# Cloud Dataproc API . projects . regions . workflowTemplates

## Instance Methods

Close httplib2 connections.

Creates new workflow template.

Deletes a workflow template. It does not cancel in-progress workflows.

Retrieves the latest workflow template.Can retrieve previously instantiated template by specifying optional version parameter.

Gets the access control policy for a resource. Returns an empty policy if the resource exists and does not have a policy set.

Instantiates a template and begins execution.The returned Operation can be used to track execution of workflow by polling operations.get. The Operation will complete when entire workflow is finished.The running workflow can be aborted via operations.cancel. This will cause any inflight jobs to be cancelled and workflow-owned clusters to be deleted.The Operation.metadata will be WorkflowMetadata (https://cloud.google.com/dataproc/docs/reference/rpc/google.cloud.dataproc.v1#workflowmetadata). Also see Using WorkflowMetadata (https://cloud.google.com/dataproc/docs/concepts/workflows/debugging#using_workflowmetadata).On successful completion, Operation.response will be Empty.

Instantiates a template and begins execution.This method is equivalent to executing the sequence CreateWorkflowTemplate, InstantiateWorkflowTemplate, DeleteWorkflowTemplate.The returned Operation can be used to track execution of workflow by polling operations.get. The Operation will complete when entire workflow is finished.The running workflow can be aborted via operations.cancel. This will cause any inflight jobs to be cancelled and workflow-owned clusters to be deleted.The Operation.metadata will be WorkflowMetadata (https://cloud.google.com/dataproc/docs/reference/rpc/google.cloud.dataproc.v1#workflowmetadata). Also see Using WorkflowMetadata (https://cloud.google.com/dataproc/docs/concepts/workflows/debugging#using_workflowmetadata).On successful completion, Operation.response will be Empty.

Lists workflows that match the specified filter in the request.

Retrieves the next page of results.

Sets the access control policy on the specified resource. Replaces any existing policy.Can return NOT_FOUND, INVALID_ARGUMENT, and PERMISSION_DENIED errors.

Returns permissions that a caller has on the specified resource. If the resource does not exist, this will return an empty set of permissions, not a NOT_FOUND error.Note: This operation is designed to be used for building permission-aware UIs and command-line tools, not for authorization checking. This operation may "fail open" without warning.

Updates (replaces) workflow template. The updated template must contain version that matches the current server version.

### Method Details

`close()`

    Close httplib2 connections.

`create(parent, body=None, x__xgafv=None)`

    Creates new workflow template.

    Args:
      parent: string, Required. The resource name of the region or location, as described in https://cloud.google.com/apis/design/resource_names. For projects.regions.workflowTemplates.create, the resource name of the region has the following format: projects/{project_id}/regions/{region} For projects.locations.workflowTemplates.create, the resource name of the location has the following format: projects/{project_id}/locations/{location} (required)
      body: object, The request body.
        The object takes the form of:

    { # A Dataproc workflow template resource.
      "createTime": "A String", # Output only. The time template was created.
      "dagTimeout": "A String", # Optional. Timeout duration for the DAG of jobs, expressed in seconds (see JSON representation of duration (https://developers.google.com/protocol-buffers/docs/proto3#json)). The timeout duration must be from 10 minutes ("600s") to 24 hours ("86400s"). The timer begins when the first job is submitted. If the workflow is running at the end of the timeout period, any remaining jobs are cancelled, the workflow is ended, and if the workflow was running on a managed cluster, the cluster is deleted.
      "encryptionConfig": { # Encryption settings for encrypting workflow template job arguments. # Optional. Encryption settings for encrypting workflow template job arguments.
        "kmsKey": "A String", # Optional. The Cloud KMS key name to use for encrypting workflow template job arguments.When this this key is provided, the following workflow template job arguments (https://cloud.google.com/dataproc/docs/concepts/workflows/use-workflows#adding_jobs_to_a_template), if present, are CMEK encrypted (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/customer-managed-encryption#use_cmek_with_workflow_template_data): FlinkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/FlinkJob) HadoopJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/HadoopJob) SparkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkJob) SparkRJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkRJob) PySparkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/PySparkJob) SparkSqlJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkSqlJob) scriptVariables and queryList.queries HiveJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/HiveJob) scriptVariables and queryList.queries PigJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/PigJob) scriptVariables and queryList.queries PrestoJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/PrestoJob) scriptVariables and queryList.queries
      },
      "id": "A String",
      "jobs": [ # Required. The Directed Acyclic Graph of Jobs to submit.
        { # A job executed by the workflow.
          "flinkJob": { # A Dataproc job for running Apache Flink applications on YARN. # Optional. Job is a Flink job.
            "args": [ # Optional. The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision might occur that causes an incorrect job submission.
              "A String",
            ],
            "jarFileUris": [ # Optional. HCFS URIs of jar files to add to the CLASSPATHs of the Flink driver and tasks.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "mainClass": "A String", # The name of the driver's main class. The jar file that contains the class must be in the default CLASSPATH or specified in jarFileUris.
            "mainJarFileUri": "A String", # The HCFS URI of the jar file that contains the main class.
            "properties": { # Optional. A mapping of property names to values, used to configure Flink. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/flink/conf/flink-defaults.conf and classes in user code.
              "a_key": "A String",
            },
            "savepointUri": "A String", # Optional. HCFS URI of the savepoint, which contains the last saved progress for starting the current job.
          },
          "hadoopJob": { # A Dataproc job for running Apache Hadoop MapReduce (https://hadoop.apache.org/docs/current/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html) jobs on Apache Hadoop YARN (https://hadoop.apache.org/docs/r2.7.1/hadoop-yarn/hadoop-yarn-site/YARN.html). # Optional. Job is a Hadoop job.
            "archiveUris": [ # Optional. HCFS URIs of archives to be extracted in the working directory of Hadoop drivers and tasks. Supported file types: .jar, .tar, .tar.gz, .tgz, or .zip.
              "A String",
            ],
            "args": [ # Optional. The arguments to pass to the driver. Do not include arguments, such as -libjars or -Dfoo=bar, that can be set as job properties, since a collision might occur that causes an incorrect job submission.
              "A String",
            ],
            "fileUris": [ # Optional. HCFS (Hadoop Compatible Filesystem) URIs of files to be copied to the working directory of Hadoop drivers and distributed tasks. Useful for naively parallel tasks.
              "A String",
            ],
            "jarFileUris": [ # Optional. Jar file URIs to add to the CLASSPATHs of the Hadoop driver and tasks.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "mainClass": "A String", # The name of the driver's main class. The jar file containing the class must be in the default CLASSPATH or specified in jar_file_uris.
            "mainJarFileUri": "A String", # The HCFS URI of the jar file containing the main class. Examples: 'gs://foo-bucket/analytics-binaries/extract-useful-metrics-mr.jar' 'hdfs:/tmp/test-samples/custom-wordcount.jar' 'file:///home/usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar'
            "properties": { # Optional. A mapping of property names to values, used to configure Hadoop. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/hadoop/conf/*-site and classes in user code.
              "a_key": "A String",
            },
          },
          "hiveJob": { # A Dataproc job for running Apache Hive (https://hive.apache.org/) queries on YARN. # Optional. Job is a Hive job.
            "continueOnFailure": True or False, # Optional. Whether to continue executing queries if a query fails. The default value is false. Setting to true can be useful when executing independent parallel queries.
            "jarFileUris": [ # Optional. HCFS URIs of jar files to add to the CLASSPATH of the Hive server and Hadoop MapReduce (MR) tasks. Can contain Hive SerDes and UDFs.
              "A String",
            ],
            "properties": { # Optional. A mapping of property names and values, used to configure Hive. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/hadoop/conf/*-site.xml, /etc/hive/conf/hive-site.xml, and classes in user code.
              "a_key": "A String",
            },
            "queryFileUri": "A String", # The HCFS URI of the script that contains Hive queries.
            "queryList": { # A list of queries to run on a cluster. # A list of queries.
              "queries": [ # Required. The queries to execute. You do not need to end a query expression with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of a Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } }
                "A String",
              ],
            },
            "scriptVariables": { # Optional. Mapping of query variable names to values (equivalent to the Hive command: SET name="value";).
              "a_key": "A String",
            },
          },
          "labels": { # Optional. The labels to associate with this job.Label keys must be between 1 and 63 characters long, and must conform to the following regular expression: \p{Ll}\p{Lo}{0,62}Label values must be between 1 and 63 characters long, and must conform to the following regular expression: \p{Ll}\p{Lo}\p{N}_-{0,63}No more than 32 labels can be associated with a given job.
            "a_key": "A String",
          },
          "pigJob": { # A Dataproc job for running Apache Pig (https://pig.apache.org/) queries on YARN. # Optional. Job is a Pig job.
            "continueOnFailure": True or False, # Optional. Whether to continue executing queries if a query fails. The default value is false. Setting to true can be useful when executing independent parallel queries.
            "jarFileUris": [ # Optional. HCFS URIs of jar files to add to the CLASSPATH of the Pig Client and Hadoop MapReduce (MR) tasks. Can contain Pig UDFs.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "properties": { # Optional. A mapping of property names to values, used to configure Pig. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/hadoop/conf/*-site.xml, /etc/pig/conf/pig.properties, and classes in user code.
              "a_key": "A String",
            },
            "queryFileUri": "A String", # The HCFS URI of the script that contains the Pig queries.
            "queryList": { # A list of queries to run on a cluster. # A list of queries.
              "queries": [ # Required. The queries to execute. You do not need to end a query expression with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of a Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } }
                "A String",
              ],
            },
            "scriptVariables": { # Optional. Mapping of query variable names to values (equivalent to the Pig command: name=[value]).
              "a_key": "A String",
            },
          },
          "prerequisiteStepIds": [ # Optional. The optional list of prerequisite job step_ids. If not specified, the job will start at the beginning of workflow.
            "A String",
          ],
          "prestoJob": { # A Dataproc job for running Presto (https://prestosql.io/) queries. IMPORTANT: The Dataproc Presto Optional Component (https://cloud.google.com/dataproc/docs/concepts/components/presto) must be enabled when the cluster is created to submit a Presto job to the cluster. # Optional. Job is a Presto job.
            "clientTags": [ # Optional. Presto client tags to attach to this query
              "A String",
            ],
            "continueOnFailure": True or False, # Optional. Whether to continue executing queries if a query fails. The default value is false. Setting to true can be useful when executing independent parallel queries.
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "outputFormat": "A String", # Optional. The format in which query output will be displayed. See the Presto documentation for supported output formats
            "properties": { # Optional. A mapping of property names to values. Used to set Presto session properties (https://prestodb.io/docs/current/sql/set-session.html) Equivalent to using the --session flag in the Presto CLI
              "a_key": "A String",
            },
            "queryFileUri": "A String", # The HCFS URI of the script that contains SQL queries.
            "queryList": { # A list of queries to run on a cluster. # A list of queries.
              "queries": [ # Required. The queries to execute. You do not need to end a query expression with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of a Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } }
                "A String",
              ],
            },
          },
          "pysparkJob": { # A Dataproc job for running Apache PySpark (https://spark.apache.org/docs/latest/api/python/index.html#pyspark-overview) applications on YARN. # Optional. Job is a PySpark job.
            "archiveUris": [ # Optional. HCFS URIs of archives to be extracted into the working directory of each executor. Supported file types: .jar, .tar, .tar.gz, .tgz, and .zip.
              "A String",
            ],
            "args": [ # Optional. The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision may occur that causes an incorrect job submission.
              "A String",
            ],
            "fileUris": [ # Optional. HCFS URIs of files to be placed in the working directory of each executor. Useful for naively parallel tasks.
              "A String",
            ],
            "jarFileUris": [ # Optional. HCFS URIs of jar files to add to the CLASSPATHs of the Python driver and tasks.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "mainPythonFileUri": "A String", # Required. The HCFS URI of the main Python file to use as the driver. Must be a .py file.
            "properties": { # Optional. A mapping of property names to values, used to configure PySpark. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/spark/conf/spark-defaults.conf and classes in user code.
              "a_key": "A String",
            },
            "pythonFileUris": [ # Optional. HCFS file URIs of Python files to pass to the PySpark framework. Supported file types: .py, .egg, and .zip.
              "A String",
            ],
          },
          "scheduling": { # Job scheduling options. # Optional. Job scheduling configuration.
            "maxFailuresPerHour": 42, # Optional. Maximum number of times per hour a driver can be restarted as a result of driver exiting with non-zero code before job is reported failed.A job might be reported as thrashing if the driver exits with a non-zero code four times within a 10-minute window.Maximum value is 10.Note: This restartable job option is not supported in Dataproc workflow templates (https://cloud.google.com/dataproc/docs/concepts/workflows/using-workflows#adding_jobs_to_a_template).
            "maxFailuresTotal": 42, # Optional. Maximum total number of times a driver can be restarted as a result of the driver exiting with a non-zero code. After the maximum number is reached, the job will be reported as failed.Maximum value is 240.Note: Currently, this restartable job option is not supported in Dataproc workflow templates (https://cloud.google.com/dataproc/docs/concepts/workflows/using-workflows#adding_jobs_to_a_template).
          },
          "sparkJob": { # A Dataproc job for running Apache Spark (https://spark.apache.org/) applications on YARN. # Optional. Job is a Spark job.
            "archiveUris": [ # Optional. HCFS URIs of archives to be extracted into the working directory of each executor. Supported file types: .jar, .tar, .tar.gz, .tgz, and .zip.
              "A String",
            ],
            "args": [ # Optional. The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision may occur that causes an incorrect job submission.
              "A String",
            ],
            "fileUris": [ # Optional. HCFS URIs of files to be placed in the working directory of each executor. Useful for naively parallel tasks.
              "A String",
            ],
            "jarFileUris": [ # Optional. HCFS URIs of jar files to add to the CLASSPATHs of the Spark driver and tasks.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "mainClass": "A String", # The name of the driver's main class. The jar file that contains the class must be in the default CLASSPATH or specified in SparkJob.jar_file_uris.
            "mainJarFileUri": "A String", # The HCFS URI of the jar file that contains the main class.
            "properties": { # Optional. A mapping of property names to values, used to configure Spark. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/spark/conf/spark-defaults.conf and classes in user code.
              "a_key": "A String",
            },
          },
          "sparkRJob": { # A Dataproc job for running Apache SparkR (https://spark.apache.org/docs/latest/sparkr.html) applications on YARN. # Optional. Job is a SparkR job.
            "archiveUris": [ # Optional. HCFS URIs of archives to be extracted into the working directory of each executor. Supported file types: .jar, .tar, .tar.gz, .tgz, and .zip.
              "A String",
            ],
            "args": [ # Optional. The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision may occur that causes an incorrect job submission.
              "A String",
            ],
            "fileUris": [ # Optional. HCFS URIs of files to be placed in the working directory of each executor. Useful for naively parallel tasks.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "mainRFileUri": "A String", # Required. The HCFS URI of the main R file to use as the driver. Must be a .R file.
            "properties": { # Optional. A mapping of property names to values, used to configure SparkR. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/spark/conf/spark-defaults.conf and classes in user code.
              "a_key": "A String",
            },
          },
          "sparkSqlJob": { # A Dataproc job for running Apache Spark SQL (https://spark.apache.org/sql/) queries. # Optional. Job is a SparkSql job.
            "jarFileUris": [ # Optional. HCFS URIs of jar files to be added to the Spark CLASSPATH.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "properties": { # Optional. A mapping of property names to values, used to configure Spark SQL's SparkConf. Properties that conflict with values set by the Dataproc API might be overwritten.
              "a_key": "A String",
            },
            "queryFileUri": "A String", # The HCFS URI of the script that contains SQL queries.
            "queryList": { # A list of queries to run on a cluster. # A list of queries.
              "queries": [ # Required. The queries to execute. You do not need to end a query expression with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of a Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } }
                "A String",
              ],
            },
            "scriptVariables": { # Optional. Mapping of query variable names to values (equivalent to the Spark SQL command: SET name="value";).
              "a_key": "A String",
            },
          },
          "stepId": "A String", # Required. The step id. The id must be unique among all jobs within the template.The step id is used as prefix for job id, as job goog-dataproc-workflow-step-id label, and in prerequisiteStepIds field from other steps.The id must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). Cannot begin or end with underscore or hyphen. Must consist of between 3 and 50 characters.
          "trinoJob": { # A Dataproc job for running Trino (https://trino.io/) queries. IMPORTANT: The Dataproc Trino Optional Component (https://cloud.google.com/dataproc/docs/concepts/components/trino) must be enabled when the cluster is created to submit a Trino job to the cluster. # Optional. Job is a Trino job.
            "clientTags": [ # Optional. Trino client tags to attach to this query
              "A String",
            ],
            "continueOnFailure": True or False, # Optional. Whether to continue executing queries if a query fails. The default value is false. Setting to true can be useful when executing independent parallel queries.
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "outputFormat": "A String", # Optional. The format in which query output will be displayed. See the Trino documentation for supported output formats
            "properties": { # Optional. A mapping of property names to values. Used to set Trino session properties (https://trino.io/docs/current/sql/set-session.html) Equivalent to using the --session flag in the Trino CLI
              "a_key": "A String",
            },
            "queryFileUri": "A String", # The HCFS URI of the script that contains SQL queries.
            "queryList": { # A list of queries to run on a cluster. # A list of queries.
              "queries": [ # Required. The queries to execute. You do not need to end a query expression with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of a Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } }
                "A String",
              ],
            },
          },
        },
      ],
      "labels": { # Optional. The labels to associate with this template. These labels will be propagated to all jobs and clusters created by the workflow instance.Label keys must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt).Label values may be empty, but, if present, must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt).No more than 32 labels can be associated with a template.
        "a_key": "A String",
      },
      "name": "A String", # Output only. The resource name of the workflow template, as described in https://cloud.google.com/apis/design/resource_names. For projects.regions.workflowTemplates, the resource name of the template has the following format: projects/{project_id}/regions/{region}/workflowTemplates/{template_id} For projects.locations.workflowTemplates, the resource name of the template has the following format: projects/{project_id}/locations/{location}/workflowTemplates/{template_id}
      "parameters": [ # Optional. Template parameters whose values are substituted into the template. Values for parameters must be provided when the template is instantiated.
        { # A configurable parameter that replaces one or more fields in the template. Parameterizable fields: - Labels - File uris - Job properties - Job arguments - Script variables - Main class (in HadoopJob and SparkJob) - Zone (in ClusterSelector)
          "description": "A String", # Optional. Brief description of the parameter. Must not exceed 1024 characters.
          "fields": [ # Required. Paths to all fields that the parameter replaces. A field is allowed to appear in at most one parameter's list of field paths.A field path is similar in syntax to a google.protobuf.FieldMask. For example, a field path that references the zone field of a workflow template's cluster selector would be specified as placement.clusterSelector.zone.Also, field paths can reference fields using the following syntax: Values in maps can be referenced by key: labels'key' placement.clusterSelector.clusterLabels'key' placement.managedCluster.labels'key' placement.clusterSelector.clusterLabels'key' jobs'step-id'.labels'key' Jobs in the jobs list can be referenced by step-id: jobs'step-id'.hadoopJob.mainJarFileUri jobs'step-id'.hiveJob.queryFileUri jobs'step-id'.pySparkJob.mainPythonFileUri jobs'step-id'.hadoopJob.jarFileUris0 jobs'step-id'.hadoopJob.archiveUris0 jobs'step-id'.hadoopJob.fileUris0 jobs'step-id'.pySparkJob.pythonFileUris0 Items in repeated fields can be referenced by a zero-based index: jobs'step-id'.sparkJob.args0 Other examples: jobs'step-id'.hadoopJob.properties'key' jobs'step-id'.hadoopJob.args0 jobs'step-id'.hiveJob.scriptVariables'key' jobs'step-id'.hadoopJob.mainJarFileUri placement.clusterSelector.zoneIt may not be possible to parameterize maps and repeated fields in their entirety since only individual map values and individual items in repeated fields can be referenced. For example, the following field paths are invalid: placement.clusterSelector.clusterLabels jobs'step-id'.sparkJob.args
            "A String",
          ],
          "name": "A String", # Required. Parameter name. The parameter name is used as the key, and paired with the parameter value, which are passed to the template when the template is instantiated. The name must contain only capital letters (A-Z), numbers (0-9), and underscores (_), and must not start with a number. The maximum length is 40 characters.
          "validation": { # Configuration for parameter validation. # Optional. Validation rules to be applied to this parameter's value.
            "regex": { # Validation based on regular expressions. # Validation based on regular expressions.
              "regexes": [ # Required. RE2 regular expressions used to validate the parameter's value. The value must match the regex in its entirety (substring matches are not sufficient).
                "A String",
              ],
            },
            "values": { # Validation based on a list of allowed values. # Validation based on a list of allowed values.
              "values": [ # Required. List of allowed values for the parameter.
                "A String",
              ],
            },
          },
        },
      ],
      "placement": { # Specifies workflow execution target.Either managed_cluster or cluster_selector is required. # Required. WorkflowTemplate scheduling information.
        "clusterSelector": { # A selector that chooses target cluster for jobs based on metadata. # Optional. A selector that chooses target cluster for jobs based on metadata.The selector is evaluated at the time each job is submitted.
          "clusterLabels": { # Required. The cluster labels. Cluster must have all labels to match.
            "a_key": "A String",
          },
          "zone": "A String", # Optional. The zone where workflow process executes. This parameter does not affect the selection of the cluster.If unspecified, the zone of the first cluster matching the selector is used.
        },
        "managedCluster": { # Cluster that is managed by the workflow. # A cluster that is managed by the workflow.
          "clusterName": "A String", # Required. The cluster name prefix. A unique cluster name will be formed by appending a random suffix.The name must contain only lower-case letters (a-z), numbers (0-9), and hyphens (-). Must begin with a letter. Cannot begin or end with hyphen. Must consist of between 2 and 35 characters.
          "config": { # The cluster config. # Required. The cluster configuration.
            "autoscalingConfig": { # Autoscaling Policy config associated with the cluster. # Optional. Autoscaling config for the policy associated with the cluster. Cluster does not autoscale if this field is unset.
              "policyUri": "A String", # Optional. The autoscaling policy used by the cluster.Only resource names including projectid and location (region) are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/locations/[dataproc_region]/autoscalingPolicies/[policy_id] projects/[project_id]/locations/[dataproc_region]/autoscalingPolicies/[policy_id]Note that the policy must be in the same project and Dataproc region.
            },
            "auxiliaryNodeGroups": [ # Optional. The node group settings.
              { # Node group identification and configuration information.
                "nodeGroup": { # Dataproc Node Group. The Dataproc NodeGroup resource is not related to the Dataproc NodeGroupAffinity resource. # Required. Node group configuration.
                  "labels": { # Optional. Node group labels. Label keys must consist of from 1 to 63 characters and conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). Label values can be empty. If specified, they must consist of from 1 to 63 characters and conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). The node group must have no more than 32 labels.
                    "a_key": "A String",
                  },
                  "name": "A String", # The Node group resource name (https://aip.dev/122).
                  "nodeGroupConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The node group instance group configuration.
                    "accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
                      { # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
                        "acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
                        "acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
                      },
                    ],
                    "diskConfig": { # Specifies the config of boot disk and attached disk options for a group of VM instances. # Optional. Disk option config settings.
                      "bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. This field is supported only if boot_disk_type is hyperdisk-balanced.
                      "bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. This field is supported only if boot_disk_type is hyperdisk-balanced.
                      "bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
                      "bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
                      "localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
                      "numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
                    },
                    "imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
                    "instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
                      "instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
                        { # Defines machines types and a rank to which the machines types belong.
                          "machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
                            "A String",
                          ],
                          "rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
                        },
                      ],
                      "instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
                        { # Defines a mapping from machine types to the number of VMs that are created with each machine type.
                          "machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
                          "vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
                        },
                      ],
                      "provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
                        "standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
                        "standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
                      },
                    },
                    "instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
                      "A String",
                    ],
                    "instanceReferences": [ # Output only. List of references to Compute Engine instances.
                      { # A reference to a Compute Engine instance.
                        "instanceId": "A String", # The unique identifier of the Compute Engine instance.
                        "instanceName": "A String", # The user-friendly name of the Compute Engine instance.
                        "publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
                        "publicKey": "A String", # The public RSA key used for sharing data with this instance.
                      },
                    ],
                    "isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
                    "machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
                    "managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
                      "instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
                      "instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
                      "instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
                    },
                    "minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
                    "minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
                    "numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
                    "preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
                    "startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
                      "requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
                    },
                  },
                  "roles": [ # Required. Node group roles.
                    "A String",
                  ],
                },
                "nodeGroupId": "A String", # Optional. A node group ID. Generated if not specified.The ID must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). Cannot begin or end with underscore or hyphen. Must consist of from 3 to 33 characters.
              },
            ],
            "clusterTier": "A String", # Optional. The cluster tier.
            "clusterType": "A String", # Optional. The type of the cluster.
            "configBucket": "A String", # Optional. A Cloud Storage bucket used to stage job dependencies, config files, and job driver console output. If you do not specify a staging bucket, Cloud Dataproc will determine a Cloud Storage location (US, ASIA, or EU) for your cluster's staging bucket according to the Compute Engine zone where your cluster is deployed, and then create and manage this project-level, per-location bucket (see Dataproc staging and temp buckets (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/staging-bucket)). This field requires a Cloud Storage bucket name, not a gs://... URI to a Cloud Storage bucket.
            "dataprocMetricConfig": { # Dataproc metric config. # Optional. The config for Dataproc metrics.
              "metrics": [ # Required. Metrics sources to enable.
                { # A Dataproc custom metric.
                  "metricOverrides": [ # Optional. Specify one or more Custom metrics (https://cloud.google.com/dataproc/docs/guides/dataproc-metrics#custom_metrics) to collect for the metric course (for the SPARK metric source (any Spark metric (https://spark.apache.org/docs/latest/monitoring.html#metrics) can be specified).Provide metrics in the following format: METRIC_SOURCE: INSTANCE:GROUP:METRIC Use camelcase as appropriate.Examples: yarn:ResourceManager:QueueMetrics:AppsCompleted spark:driver:DAGScheduler:job.allJobs sparkHistoryServer:JVM:Memory:NonHeapMemoryUsage.committed hiveserver2:JVM:Memory:NonHeapMemoryUsage.used Notes: Only the specified overridden metrics are collected for the metric source. For example, if one or more spark:executive metrics are listed as metric overrides, other SPARK metrics are not collected. The collection of the metrics for other enabled custom metric sources is unaffected. For example, if both SPARK and YARN metric sources are enabled, and overrides are provided for Spark metrics only, all YARN metrics are collected.
                    "A String",
                  ],
                  "metricSource": "A String", # Required. A standard set of metrics is collected unless metricOverrides are specified for the metric source (see Custom metrics (https://cloud.google.com/dataproc/docs/guides/dataproc-metrics#custom_metrics) for more information).
                },
              ],
            },
            "encryptionConfig": { # Encryption settings for the cluster. # Optional. Encryption settings for the cluster.
              "gcePdKmsKeyName": "A String", # Optional. The Cloud KMS key resource name to use for persistent disk encryption for all instances in the cluster. See Use CMEK with cluster data (https://cloud.google.com//dataproc/docs/concepts/configuring-clusters/customer-managed-encryption#use_cmek_with_cluster_data) for more information.
              "kmsKey": "A String", # Optional. The Cloud KMS key resource name to use for cluster persistent disk and job argument encryption. See Use CMEK with cluster data (https://cloud.google.com//dataproc/docs/concepts/configuring-clusters/customer-managed-encryption#use_cmek_with_cluster_data) for more information.When this key resource name is provided, the following job arguments of the following job types submitted to the cluster are encrypted using CMEK: FlinkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/FlinkJob) HadoopJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/HadoopJob) SparkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkJob) SparkRJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkRJob) PySparkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/PySparkJob) SparkSqlJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkSqlJob) scriptVariables and queryList.queries HiveJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/HiveJob) scriptVariables and queryList.queries PigJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/PigJob) scriptVariables and queryList.queries PrestoJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/PrestoJob) scriptVariables and queryList.queries
            },
            "endpointConfig": { # Endpoint config for this cluster # Optional. Port/endpoint configuration for this cluster
              "enableHttpPortAccess": True or False, # Optional. If true, enable http access to specific ports on the cluster from external sources. Defaults to false.
              "httpPorts": { # Output only. The map of port descriptions to URLs. Will only be populated if enable_http_port_access is true.
                "a_key": "A String",
              },
            },
            "gceClusterConfig": { # Common config settings for resources of Compute Engine cluster instances, applicable to all instances in the cluster. # Optional. The shared Compute Engine config settings for all instances in a cluster.
              "confidentialInstanceConfig": { # Confidential Instance Config for clusters using Confidential VMs (https://cloud.google.com/compute/confidential-vm/docs) # Optional. Confidential Instance Config for clusters using Confidential VMs (https://cloud.google.com/compute/confidential-vm/docs).
                "enableConfidentialCompute": True or False, # Optional. Defines whether the instance should have confidential compute enabled.
              },
              "internalIpOnly": True or False, # Optional. This setting applies to subnetwork-enabled networks. It is set to true by default in clusters created with image versions 2.2.x.When set to true: All cluster VMs have internal IP addresses. Google Private Access (https://cloud.google.com/vpc/docs/private-google-access) must be enabled to access Dataproc and other Google Cloud APIs. Off-cluster dependencies must be configured to be accessible without external IP addresses.When set to false: Cluster VMs are not restricted to internal IP addresses. Ephemeral external IP addresses are assigned to each cluster VM.
              "metadata": { # Optional. The Compute Engine metadata entries to add to all instances (see Project and instance metadata (https://cloud.google.com/compute/docs/storing-retrieving-metadata#project_and_instance_metadata)).
                "a_key": "A String",
              },
              "networkUri": "A String", # Optional. The Compute Engine network to be used for machine communications. Cannot be specified with subnetwork_uri. If neither network_uri nor subnetwork_uri is specified, the "default" network of the project is used, if it exists. Cannot be a "Custom Subnet Network" (see Using Subnetworks (https://cloud.google.com/compute/docs/subnetworks) for more information).A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/networks/default projects/[project_id]/global/networks/default default
              "nodeGroupAffinity": { # Node Group Affinity for clusters using sole-tenant node groups. The Dataproc NodeGroupAffinity resource is not related to the Dataproc NodeGroup resource. # Optional. Node Group Affinity for sole-tenant clusters.
                "nodeGroupUri": "A String", # Required. The URI of a sole-tenant node group resource (https://cloud.google.com/compute/docs/reference/rest/v1/nodeGroups) that the cluster will be created on.A full URL, partial URI, or node group name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/nodeGroups/node-group-1 projects/[project_id]/zones/[zone]/nodeGroups/node-group-1 node-group-1
              },
              "privateIpv6GoogleAccess": "A String", # Optional. The type of IPv6 access for a cluster.
              "reservationAffinity": { # Reservation Affinity for consuming Zonal reservation. # Optional. Reservation Affinity for consuming Zonal reservation.
                "consumeReservationType": "A String", # Optional. Type of reservation to consume
                "key": "A String", # Optional. Corresponds to the label key of reservation resource.
                "values": [ # Optional. Corresponds to the label values of reservation resource.
                  "A String",
                ],
              },
              "resourceManagerTags": { # Optional. Resource manager tags (https://cloud.google.com/resource-manager/docs/tags/tags-creating-and-managing) to add to all instances (see Use secure tags in Dataproc (https://cloud.google.com/dataproc/docs/guides/attach-secure-tags)).
                "a_key": "A String",
              },
              "serviceAccount": "A String", # Optional. The Dataproc service account (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/service-accounts#service_accounts_in_dataproc) (also see VM Data Plane identity (https://cloud.google.com/dataproc/docs/concepts/iam/dataproc-principals#vm_service_account_data_plane_identity)) used by Dataproc cluster VM instances to access Google Cloud Platform services.If not specified, the Compute Engine default service account (https://cloud.google.com/compute/docs/access/service-accounts#default_service_account) is used.
              "serviceAccountScopes": [ # Optional. The URIs of service account scopes to be included in Compute Engine instances. The following base set of scopes is always included: https://www.googleapis.com/auth/cloud.useraccounts.readonly https://www.googleapis.com/auth/devstorage.read_write https://www.googleapis.com/auth/logging.writeIf no scopes are specified, the following defaults are also provided: https://www.googleapis.com/auth/bigquery https://www.googleapis.com/auth/bigtable.admin.table https://www.googleapis.com/auth/bigtable.data https://www.googleapis.com/auth/devstorage.full_control
                "A String",
              ],
              "shieldedInstanceConfig": { # Shielded Instance Config for clusters using Compute Engine Shielded VMs (https://cloud.google.com/security/shielded-cloud/shielded-vm). # Optional. Shielded Instance Config for clusters using Compute Engine Shielded VMs (https://cloud.google.com/security/shielded-cloud/shielded-vm).
                "enableIntegrityMonitoring": True or False, # Optional. Defines whether instances have integrity monitoring enabled.
                "enableSecureBoot": True or False, # Optional. Defines whether instances have Secure Boot enabled.
                "enableVtpm": True or False, # Optional. Defines whether instances have the vTPM enabled.
              },
              "subnetworkUri": "A String", # Optional. The Compute Engine subnetwork to be used for machine communications. Cannot be specified with network_uri.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/regions/[region]/subnetworks/sub0 projects/[project_id]/regions/[region]/subnetworks/sub0 sub0
              "tags": [ # The Compute Engine network tags to add to all instances (see Tagging instances (https://cloud.google.com/vpc/docs/add-remove-network-tags)).
                "A String",
              ],
              "zoneUri": "A String", # Optional. The Compute Engine zone where the Dataproc cluster will be located. If omitted, the service will pick a zone in the cluster's Compute Engine region. On a get request, zone will always be present.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone] projects/[project_id]/zones/[zone] [zone]
            },
            "gkeClusterConfig": { # The cluster's GKE config. # Optional. BETA. The Kubernetes Engine config for Dataproc clusters deployed to The Kubernetes Engine config for Dataproc clusters deployed to Kubernetes. These config settings are mutually exclusive with Compute Engine-based options, such as gce_cluster_config, master_config, worker_config, secondary_worker_config, and autoscaling_config.
              "gkeClusterTarget": "A String", # Optional. A target GKE cluster to deploy to. It must be in the same project and region as the Dataproc cluster (the GKE cluster can be zonal or regional). Format: 'projects/{project}/locations/{location}/clusters/{cluster_id}'
              "namespacedGkeDeploymentTarget": { # Deprecated. Used only for the deprecated beta. A full, namespace-isolated deployment target for an existing GKE cluster. # Optional. Deprecated. Use gkeClusterTarget. Used only for the deprecated beta. A target for the deployment.
                "clusterNamespace": "A String", # Optional. A namespace within the GKE cluster to deploy into.
                "targetGkeCluster": "A String", # Optional. The target GKE cluster to deploy to. Format: 'projects/{project}/locations/{location}/clusters/{cluster_id}'
              },
              "nodePoolTarget": [ # Optional. GKE node pools where workloads will be scheduled. At least one node pool must be assigned the DEFAULT GkeNodePoolTarget.Role. If a GkeNodePoolTarget is not specified, Dataproc constructs a DEFAULT GkeNodePoolTarget. Each role can be given to only one GkeNodePoolTarget. All node pools must have the same location settings.
                { # GKE node pools that Dataproc workloads run on.
                  "nodePool": "A String", # Required. The target GKE node pool. Format: 'projects/{project}/locations/{location}/clusters/{cluster}/nodePools/{node_pool}'
                  "nodePoolConfig": { # The configuration of a GKE node pool used by a Dataproc-on-GKE cluster (https://cloud.google.com/dataproc/docs/concepts/jobs/dataproc-gke#create-a-dataproc-on-gke-cluster). # Input only. The configuration for the GKE node pool.If specified, Dataproc attempts to create a node pool with the specified shape. If one with the same name already exists, it is verified against all specified fields. If a field differs, the virtual cluster creation will fail.If omitted, any node pool with the specified name is used. If a node pool with the specified name does not exist, Dataproc create a node pool with default values.This is an input only field. It will not be returned by the API.
                    "autoscaling": { # GkeNodePoolAutoscaling contains information the cluster autoscaler needs to adjust the size of the node pool to the current cluster usage. # Optional. The autoscaler configuration for this node pool. The autoscaler is enabled only when a valid configuration is present.
                      "maxNodeCount": 42, # The maximum number of nodes in the node pool. Must be >= min_node_count, and must be > 0. Note: Quota must be sufficient to scale up the cluster.
                      "minNodeCount": 42, # The minimum number of nodes in the node pool. Must be >= 0 and <= max_node_count.
                    },
                    "config": { # Parameters that describe cluster nodes. # Optional. The node pool configuration.
                      "accelerators": [ # Optional. A list of hardware accelerators (https://cloud.google.com/compute/docs/gpus) to attach to each node.
                        { # A GkeNodeConfigAcceleratorConfig represents a Hardware Accelerator request for a node pool.
                          "acceleratorCount": "A String", # The number of accelerator cards exposed to an instance.
                          "acceleratorType": "A String", # The accelerator type resource namename (see GPUs on Compute Engine).
                          "gpuPartitionSize": "A String", # Size of partitions to create on the GPU. Valid values are described in the NVIDIA mig user guide (https://docs.nvidia.com/datacenter/tesla/mig-user-guide/#partitioning).
                        },
                      ],
                      "bootDiskKmsKey": "A String", # Optional. The Customer Managed Encryption Key (CMEK) (https://cloud.google.com/kubernetes-engine/docs/how-to/using-cmek) used to encrypt the boot disk attached to each node in the node pool. Specify the key using the following format: projects/{project}/locations/{location}/keyRings/{key_ring}/cryptoKeys/{crypto_key}
                      "localSsdCount": 42, # Optional. The number of local SSD disks to attach to the node, which is limited by the maximum number of disks allowable per zone (see Adding Local SSDs (https://cloud.google.com/compute/docs/disks/local-ssd)).
                      "machineType": "A String", # Optional. The name of a Compute Engine machine type (https://cloud.google.com/compute/docs/machine-types).
                      "minCpuPlatform": "A String", # Optional. Minimum CPU platform (https://cloud.google.com/compute/docs/instances/specify-min-cpu-platform) to be used by this instance. The instance may be scheduled on the specified or a newer CPU platform. Specify the friendly names of CPU platforms, such as "Intel Haswell"` or Intel Sandy Bridge".
                      "preemptible": True or False, # Optional. Whether the nodes are created as legacy preemptible VM instances (https://cloud.google.com/compute/docs/instances/preemptible). Also see Spot VMs, preemptible VM instances without a maximum lifetime. Legacy and Spot preemptible nodes cannot be used in a node pool with the CONTROLLER role or in the DEFAULT node pool if the CONTROLLER role is not assigned (the DEFAULT node pool will assume the CONTROLLER role).
                      "spot": True or False, # Optional. Whether the nodes are created as Spot VM instances (https://cloud.google.com/compute/docs/instances/spot). Spot VMs are the latest update to legacy preemptible VMs. Spot VMs do not have a maximum lifetime. Legacy and Spot preemptible nodes cannot be used in a node pool with the CONTROLLER role or in the DEFAULT node pool if the CONTROLLER role is not assigned (the DEFAULT node pool will assume the CONTROLLER role).
                    },
                    "locations": [ # Optional. The list of Compute Engine zones (https://cloud.google.com/compute/docs/zones#available) where node pool nodes associated with a Dataproc on GKE virtual cluster will be located.Note: All node pools associated with a virtual cluster must be located in the same region as the virtual cluster, and they must be located in the same zone within that region.If a location is not specified during node pool creation, Dataproc on GKE will choose the zone.
                      "A String",
                    ],
                  },
                  "roles": [ # Required. The roles associated with the GKE node pool.
                    "A String",
                  ],
                },
              ],
            },
            "initializationActions": [ # Optional. Commands to execute on each node after config is completed. By default, executables are run on master and all worker nodes. You can test a node's role metadata to run an executable on a master or worker node, as shown below using curl (you can also use wget): ROLE=$(curl -H Metadata-Flavor:Google http://metadata/computeMetadata/v1/instance/attributes/dataproc-role) if [[ "${ROLE}" == 'Master' ]]; then ... master specific actions ... else ... worker specific actions ... fi
              { # Specifies an executable to run on a fully configured node and a timeout period for executable completion.
                "executableFile": "A String", # Required. Cloud Storage URI of executable file.
                "executionTimeout": "A String", # Optional. Amount of time executable has to complete. Default is 10 minutes (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).Cluster creation fails with an explanatory error message (the name of the executable that caused the error and the exceeded timeout period) if the executable is not completed at end of the timeout period.
              },
            ],
            "lifecycleConfig": { # Specifies the cluster auto-delete schedule configuration. # Optional. Lifecycle setting for the cluster.
              "autoDeleteTime": "A String", # Optional. The time when cluster will be auto-deleted (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
              "autoDeleteTtl": "A String", # Optional. The lifetime duration of cluster. The cluster will be auto-deleted at the end of this period. Minimum value is 10 minutes; maximum value is 14 days (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).
              "autoStopTime": "A String", # Optional. The time when cluster will be auto-stopped (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
              "autoStopTtl": "A String", # Optional. The lifetime duration of the cluster. The cluster will be auto-stopped at the end of this period, calculated from the time of submission of the create or update cluster request. Minimum value is 10 minutes; maximum value is 14 days (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).
              "idleDeleteTtl": "A String", # Optional. The duration to keep the cluster alive while idling (when no jobs are running). Passing this threshold will cause the cluster to be deleted. Minimum value is 5 minutes; maximum value is 14 days (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).
              "idleStartTime": "A String", # Output only. The time when cluster became idle (most recent job finished) and became eligible for deletion due to idleness (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
              "idleStopTtl": "A String", # Optional. The duration to keep the cluster started while idling (when no jobs are running). Passing this threshold will cause the cluster to be stopped. Minimum value is 5 minutes; maximum value is 14 days (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).
            },
            "masterConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The Compute Engine config settings for the cluster's master instance.
              "accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
                { # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
                  "acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
                  "acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
                },
              ],
              "diskConfig": { # Specifies the config of boot disk and attached disk options for a group of VM instances. # Optional. Disk option config settings.
                "bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. This field is supported only if boot_disk_type is hyperdisk-balanced.
                "bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. This field is supported only if boot_disk_type is hyperdisk-balanced.
                "bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
                "bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
                "localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
                "numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
              },
              "imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
              "instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
                "instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
                  { # Defines machines types and a rank to which the machines types belong.
                    "machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
                      "A String",
                    ],
                    "rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
                  },
                ],
                "instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
                  { # Defines a mapping from machine types to the number of VMs that are created with each machine type.
                    "machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
                    "vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
                  },
                ],
                "provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
                  "standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
                  "standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
                },
              },
              "instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
                "A String",
              ],
              "instanceReferences": [ # Output only. List of references to Compute Engine instances.
                { # A reference to a Compute Engine instance.
                  "instanceId": "A String", # The unique identifier of the Compute Engine instance.
                  "instanceName": "A String", # The user-friendly name of the Compute Engine instance.
                  "publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
                  "publicKey": "A String", # The public RSA key used for sharing data with this instance.
                },
              ],
              "isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
              "machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
              "managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
                "instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
                "instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
                "instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
              },
              "minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
              "minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
              "numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
              "preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
              "startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
                "requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
              },
            },
            "metastoreConfig": { # Specifies a Metastore configuration. # Optional. Metastore configuration.
              "dataprocMetastoreService": "A String", # Required. Resource name of an existing Dataproc Metastore service.Example: projects/[project_id]/locations/[dataproc_region]/services/[service-name]
            },
            "secondaryWorkerConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The Compute Engine config settings for a cluster's secondary worker instances
              "accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
                { # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
                  "acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
                  "acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
                },
              ],
              "diskConfig": { # Specifies the config of boot disk and attached disk options for a group of VM instances. # Optional. Disk option config settings.
                "bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. This field is supported only if boot_disk_type is hyperdisk-balanced.
                "bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. This field is supported only if boot_disk_type is hyperdisk-balanced.
                "bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
                "bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
                "localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
                "numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
              },
              "imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
              "instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
                "instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
                  { # Defines machines types and a rank to which the machines types belong.
                    "machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
                      "A String",
                    ],
                    "rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
                  },
                ],
                "instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
                  { # Defines a mapping from machine types to the number of VMs that are created with each machine type.
                    "machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
                    "vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
                  },
                ],
                "provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
                  "standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
                  "standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
                },
              },
              "instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
                "A String",
              ],
              "instanceReferences": [ # Output only. List of references to Compute Engine instances.
                { # A reference to a Compute Engine instance.
                  "instanceId": "A String", # The unique identifier of the Compute Engine instance.
                  "instanceName": "A String", # The user-friendly name of the Compute Engine instance.
                  "publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
                  "publicKey": "A String", # The public RSA key used for sharing data with this instance.
                },
              ],
              "isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
              "machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
              "managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
                "instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
                "instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
                "instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
              },
              "minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
              "minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
              "numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
              "preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
              "startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
                "requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
              },
            },
            "securityConfig": { # Security related configuration, including encryption, Kerberos, etc. # Optional. Security settings for the cluster.
              "identityConfig": { # Identity related configuration, including service account based secure multi-tenancy user mappings. # Optional. Identity related configuration, including service account based secure multi-tenancy user mappings.
                "userServiceAccountMapping": { # Required. Map of user to service account.
                  "a_key": "A String",
                },
              },
              "kerberosConfig": { # Specifies Kerberos related configuration. # Optional. Kerberos related configuration.
                "crossRealmTrustAdminServer": "A String", # Optional. The admin server (IP or hostname) for the remote trusted realm in a cross realm trust relationship.
                "crossRealmTrustKdc": "A String", # Optional. The KDC (IP or hostname) for the remote trusted realm in a cross realm trust relationship.
                "crossRealmTrustRealm": "A String", # Optional. The remote realm the Dataproc on-cluster KDC will trust, should the user enable cross realm trust.
                "crossRealmTrustSharedPasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the shared password between the on-cluster Kerberos realm and the remote trusted realm, in a cross realm trust relationship.
                "enableKerberos": True or False, # Optional. Flag to indicate whether to Kerberize the cluster (default: false). Set this field to true to enable Kerberos on a cluster.
                "kdcDbKeyUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the master key of the KDC database.
                "keyPasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the password to the user provided key. For the self-signed certificate, this password is generated by Dataproc.
                "keystorePasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the password to the user provided keystore. For the self-signed certificate, this password is generated by Dataproc.
                "keystoreUri": "A String", # Optional. The Cloud Storage URI of the keystore file used for SSL encryption. If not provided, Dataproc will provide a self-signed certificate.
                "kmsKeyUri": "A String", # Optional. The URI of the KMS key used to encrypt sensitive files.
                "realm": "A String", # Optional. The name of the on-cluster Kerberos realm. If not specified, the uppercased domain of hostnames will be the realm.
                "rootPrincipalPasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the root principal password.
                "tgtLifetimeHours": 42, # Optional. The lifetime of the ticket granting ticket, in hours. If not specified, or user specifies 0, then default value 10 will be used.
                "truststorePasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the password to the user provided truststore. For the self-signed certificate, this password is generated by Dataproc.
                "truststoreUri": "A String", # Optional. The Cloud Storage URI of the truststore file used for SSL encryption. If not provided, Dataproc will provide a self-signed certificate.
              },
            },
            "softwareConfig": { # Specifies the selection and config of software inside the cluster. # Optional. The config settings for cluster software.
              "imageVersion": "A String", # Optional. The version of software inside the cluster. It must be one of the supported Dataproc Versions (https://cloud.google.com/dataproc/docs/concepts/versioning/dataproc-versions#supported-dataproc-image-versions), such as "1.2" (including a subminor version, such as "1.2.29"), or the "preview" version (https://cloud.google.com/dataproc/docs/concepts/versioning/dataproc-versions#other_versions). If unspecified, it defaults to the latest Debian version.
              "optionalComponents": [ # Optional. The set of components to activate on the cluster.
                "A String",
              ],
              "properties": { # Optional. The properties to set on daemon config files.Property keys are specified in prefix:property format, for example core:hadoop.tmp.dir. The following are supported prefixes and their mappings: capacity-scheduler: capacity-scheduler.xml core: core-site.xml distcp: distcp-default.xml hdfs: hdfs-site.xml hive: hive-site.xml mapred: mapred-site.xml pig: pig.properties spark: spark-defaults.conf yarn: yarn-site.xmlFor more information, see Cluster properties (https://cloud.google.com/dataproc/docs/concepts/cluster-properties).
                "a_key": "A String",
              },
            },
            "tempBucket": "A String", # Optional. A Cloud Storage bucket used to store ephemeral cluster and jobs data, such as Spark and MapReduce history files. If you do not specify a temp bucket, Dataproc will determine a Cloud Storage location (US, ASIA, or EU) for your cluster's temp bucket according to the Compute Engine zone where your cluster is deployed, and then create and manage this project-level, per-location bucket. The default bucket has a TTL of 90 days, but you can use any TTL (or none) if you specify a bucket (see Dataproc staging and temp buckets (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/staging-bucket)). This field requires a Cloud Storage bucket name, not a gs://... URI to a Cloud Storage bucket.
            "workerConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The Compute Engine config settings for the cluster's worker instances.
              "accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
                { # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
                  "acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
                  "acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
                },
              ],
              "diskConfig": { # Specifies the config of boot disk and attached disk options for a group of VM instances. # Optional. Disk option config settings.
                "bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. This field is supported only if boot_disk_type is hyperdisk-balanced.
                "bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. This field is supported only if boot_disk_type is hyperdisk-balanced.
                "bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
                "bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
                "localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
                "numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
              },
              "imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
              "instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
                "instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
                  { # Defines machines types and a rank to which the machines types belong.
                    "machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
                      "A String",
                    ],
                    "rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
                  },
                ],
                "instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
                  { # Defines a mapping from machine types to the number of VMs that are created with each machine type.
                    "machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
                    "vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
                  },
                ],
                "provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
                  "standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
                  "standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
                },
              },
              "instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
                "A String",
              ],
              "instanceReferences": [ # Output only. List of references to Compute Engine instances.
                { # A reference to a Compute Engine instance.
                  "instanceId": "A String", # The unique identifier of the Compute Engine instance.
                  "instanceName": "A String", # The user-friendly name of the Compute Engine instance.
                  "publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
                  "publicKey": "A String", # The public RSA key used for sharing data with this instance.
                },
              ],
              "isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
              "machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
              "managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
                "instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
                "instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
                "instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
              },
              "minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
              "minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
              "numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
              "preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
              "startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
                "requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
              },
            },
          },
          "labels": { # Optional. The labels to associate with this cluster.Label keys must be between 1 and 63 characters long, and must conform to the following PCRE regular expression: \p{Ll}\p{Lo}{0,62}Label values must be between 1 and 63 characters long, and must conform to the following PCRE regular expression: \p{Ll}\p{Lo}\p{N}_-{0,63}No more than 32 labels can be associated with a given cluster.
            "a_key": "A String",
          },
        },
      },
      "updateTime": "A String", # Output only. The time template was last updated.
      "version": 42, # Optional. Used to perform a consistent read-modify-write.This field should be left blank for a CreateWorkflowTemplate request. It is required for an UpdateWorkflowTemplate request, and must match the current server version. A typical update template flow would fetch the current template with a GetWorkflowTemplate request, which will return the current template with the version field filled in with the current server version. The user updates other fields in the template, then returns it as part of the UpdateWorkflowTemplate request.
    }

      x__xgafv: string, V1 error format.
        Allowed values
          1 - v1 error format
          2 - v2 error format

    Returns:
      An object of the form:

        { # A Dataproc workflow template resource.
      "createTime": "A String", # Output only. The time template was created.
      "dagTimeout": "A String", # Optional. Timeout duration for the DAG of jobs, expressed in seconds (see JSON representation of duration (https://developers.google.com/protocol-buffers/docs/proto3#json)). The timeout duration must be from 10 minutes ("600s") to 24 hours ("86400s"). The timer begins when the first job is submitted. If the workflow is running at the end of the timeout period, any remaining jobs are cancelled, the workflow is ended, and if the workflow was running on a managed cluster, the cluster is deleted.
      "encryptionConfig": { # Encryption settings for encrypting workflow template job arguments. # Optional. Encryption settings for encrypting workflow template job arguments.
        "kmsKey": "A String", # Optional. The Cloud KMS key name to use for encrypting workflow template job arguments.When this this key is provided, the following workflow template job arguments (https://cloud.google.com/dataproc/docs/concepts/workflows/use-workflows#adding_jobs_to_a_template), if present, are CMEK encrypted (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/customer-managed-encryption#use_cmek_with_workflow_template_data): FlinkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/FlinkJob) HadoopJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/HadoopJob) SparkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkJob) SparkRJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkRJob) PySparkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/PySparkJob) SparkSqlJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkSqlJob) scriptVariables and queryList.queries HiveJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/HiveJob) scriptVariables and queryList.queries PigJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/PigJob) scriptVariables and queryList.queries PrestoJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/PrestoJob) scriptVariables and queryList.queries
      },
      "id": "A String",
      "jobs": [ # Required. The Directed Acyclic Graph of Jobs to submit.
        { # A job executed by the workflow.
          "flinkJob": { # A Dataproc job for running Apache Flink applications on YARN. # Optional. Job is a Flink job.
            "args": [ # Optional. The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision might occur that causes an incorrect job submission.
              "A String",
            ],
            "jarFileUris": [ # Optional. HCFS URIs of jar files to add to the CLASSPATHs of the Flink driver and tasks.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "mainClass": "A String", # The name of the driver's main class. The jar file that contains the class must be in the default CLASSPATH or specified in jarFileUris.
            "mainJarFileUri": "A String", # The HCFS URI of the jar file that contains the main class.
            "properties": { # Optional. A mapping of property names to values, used to configure Flink. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/flink/conf/flink-defaults.conf and classes in user code.
              "a_key": "A String",
            },
            "savepointUri": "A String", # Optional. HCFS URI of the savepoint, which contains the last saved progress for starting the current job.
          },
          "hadoopJob": { # A Dataproc job for running Apache Hadoop MapReduce (https://hadoop.apache.org/docs/current/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html) jobs on Apache Hadoop YARN (https://hadoop.apache.org/docs/r2.7.1/hadoop-yarn/hadoop-yarn-site/YARN.html). # Optional. Job is a Hadoop job.
            "archiveUris": [ # Optional. HCFS URIs of archives to be extracted in the working directory of Hadoop drivers and tasks. Supported file types: .jar, .tar, .tar.gz, .tgz, or .zip.
              "A String",
            ],
            "args": [ # Optional. The arguments to pass to the driver. Do not include arguments, such as -libjars or -Dfoo=bar, that can be set as job properties, since a collision might occur that causes an incorrect job submission.
              "A String",
            ],
            "fileUris": [ # Optional. HCFS (Hadoop Compatible Filesystem) URIs of files to be copied to the working directory of Hadoop drivers and distributed tasks. Useful for naively parallel tasks.
              "A String",
            ],
            "jarFileUris": [ # Optional. Jar file URIs to add to the CLASSPATHs of the Hadoop driver and tasks.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "mainClass": "A String", # The name of the driver's main class. The jar file containing the class must be in the default CLASSPATH or specified in jar_file_uris.
            "mainJarFileUri": "A String", # The HCFS URI of the jar file containing the main class. Examples: 'gs://foo-bucket/analytics-binaries/extract-useful-metrics-mr.jar' 'hdfs:/tmp/test-samples/custom-wordcount.jar' 'file:///home/usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar'
            "properties": { # Optional. A mapping of property names to values, used to configure Hadoop. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/hadoop/conf/*-site and classes in user code.
              "a_key": "A String",
            },
          },
          "hiveJob": { # A Dataproc job for running Apache Hive (https://hive.apache.org/) queries on YARN. # Optional. Job is a Hive job.
            "continueOnFailure": True or False, # Optional. Whether to continue executing queries if a query fails. The default value is false. Setting to true can be useful when executing independent parallel queries.
            "jarFileUris": [ # Optional. HCFS URIs of jar files to add to the CLASSPATH of the Hive server and Hadoop MapReduce (MR) tasks. Can contain Hive SerDes and UDFs.
              "A String",
            ],
            "properties": { # Optional. A mapping of property names and values, used to configure Hive. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/hadoop/conf/*-site.xml, /etc/hive/conf/hive-site.xml, and classes in user code.
              "a_key": "A String",
            },
            "queryFileUri": "A String", # The HCFS URI of the script that contains Hive queries.
            "queryList": { # A list of queries to run on a cluster. # A list of queries.
              "queries": [ # Required. The queries to execute. You do not need to end a query expression with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of a Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } }
                "A String",
              ],
            },
            "scriptVariables": { # Optional. Mapping of query variable names to values (equivalent to the Hive command: SET name="value";).
              "a_key": "A String",
            },
          },
          "labels": { # Optional. The labels to associate with this job.Label keys must be between 1 and 63 characters long, and must conform to the following regular expression: \p{Ll}\p{Lo}{0,62}Label values must be between 1 and 63 characters long, and must conform to the following regular expression: \p{Ll}\p{Lo}\p{N}_-{0,63}No more than 32 labels can be associated with a given job.
            "a_key": "A String",
          },
          "pigJob": { # A Dataproc job for running Apache Pig (https://pig.apache.org/) queries on YARN. # Optional. Job is a Pig job.
            "continueOnFailure": True or False, # Optional. Whether to continue executing queries if a query fails. The default value is false. Setting to true can be useful when executing independent parallel queries.
            "jarFileUris": [ # Optional. HCFS URIs of jar files to add to the CLASSPATH of the Pig Client and Hadoop MapReduce (MR) tasks. Can contain Pig UDFs.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "properties": { # Optional. A mapping of property names to values, used to configure Pig. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/hadoop/conf/*-site.xml, /etc/pig/conf/pig.properties, and classes in user code.
              "a_key": "A String",
            },
            "queryFileUri": "A String", # The HCFS URI of the script that contains the Pig queries.
            "queryList": { # A list of queries to run on a cluster. # A list of queries.
              "queries": [ # Required. The queries to execute. You do not need to end a query expression with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of a Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } }
                "A String",
              ],
            },
            "scriptVariables": { # Optional. Mapping of query variable names to values (equivalent to the Pig command: name=[value]).
              "a_key": "A String",
            },
          },
          "prerequisiteStepIds": [ # Optional. The optional list of prerequisite job step_ids. If not specified, the job will start at the beginning of workflow.
            "A String",
          ],
          "prestoJob": { # A Dataproc job for running Presto (https://prestosql.io/) queries. IMPORTANT: The Dataproc Presto Optional Component (https://cloud.google.com/dataproc/docs/concepts/components/presto) must be enabled when the cluster is created to submit a Presto job to the cluster. # Optional. Job is a Presto job.
            "clientTags": [ # Optional. Presto client tags to attach to this query
              "A String",
            ],
            "continueOnFailure": True or False, # Optional. Whether to continue executing queries if a query fails. The default value is false. Setting to true can be useful when executing independent parallel queries.
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "outputFormat": "A String", # Optional. The format in which query output will be displayed. See the Presto documentation for supported output formats
            "properties": { # Optional. A mapping of property names to values. Used to set Presto session properties (https://prestodb.io/docs/current/sql/set-session.html) Equivalent to using the --session flag in the Presto CLI
              "a_key": "A String",
            },
            "queryFileUri": "A String", # The HCFS URI of the script that contains SQL queries.
            "queryList": { # A list of queries to run on a cluster. # A list of queries.
              "queries": [ # Required. The queries to execute. You do not need to end a query expression with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of a Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } }
                "A String",
              ],
            },
          },
          "pysparkJob": { # A Dataproc job for running Apache PySpark (https://spark.apache.org/docs/latest/api/python/index.html#pyspark-overview) applications on YARN. # Optional. Job is a PySpark job.
            "archiveUris": [ # Optional. HCFS URIs of archives to be extracted into the working directory of each executor. Supported file types: .jar, .tar, .tar.gz, .tgz, and .zip.
              "A String",
            ],
            "args": [ # Optional. The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision may occur that causes an incorrect job submission.
              "A String",
            ],
            "fileUris": [ # Optional. HCFS URIs of files to be placed in the working directory of each executor. Useful for naively parallel tasks.
              "A String",
            ],
            "jarFileUris": [ # Optional. HCFS URIs of jar files to add to the CLASSPATHs of the Python driver and tasks.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "mainPythonFileUri": "A String", # Required. The HCFS URI of the main Python file to use as the driver. Must be a .py file.
            "properties": { # Optional. A mapping of property names to values, used to configure PySpark. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/spark/conf/spark-defaults.conf and classes in user code.
              "a_key": "A String",
            },
            "pythonFileUris": [ # Optional. HCFS file URIs of Python files to pass to the PySpark framework. Supported file types: .py, .egg, and .zip.
              "A String",
            ],
          },
          "scheduling": { # Job scheduling options. # Optional. Job scheduling configuration.
            "maxFailuresPerHour": 42, # Optional. Maximum number of times per hour a driver can be restarted as a result of driver exiting with non-zero code before job is reported failed.A job might be reported as thrashing if the driver exits with a non-zero code four times within a 10-minute window.Maximum value is 10.Note: This restartable job option is not supported in Dataproc workflow templates (https://cloud.google.com/dataproc/docs/concepts/workflows/using-workflows#adding_jobs_to_a_template).
            "maxFailuresTotal": 42, # Optional. Maximum total number of times a driver can be restarted as a result of the driver exiting with a non-zero code. After the maximum number is reached, the job will be reported as failed.Maximum value is 240.Note: Currently, this restartable job option is not supported in Dataproc workflow templates (https://cloud.google.com/dataproc/docs/concepts/workflows/using-workflows#adding_jobs_to_a_template).
          },
          "sparkJob": { # A Dataproc job for running Apache Spark (https://spark.apache.org/) applications on YARN. # Optional. Job is a Spark job.
            "archiveUris": [ # Optional. HCFS URIs of archives to be extracted into the working directory of each executor. Supported file types: .jar, .tar, .tar.gz, .tgz, and .zip.
              "A String",
            ],
            "args": [ # Optional. The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision may occur that causes an incorrect job submission.
              "A String",
            ],
            "fileUris": [ # Optional. HCFS URIs of files to be placed in the working directory of each executor. Useful for naively parallel tasks.
              "A String",
            ],
            "jarFileUris": [ # Optional. HCFS URIs of jar files to add to the CLASSPATHs of the Spark driver and tasks.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "mainClass": "A String", # The name of the driver's main class. The jar file that contains the class must be in the default CLASSPATH or specified in SparkJob.jar_file_uris.
            "mainJarFileUri": "A String", # The HCFS URI of the jar file that contains the main class.
            "properties": { # Optional. A mapping of property names to values, used to configure Spark. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/spark/conf/spark-defaults.conf and classes in user code.
              "a_key": "A String",
            },
          },
          "sparkRJob": { # A Dataproc job for running Apache SparkR (https://spark.apache.org/docs/latest/sparkr.html) applications on YARN. # Optional. Job is a SparkR job.
            "archiveUris": [ # Optional. HCFS URIs of archives to be extracted into the working directory of each executor. Supported file types: .jar, .tar, .tar.gz, .tgz, and .zip.
              "A String",
            ],
            "args": [ # Optional. The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision may occur that causes an incorrect job submission.
              "A String",
            ],
            "fileUris": [ # Optional. HCFS URIs of files to be placed in the working directory of each executor. Useful for naively parallel tasks.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "mainRFileUri": "A String", # Required. The HCFS URI of the main R file to use as the driver. Must be a .R file.
            "properties": { # Optional. A mapping of property names to values, used to configure SparkR. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/spark/conf/spark-defaults.conf and classes in user code.
              "a_key": "A String",
            },
          },
          "sparkSqlJob": { # A Dataproc job for running Apache Spark SQL (https://spark.apache.org/sql/) queries. # Optional. Job is a SparkSql job.
            "jarFileUris": [ # Optional. HCFS URIs of jar files to be added to the Spark CLASSPATH.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "properties": { # Optional. A mapping of property names to values, used to configure Spark SQL's SparkConf. Properties that conflict with values set by the Dataproc API might be overwritten.
              "a_key": "A String",
            },
            "queryFileUri": "A String", # The HCFS URI of the script that contains SQL queries.
            "queryList": { # A list of queries to run on a cluster. # A list of queries.
              "queries": [ # Required. The queries to execute. You do not need to end a query expression with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of a Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } }
                "A String",
              ],
            },
            "scriptVariables": { # Optional. Mapping of query variable names to values (equivalent to the Spark SQL command: SET name="value";).
              "a_key": "A String",
            },
          },
          "stepId": "A String", # Required. The step id. The id must be unique among all jobs within the template.The step id is used as prefix for job id, as job goog-dataproc-workflow-step-id label, and in prerequisiteStepIds field from other steps.The id must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). Cannot begin or end with underscore or hyphen. Must consist of between 3 and 50 characters.
          "trinoJob": { # A Dataproc job for running Trino (https://trino.io/) queries. IMPORTANT: The Dataproc Trino Optional Component (https://cloud.google.com/dataproc/docs/concepts/components/trino) must be enabled when the cluster is created to submit a Trino job to the cluster. # Optional. Job is a Trino job.
            "clientTags": [ # Optional. Trino client tags to attach to this query
              "A String",
            ],
            "continueOnFailure": True or False, # Optional. Whether to continue executing queries if a query fails. The default value is false. Setting to true can be useful when executing independent parallel queries.
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "outputFormat": "A String", # Optional. The format in which query output will be displayed. See the Trino documentation for supported output formats
            "properties": { # Optional. A mapping of property names to values. Used to set Trino session properties (https://trino.io/docs/current/sql/set-session.html) Equivalent to using the --session flag in the Trino CLI
              "a_key": "A String",
            },
            "queryFileUri": "A String", # The HCFS URI of the script that contains SQL queries.
            "queryList": { # A list of queries to run on a cluster. # A list of queries.
              "queries": [ # Required. The queries to execute. You do not need to end a query expression with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of a Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } }
                "A String",
              ],
            },
          },
        },
      ],
      "labels": { # Optional. The labels to associate with this template. These labels will be propagated to all jobs and clusters created by the workflow instance.Label keys must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt).Label values may be empty, but, if present, must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt).No more than 32 labels can be associated with a template.
        "a_key": "A String",
      },
      "name": "A String", # Output only. The resource name of the workflow template, as described in https://cloud.google.com/apis/design/resource_names. For projects.regions.workflowTemplates, the resource name of the template has the following format: projects/{project_id}/regions/{region}/workflowTemplates/{template_id} For projects.locations.workflowTemplates, the resource name of the template has the following format: projects/{project_id}/locations/{location}/workflowTemplates/{template_id}
      "parameters": [ # Optional. Template parameters whose values are substituted into the template. Values for parameters must be provided when the template is instantiated.
        { # A configurable parameter that replaces one or more fields in the template. Parameterizable fields: - Labels - File uris - Job properties - Job arguments - Script variables - Main class (in HadoopJob and SparkJob) - Zone (in ClusterSelector)
          "description": "A String", # Optional. Brief description of the parameter. Must not exceed 1024 characters.
          "fields": [ # Required. Paths to all fields that the parameter replaces. A field is allowed to appear in at most one parameter's list of field paths.A field path is similar in syntax to a google.protobuf.FieldMask. For example, a field path that references the zone field of a workflow template's cluster selector would be specified as placement.clusterSelector.zone.Also, field paths can reference fields using the following syntax: Values in maps can be referenced by key: labels'key' placement.clusterSelector.clusterLabels'key' placement.managedCluster.labels'key' placement.clusterSelector.clusterLabels'key' jobs'step-id'.labels'key' Jobs in the jobs list can be referenced by step-id: jobs'step-id'.hadoopJob.mainJarFileUri jobs'step-id'.hiveJob.queryFileUri jobs'step-id'.pySparkJob.mainPythonFileUri jobs'step-id'.hadoopJob.jarFileUris0 jobs'step-id'.hadoopJob.archiveUris0 jobs'step-id'.hadoopJob.fileUris0 jobs'step-id'.pySparkJob.pythonFileUris0 Items in repeated fields can be referenced by a zero-based index: jobs'step-id'.sparkJob.args0 Other examples: jobs'step-id'.hadoopJob.properties'key' jobs'step-id'.hadoopJob.args0 jobs'step-id'.hiveJob.scriptVariables'key' jobs'step-id'.hadoopJob.mainJarFileUri placement.clusterSelector.zoneIt may not be possible to parameterize maps and repeated fields in their entirety since only individual map values and individual items in repeated fields can be referenced. For example, the following field paths are invalid: placement.clusterSelector.clusterLabels jobs'step-id'.sparkJob.args
            "A String",
          ],
          "name": "A String", # Required. Parameter name. The parameter name is used as the key, and paired with the parameter value, which are passed to the template when the template is instantiated. The name must contain only capital letters (A-Z), numbers (0-9), and underscores (_), and must not start with a number. The maximum length is 40 characters.
          "validation": { # Configuration for parameter validation. # Optional. Validation rules to be applied to this parameter's value.
            "regex": { # Validation based on regular expressions. # Validation based on regular expressions.
              "regexes": [ # Required. RE2 regular expressions used to validate the parameter's value. The value must match the regex in its entirety (substring matches are not sufficient).
                "A String",
              ],
            },
            "values": { # Validation based on a list of allowed values. # Validation based on a list of allowed values.
              "values": [ # Required. List of allowed values for the parameter.
                "A String",
              ],
            },
          },
        },
      ],
      "placement": { # Specifies workflow execution target.Either managed_cluster or cluster_selector is required. # Required. WorkflowTemplate scheduling information.
        "clusterSelector": { # A selector that chooses target cluster for jobs based on metadata. # Optional. A selector that chooses target cluster for jobs based on metadata.The selector is evaluated at the time each job is submitted.
          "clusterLabels": { # Required. The cluster labels. Cluster must have all labels to match.
            "a_key": "A String",
          },
          "zone": "A String", # Optional. The zone where workflow process executes. This parameter does not affect the selection of the cluster.If unspecified, the zone of the first cluster matching the selector is used.
        },
        "managedCluster": { # Cluster that is managed by the workflow. # A cluster that is managed by the workflow.
          "clusterName": "A String", # Required. The cluster name prefix. A unique cluster name will be formed by appending a random suffix.The name must contain only lower-case letters (a-z), numbers (0-9), and hyphens (-). Must begin with a letter. Cannot begin or end with hyphen. Must consist of between 2 and 35 characters.
          "config": { # The cluster config. # Required. The cluster configuration.
            "autoscalingConfig": { # Autoscaling Policy config associated with the cluster. # Optional. Autoscaling config for the policy associated with the cluster. Cluster does not autoscale if this field is unset.
              "policyUri": "A String", # Optional. The autoscaling policy used by the cluster.Only resource names including projectid and location (region) are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/locations/[dataproc_region]/autoscalingPolicies/[policy_id] projects/[project_id]/locations/[dataproc_region]/autoscalingPolicies/[policy_id]Note that the policy must be in the same project and Dataproc region.
            },
            "auxiliaryNodeGroups": [ # Optional. The node group settings.
              { # Node group identification and configuration information.
                "nodeGroup": { # Dataproc Node Group. The Dataproc NodeGroup resource is not related to the Dataproc NodeGroupAffinity resource. # Required. Node group configuration.
                  "labels": { # Optional. Node group labels. Label keys must consist of from 1 to 63 characters and conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). Label values can be empty. If specified, they must consist of from 1 to 63 characters and conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). The node group must have no more than 32 labels.
                    "a_key": "A String",
                  },
                  "name": "A String", # The Node group resource name (https://aip.dev/122).
                  "nodeGroupConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The node group instance group configuration.
                    "accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
                      { # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
                        "acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
                        "acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
                      },
                    ],
                    "diskConfig": { # Specifies the config of boot disk and attached disk options for a group of VM instances. # Optional. Disk option config settings.
                      "bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. This field is supported only if boot_disk_type is hyperdisk-balanced.
                      "bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. This field is supported only if boot_disk_type is hyperdisk-balanced.
                      "bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
                      "bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
                      "localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
                      "numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
                    },
                    "imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
                    "instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
                      "instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
                        { # Defines machines types and a rank to which the machines types belong.
                          "machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
                            "A String",
                          ],
                          "rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
                        },
                      ],
                      "instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
                        { # Defines a mapping from machine types to the number of VMs that are created with each machine type.
                          "machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
                          "vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
                        },
                      ],
                      "provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
                        "standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
                        "standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
                      },
                    },
                    "instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
                      "A String",
                    ],
                    "instanceReferences": [ # Output only. List of references to Compute Engine instances.
                      { # A reference to a Compute Engine instance.
                        "instanceId": "A String", # The unique identifier of the Compute Engine instance.
                        "instanceName": "A String", # The user-friendly name of the Compute Engine instance.
                        "publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
                        "publicKey": "A String", # The public RSA key used for sharing data with this instance.
                      },
                    ],
                    "isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
                    "machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
                    "managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
                      "instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
                      "instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
                      "instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
                    },
                    "minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
                    "minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
                    "numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
                    "preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
                    "startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
                      "requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
                    },
                  },
                  "roles": [ # Required. Node group roles.
                    "A String",
                  ],
                },
                "nodeGroupId": "A String", # Optional. A node group ID. Generated if not specified.The ID must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). Cannot begin or end with underscore or hyphen. Must consist of from 3 to 33 characters.
              },
            ],
            "clusterTier": "A String", # Optional. The cluster tier.
            "clusterType": "A String", # Optional. The type of the cluster.
            "configBucket": "A String", # Optional. A Cloud Storage bucket used to stage job dependencies, config files, and job driver console output. If you do not specify a staging bucket, Cloud Dataproc will determine a Cloud Storage location (US, ASIA, or EU) for your cluster's staging bucket according to the Compute Engine zone where your cluster is deployed, and then create and manage this project-level, per-location bucket (see Dataproc staging and temp buckets (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/staging-bucket)). This field requires a Cloud Storage bucket name, not a gs://... URI to a Cloud Storage bucket.
            "dataprocMetricConfig": { # Dataproc metric config. # Optional. The config for Dataproc metrics.
              "metrics": [ # Required. Metrics sources to enable.
                { # A Dataproc custom metric.
                  "metricOverrides": [ # Optional. Specify one or more Custom metrics (https://cloud.google.com/dataproc/docs/guides/dataproc-metrics#custom_metrics) to collect for the metric course (for the SPARK metric source (any Spark metric (https://spark.apache.org/docs/latest/monitoring.html#metrics) can be specified).Provide metrics in the following format: METRIC_SOURCE: INSTANCE:GROUP:METRIC Use camelcase as appropriate.Examples: yarn:ResourceManager:QueueMetrics:AppsCompleted spark:driver:DAGScheduler:job.allJobs sparkHistoryServer:JVM:Memory:NonHeapMemoryUsage.committed hiveserver2:JVM:Memory:NonHeapMemoryUsage.used Notes: Only the specified overridden metrics are collected for the metric source. For example, if one or more spark:executive metrics are listed as metric overrides, other SPARK metrics are not collected. The collection of the metrics for other enabled custom metric sources is unaffected. For example, if both SPARK and YARN metric sources are enabled, and overrides are provided for Spark metrics only, all YARN metrics are collected.
                    "A String",
                  ],
                  "metricSource": "A String", # Required. A standard set of metrics is collected unless metricOverrides are specified for the metric source (see Custom metrics (https://cloud.google.com/dataproc/docs/guides/dataproc-metrics#custom_metrics) for more information).
                },
              ],
            },
            "encryptionConfig": { # Encryption settings for the cluster. # Optional. Encryption settings for the cluster.
              "gcePdKmsKeyName": "A String", # Optional. The Cloud KMS key resource name to use for persistent disk encryption for all instances in the cluster. See Use CMEK with cluster data (https://cloud.google.com//dataproc/docs/concepts/configuring-clusters/customer-managed-encryption#use_cmek_with_cluster_data) for more information.
              "kmsKey": "A String", # Optional. The Cloud KMS key resource name to use for cluster persistent disk and job argument encryption. See Use CMEK with cluster data (https://cloud.google.com//dataproc/docs/concepts/configuring-clusters/customer-managed-encryption#use_cmek_with_cluster_data) for more information.When this key resource name is provided, the following job arguments of the following job types submitted to the cluster are encrypted using CMEK: FlinkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/FlinkJob) HadoopJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/HadoopJob) SparkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkJob) SparkRJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkRJob) PySparkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/PySparkJob) SparkSqlJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkSqlJob) scriptVariables and queryList.queries HiveJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/HiveJob) scriptVariables and queryList.queries PigJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/PigJob) scriptVariables and queryList.queries PrestoJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/PrestoJob) scriptVariables and queryList.queries
            },
            "endpointConfig": { # Endpoint config for this cluster # Optional. Port/endpoint configuration for this cluster
              "enableHttpPortAccess": True or False, # Optional. If true, enable http access to specific ports on the cluster from external sources. Defaults to false.
              "httpPorts": { # Output only. The map of port descriptions to URLs. Will only be populated if enable_http_port_access is true.
                "a_key": "A String",
              },
            },
            "gceClusterConfig": { # Common config settings for resources of Compute Engine cluster instances, applicable to all instances in the cluster. # Optional. The shared Compute Engine config settings for all instances in a cluster.
              "confidentialInstanceConfig": { # Confidential Instance Config for clusters using Confidential VMs (https://cloud.google.com/compute/confidential-vm/docs) # Optional. Confidential Instance Config for clusters using Confidential VMs (https://cloud.google.com/compute/confidential-vm/docs).
                "enableConfidentialCompute": True or False, # Optional. Defines whether the instance should have confidential compute enabled.
              },
              "internalIpOnly": True or False, # Optional. This setting applies to subnetwork-enabled networks. It is set to true by default in clusters created with image versions 2.2.x.When set to true: All cluster VMs have internal IP addresses. Google Private Access (https://cloud.google.com/vpc/docs/private-google-access) must be enabled to access Dataproc and other Google Cloud APIs. Off-cluster dependencies must be configured to be accessible without external IP addresses.When set to false: Cluster VMs are not restricted to internal IP addresses. Ephemeral external IP addresses are assigned to each cluster VM.
              "metadata": { # Optional. The Compute Engine metadata entries to add to all instances (see Project and instance metadata (https://cloud.google.com/compute/docs/storing-retrieving-metadata#project_and_instance_metadata)).
                "a_key": "A String",
              },
              "networkUri": "A String", # Optional. The Compute Engine network to be used for machine communications. Cannot be specified with subnetwork_uri. If neither network_uri nor subnetwork_uri is specified, the "default" network of the project is used, if it exists. Cannot be a "Custom Subnet Network" (see Using Subnetworks (https://cloud.google.com/compute/docs/subnetworks) for more information).A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/networks/default projects/[project_id]/global/networks/default default
              "nodeGroupAffinity": { # Node Group Affinity for clusters using sole-tenant node groups. The Dataproc NodeGroupAffinity resource is not related to the Dataproc NodeGroup resource. # Optional. Node Group Affinity for sole-tenant clusters.
                "nodeGroupUri": "A String", # Required. The URI of a sole-tenant node group resource (https://cloud.google.com/compute/docs/reference/rest/v1/nodeGroups) that the cluster will be created on.A full URL, partial URI, or node group name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/nodeGroups/node-group-1 projects/[project_id]/zones/[zone]/nodeGroups/node-group-1 node-group-1
              },
              "privateIpv6GoogleAccess": "A String", # Optional. The type of IPv6 access for a cluster.
              "reservationAffinity": { # Reservation Affinity for consuming Zonal reservation. # Optional. Reservation Affinity for consuming Zonal reservation.
                "consumeReservationType": "A String", # Optional. Type of reservation to consume
                "key": "A String", # Optional. Corresponds to the label key of reservation resource.
                "values": [ # Optional. Corresponds to the label values of reservation resource.
                  "A String",
                ],
              },
              "resourceManagerTags": { # Optional. Resource manager tags (https://cloud.google.com/resource-manager/docs/tags/tags-creating-and-managing) to add to all instances (see Use secure tags in Dataproc (https://cloud.google.com/dataproc/docs/guides/attach-secure-tags)).
                "a_key": "A String",
              },
              "serviceAccount": "A String", # Optional. The Dataproc service account (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/service-accounts#service_accounts_in_dataproc) (also see VM Data Plane identity (https://cloud.google.com/dataproc/docs/concepts/iam/dataproc-principals#vm_service_account_data_plane_identity)) used by Dataproc cluster VM instances to access Google Cloud Platform services.If not specified, the Compute Engine default service account (https://cloud.google.com/compute/docs/access/service-accounts#default_service_account) is used.
              "serviceAccountScopes": [ # Optional. The URIs of service account scopes to be included in Compute Engine instances. The following base set of scopes is always included: https://www.googleapis.com/auth/cloud.useraccounts.readonly https://www.googleapis.com/auth/devstorage.read_write https://www.googleapis.com/auth/logging.writeIf no scopes are specified, the following defaults are also provided: https://www.googleapis.com/auth/bigquery https://www.googleapis.com/auth/bigtable.admin.table https://www.googleapis.com/auth/bigtable.data https://www.googleapis.com/auth/devstorage.full_control
                "A String",
              ],
              "shieldedInstanceConfig": { # Shielded Instance Config for clusters using Compute Engine Shielded VMs (https://cloud.google.com/security/shielded-cloud/shielded-vm). # Optional. Shielded Instance Config for clusters using Compute Engine Shielded VMs (https://cloud.google.com/security/shielded-cloud/shielded-vm).
                "enableIntegrityMonitoring": True or False, # Optional. Defines whether instances have integrity monitoring enabled.
                "enableSecureBoot": True or False, # Optional. Defines whether instances have Secure Boot enabled.
                "enableVtpm": True or False, # Optional. Defines whether instances have the vTPM enabled.
              },
              "subnetworkUri": "A String", # Optional. The Compute Engine subnetwork to be used for machine communications. Cannot be specified with network_uri.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/regions/[region]/subnetworks/sub0 projects/[project_id]/regions/[region]/subnetworks/sub0 sub0
              "tags": [ # The Compute Engine network tags to add to all instances (see Tagging instances (https://cloud.google.com/vpc/docs/add-remove-network-tags)).
                "A String",
              ],
              "zoneUri": "A String", # Optional. The Compute Engine zone where the Dataproc cluster will be located. If omitted, the service will pick a zone in the cluster's Compute Engine region. On a get request, zone will always be present.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone] projects/[project_id]/zones/[zone] [zone]
            },
            "gkeClusterConfig": { # The cluster's GKE config. # Optional. BETA. The Kubernetes Engine config for Dataproc clusters deployed to The Kubernetes Engine config for Dataproc clusters deployed to Kubernetes. These config settings are mutually exclusive with Compute Engine-based options, such as gce_cluster_config, master_config, worker_config, secondary_worker_config, and autoscaling_config.
              "gkeClusterTarget": "A String", # Optional. A target GKE cluster to deploy to. It must be in the same project and region as the Dataproc cluster (the GKE cluster can be zonal or regional). Format: 'projects/{project}/locations/{location}/clusters/{cluster_id}'
              "namespacedGkeDeploymentTarget": { # Deprecated. Used only for the deprecated beta. A full, namespace-isolated deployment target for an existing GKE cluster. # Optional. Deprecated. Use gkeClusterTarget. Used only for the deprecated beta. A target for the deployment.
                "clusterNamespace": "A String", # Optional. A namespace within the GKE cluster to deploy into.
                "targetGkeCluster": "A String", # Optional. The target GKE cluster to deploy to. Format: 'projects/{project}/locations/{location}/clusters/{cluster_id}'
              },
              "nodePoolTarget": [ # Optional. GKE node pools where workloads will be scheduled. At least one node pool must be assigned the DEFAULT GkeNodePoolTarget.Role. If a GkeNodePoolTarget is not specified, Dataproc constructs a DEFAULT GkeNodePoolTarget. Each role can be given to only one GkeNodePoolTarget. All node pools must have the same location settings.
                { # GKE node pools that Dataproc workloads run on.
                  "nodePool": "A String", # Required. The target GKE node pool. Format: 'projects/{project}/locations/{location}/clusters/{cluster}/nodePools/{node_pool}'
                  "nodePoolConfig": { # The configuration of a GKE node pool used by a Dataproc-on-GKE cluster (https://cloud.google.com/dataproc/docs/concepts/jobs/dataproc-gke#create-a-dataproc-on-gke-cluster). # Input only. The configuration for the GKE node pool.If specified, Dataproc attempts to create a node pool with the specified shape. If one with the same name already exists, it is verified against all specified fields. If a field differs, the virtual cluster creation will fail.If omitted, any node pool with the specified name is used. If a node pool with the specified name does not exist, Dataproc create a node pool with default values.This is an input only field. It will not be returned by the API.
                    "autoscaling": { # GkeNodePoolAutoscaling contains information the cluster autoscaler needs to adjust the size of the node pool to the current cluster usage. # Optional. The autoscaler configuration for this node pool. The autoscaler is enabled only when a valid configuration is present.
                      "maxNodeCount": 42, # The maximum number of nodes in the node pool. Must be >= min_node_count, and must be > 0. Note: Quota must be sufficient to scale up the cluster.
                      "minNodeCount": 42, # The minimum number of nodes in the node pool. Must be >= 0 and <= max_node_count.
                    },
                    "config": { # Parameters that describe cluster nodes. # Optional. The node pool configuration.
                      "accelerators": [ # Optional. A list of hardware accelerators (https://cloud.google.com/compute/docs/gpus) to attach to each node.
                        { # A GkeNodeConfigAcceleratorConfig represents a Hardware Accelerator request for a node pool.
                          "acceleratorCount": "A String", # The number of accelerator cards exposed to an instance.
                          "acceleratorType": "A String", # The accelerator type resource namename (see GPUs on Compute Engine).
                          "gpuPartitionSize": "A String", # Size of partitions to create on the GPU. Valid values are described in the NVIDIA mig user guide (https://docs.nvidia.com/datacenter/tesla/mig-user-guide/#partitioning).
                        },
                      ],
                      "bootDiskKmsKey": "A String", # Optional. The Customer Managed Encryption Key (CMEK) (https://cloud.google.com/kubernetes-engine/docs/how-to/using-cmek) used to encrypt the boot disk attached to each node in the node pool. Specify the key using the following format: projects/{project}/locations/{location}/keyRings/{key_ring}/cryptoKeys/{crypto_key}
                      "localSsdCount": 42, # Optional. The number of local SSD disks to attach to the node, which is limited by the maximum number of disks allowable per zone (see Adding Local SSDs (https://cloud.google.com/compute/docs/disks/local-ssd)).
                      "machineType": "A String", # Optional. The name of a Compute Engine machine type (https://cloud.google.com/compute/docs/machine-types).
                      "minCpuPlatform": "A String", # Optional. Minimum CPU platform (https://cloud.google.com/compute/docs/instances/specify-min-cpu-platform) to be used by this instance. The instance may be scheduled on the specified or a newer CPU platform. Specify the friendly names of CPU platforms, such as "Intel Haswell"` or Intel Sandy Bridge".
                      "preemptible": True or False, # Optional. Whether the nodes are created as legacy preemptible VM instances (https://cloud.google.com/compute/docs/instances/preemptible). Also see Spot VMs, preemptible VM instances without a maximum lifetime. Legacy and Spot preemptible nodes cannot be used in a node pool with the CONTROLLER role or in the DEFAULT node pool if the CONTROLLER role is not assigned (the DEFAULT node pool will assume the CONTROLLER role).
                      "spot": True or False, # Optional. Whether the nodes are created as Spot VM instances (https://cloud.google.com/compute/docs/instances/spot). Spot VMs are the latest update to legacy preemptible VMs. Spot VMs do not have a maximum lifetime. Legacy and Spot preemptible nodes cannot be used in a node pool with the CONTROLLER role or in the DEFAULT node pool if the CONTROLLER role is not assigned (the DEFAULT node pool will assume the CONTROLLER role).
                    },
                    "locations": [ # Optional. The list of Compute Engine zones (https://cloud.google.com/compute/docs/zones#available) where node pool nodes associated with a Dataproc on GKE virtual cluster will be located.Note: All node pools associated with a virtual cluster must be located in the same region as the virtual cluster, and they must be located in the same zone within that region.If a location is not specified during node pool creation, Dataproc on GKE will choose the zone.
                      "A String",
                    ],
                  },
                  "roles": [ # Required. The roles associated with the GKE node pool.
                    "A String",
                  ],
                },
              ],
            },
            "initializationActions": [ # Optional. Commands to execute on each node after config is completed. By default, executables are run on master and all worker nodes. You can test a node's role metadata to run an executable on a master or worker node, as shown below using curl (you can also use wget): ROLE=$(curl -H Metadata-Flavor:Google http://metadata/computeMetadata/v1/instance/attributes/dataproc-role) if [[ "${ROLE}" == 'Master' ]]; then ... master specific actions ... else ... worker specific actions ... fi
              { # Specifies an executable to run on a fully configured node and a timeout period for executable completion.
                "executableFile": "A String", # Required. Cloud Storage URI of executable file.
                "executionTimeout": "A String", # Optional. Amount of time executable has to complete. Default is 10 minutes (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).Cluster creation fails with an explanatory error message (the name of the executable that caused the error and the exceeded timeout period) if the executable is not completed at end of the timeout period.
              },
            ],
            "lifecycleConfig": { # Specifies the cluster auto-delete schedule configuration. # Optional. Lifecycle setting for the cluster.
              "autoDeleteTime": "A String", # Optional. The time when cluster will be auto-deleted (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
              "autoDeleteTtl": "A String", # Optional. The lifetime duration of cluster. The cluster will be auto-deleted at the end of this period. Minimum value is 10 minutes; maximum value is 14 days (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).
              "autoStopTime": "A String", # Optional. The time when cluster will be auto-stopped (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
              "autoStopTtl": "A String", # Optional. The lifetime duration of the cluster. The cluster will be auto-stopped at the end of this period, calculated from the time of submission of the create or update cluster request. Minimum value is 10 minutes; maximum value is 14 days (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).
              "idleDeleteTtl": "A String", # Optional. The duration to keep the cluster alive while idling (when no jobs are running). Passing this threshold will cause the cluster to be deleted. Minimum value is 5 minutes; maximum value is 14 days (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).
              "idleStartTime": "A String", # Output only. The time when cluster became idle (most recent job finished) and became eligible for deletion due to idleness (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
              "idleStopTtl": "A String", # Optional. The duration to keep the cluster started while idling (when no jobs are running). Passing this threshold will cause the cluster to be stopped. Minimum value is 5 minutes; maximum value is 14 days (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).
            },
            "masterConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The Compute Engine config settings for the cluster's master instance.
              "accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
                { # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
                  "acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
                  "acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
                },
              ],
              "diskConfig": { # Specifies the config of boot disk and attached disk options for a group of VM instances. # Optional. Disk option config settings.
                "bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. This field is supported only if boot_disk_type is hyperdisk-balanced.
                "bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. This field is supported only if boot_disk_type is hyperdisk-balanced.
                "bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
                "bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
                "localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
                "numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
              },
              "imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
              "instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
                "instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
                  { # Defines machines types and a rank to which the machines types belong.
                    "machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
                      "A String",
                    ],
                    "rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
                  },
                ],
                "instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
                  { # Defines a mapping from machine types to the number of VMs that are created with each machine type.
                    "machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
                    "vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
                  },
                ],
                "provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
                  "standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
                  "standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
                },
              },
              "instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
                "A String",
              ],
              "instanceReferences": [ # Output only. List of references to Compute Engine instances.
                { # A reference to a Compute Engine instance.
                  "instanceId": "A String", # The unique identifier of the Compute Engine instance.
                  "instanceName": "A String", # The user-friendly name of the Compute Engine instance.
                  "publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
                  "publicKey": "A String", # The public RSA key used for sharing data with this instance.
                },
              ],
              "isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
              "machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
              "managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
                "instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
                "instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
                "instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
              },
              "minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
              "minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
              "numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
              "preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
              "startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
                "requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
              },
            },
            "metastoreConfig": { # Specifies a Metastore configuration. # Optional. Metastore configuration.
              "dataprocMetastoreService": "A String", # Required. Resource name of an existing Dataproc Metastore service.Example: projects/[project_id]/locations/[dataproc_region]/services/[service-name]
            },
            "secondaryWorkerConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The Compute Engine config settings for a cluster's secondary worker instances
              "accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
                { # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
                  "acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
                  "acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
                },
              ],
              "diskConfig": { # Specifies the config of boot disk and attached disk options for a group of VM instances. # Optional. Disk option config settings.
                "bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. This field is supported only if boot_disk_type is hyperdisk-balanced.
                "bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. This field is supported only if boot_disk_type is hyperdisk-balanced.
                "bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
                "bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
                "localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
                "numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
              },
              "imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
              "instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
                "instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
                  { # Defines machines types and a rank to which the machines types belong.
                    "machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
                      "A String",
                    ],
                    "rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
                  },
                ],
                "instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
                  { # Defines a mapping from machine types to the number of VMs that are created with each machine type.
                    "machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
                    "vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
                  },
                ],
                "provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
                  "standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
                  "standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
                },
              },
              "instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
                "A String",
              ],
              "instanceReferences": [ # Output only. List of references to Compute Engine instances.
                { # A reference to a Compute Engine instance.
                  "instanceId": "A String", # The unique identifier of the Compute Engine instance.
                  "instanceName": "A String", # The user-friendly name of the Compute Engine instance.
                  "publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
                  "publicKey": "A String", # The public RSA key used for sharing data with this instance.
                },
              ],
              "isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
              "machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
              "managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
                "instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
                "instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
                "instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
              },
              "minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
              "minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
              "numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
              "preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
              "startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
                "requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
              },
            },
            "securityConfig": { # Security related configuration, including encryption, Kerberos, etc. # Optional. Security settings for the cluster.
              "identityConfig": { # Identity related configuration, including service account based secure multi-tenancy user mappings. # Optional. Identity related configuration, including service account based secure multi-tenancy user mappings.
                "userServiceAccountMapping": { # Required. Map of user to service account.
                  "a_key": "A String",
                },
              },
              "kerberosConfig": { # Specifies Kerberos related configuration. # Optional. Kerberos related configuration.
                "crossRealmTrustAdminServer": "A String", # Optional. The admin server (IP or hostname) for the remote trusted realm in a cross realm trust relationship.
                "crossRealmTrustKdc": "A String", # Optional. The KDC (IP or hostname) for the remote trusted realm in a cross realm trust relationship.
                "crossRealmTrustRealm": "A String", # Optional. The remote realm the Dataproc on-cluster KDC will trust, should the user enable cross realm trust.
                "crossRealmTrustSharedPasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the shared password between the on-cluster Kerberos realm and the remote trusted realm, in a cross realm trust relationship.
                "enableKerberos": True or False, # Optional. Flag to indicate whether to Kerberize the cluster (default: false). Set this field to true to enable Kerberos on a cluster.
                "kdcDbKeyUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the master key of the KDC database.
                "keyPasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the password to the user provided key. For the self-signed certificate, this password is generated by Dataproc.
                "keystorePasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the password to the user provided keystore. For the self-signed certificate, this password is generated by Dataproc.
                "keystoreUri": "A String", # Optional. The Cloud Storage URI of the keystore file used for SSL encryption. If not provided, Dataproc will provide a self-signed certificate.
                "kmsKeyUri": "A String", # Optional. The URI of the KMS key used to encrypt sensitive files.
                "realm": "A String", # Optional. The name of the on-cluster Kerberos realm. If not specified, the uppercased domain of hostnames will be the realm.
                "rootPrincipalPasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the root principal password.
                "tgtLifetimeHours": 42, # Optional. The lifetime of the ticket granting ticket, in hours. If not specified, or user specifies 0, then default value 10 will be used.
                "truststorePasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the password to the user provided truststore. For the self-signed certificate, this password is generated by Dataproc.
                "truststoreUri": "A String", # Optional. The Cloud Storage URI of the truststore file used for SSL encryption. If not provided, Dataproc will provide a self-signed certificate.
              },
            },
            "softwareConfig": { # Specifies the selection and config of software inside the cluster. # Optional. The config settings for cluster software.
              "imageVersion": "A String", # Optional. The version of software inside the cluster. It must be one of the supported Dataproc Versions (https://cloud.google.com/dataproc/docs/concepts/versioning/dataproc-versions#supported-dataproc-image-versions), such as "1.2" (including a subminor version, such as "1.2.29"), or the "preview" version (https://cloud.google.com/dataproc/docs/concepts/versioning/dataproc-versions#other_versions). If unspecified, it defaults to the latest Debian version.
              "optionalComponents": [ # Optional. The set of components to activate on the cluster.
                "A String",
              ],
              "properties": { # Optional. The properties to set on daemon config files.Property keys are specified in prefix:property format, for example core:hadoop.tmp.dir. The following are supported prefixes and their mappings: capacity-scheduler: capacity-scheduler.xml core: core-site.xml distcp: distcp-default.xml hdfs: hdfs-site.xml hive: hive-site.xml mapred: mapred-site.xml pig: pig.properties spark: spark-defaults.conf yarn: yarn-site.xmlFor more information, see Cluster properties (https://cloud.google.com/dataproc/docs/concepts/cluster-properties).
                "a_key": "A String",
              },
            },
            "tempBucket": "A String", # Optional. A Cloud Storage bucket used to store ephemeral cluster and jobs data, such as Spark and MapReduce history files. If you do not specify a temp bucket, Dataproc will determine a Cloud Storage location (US, ASIA, or EU) for your cluster's temp bucket according to the Compute Engine zone where your cluster is deployed, and then create and manage this project-level, per-location bucket. The default bucket has a TTL of 90 days, but you can use any TTL (or none) if you specify a bucket (see Dataproc staging and temp buckets (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/staging-bucket)). This field requires a Cloud Storage bucket name, not a gs://... URI to a Cloud Storage bucket.
            "workerConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The Compute Engine config settings for the cluster's worker instances.
              "accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
                { # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
                  "acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
                  "acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
                },
              ],
              "diskConfig": { # Specifies the config of boot disk and attached disk options for a group of VM instances. # Optional. Disk option config settings.
                "bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. This field is supported only if boot_disk_type is hyperdisk-balanced.
                "bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. This field is supported only if boot_disk_type is hyperdisk-balanced.
                "bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
                "bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
                "localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
                "numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
              },
              "imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
              "instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
                "instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
                  { # Defines machines types and a rank to which the machines types belong.
                    "machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
                      "A String",
                    ],
                    "rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
                  },
                ],
                "instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
                  { # Defines a mapping from machine types to the number of VMs that are created with each machine type.
                    "machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
                    "vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
                  },
                ],
                "provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
                  "standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
                  "standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
                },
              },
              "instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
                "A String",
              ],
              "instanceReferences": [ # Output only. List of references to Compute Engine instances.
                { # A reference to a Compute Engine instance.
                  "instanceId": "A String", # The unique identifier of the Compute Engine instance.
                  "instanceName": "A String", # The user-friendly name of the Compute Engine instance.
                  "publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
                  "publicKey": "A String", # The public RSA key used for sharing data with this instance.
                },
              ],
              "isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
              "machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
              "managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
                "instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
                "instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
                "instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
              },
              "minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
              "minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
              "numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
              "preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
              "startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
                "requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
              },
            },
          },
          "labels": { # Optional. The labels to associate with this cluster.Label keys must be between 1 and 63 characters long, and must conform to the following PCRE regular expression: \p{Ll}\p{Lo}{0,62}Label values must be between 1 and 63 characters long, and must conform to the following PCRE regular expression: \p{Ll}\p{Lo}\p{N}_-{0,63}No more than 32 labels can be associated with a given cluster.
            "a_key": "A String",
          },
        },
      },
      "updateTime": "A String", # Output only. The time template was last updated.
      "version": 42, # Optional. Used to perform a consistent read-modify-write.This field should be left blank for a CreateWorkflowTemplate request. It is required for an UpdateWorkflowTemplate request, and must match the current server version. A typical update template flow would fetch the current template with a GetWorkflowTemplate request, which will return the current template with the version field filled in with the current server version. The user updates other fields in the template, then returns it as part of the UpdateWorkflowTemplate request.
    }

`delete(name, version=None, x__xgafv=None)`

    Deletes a workflow template. It does not cancel in-progress workflows.

    Args:
      name: string, Required. The resource name of the workflow template, as described in https://cloud.google.com/apis/design/resource_names. For projects.regions.workflowTemplates.delete, the resource name of the template has the following format: projects/{project_id}/regions/{region}/workflowTemplates/{template_id} For projects.locations.workflowTemplates.instantiate, the resource name of the template has the following format: projects/{project_id}/locations/{location}/workflowTemplates/{template_id} (required)
      version: integer, Optional. The version of workflow template to delete. If specified, will only delete the template if the current server version matches specified version.
      x__xgafv: string, V1 error format.
        Allowed values
          1 - v1 error format
          2 - v2 error format

    Returns:
      An object of the form:

        { # A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); }
    }

`get(name, version=None, x__xgafv=None)`

    Retrieves the latest workflow template.Can retrieve previously instantiated template by specifying optional version parameter.

    Args:
      name: string, Required. The resource name of the workflow template, as described in https://cloud.google.com/apis/design/resource_names. For projects.regions.workflowTemplates.get, the resource name of the template has the following format: projects/{project_id}/regions/{region}/workflowTemplates/{template_id} For projects.locations.workflowTemplates.get, the resource name of the template has the following format: projects/{project_id}/locations/{location}/workflowTemplates/{template_id} (required)
      version: integer, Optional. The version of workflow template to retrieve. Only previously instantiated versions can be retrieved.If unspecified, retrieves the current version.
      x__xgafv: string, V1 error format.
        Allowed values
          1 - v1 error format
          2 - v2 error format

    Returns:
      An object of the form:

        { # A Dataproc workflow template resource.
      "createTime": "A String", # Output only. The time template was created.
      "dagTimeout": "A String", # Optional. Timeout duration for the DAG of jobs, expressed in seconds (see JSON representation of duration (https://developers.google.com/protocol-buffers/docs/proto3#json)). The timeout duration must be from 10 minutes ("600s") to 24 hours ("86400s"). The timer begins when the first job is submitted. If the workflow is running at the end of the timeout period, any remaining jobs are cancelled, the workflow is ended, and if the workflow was running on a managed cluster, the cluster is deleted.
      "encryptionConfig": { # Encryption settings for encrypting workflow template job arguments. # Optional. Encryption settings for encrypting workflow template job arguments.
        "kmsKey": "A String", # Optional. The Cloud KMS key name to use for encrypting workflow template job arguments.When this this key is provided, the following workflow template job arguments (https://cloud.google.com/dataproc/docs/concepts/workflows/use-workflows#adding_jobs_to_a_template), if present, are CMEK encrypted (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/customer-managed-encryption#use_cmek_with_workflow_template_data): FlinkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/FlinkJob) HadoopJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/HadoopJob) SparkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkJob) SparkRJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkRJob) PySparkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/PySparkJob) SparkSqlJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkSqlJob) scriptVariables and queryList.queries HiveJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/HiveJob) scriptVariables and queryList.queries PigJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/PigJob) scriptVariables and queryList.queries PrestoJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/PrestoJob) scriptVariables and queryList.queries
      },
      "id": "A String",
      "jobs": [ # Required. The Directed Acyclic Graph of Jobs to submit.
        { # A job executed by the workflow.
          "flinkJob": { # A Dataproc job for running Apache Flink applications on YARN. # Optional. Job is a Flink job.
            "args": [ # Optional. The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision might occur that causes an incorrect job submission.
              "A String",
            ],
            "jarFileUris": [ # Optional. HCFS URIs of jar files to add to the CLASSPATHs of the Flink driver and tasks.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "mainClass": "A String", # The name of the driver's main class. The jar file that contains the class must be in the default CLASSPATH or specified in jarFileUris.
            "mainJarFileUri": "A String", # The HCFS URI of the jar file that contains the main class.
            "properties": { # Optional. A mapping of property names to values, used to configure Flink. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/flink/conf/flink-defaults.conf and classes in user code.
              "a_key": "A String",
            },
            "savepointUri": "A String", # Optional. HCFS URI of the savepoint, which contains the last saved progress for starting the current job.
          },
          "hadoopJob": { # A Dataproc job for running Apache Hadoop MapReduce (https://hadoop.apache.org/docs/current/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html) jobs on Apache Hadoop YARN (https://hadoop.apache.org/docs/r2.7.1/hadoop-yarn/hadoop-yarn-site/YARN.html). # Optional. Job is a Hadoop job.
            "archiveUris": [ # Optional. HCFS URIs of archives to be extracted in the working directory of Hadoop drivers and tasks. Supported file types: .jar, .tar, .tar.gz, .tgz, or .zip.
              "A String",
            ],
            "args": [ # Optional. The arguments to pass to the driver. Do not include arguments, such as -libjars or -Dfoo=bar, that can be set as job properties, since a collision might occur that causes an incorrect job submission.
              "A String",
            ],
            "fileUris": [ # Optional. HCFS (Hadoop Compatible Filesystem) URIs of files to be copied to the working directory of Hadoop drivers and distributed tasks. Useful for naively parallel tasks.
              "A String",
            ],
            "jarFileUris": [ # Optional. Jar file URIs to add to the CLASSPATHs of the Hadoop driver and tasks.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "mainClass": "A String", # The name of the driver's main class. The jar file containing the class must be in the default CLASSPATH or specified in jar_file_uris.
            "mainJarFileUri": "A String", # The HCFS URI of the jar file containing the main class. Examples: 'gs://foo-bucket/analytics-binaries/extract-useful-metrics-mr.jar' 'hdfs:/tmp/test-samples/custom-wordcount.jar' 'file:///home/usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar'
            "properties": { # Optional. A mapping of property names to values, used to configure Hadoop. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/hadoop/conf/*-site and classes in user code.
              "a_key": "A String",
            },
          },
          "hiveJob": { # A Dataproc job for running Apache Hive (https://hive.apache.org/) queries on YARN. # Optional. Job is a Hive job.
            "continueOnFailure": True or False, # Optional. Whether to continue executing queries if a query fails. The default value is false. Setting to true can be useful when executing independent parallel queries.
            "jarFileUris": [ # Optional. HCFS URIs of jar files to add to the CLASSPATH of the Hive server and Hadoop MapReduce (MR) tasks. Can contain Hive SerDes and UDFs.
              "A String",
            ],
            "properties": { # Optional. A mapping of property names and values, used to configure Hive. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/hadoop/conf/*-site.xml, /etc/hive/conf/hive-site.xml, and classes in user code.
              "a_key": "A String",
            },
            "queryFileUri": "A String", # The HCFS URI of the script that contains Hive queries.
            "queryList": { # A list of queries to run on a cluster. # A list of queries.
              "queries": [ # Required. The queries to execute. You do not need to end a query expression with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of a Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } }
                "A String",
              ],
            },
            "scriptVariables": { # Optional. Mapping of query variable names to values (equivalent to the Hive command: SET name="value";).
              "a_key": "A String",
            },
          },
          "labels": { # Optional. The labels to associate with this job.Label keys must be between 1 and 63 characters long, and must conform to the following regular expression: \p{Ll}\p{Lo}{0,62}Label values must be between 1 and 63 characters long, and must conform to the following regular expression: \p{Ll}\p{Lo}\p{N}_-{0,63}No more than 32 labels can be associated with a given job.
            "a_key": "A String",
          },
          "pigJob": { # A Dataproc job for running Apache Pig (https://pig.apache.org/) queries on YARN. # Optional. Job is a Pig job.
            "continueOnFailure": True or False, # Optional. Whether to continue executing queries if a query fails. The default value is false. Setting to true can be useful when executing independent parallel queries.
            "jarFileUris": [ # Optional. HCFS URIs of jar files to add to the CLASSPATH of the Pig Client and Hadoop MapReduce (MR) tasks. Can contain Pig UDFs.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "properties": { # Optional. A mapping of property names to values, used to configure Pig. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/hadoop/conf/*-site.xml, /etc/pig/conf/pig.properties, and classes in user code.
              "a_key": "A String",
            },
            "queryFileUri": "A String", # The HCFS URI of the script that contains the Pig queries.
            "queryList": { # A list of queries to run on a cluster. # A list of queries.
              "queries": [ # Required. The queries to execute. You do not need to end a query expression with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of a Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } }
                "A String",
              ],
            },
            "scriptVariables": { # Optional. Mapping of query variable names to values (equivalent to the Pig command: name=[value]).
              "a_key": "A String",
            },
          },
          "prerequisiteStepIds": [ # Optional. The optional list of prerequisite job step_ids. If not specified, the job will start at the beginning of workflow.
            "A String",
          ],
          "prestoJob": { # A Dataproc job for running Presto (https://prestosql.io/) queries. IMPORTANT: The Dataproc Presto Optional Component (https://cloud.google.com/dataproc/docs/concepts/components/presto) must be enabled when the cluster is created to submit a Presto job to the cluster. # Optional. Job is a Presto job.
            "clientTags": [ # Optional. Presto client tags to attach to this query
              "A String",
            ],
            "continueOnFailure": True or False, # Optional. Whether to continue executing queries if a query fails. The default value is false. Setting to true can be useful when executing independent parallel queries.
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "outputFormat": "A String", # Optional. The format in which query output will be displayed. See the Presto documentation for supported output formats
            "properties": { # Optional. A mapping of property names to values. Used to set Presto session properties (https://prestodb.io/docs/current/sql/set-session.html) Equivalent to using the --session flag in the Presto CLI
              "a_key": "A String",
            },
            "queryFileUri": "A String", # The HCFS URI of the script that contains SQL queries.
            "queryList": { # A list of queries to run on a cluster. # A list of queries.
              "queries": [ # Required. The queries to execute. You do not need to end a query expression with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of a Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } }
                "A String",
              ],
            },
          },
          "pysparkJob": { # A Dataproc job for running Apache PySpark (https://spark.apache.org/docs/latest/api/python/index.html#pyspark-overview) applications on YARN. # Optional. Job is a PySpark job.
            "archiveUris": [ # Optional. HCFS URIs of archives to be extracted into the working directory of each executor. Supported file types: .jar, .tar, .tar.gz, .tgz, and .zip.
              "A String",
            ],
            "args": [ # Optional. The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision may occur that causes an incorrect job submission.
              "A String",
            ],
            "fileUris": [ # Optional. HCFS URIs of files to be placed in the working directory of each executor. Useful for naively parallel tasks.
              "A String",
            ],
            "jarFileUris": [ # Optional. HCFS URIs of jar files to add to the CLASSPATHs of the Python driver and tasks.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "mainPythonFileUri": "A String", # Required. The HCFS URI of the main Python file to use as the driver. Must be a .py file.
            "properties": { # Optional. A mapping of property names to values, used to configure PySpark. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/spark/conf/spark-defaults.conf and classes in user code.
              "a_key": "A String",
            },
            "pythonFileUris": [ # Optional. HCFS file URIs of Python files to pass to the PySpark framework. Supported file types: .py, .egg, and .zip.
              "A String",
            ],
          },
          "scheduling": { # Job scheduling options. # Optional. Job scheduling configuration.
            "maxFailuresPerHour": 42, # Optional. Maximum number of times per hour a driver can be restarted as a result of driver exiting with non-zero code before job is reported failed.A job might be reported as thrashing if the driver exits with a non-zero code four times within a 10-minute window.Maximum value is 10.Note: This restartable job option is not supported in Dataproc workflow templates (https://cloud.google.com/dataproc/docs/concepts/workflows/using-workflows#adding_jobs_to_a_template).
            "maxFailuresTotal": 42, # Optional. Maximum total number of times a driver can be restarted as a result of the driver exiting with a non-zero code. After the maximum number is reached, the job will be reported as failed.Maximum value is 240.Note: Currently, this restartable job option is not supported in Dataproc workflow templates (https://cloud.google.com/dataproc/docs/concepts/workflows/using-workflows#adding_jobs_to_a_template).
          },
          "sparkJob": { # A Dataproc job for running Apache Spark (https://spark.apache.org/) applications on YARN. # Optional. Job is a Spark job.
            "archiveUris": [ # Optional. HCFS URIs of archives to be extracted into the working directory of each executor. Supported file types: .jar, .tar, .tar.gz, .tgz, and .zip.
              "A String",
            ],
            "args": [ # Optional. The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision may occur that causes an incorrect job submission.
              "A String",
            ],
            "fileUris": [ # Optional. HCFS URIs of files to be placed in the working directory of each executor. Useful for naively parallel tasks.
              "A String",
            ],
            "jarFileUris": [ # Optional. HCFS URIs of jar files to add to the CLASSPATHs of the Spark driver and tasks.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "mainClass": "A String", # The name of the driver's main class. The jar file that contains the class must be in the default CLASSPATH or specified in SparkJob.jar_file_uris.
            "mainJarFileUri": "A String", # The HCFS URI of the jar file that contains the main class.
            "properties": { # Optional. A mapping of property names to values, used to configure Spark. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/spark/conf/spark-defaults.conf and classes in user code.
              "a_key": "A String",
            },
          },
          "sparkRJob": { # A Dataproc job for running Apache SparkR (https://spark.apache.org/docs/latest/sparkr.html) applications on YARN. # Optional. Job is a SparkR job.
            "archiveUris": [ # Optional. HCFS URIs of archives to be extracted into the working directory of each executor. Supported file types: .jar, .tar, .tar.gz, .tgz, and .zip.
              "A String",
            ],
            "args": [ # Optional. The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision may occur that causes an incorrect job submission.
              "A String",
            ],
            "fileUris": [ # Optional. HCFS URIs of files to be placed in the working directory of each executor. Useful for naively parallel tasks.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "mainRFileUri": "A String", # Required. The HCFS URI of the main R file to use as the driver. Must be a .R file.
            "properties": { # Optional. A mapping of property names to values, used to configure SparkR. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/spark/conf/spark-defaults.conf and classes in user code.
              "a_key": "A String",
            },
          },
          "sparkSqlJob": { # A Dataproc job for running Apache Spark SQL (https://spark.apache.org/sql/) queries. # Optional. Job is a SparkSql job.
            "jarFileUris": [ # Optional. HCFS URIs of jar files to be added to the Spark CLASSPATH.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "properties": { # Optional. A mapping of property names to values, used to configure Spark SQL's SparkConf. Properties that conflict with values set by the Dataproc API might be overwritten.
              "a_key": "A String",
            },
            "queryFileUri": "A String", # The HCFS URI of the script that contains SQL queries.
            "queryList": { # A list of queries to run on a cluster. # A list of queries.
              "queries": [ # Required. The queries to execute. You do not need to end a query expression with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of a Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } }
                "A String",
              ],
            },
            "scriptVariables": { # Optional. Mapping of query variable names to values (equivalent to the Spark SQL command: SET name="value";).
              "a_key": "A String",
            },
          },
          "stepId": "A String", # Required. The step id. The id must be unique among all jobs within the template.The step id is used as prefix for job id, as job goog-dataproc-workflow-step-id label, and in prerequisiteStepIds field from other steps.The id must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). Cannot begin or end with underscore or hyphen. Must consist of between 3 and 50 characters.
          "trinoJob": { # A Dataproc job for running Trino (https://trino.io/) queries. IMPORTANT: The Dataproc Trino Optional Component (https://cloud.google.com/dataproc/docs/concepts/components/trino) must be enabled when the cluster is created to submit a Trino job to the cluster. # Optional. Job is a Trino job.
            "clientTags": [ # Optional. Trino client tags to attach to this query
              "A String",
            ],
            "continueOnFailure": True or False, # Optional. Whether to continue executing queries if a query fails. The default value is false. Setting to true can be useful when executing independent parallel queries.
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "outputFormat": "A String", # Optional. The format in which query output will be displayed. See the Trino documentation for supported output formats
            "properties": { # Optional. A mapping of property names to values. Used to set Trino session properties (https://trino.io/docs/current/sql/set-session.html) Equivalent to using the --session flag in the Trino CLI
              "a_key": "A String",
            },
            "queryFileUri": "A String", # The HCFS URI of the script that contains SQL queries.
            "queryList": { # A list of queries to run on a cluster. # A list of queries.
              "queries": [ # Required. The queries to execute. You do not need to end a query expression with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of a Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } }
                "A String",
              ],
            },
          },
        },
      ],
      "labels": { # Optional. The labels to associate with this template. These labels will be propagated to all jobs and clusters created by the workflow instance.Label keys must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt).Label values may be empty, but, if present, must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt).No more than 32 labels can be associated with a template.
        "a_key": "A String",
      },
      "name": "A String", # Output only. The resource name of the workflow template, as described in https://cloud.google.com/apis/design/resource_names. For projects.regions.workflowTemplates, the resource name of the template has the following format: projects/{project_id}/regions/{region}/workflowTemplates/{template_id} For projects.locations.workflowTemplates, the resource name of the template has the following format: projects/{project_id}/locations/{location}/workflowTemplates/{template_id}
      "parameters": [ # Optional. Template parameters whose values are substituted into the template. Values for parameters must be provided when the template is instantiated.
        { # A configurable parameter that replaces one or more fields in the template. Parameterizable fields: - Labels - File uris - Job properties - Job arguments - Script variables - Main class (in HadoopJob and SparkJob) - Zone (in ClusterSelector)
          "description": "A String", # Optional. Brief description of the parameter. Must not exceed 1024 characters.
          "fields": [ # Required. Paths to all fields that the parameter replaces. A field is allowed to appear in at most one parameter's list of field paths.A field path is similar in syntax to a google.protobuf.FieldMask. For example, a field path that references the zone field of a workflow template's cluster selector would be specified as placement.clusterSelector.zone.Also, field paths can reference fields using the following syntax: Values in maps can be referenced by key: labels'key' placement.clusterSelector.clusterLabels'key' placement.managedCluster.labels'key' placement.clusterSelector.clusterLabels'key' jobs'step-id'.labels'key' Jobs in the jobs list can be referenced by step-id: jobs'step-id'.hadoopJob.mainJarFileUri jobs'step-id'.hiveJob.queryFileUri jobs'step-id'.pySparkJob.mainPythonFileUri jobs'step-id'.hadoopJob.jarFileUris0 jobs'step-id'.hadoopJob.archiveUris0 jobs'step-id'.hadoopJob.fileUris0 jobs'step-id'.pySparkJob.pythonFileUris0 Items in repeated fields can be referenced by a zero-based index: jobs'step-id'.sparkJob.args0 Other examples: jobs'step-id'.hadoopJob.properties'key' jobs'step-id'.hadoopJob.args0 jobs'step-id'.hiveJob.scriptVariables'key' jobs'step-id'.hadoopJob.mainJarFileUri placement.clusterSelector.zoneIt may not be possible to parameterize maps and repeated fields in their entirety since only individual map values and individual items in repeated fields can be referenced. For example, the following field paths are invalid: placement.clusterSelector.clusterLabels jobs'step-id'.sparkJob.args
            "A String",
          ],
          "name": "A String", # Required. Parameter name. The parameter name is used as the key, and paired with the parameter value, which are passed to the template when the template is instantiated. The name must contain only capital letters (A-Z), numbers (0-9), and underscores (_), and must not start with a number. The maximum length is 40 characters.
          "validation": { # Configuration for parameter validation. # Optional. Validation rules to be applied to this parameter's value.
            "regex": { # Validation based on regular expressions. # Validation based on regular expressions.
              "regexes": [ # Required. RE2 regular expressions used to validate the parameter's value. The value must match the regex in its entirety (substring matches are not sufficient).
                "A String",
              ],
            },
            "values": { # Validation based on a list of allowed values. # Validation based on a list of allowed values.
              "values": [ # Required. List of allowed values for the parameter.
                "A String",
              ],
            },
          },
        },
      ],
      "placement": { # Specifies workflow execution target.Either managed_cluster or cluster_selector is required. # Required. WorkflowTemplate scheduling information.
        "clusterSelector": { # A selector that chooses target cluster for jobs based on metadata. # Optional. A selector that chooses target cluster for jobs based on metadata.The selector is evaluated at the time each job is submitted.
          "clusterLabels": { # Required. The cluster labels. Cluster must have all labels to match.
            "a_key": "A String",
          },
          "zone": "A String", # Optional. The zone where workflow process executes. This parameter does not affect the selection of the cluster.If unspecified, the zone of the first cluster matching the selector is used.
        },
        "managedCluster": { # Cluster that is managed by the workflow. # A cluster that is managed by the workflow.
          "clusterName": "A String", # Required. The cluster name prefix. A unique cluster name will be formed by appending a random suffix.The name must contain only lower-case letters (a-z), numbers (0-9), and hyphens (-). Must begin with a letter. Cannot begin or end with hyphen. Must consist of between 2 and 35 characters.
          "config": { # The cluster config. # Required. The cluster configuration.
            "autoscalingConfig": { # Autoscaling Policy config associated with the cluster. # Optional. Autoscaling config for the policy associated with the cluster. Cluster does not autoscale if this field is unset.
              "policyUri": "A String", # Optional. The autoscaling policy used by the cluster.Only resource names including projectid and location (region) are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/locations/[dataproc_region]/autoscalingPolicies/[policy_id] projects/[project_id]/locations/[dataproc_region]/autoscalingPolicies/[policy_id]Note that the policy must be in the same project and Dataproc region.
            },
            "auxiliaryNodeGroups": [ # Optional. The node group settings.
              { # Node group identification and configuration information.
                "nodeGroup": { # Dataproc Node Group. The Dataproc NodeGroup resource is not related to the Dataproc NodeGroupAffinity resource. # Required. Node group configuration.
                  "labels": { # Optional. Node group labels. Label keys must consist of from 1 to 63 characters and conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). Label values can be empty. If specified, they must consist of from 1 to 63 characters and conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). The node group must have no more than 32 labels.
                    "a_key": "A String",
                  },
                  "name": "A String", # The Node group resource name (https://aip.dev/122).
                  "nodeGroupConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The node group instance group configuration.
                    "accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
                      { # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
                        "acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
                        "acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
                      },
                    ],
                    "diskConfig": { # Specifies the config of boot disk and attached disk options for a group of VM instances. # Optional. Disk option config settings.
                      "bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. This field is supported only if boot_disk_type is hyperdisk-balanced.
                      "bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. This field is supported only if boot_disk_type is hyperdisk-balanced.
                      "bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
                      "bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
                      "localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
                      "numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
                    },
                    "imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
                    "instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
                      "instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
                        { # Defines machines types and a rank to which the machines types belong.
                          "machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
                            "A String",
                          ],
                          "rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
                        },
                      ],
                      "instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
                        { # Defines a mapping from machine types to the number of VMs that are created with each machine type.
                          "machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
                          "vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
                        },
                      ],
                      "provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
                        "standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
                        "standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
                      },
                    },
                    "instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
                      "A String",
                    ],
                    "instanceReferences": [ # Output only. List of references to Compute Engine instances.
                      { # A reference to a Compute Engine instance.
                        "instanceId": "A String", # The unique identifier of the Compute Engine instance.
                        "instanceName": "A String", # The user-friendly name of the Compute Engine instance.
                        "publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
                        "publicKey": "A String", # The public RSA key used for sharing data with this instance.
                      },
                    ],
                    "isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
                    "machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
                    "managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
                      "instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
                      "instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
                      "instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
                    },
                    "minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
                    "minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
                    "numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
                    "preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
                    "startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
                      "requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
                    },
                  },
                  "roles": [ # Required. Node group roles.
                    "A String",
                  ],
                },
                "nodeGroupId": "A String", # Optional. A node group ID. Generated if not specified.The ID must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). Cannot begin or end with underscore or hyphen. Must consist of from 3 to 33 characters.
              },
            ],
            "clusterTier": "A String", # Optional. The cluster tier.
            "clusterType": "A String", # Optional. The type of the cluster.
            "configBucket": "A String", # Optional. A Cloud Storage bucket used to stage job dependencies, config files, and job driver console output. If you do not specify a staging bucket, Cloud Dataproc will determine a Cloud Storage location (US, ASIA, or EU) for your cluster's staging bucket according to the Compute Engine zone where your cluster is deployed, and then create and manage this project-level, per-location bucket (see Dataproc staging and temp buckets (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/staging-bucket)). This field requires a Cloud Storage bucket name, not a gs://... URI to a Cloud Storage bucket.
            "dataprocMetricConfig": { # Dataproc metric config. # Optional. The config for Dataproc metrics.
              "metrics": [ # Required. Metrics sources to enable.
                { # A Dataproc custom metric.
                  "metricOverrides": [ # Optional. Specify one or more Custom metrics (https://cloud.google.com/dataproc/docs/guides/dataproc-metrics#custom_metrics) to collect for the metric course (for the SPARK metric source (any Spark metric (https://spark.apache.org/docs/latest/monitoring.html#metrics) can be specified).Provide metrics in the following format: METRIC_SOURCE: INSTANCE:GROUP:METRIC Use camelcase as appropriate.Examples: yarn:ResourceManager:QueueMetrics:AppsCompleted spark:driver:DAGScheduler:job.allJobs sparkHistoryServer:JVM:Memory:NonHeapMemoryUsage.committed hiveserver2:JVM:Memory:NonHeapMemoryUsage.used Notes: Only the specified overridden metrics are collected for the metric source. For example, if one or more spark:executive metrics are listed as metric overrides, other SPARK metrics are not collected. The collection of the metrics for other enabled custom metric sources is unaffected. For example, if both SPARK and YARN metric sources are enabled, and overrides are provided for Spark metrics only, all YARN metrics are collected.
                    "A String",
                  ],
                  "metricSource": "A String", # Required. A standard set of metrics is collected unless metricOverrides are specified for the metric source (see Custom metrics (https://cloud.google.com/dataproc/docs/guides/dataproc-metrics#custom_metrics) for more information).
                },
              ],
            },
            "encryptionConfig": { # Encryption settings for the cluster. # Optional. Encryption settings for the cluster.
              "gcePdKmsKeyName": "A String", # Optional. The Cloud KMS key resource name to use for persistent disk encryption for all instances in the cluster. See Use CMEK with cluster data (https://cloud.google.com//dataproc/docs/concepts/configuring-clusters/customer-managed-encryption#use_cmek_with_cluster_data) for more information.
              "kmsKey": "A String", # Optional. The Cloud KMS key resource name to use for cluster persistent disk and job argument encryption. See Use CMEK with cluster data (https://cloud.google.com//dataproc/docs/concepts/configuring-clusters/customer-managed-encryption#use_cmek_with_cluster_data) for more information.When this key resource name is provided, the following job arguments of the following job types submitted to the cluster are encrypted using CMEK: FlinkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/FlinkJob) HadoopJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/HadoopJob) SparkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkJob) SparkRJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkRJob) PySparkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/PySparkJob) SparkSqlJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkSqlJob) scriptVariables and queryList.queries HiveJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/HiveJob) scriptVariables and queryList.queries PigJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/PigJob) scriptVariables and queryList.queries PrestoJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/PrestoJob) scriptVariables and queryList.queries
            },
            "endpointConfig": { # Endpoint config for this cluster # Optional. Port/endpoint configuration for this cluster
              "enableHttpPortAccess": True or False, # Optional. If true, enable http access to specific ports on the cluster from external sources. Defaults to false.
              "httpPorts": { # Output only. The map of port descriptions to URLs. Will only be populated if enable_http_port_access is true.
                "a_key": "A String",
              },
            },
            "gceClusterConfig": { # Common config settings for resources of Compute Engine cluster instances, applicable to all instances in the cluster. # Optional. The shared Compute Engine config settings for all instances in a cluster.
              "confidentialInstanceConfig": { # Confidential Instance Config for clusters using Confidential VMs (https://cloud.google.com/compute/confidential-vm/docs) # Optional. Confidential Instance Config for clusters using Confidential VMs (https://cloud.google.com/compute/confidential-vm/docs).
                "enableConfidentialCompute": True or False, # Optional. Defines whether the instance should have confidential compute enabled.
              },
              "internalIpOnly": True or False, # Optional. This setting applies to subnetwork-enabled networks. It is set to true by default in clusters created with image versions 2.2.x.When set to true: All cluster VMs have internal IP addresses. Google Private Access (https://cloud.google.com/vpc/docs/private-google-access) must be enabled to access Dataproc and other Google Cloud APIs. Off-cluster dependencies must be configured to be accessible without external IP addresses.When set to false: Cluster VMs are not restricted to internal IP addresses. Ephemeral external IP addresses are assigned to each cluster VM.
              "metadata": { # Optional. The Compute Engine metadata entries to add to all instances (see Project and instance metadata (https://cloud.google.com/compute/docs/storing-retrieving-metadata#project_and_instance_metadata)).
                "a_key": "A String",
              },
              "networkUri": "A String", # Optional. The Compute Engine network to be used for machine communications. Cannot be specified with subnetwork_uri. If neither network_uri nor subnetwork_uri is specified, the "default" network of the project is used, if it exists. Cannot be a "Custom Subnet Network" (see Using Subnetworks (https://cloud.google.com/compute/docs/subnetworks) for more information).A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/networks/default projects/[project_id]/global/networks/default default
              "nodeGroupAffinity": { # Node Group Affinity for clusters using sole-tenant node groups. The Dataproc NodeGroupAffinity resource is not related to the Dataproc NodeGroup resource. # Optional. Node Group Affinity for sole-tenant clusters.
                "nodeGroupUri": "A String", # Required. The URI of a sole-tenant node group resource (https://cloud.google.com/compute/docs/reference/rest/v1/nodeGroups) that the cluster will be created on.A full URL, partial URI, or node group name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/nodeGroups/node-group-1 projects/[project_id]/zones/[zone]/nodeGroups/node-group-1 node-group-1
              },
              "privateIpv6GoogleAccess": "A String", # Optional. The type of IPv6 access for a cluster.
              "reservationAffinity": { # Reservation Affinity for consuming Zonal reservation. # Optional. Reservation Affinity for consuming Zonal reservation.
                "consumeReservationType": "A String", # Optional. Type of reservation to consume
                "key": "A String", # Optional. Corresponds to the label key of reservation resource.
                "values": [ # Optional. Corresponds to the label values of reservation resource.
                  "A String",
                ],
              },
              "resourceManagerTags": { # Optional. Resource manager tags (https://cloud.google.com/resource-manager/docs/tags/tags-creating-and-managing) to add to all instances (see Use secure tags in Dataproc (https://cloud.google.com/dataproc/docs/guides/attach-secure-tags)).
                "a_key": "A String",
              },
              "serviceAccount": "A String", # Optional. The Dataproc service account (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/service-accounts#service_accounts_in_dataproc) (also see VM Data Plane identity (https://cloud.google.com/dataproc/docs/concepts/iam/dataproc-principals#vm_service_account_data_plane_identity)) used by Dataproc cluster VM instances to access Google Cloud Platform services.If not specified, the Compute Engine default service account (https://cloud.google.com/compute/docs/access/service-accounts#default_service_account) is used.
              "serviceAccountScopes": [ # Optional. The URIs of service account scopes to be included in Compute Engine instances. The following base set of scopes is always included: https://www.googleapis.com/auth/cloud.useraccounts.readonly https://www.googleapis.com/auth/devstorage.read_write https://www.googleapis.com/auth/logging.writeIf no scopes are specified, the following defaults are also provided: https://www.googleapis.com/auth/bigquery https://www.googleapis.com/auth/bigtable.admin.table https://www.googleapis.com/auth/bigtable.data https://www.googleapis.com/auth/devstorage.full_control
                "A String",
              ],
              "shieldedInstanceConfig": { # Shielded Instance Config for clusters using Compute Engine Shielded VMs (https://cloud.google.com/security/shielded-cloud/shielded-vm). # Optional. Shielded Instance Config for clusters using Compute Engine Shielded VMs (https://cloud.google.com/security/shielded-cloud/shielded-vm).
                "enableIntegrityMonitoring": True or False, # Optional. Defines whether instances have integrity monitoring enabled.
                "enableSecureBoot": True or False, # Optional. Defines whether instances have Secure Boot enabled.
                "enableVtpm": True or False, # Optional. Defines whether instances have the vTPM enabled.
              },
              "subnetworkUri": "A String", # Optional. The Compute Engine subnetwork to be used for machine communications. Cannot be specified with network_uri.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/regions/[region]/subnetworks/sub0 projects/[project_id]/regions/[region]/subnetworks/sub0 sub0
              "tags": [ # The Compute Engine network tags to add to all instances (see Tagging instances (https://cloud.google.com/vpc/docs/add-remove-network-tags)).
                "A String",
              ],
              "zoneUri": "A String", # Optional. The Compute Engine zone where the Dataproc cluster will be located. If omitted, the service will pick a zone in the cluster's Compute Engine region. On a get request, zone will always be present.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone] projects/[project_id]/zones/[zone] [zone]
            },
            "gkeClusterConfig": { # The cluster's GKE config. # Optional. BETA. The Kubernetes Engine config for Dataproc clusters deployed to The Kubernetes Engine config for Dataproc clusters deployed to Kubernetes. These config settings are mutually exclusive with Compute Engine-based options, such as gce_cluster_config, master_config, worker_config, secondary_worker_config, and autoscaling_config.
              "gkeClusterTarget": "A String", # Optional. A target GKE cluster to deploy to. It must be in the same project and region as the Dataproc cluster (the GKE cluster can be zonal or regional). Format: 'projects/{project}/locations/{location}/clusters/{cluster_id}'
              "namespacedGkeDeploymentTarget": { # Deprecated. Used only for the deprecated beta. A full, namespace-isolated deployment target for an existing GKE cluster. # Optional. Deprecated. Use gkeClusterTarget. Used only for the deprecated beta. A target for the deployment.
                "clusterNamespace": "A String", # Optional. A namespace within the GKE cluster to deploy into.
                "targetGkeCluster": "A String", # Optional. The target GKE cluster to deploy to. Format: 'projects/{project}/locations/{location}/clusters/{cluster_id}'
              },
              "nodePoolTarget": [ # Optional. GKE node pools where workloads will be scheduled. At least one node pool must be assigned the DEFAULT GkeNodePoolTarget.Role. If a GkeNodePoolTarget is not specified, Dataproc constructs a DEFAULT GkeNodePoolTarget. Each role can be given to only one GkeNodePoolTarget. All node pools must have the same location settings.
                { # GKE node pools that Dataproc workloads run on.
                  "nodePool": "A String", # Required. The target GKE node pool. Format: 'projects/{project}/locations/{location}/clusters/{cluster}/nodePools/{node_pool}'
                  "nodePoolConfig": { # The configuration of a GKE node pool used by a Dataproc-on-GKE cluster (https://cloud.google.com/dataproc/docs/concepts/jobs/dataproc-gke#create-a-dataproc-on-gke-cluster). # Input only. The configuration for the GKE node pool.If specified, Dataproc attempts to create a node pool with the specified shape. If one with the same name already exists, it is verified against all specified fields. If a field differs, the virtual cluster creation will fail.If omitted, any node pool with the specified name is used. If a node pool with the specified name does not exist, Dataproc create a node pool with default values.This is an input only field. It will not be returned by the API.
                    "autoscaling": { # GkeNodePoolAutoscaling contains information the cluster autoscaler needs to adjust the size of the node pool to the current cluster usage. # Optional. The autoscaler configuration for this node pool. The autoscaler is enabled only when a valid configuration is present.
                      "maxNodeCount": 42, # The maximum number of nodes in the node pool. Must be >= min_node_count, and must be > 0. Note: Quota must be sufficient to scale up the cluster.
                      "minNodeCount": 42, # The minimum number of nodes in the node pool. Must be >= 0 and <= max_node_count.
                    },
                    "config": { # Parameters that describe cluster nodes. # Optional. The node pool configuration.
                      "accelerators": [ # Optional. A list of hardware accelerators (https://cloud.google.com/compute/docs/gpus) to attach to each node.
                        { # A GkeNodeConfigAcceleratorConfig represents a Hardware Accelerator request for a node pool.
                          "acceleratorCount": "A String", # The number of accelerator cards exposed to an instance.
                          "acceleratorType": "A String", # The accelerator type resource namename (see GPUs on Compute Engine).
                          "gpuPartitionSize": "A String", # Size of partitions to create on the GPU. Valid values are described in the NVIDIA mig user guide (https://docs.nvidia.com/datacenter/tesla/mig-user-guide/#partitioning).
                        },
                      ],
                      "bootDiskKmsKey": "A String", # Optional. The Customer Managed Encryption Key (CMEK) (https://cloud.google.com/kubernetes-engine/docs/how-to/using-cmek) used to encrypt the boot disk attached to each node in the node pool. Specify the key using the following format: projects/{project}/locations/{location}/keyRings/{key_ring}/cryptoKeys/{crypto_key}
                      "localSsdCount": 42, # Optional. The number of local SSD disks to attach to the node, which is limited by the maximum number of disks allowable per zone (see Adding Local SSDs (https://cloud.google.com/compute/docs/disks/local-ssd)).
                      "machineType": "A String", # Optional. The name of a Compute Engine machine type (https://cloud.google.com/compute/docs/machine-types).
                      "minCpuPlatform": "A String", # Optional. Minimum CPU platform (https://cloud.google.com/compute/docs/instances/specify-min-cpu-platform) to be used by this instance. The instance may be scheduled on the specified or a newer CPU platform. Specify the friendly names of CPU platforms, such as "Intel Haswell"` or Intel Sandy Bridge".
                      "preemptible": True or False, # Optional. Whether the nodes are created as legacy preemptible VM instances (https://cloud.google.com/compute/docs/instances/preemptible). Also see Spot VMs, preemptible VM instances without a maximum lifetime. Legacy and Spot preemptible nodes cannot be used in a node pool with the CONTROLLER role or in the DEFAULT node pool if the CONTROLLER role is not assigned (the DEFAULT node pool will assume the CONTROLLER role).
                      "spot": True or False, # Optional. Whether the nodes are created as Spot VM instances (https://cloud.google.com/compute/docs/instances/spot). Spot VMs are the latest update to legacy preemptible VMs. Spot VMs do not have a maximum lifetime. Legacy and Spot preemptible nodes cannot be used in a node pool with the CONTROLLER role or in the DEFAULT node pool if the CONTROLLER role is not assigned (the DEFAULT node pool will assume the CONTROLLER role).
                    },
                    "locations": [ # Optional. The list of Compute Engine zones (https://cloud.google.com/compute/docs/zones#available) where node pool nodes associated with a Dataproc on GKE virtual cluster will be located.Note: All node pools associated with a virtual cluster must be located in the same region as the virtual cluster, and they must be located in the same zone within that region.If a location is not specified during node pool creation, Dataproc on GKE will choose the zone.
                      "A String",
                    ],
                  },
                  "roles": [ # Required. The roles associated with the GKE node pool.
                    "A String",
                  ],
                },
              ],
            },
            "initializationActions": [ # Optional. Commands to execute on each node after config is completed. By default, executables are run on master and all worker nodes. You can test a node's role metadata to run an executable on a master or worker node, as shown below using curl (you can also use wget): ROLE=$(curl -H Metadata-Flavor:Google http://metadata/computeMetadata/v1/instance/attributes/dataproc-role) if [[ "${ROLE}" == 'Master' ]]; then ... master specific actions ... else ... worker specific actions ... fi
              { # Specifies an executable to run on a fully configured node and a timeout period for executable completion.
                "executableFile": "A String", # Required. Cloud Storage URI of executable file.
                "executionTimeout": "A String", # Optional. Amount of time executable has to complete. Default is 10 minutes (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).Cluster creation fails with an explanatory error message (the name of the executable that caused the error and the exceeded timeout period) if the executable is not completed at end of the timeout period.
              },
            ],
            "lifecycleConfig": { # Specifies the cluster auto-delete schedule configuration. # Optional. Lifecycle setting for the cluster.
              "autoDeleteTime": "A String", # Optional. The time when cluster will be auto-deleted (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
              "autoDeleteTtl": "A String", # Optional. The lifetime duration of cluster. The cluster will be auto-deleted at the end of this period. Minimum value is 10 minutes; maximum value is 14 days (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).
              "autoStopTime": "A String", # Optional. The time when cluster will be auto-stopped (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
              "autoStopTtl": "A String", # Optional. The lifetime duration of the cluster. The cluster will be auto-stopped at the end of this period, calculated from the time of submission of the create or update cluster request. Minimum value is 10 minutes; maximum value is 14 days (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).
              "idleDeleteTtl": "A String", # Optional. The duration to keep the cluster alive while idling (when no jobs are running). Passing this threshold will cause the cluster to be deleted. Minimum value is 5 minutes; maximum value is 14 days (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).
              "idleStartTime": "A String", # Output only. The time when cluster became idle (most recent job finished) and became eligible for deletion due to idleness (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
              "idleStopTtl": "A String", # Optional. The duration to keep the cluster started while idling (when no jobs are running). Passing this threshold will cause the cluster to be stopped. Minimum value is 5 minutes; maximum value is 14 days (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).
            },
            "masterConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The Compute Engine config settings for the cluster's master instance.
              "accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
                { # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
                  "acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
                  "acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
                },
              ],
              "diskConfig": { # Specifies the config of boot disk and attached disk options for a group of VM instances. # Optional. Disk option config settings.
                "bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. This field is supported only if boot_disk_type is hyperdisk-balanced.
                "bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. This field is supported only if boot_disk_type is hyperdisk-balanced.
                "bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
                "bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
                "localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
                "numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
              },
              "imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
              "instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
                "instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
                  { # Defines machines types and a rank to which the machines types belong.
                    "machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
                      "A String",
                    ],
                    "rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
                  },
                ],
                "instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
                  { # Defines a mapping from machine types to the number of VMs that are created with each machine type.
                    "machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
                    "vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
                  },
                ],
                "provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
                  "standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
                  "standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
                },
              },
              "instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
                "A String",
              ],
              "instanceReferences": [ # Output only. List of references to Compute Engine instances.
                { # A reference to a Compute Engine instance.
                  "instanceId": "A String", # The unique identifier of the Compute Engine instance.
                  "instanceName": "A String", # The user-friendly name of the Compute Engine instance.
                  "publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
                  "publicKey": "A String", # The public RSA key used for sharing data with this instance.
                },
              ],
              "isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
              "machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
              "managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
                "instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
                "instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
                "instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
              },
              "minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
              "minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
              "numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
              "preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
              "startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
                "requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
              },
            },
            "metastoreConfig": { # Specifies a Metastore configuration. # Optional. Metastore configuration.
              "dataprocMetastoreService": "A String", # Required. Resource name of an existing Dataproc Metastore service.Example: projects/[project_id]/locations/[dataproc_region]/services/[service-name]
            },
            "secondaryWorkerConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The Compute Engine config settings for a cluster's secondary worker instances
              "accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
                { # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
                  "acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
                  "acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
                },
              ],
              "diskConfig": { # Specifies the config of boot disk and attached disk options for a group of VM instances. # Optional. Disk option config settings.
                "bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. This field is supported only if boot_disk_type is hyperdisk-balanced.
                "bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. This field is supported only if boot_disk_type is hyperdisk-balanced.
                "bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
                "bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
                "localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
                "numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
              },
              "imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
              "instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
                "instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
                  { # Defines machines types and a rank to which the machines types belong.
                    "machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
                      "A String",
                    ],
                    "rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
                  },
                ],
                "instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
                  { # Defines a mapping from machine types to the number of VMs that are created with each machine type.
                    "machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
                    "vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
                  },
                ],
                "provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
                  "standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
                  "standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
                },
              },
              "instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
                "A String",
              ],
              "instanceReferences": [ # Output only. List of references to Compute Engine instances.
                { # A reference to a Compute Engine instance.
                  "instanceId": "A String", # The unique identifier of the Compute Engine instance.
                  "instanceName": "A String", # The user-friendly name of the Compute Engine instance.
                  "publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
                  "publicKey": "A String", # The public RSA key used for sharing data with this instance.
                },
              ],
              "isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
              "machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
              "managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
                "instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
                "instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
                "instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
              },
              "minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
              "minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
              "numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
              "preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
              "startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
                "requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
              },
            },
            "securityConfig": { # Security related configuration, including encryption, Kerberos, etc. # Optional. Security settings for the cluster.
              "identityConfig": { # Identity related configuration, including service account based secure multi-tenancy user mappings. # Optional. Identity related configuration, including service account based secure multi-tenancy user mappings.
                "userServiceAccountMapping": { # Required. Map of user to service account.
                  "a_key": "A String",
                },
              },
              "kerberosConfig": { # Specifies Kerberos related configuration. # Optional. Kerberos related configuration.
                "crossRealmTrustAdminServer": "A String", # Optional. The admin server (IP or hostname) for the remote trusted realm in a cross realm trust relationship.
                "crossRealmTrustKdc": "A String", # Optional. The KDC (IP or hostname) for the remote trusted realm in a cross realm trust relationship.
                "crossRealmTrustRealm": "A String", # Optional. The remote realm the Dataproc on-cluster KDC will trust, should the user enable cross realm trust.
                "crossRealmTrustSharedPasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the shared password between the on-cluster Kerberos realm and the remote trusted realm, in a cross realm trust relationship.
                "enableKerberos": True or False, # Optional. Flag to indicate whether to Kerberize the cluster (default: false). Set this field to true to enable Kerberos on a cluster.
                "kdcDbKeyUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the master key of the KDC database.
                "keyPasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the password to the user provided key. For the self-signed certificate, this password is generated by Dataproc.
                "keystorePasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the password to the user provided keystore. For the self-signed certificate, this password is generated by Dataproc.
                "keystoreUri": "A String", # Optional. The Cloud Storage URI of the keystore file used for SSL encryption. If not provided, Dataproc will provide a self-signed certificate.
                "kmsKeyUri": "A String", # Optional. The URI of the KMS key used to encrypt sensitive files.
                "realm": "A String", # Optional. The name of the on-cluster Kerberos realm. If not specified, the uppercased domain of hostnames will be the realm.
                "rootPrincipalPasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the root principal password.
                "tgtLifetimeHours": 42, # Optional. The lifetime of the ticket granting ticket, in hours. If not specified, or user specifies 0, then default value 10 will be used.
                "truststorePasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the password to the user provided truststore. For the self-signed certificate, this password is generated by Dataproc.
                "truststoreUri": "A String", # Optional. The Cloud Storage URI of the truststore file used for SSL encryption. If not provided, Dataproc will provide a self-signed certificate.
              },
            },
            "softwareConfig": { # Specifies the selection and config of software inside the cluster. # Optional. The config settings for cluster software.
              "imageVersion": "A String", # Optional. The version of software inside the cluster. It must be one of the supported Dataproc Versions (https://cloud.google.com/dataproc/docs/concepts/versioning/dataproc-versions#supported-dataproc-image-versions), such as "1.2" (including a subminor version, such as "1.2.29"), or the "preview" version (https://cloud.google.com/dataproc/docs/concepts/versioning/dataproc-versions#other_versions). If unspecified, it defaults to the latest Debian version.
              "optionalComponents": [ # Optional. The set of components to activate on the cluster.
                "A String",
              ],
              "properties": { # Optional. The properties to set on daemon config files.Property keys are specified in prefix:property format, for example core:hadoop.tmp.dir. The following are supported prefixes and their mappings: capacity-scheduler: capacity-scheduler.xml core: core-site.xml distcp: distcp-default.xml hdfs: hdfs-site.xml hive: hive-site.xml mapred: mapred-site.xml pig: pig.properties spark: spark-defaults.conf yarn: yarn-site.xmlFor more information, see Cluster properties (https://cloud.google.com/dataproc/docs/concepts/cluster-properties).
                "a_key": "A String",
              },
            },
            "tempBucket": "A String", # Optional. A Cloud Storage bucket used to store ephemeral cluster and jobs data, such as Spark and MapReduce history files. If you do not specify a temp bucket, Dataproc will determine a Cloud Storage location (US, ASIA, or EU) for your cluster's temp bucket according to the Compute Engine zone where your cluster is deployed, and then create and manage this project-level, per-location bucket. The default bucket has a TTL of 90 days, but you can use any TTL (or none) if you specify a bucket (see Dataproc staging and temp buckets (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/staging-bucket)). This field requires a Cloud Storage bucket name, not a gs://... URI to a Cloud Storage bucket.
            "workerConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The Compute Engine config settings for the cluster's worker instances.
              "accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
                { # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
                  "acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
                  "acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
                },
              ],
              "diskConfig": { # Specifies the config of boot disk and attached disk options for a group of VM instances. # Optional. Disk option config settings.
                "bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. This field is supported only if boot_disk_type is hyperdisk-balanced.
                "bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. This field is supported only if boot_disk_type is hyperdisk-balanced.
                "bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
                "bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
                "localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
                "numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
              },
              "imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
              "instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
                "instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
                  { # Defines machines types and a rank to which the machines types belong.
                    "machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
                      "A String",
                    ],
                    "rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
                  },
                ],
                "instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
                  { # Defines a mapping from machine types to the number of VMs that are created with each machine type.
                    "machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
                    "vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
                  },
                ],
                "provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
                  "standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
                  "standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
                },
              },
              "instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
                "A String",
              ],
              "instanceReferences": [ # Output only. List of references to Compute Engine instances.
                { # A reference to a Compute Engine instance.
                  "instanceId": "A String", # The unique identifier of the Compute Engine instance.
                  "instanceName": "A String", # The user-friendly name of the Compute Engine instance.
                  "publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
                  "publicKey": "A String", # The public RSA key used for sharing data with this instance.
                },
              ],
              "isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
              "machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
              "managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
                "instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
                "instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
                "instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
              },
              "minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
              "minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
              "numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
              "preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
              "startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
                "requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
              },
            },
          },
          "labels": { # Optional. The labels to associate with this cluster.Label keys must be between 1 and 63 characters long, and must conform to the following PCRE regular expression: \p{Ll}\p{Lo}{0,62}Label values must be between 1 and 63 characters long, and must conform to the following PCRE regular expression: \p{Ll}\p{Lo}\p{N}_-{0,63}No more than 32 labels can be associated with a given cluster.
            "a_key": "A String",
          },
        },
      },
      "updateTime": "A String", # Output only. The time template was last updated.
      "version": 42, # Optional. Used to perform a consistent read-modify-write.This field should be left blank for a CreateWorkflowTemplate request. It is required for an UpdateWorkflowTemplate request, and must match the current server version. A typical update template flow would fetch the current template with a GetWorkflowTemplate request, which will return the current template with the version field filled in with the current server version. The user updates other fields in the template, then returns it as part of the UpdateWorkflowTemplate request.
    }

`getIamPolicy(resource, body=None, x__xgafv=None)`

    Gets the access control policy for a resource. Returns an empty policy if the resource exists and does not have a policy set.

    Args:
      resource: string, REQUIRED: The resource for which the policy is being requested. See Resource names (https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. (required)
      body: object, The request body.
        The object takes the form of:

    { # Request message for GetIamPolicy method.
      "options": { # Encapsulates settings provided to GetIamPolicy. # OPTIONAL: A GetPolicyOptions object for specifying options to GetIamPolicy.
        "requestedPolicyVersion": 42, # Optional. The maximum policy version that will be used to format the policy.Valid values are 0, 1, and 3. Requests specifying an invalid value will be rejected.Requests for policies with any conditional role bindings must specify version 3. Policies with no conditional role bindings may specify any valid value or leave the field unset.The policy in the response might use the policy version that you specified, or it might use a lower policy version. For example, if you specify version 3, but the policy has no conditional role bindings, the response uses version 1.To learn which resources support conditions in their IAM policies, see the IAM documentation (https://cloud.google.com/iam/help/conditions/resource-policies).
      },
    }

      x__xgafv: string, V1 error format.
        Allowed values
          1 - v1 error format
          2 - v2 error format

    Returns:
      An object of the form:

        { # An Identity and Access Management (IAM) policy, which specifies access controls for Google Cloud resources.A Policy is a collection of bindings. A binding binds one or more members, or principals, to a single role. Principals can be user accounts, service accounts, Google groups, and domains (such as G Suite). A role is a named list of permissions; each role can be an IAM predefined role or a user-created custom role.For some types of Google Cloud resources, a binding can also specify a condition, which is a logical expression that allows access to a resource only if the expression evaluates to true. A condition can add constraints based on attributes of the request, the resource, or both. To learn which resources support conditions in their IAM policies, see the IAM documentation (https://cloud.google.com/iam/help/conditions/resource-policies).JSON example: { "bindings": [ { "role": "roles/resourcemanager.organizationAdmin", "members": [ "user:mike@example.com", "group:admins@example.com", "domain:google.com", "serviceAccount:my-project-id@appspot.gserviceaccount.com" ] }, { "role": "roles/resourcemanager.organizationViewer", "members": [ "user:eve@example.com" ], "condition": { "title": "expirable access", "description": "Does not grant access after Sep 2020", "expression": "request.time < timestamp('2020-10-01T00:00:00.000Z')", } } ], "etag": "BwWWja0YfJA=", "version": 3 } YAML example: bindings: - members: - user:mike@example.com - group:admins@example.com - domain:google.com - serviceAccount:my-project-id@appspot.gserviceaccount.com role: roles/resourcemanager.organizationAdmin - members: - user:eve@example.com role: roles/resourcemanager.organizationViewer condition: title: expirable access description: Does not grant access after Sep 2020 expression: request.time < timestamp('2020-10-01T00:00:00.000Z') etag: BwWWja0YfJA= version: 3 For a description of IAM and its features, see the IAM documentation (https://cloud.google.com/iam/docs/).
      "bindings": [ # Associates a list of members, or principals, with a role. Optionally, may specify a condition that determines how and when the bindings are applied. Each of the bindings must contain at least one principal.The bindings in a Policy can refer to up to 1,500 principals; up to 250 of these principals can be Google groups. Each occurrence of a principal counts towards these limits. For example, if the bindings grant 50 different roles to user:alice@example.com, and not to any other principal, then you can add another 1,450 principals to the bindings in the Policy.
        { # Associates members, or principals, with a role.
          "condition": { # Represents a textual expression in the Common Expression Language (CEL) syntax. CEL is a C-like expression language. The syntax and semantics of CEL are documented at https://github.com/google/cel-spec.Example (Comparison): title: "Summary size limit" description: "Determines if a summary is less than 100 chars" expression: "document.summary.size() < 100" Example (Equality): title: "Requestor is owner" description: "Determines if requestor is the document owner" expression: "document.owner == request.auth.claims.email" Example (Logic): title: "Public documents" description: "Determine whether the document should be publicly visible" expression: "document.type != 'private' && document.type != 'internal'" Example (Data Manipulation): title: "Notification string" description: "Create a notification string with a timestamp." expression: "'New message received at ' + string(document.create_time)" The exact variables and functions that may be referenced within an expression are determined by the service that evaluates it. See the service documentation for additional information. # The condition that is associated with this binding.If the condition evaluates to true, then this binding applies to the current request.If the condition evaluates to false, then this binding does not apply to the current request. However, a different role binding might grant the same role to one or more of the principals in this binding.To learn which resources support conditions in their IAM policies, see the IAM documentation (https://cloud.google.com/iam/help/conditions/resource-policies).
            "description": "A String", # Optional. Description of the expression. This is a longer text which describes the expression, e.g. when hovered over it in a UI.
            "expression": "A String", # Textual representation of an expression in Common Expression Language syntax.
            "location": "A String", # Optional. String indicating the location of the expression for error reporting, e.g. a file name and a position in the file.
            "title": "A String", # Optional. Title for the expression, i.e. a short string describing its purpose. This can be used e.g. in UIs which allow to enter the expression.
          },
          "members": [ # Specifies the principals requesting access for a Google Cloud resource. members can have the following values: allUsers: A special identifier that represents anyone who is on the internet; with or without a Google account. allAuthenticatedUsers: A special identifier that represents anyone who is authenticated with a Google account or a service account. Does not include identities that come from external identity providers (IdPs) through identity federation. user:{emailid}: An email address that represents a specific Google account. For example, alice@example.com . serviceAccount:{emailid}: An email address that represents a Google service account. For example, my-other-app@appspot.gserviceaccount.com. serviceAccount:{projectid}.svc.id.goog[{namespace}/{kubernetes-sa}]: An identifier for a Kubernetes service account (https://cloud.google.com/kubernetes-engine/docs/how-to/kubernetes-service-accounts). For example, my-project.svc.id.goog[my-namespace/my-kubernetes-sa]. group:{emailid}: An email address that represents a Google group. For example, admins@example.com. domain:{domain}: The G Suite domain (primary) that represents all the users of that domain. For example, google.com or example.com. principal://iam.googleapis.com/locations/global/workforcePools/{pool_id}/subject/{subject_attribute_value}: A single identity in a workforce identity pool. principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/group/{group_id}: All workforce identities in a group. principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/attribute.{attribute_name}/{attribute_value}: All workforce identities with a specific attribute value. principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/*: All identities in a workforce identity pool. principal://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/subject/{subject_attribute_value}: A single identity in a workload identity pool. principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/group/{group_id}: A workload identity pool group. principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/attribute.{attribute_name}/{attribute_value}: All identities in a workload identity pool with a certain attribute. principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/*: All identities in a workload identity pool. deleted:user:{emailid}?uid={uniqueid}: An email address (plus unique identifier) representing a user that has been recently deleted. For example, alice@example.com?uid=123456789012345678901. If the user is recovered, this value reverts to user:{emailid} and the recovered user retains the role in the binding. deleted:serviceAccount:{emailid}?uid={uniqueid}: An email address (plus unique identifier) representing a service account that has been recently deleted. For example, my-other-app@appspot.gserviceaccount.com?uid=123456789012345678901. If the service account is undeleted, this value reverts to serviceAccount:{emailid} and the undeleted service account retains the role in the binding. deleted:group:{emailid}?uid={uniqueid}: An email address (plus unique identifier) representing a Google group that has been recently deleted. For example, admins@example.com?uid=123456789012345678901. If the group is recovered, this value reverts to group:{emailid} and the recovered group retains the role in the binding. deleted:principal://iam.googleapis.com/locations/global/workforcePools/{pool_id}/subject/{subject_attribute_value}: Deleted single identity in a workforce identity pool. For example, deleted:principal://iam.googleapis.com/locations/global/workforcePools/my-pool-id/subject/my-subject-attribute-value.
            "A String",
          ],
          "role": "A String", # Role that is assigned to the list of members, or principals. For example, roles/viewer, roles/editor, or roles/owner.For an overview of the IAM roles and permissions, see the IAM documentation (https://cloud.google.com/iam/docs/roles-overview). For a list of the available pre-defined roles, see here (https://cloud.google.com/iam/docs/understanding-roles).
        },
      ],
      "etag": "A String", # etag is used for optimistic concurrency control as a way to help prevent simultaneous updates of a policy from overwriting each other. It is strongly suggested that systems make use of the etag in the read-modify-write cycle to perform policy updates in order to avoid race conditions: An etag is returned in the response to getIamPolicy, and systems are expected to put that etag in the request to setIamPolicy to ensure that their change will be applied to the same version of the policy.Important: If you use IAM Conditions, you must include the etag field whenever you call setIamPolicy. If you omit this field, then IAM allows you to overwrite a version 3 policy with a version 1 policy, and all of the conditions in the version 3 policy are lost.
      "version": 42, # Specifies the format of the policy.Valid values are 0, 1, and 3. Requests that specify an invalid value are rejected.Any operation that affects conditional role bindings must specify version 3. This requirement applies to the following operations: Getting a policy that includes a conditional role binding Adding a conditional role binding to a policy Changing a conditional role binding in a policy Removing any role binding, with or without a condition, from a policy that includes conditionsImportant: If you use IAM Conditions, you must include the etag field whenever you call setIamPolicy. If you omit this field, then IAM allows you to overwrite a version 3 policy with a version 1 policy, and all of the conditions in the version 3 policy are lost.If a policy does not include any conditions, operations on that policy may specify any valid version or leave the field unset.To learn which resources support conditions in their IAM policies, see the IAM documentation (https://cloud.google.com/iam/help/conditions/resource-policies).
    }

`instantiate(name, body=None, x__xgafv=None)`

    Instantiates a template and begins execution.The returned Operation can be used to track execution of workflow by polling operations.get. The Operation will complete when entire workflow is finished.The running workflow can be aborted via operations.cancel. This will cause any inflight jobs to be cancelled and workflow-owned clusters to be deleted.The Operation.metadata will be WorkflowMetadata (https://cloud.google.com/dataproc/docs/reference/rpc/google.cloud.dataproc.v1#workflowmetadata). Also see Using WorkflowMetadata (https://cloud.google.com/dataproc/docs/concepts/workflows/debugging#using_workflowmetadata).On successful completion, Operation.response will be Empty.

    Args:
      name: string, Required. The resource name of the workflow template, as described in https://cloud.google.com/apis/design/resource_names. For projects.regions.workflowTemplates.instantiate, the resource name of the template has the following format: projects/{project_id}/regions/{region}/workflowTemplates/{template_id} For projects.locations.workflowTemplates.instantiate, the resource name of the template has the following format: projects/{project_id}/locations/{location}/workflowTemplates/{template_id} (required)
      body: object, The request body.
        The object takes the form of:

    { # A request to instantiate a workflow template.
      "parameters": { # Optional. Map from parameter names to values that should be used for those parameters. Values may not exceed 1000 characters.
        "a_key": "A String",
      },
      "requestId": "A String", # Optional. A tag that prevents multiple concurrent workflow instances with the same tag from running. This mitigates risk of concurrent instances started due to retries.It is recommended to always set this value to a UUID (https://en.wikipedia.org/wiki/Universally_unique_identifier).The tag must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). The maximum length is 40 characters.
      "version": 42, # Optional. The version of workflow template to instantiate. If specified, the workflow will be instantiated only if the current version of the workflow template has the supplied version.This option cannot be used to instantiate a previous version of workflow template.
    }

      x__xgafv: string, V1 error format.
        Allowed values
          1 - v1 error format
          2 - v2 error format

    Returns:
      An object of the form:

        { # This resource represents a long-running operation that is the result of a network API call.
      "done": True or False, # If the value is false, it means the operation is still in progress. If true, the operation is completed, and either error or response is available.
      "error": { # The Status type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by gRPC (https://github.com/grpc). Each Status message contains three pieces of data: error code, error message, and error details.You can find out more about this error model and how to work with it in the API Design Guide (https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
        "code": 42, # The status code, which should be an enum value of google.rpc.Code.
        "details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
          {
            "a_key": "", # Properties of the object. Contains field @type with type URL.
          },
        ],
        "message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
      },
      "metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
        "a_key": "", # Properties of the object. Contains field @type with type URL.
      },
      "name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the name should be a resource name ending with operations/{unique_id}.
      "response": { # The normal, successful response of the operation. If the original method returns no data on success, such as Delete, the response is google.protobuf.Empty. If the original method is standard Get/Create/Update, the response should be the resource. For other methods, the response should have the type XxxResponse, where Xxx is the original method name. For example, if the original method name is TakeSnapshot(), the inferred response type is TakeSnapshotResponse.
        "a_key": "", # Properties of the object. Contains field @type with type URL.
      },
    }

`instantiateInline(parent, body=None, requestId=None, x__xgafv=None)`

    Instantiates a template and begins execution.This method is equivalent to executing the sequence CreateWorkflowTemplate, InstantiateWorkflowTemplate, DeleteWorkflowTemplate.The returned Operation can be used to track execution of workflow by polling operations.get. The Operation will complete when entire workflow is finished.The running workflow can be aborted via operations.cancel. This will cause any inflight jobs to be cancelled and workflow-owned clusters to be deleted.The Operation.metadata will be WorkflowMetadata (https://cloud.google.com/dataproc/docs/reference/rpc/google.cloud.dataproc.v1#workflowmetadata). Also see Using WorkflowMetadata (https://cloud.google.com/dataproc/docs/concepts/workflows/debugging#using_workflowmetadata).On successful completion, Operation.response will be Empty.

    Args:
      parent: string, Required. The resource name of the region or location, as described in https://cloud.google.com/apis/design/resource_names. For projects.regions.workflowTemplates,instantiateinline, the resource name of the region has the following format: projects/{project_id}/regions/{region} For projects.locations.workflowTemplates.instantiateinline, the resource name of the location has the following format: projects/{project_id}/locations/{location} (required)
      body: object, The request body.
        The object takes the form of:

    { # A Dataproc workflow template resource.
      "createTime": "A String", # Output only. The time template was created.
      "dagTimeout": "A String", # Optional. Timeout duration for the DAG of jobs, expressed in seconds (see JSON representation of duration (https://developers.google.com/protocol-buffers/docs/proto3#json)). The timeout duration must be from 10 minutes ("600s") to 24 hours ("86400s"). The timer begins when the first job is submitted. If the workflow is running at the end of the timeout period, any remaining jobs are cancelled, the workflow is ended, and if the workflow was running on a managed cluster, the cluster is deleted.
      "encryptionConfig": { # Encryption settings for encrypting workflow template job arguments. # Optional. Encryption settings for encrypting workflow template job arguments.
        "kmsKey": "A String", # Optional. The Cloud KMS key name to use for encrypting workflow template job arguments.When this this key is provided, the following workflow template job arguments (https://cloud.google.com/dataproc/docs/concepts/workflows/use-workflows#adding_jobs_to_a_template), if present, are CMEK encrypted (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/customer-managed-encryption#use_cmek_with_workflow_template_data): FlinkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/FlinkJob) HadoopJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/HadoopJob) SparkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkJob) SparkRJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkRJob) PySparkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/PySparkJob) SparkSqlJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkSqlJob) scriptVariables and queryList.queries HiveJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/HiveJob) scriptVariables and queryList.queries PigJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/PigJob) scriptVariables and queryList.queries PrestoJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/PrestoJob) scriptVariables and queryList.queries
      },
      "id": "A String",
      "jobs": [ # Required. The Directed Acyclic Graph of Jobs to submit.
        { # A job executed by the workflow.
          "flinkJob": { # A Dataproc job for running Apache Flink applications on YARN. # Optional. Job is a Flink job.
            "args": [ # Optional. The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision might occur that causes an incorrect job submission.
              "A String",
            ],
            "jarFileUris": [ # Optional. HCFS URIs of jar files to add to the CLASSPATHs of the Flink driver and tasks.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "mainClass": "A String", # The name of the driver's main class. The jar file that contains the class must be in the default CLASSPATH or specified in jarFileUris.
            "mainJarFileUri": "A String", # The HCFS URI of the jar file that contains the main class.
            "properties": { # Optional. A mapping of property names to values, used to configure Flink. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/flink/conf/flink-defaults.conf and classes in user code.
              "a_key": "A String",
            },
            "savepointUri": "A String", # Optional. HCFS URI of the savepoint, which contains the last saved progress for starting the current job.
          },
          "hadoopJob": { # A Dataproc job for running Apache Hadoop MapReduce (https://hadoop.apache.org/docs/current/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html) jobs on Apache Hadoop YARN (https://hadoop.apache.org/docs/r2.7.1/hadoop-yarn/hadoop-yarn-site/YARN.html). # Optional. Job is a Hadoop job.
            "archiveUris": [ # Optional. HCFS URIs of archives to be extracted in the working directory of Hadoop drivers and tasks. Supported file types: .jar, .tar, .tar.gz, .tgz, or .zip.
              "A String",
            ],
            "args": [ # Optional. The arguments to pass to the driver. Do not include arguments, such as -libjars or -Dfoo=bar, that can be set as job properties, since a collision might occur that causes an incorrect job submission.
              "A String",
            ],
            "fileUris": [ # Optional. HCFS (Hadoop Compatible Filesystem) URIs of files to be copied to the working directory of Hadoop drivers and distributed tasks. Useful for naively parallel tasks.
              "A String",
            ],
            "jarFileUris": [ # Optional. Jar file URIs to add to the CLASSPATHs of the Hadoop driver and tasks.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "mainClass": "A String", # The name of the driver's main class. The jar file containing the class must be in the default CLASSPATH or specified in jar_file_uris.
            "mainJarFileUri": "A String", # The HCFS URI of the jar file containing the main class. Examples: 'gs://foo-bucket/analytics-binaries/extract-useful-metrics-mr.jar' 'hdfs:/tmp/test-samples/custom-wordcount.jar' 'file:///home/usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar'
            "properties": { # Optional. A mapping of property names to values, used to configure Hadoop. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/hadoop/conf/*-site and classes in user code.
              "a_key": "A String",
            },
          },
          "hiveJob": { # A Dataproc job for running Apache Hive (https://hive.apache.org/) queries on YARN. # Optional. Job is a Hive job.
            "continueOnFailure": True or False, # Optional. Whether to continue executing queries if a query fails. The default value is false. Setting to true can be useful when executing independent parallel queries.
            "jarFileUris": [ # Optional. HCFS URIs of jar files to add to the CLASSPATH of the Hive server and Hadoop MapReduce (MR) tasks. Can contain Hive SerDes and UDFs.
              "A String",
            ],
            "properties": { # Optional. A mapping of property names and values, used to configure Hive. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/hadoop/conf/*-site.xml, /etc/hive/conf/hive-site.xml, and classes in user code.
              "a_key": "A String",
            },
            "queryFileUri": "A String", # The HCFS URI of the script that contains Hive queries.
            "queryList": { # A list of queries to run on a cluster. # A list of queries.
              "queries": [ # Required. The queries to execute. You do not need to end a query expression with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of a Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } }
                "A String",
              ],
            },
            "scriptVariables": { # Optional. Mapping of query variable names to values (equivalent to the Hive command: SET name="value";).
              "a_key": "A String",
            },
          },
          "labels": { # Optional. The labels to associate with this job.Label keys must be between 1 and 63 characters long, and must conform to the following regular expression: \p{Ll}\p{Lo}{0,62}Label values must be between 1 and 63 characters long, and must conform to the following regular expression: \p{Ll}\p{Lo}\p{N}_-{0,63}No more than 32 labels can be associated with a given job.
            "a_key": "A String",
          },
          "pigJob": { # A Dataproc job for running Apache Pig (https://pig.apache.org/) queries on YARN. # Optional. Job is a Pig job.
            "continueOnFailure": True or False, # Optional. Whether to continue executing queries if a query fails. The default value is false. Setting to true can be useful when executing independent parallel queries.
            "jarFileUris": [ # Optional. HCFS URIs of jar files to add to the CLASSPATH of the Pig Client and Hadoop MapReduce (MR) tasks. Can contain Pig UDFs.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "properties": { # Optional. A mapping of property names to values, used to configure Pig. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/hadoop/conf/*-site.xml, /etc/pig/conf/pig.properties, and classes in user code.
              "a_key": "A String",
            },
            "queryFileUri": "A String", # The HCFS URI of the script that contains the Pig queries.
            "queryList": { # A list of queries to run on a cluster. # A list of queries.
              "queries": [ # Required. The queries to execute. You do not need to end a query expression with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of a Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } }
                "A String",
              ],
            },
            "scriptVariables": { # Optional. Mapping of query variable names to values (equivalent to the Pig command: name=[value]).
              "a_key": "A String",
            },
          },
          "prerequisiteStepIds": [ # Optional. The optional list of prerequisite job step_ids. If not specified, the job will start at the beginning of workflow.
            "A String",
          ],
          "prestoJob": { # A Dataproc job for running Presto (https://prestosql.io/) queries. IMPORTANT: The Dataproc Presto Optional Component (https://cloud.google.com/dataproc/docs/concepts/components/presto) must be enabled when the cluster is created to submit a Presto job to the cluster. # Optional. Job is a Presto job.
            "clientTags": [ # Optional. Presto client tags to attach to this query
              "A String",
            ],
            "continueOnFailure": True or False, # Optional. Whether to continue executing queries if a query fails. The default value is false. Setting to true can be useful when executing independent parallel queries.
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "outputFormat": "A String", # Optional. The format in which query output will be displayed. See the Presto documentation for supported output formats
            "properties": { # Optional. A mapping of property names to values. Used to set Presto session properties (https://prestodb.io/docs/current/sql/set-session.html) Equivalent to using the --session flag in the Presto CLI
              "a_key": "A String",
            },
            "queryFileUri": "A String", # The HCFS URI of the script that contains SQL queries.
            "queryList": { # A list of queries to run on a cluster. # A list of queries.
              "queries": [ # Required. The queries to execute. You do not need to end a query expression with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of a Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } }
                "A String",
              ],
            },
          },
          "pysparkJob": { # A Dataproc job for running Apache PySpark (https://spark.apache.org/docs/latest/api/python/index.html#pyspark-overview) applications on YARN. # Optional. Job is a PySpark job.
            "archiveUris": [ # Optional. HCFS URIs of archives to be extracted into the working directory of each executor. Supported file types: .jar, .tar, .tar.gz, .tgz, and .zip.
              "A String",
            ],
            "args": [ # Optional. The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision may occur that causes an incorrect job submission.
              "A String",
            ],
            "fileUris": [ # Optional. HCFS URIs of files to be placed in the working directory of each executor. Useful for naively parallel tasks.
              "A String",
            ],
            "jarFileUris": [ # Optional. HCFS URIs of jar files to add to the CLASSPATHs of the Python driver and tasks.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "mainPythonFileUri": "A String", # Required. The HCFS URI of the main Python file to use as the driver. Must be a .py file.
            "properties": { # Optional. A mapping of property names to values, used to configure PySpark. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/spark/conf/spark-defaults.conf and classes in user code.
              "a_key": "A String",
            },
            "pythonFileUris": [ # Optional. HCFS file URIs of Python files to pass to the PySpark framework. Supported file types: .py, .egg, and .zip.
              "A String",
            ],
          },
          "scheduling": { # Job scheduling options. # Optional. Job scheduling configuration.
            "maxFailuresPerHour": 42, # Optional. Maximum number of times per hour a driver can be restarted as a result of driver exiting with non-zero code before job is reported failed.A job might be reported as thrashing if the driver exits with a non-zero code four times within a 10-minute window.Maximum value is 10.Note: This restartable job option is not supported in Dataproc workflow templates (https://cloud.google.com/dataproc/docs/concepts/workflows/using-workflows#adding_jobs_to_a_template).
            "maxFailuresTotal": 42, # Optional. Maximum total number of times a driver can be restarted as a result of the driver exiting with a non-zero code. After the maximum number is reached, the job will be reported as failed.Maximum value is 240.Note: Currently, this restartable job option is not supported in Dataproc workflow templates (https://cloud.google.com/dataproc/docs/concepts/workflows/using-workflows#adding_jobs_to_a_template).
          },
          "sparkJob": { # A Dataproc job for running Apache Spark (https://spark.apache.org/) applications on YARN. # Optional. Job is a Spark job.
            "archiveUris": [ # Optional. HCFS URIs of archives to be extracted into the working directory of each executor. Supported file types: .jar, .tar, .tar.gz, .tgz, and .zip.
              "A String",
            ],
            "args": [ # Optional. The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision may occur that causes an incorrect job submission.
              "A String",
            ],
            "fileUris": [ # Optional. HCFS URIs of files to be placed in the working directory of each executor. Useful for naively parallel tasks.
              "A String",
            ],
            "jarFileUris": [ # Optional. HCFS URIs of jar files to add to the CLASSPATHs of the Spark driver and tasks.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "mainClass": "A String", # The name of the driver's main class. The jar file that contains the class must be in the default CLASSPATH or specified in SparkJob.jar_file_uris.
            "mainJarFileUri": "A String", # The HCFS URI of the jar file that contains the main class.
            "properties": { # Optional. A mapping of property names to values, used to configure Spark. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/spark/conf/spark-defaults.conf and classes in user code.
              "a_key": "A String",
            },
          },
          "sparkRJob": { # A Dataproc job for running Apache SparkR (https://spark.apache.org/docs/latest/sparkr.html) applications on YARN. # Optional. Job is a SparkR job.
            "archiveUris": [ # Optional. HCFS URIs of archives to be extracted into the working directory of each executor. Supported file types: .jar, .tar, .tar.gz, .tgz, and .zip.
              "A String",
            ],
            "args": [ # Optional. The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision may occur that causes an incorrect job submission.
              "A String",
            ],
            "fileUris": [ # Optional. HCFS URIs of files to be placed in the working directory of each executor. Useful for naively parallel tasks.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "mainRFileUri": "A String", # Required. The HCFS URI of the main R file to use as the driver. Must be a .R file.
            "properties": { # Optional. A mapping of property names to values, used to configure SparkR. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/spark/conf/spark-defaults.conf and classes in user code.
              "a_key": "A String",
            },
          },
          "sparkSqlJob": { # A Dataproc job for running Apache Spark SQL (https://spark.apache.org/sql/) queries. # Optional. Job is a SparkSql job.
            "jarFileUris": [ # Optional. HCFS URIs of jar files to be added to the Spark CLASSPATH.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "properties": { # Optional. A mapping of property names to values, used to configure Spark SQL's SparkConf. Properties that conflict with values set by the Dataproc API might be overwritten.
              "a_key": "A String",
            },
            "queryFileUri": "A String", # The HCFS URI of the script that contains SQL queries.
            "queryList": { # A list of queries to run on a cluster. # A list of queries.
              "queries": [ # Required. The queries to execute. You do not need to end a query expression with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of a Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } }
                "A String",
              ],
            },
            "scriptVariables": { # Optional. Mapping of query variable names to values (equivalent to the Spark SQL command: SET name="value";).
              "a_key": "A String",
            },
          },
          "stepId": "A String", # Required. The step id. The id must be unique among all jobs within the template.The step id is used as prefix for job id, as job goog-dataproc-workflow-step-id label, and in prerequisiteStepIds field from other steps.The id must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). Cannot begin or end with underscore or hyphen. Must consist of between 3 and 50 characters.
          "trinoJob": { # A Dataproc job for running Trino (https://trino.io/) queries. IMPORTANT: The Dataproc Trino Optional Component (https://cloud.google.com/dataproc/docs/concepts/components/trino) must be enabled when the cluster is created to submit a Trino job to the cluster. # Optional. Job is a Trino job.
            "clientTags": [ # Optional. Trino client tags to attach to this query
              "A String",
            ],
            "continueOnFailure": True or False, # Optional. Whether to continue executing queries if a query fails. The default value is false. Setting to true can be useful when executing independent parallel queries.
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "outputFormat": "A String", # Optional. The format in which query output will be displayed. See the Trino documentation for supported output formats
            "properties": { # Optional. A mapping of property names to values. Used to set Trino session properties (https://trino.io/docs/current/sql/set-session.html) Equivalent to using the --session flag in the Trino CLI
              "a_key": "A String",
            },
            "queryFileUri": "A String", # The HCFS URI of the script that contains SQL queries.
            "queryList": { # A list of queries to run on a cluster. # A list of queries.
              "queries": [ # Required. The queries to execute. You do not need to end a query expression with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of a Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } }
                "A String",
              ],
            },
          },
        },
      ],
      "labels": { # Optional. The labels to associate with this template. These labels will be propagated to all jobs and clusters created by the workflow instance.Label keys must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt).Label values may be empty, but, if present, must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt).No more than 32 labels can be associated with a template.
        "a_key": "A String",
      },
      "name": "A String", # Output only. The resource name of the workflow template, as described in https://cloud.google.com/apis/design/resource_names. For projects.regions.workflowTemplates, the resource name of the template has the following format: projects/{project_id}/regions/{region}/workflowTemplates/{template_id} For projects.locations.workflowTemplates, the resource name of the template has the following format: projects/{project_id}/locations/{location}/workflowTemplates/{template_id}
      "parameters": [ # Optional. Template parameters whose values are substituted into the template. Values for parameters must be provided when the template is instantiated.
        { # A configurable parameter that replaces one or more fields in the template. Parameterizable fields: - Labels - File uris - Job properties - Job arguments - Script variables - Main class (in HadoopJob and SparkJob) - Zone (in ClusterSelector)
          "description": "A String", # Optional. Brief description of the parameter. Must not exceed 1024 characters.
          "fields": [ # Required. Paths to all fields that the parameter replaces. A field is allowed to appear in at most one parameter's list of field paths.A field path is similar in syntax to a google.protobuf.FieldMask. For example, a field path that references the zone field of a workflow template's cluster selector would be specified as placement.clusterSelector.zone.Also, field paths can reference fields using the following syntax: Values in maps can be referenced by key: labels'key' placement.clusterSelector.clusterLabels'key' placement.managedCluster.labels'key' placement.clusterSelector.clusterLabels'key' jobs'step-id'.labels'key' Jobs in the jobs list can be referenced by step-id: jobs'step-id'.hadoopJob.mainJarFileUri jobs'step-id'.hiveJob.queryFileUri jobs'step-id'.pySparkJob.mainPythonFileUri jobs'step-id'.hadoopJob.jarFileUris0 jobs'step-id'.hadoopJob.archiveUris0 jobs'step-id'.hadoopJob.fileUris0 jobs'step-id'.pySparkJob.pythonFileUris0 Items in repeated fields can be referenced by a zero-based index: jobs'step-id'.sparkJob.args0 Other examples: jobs'step-id'.hadoopJob.properties'key' jobs'step-id'.hadoopJob.args0 jobs'step-id'.hiveJob.scriptVariables'key' jobs'step-id'.hadoopJob.mainJarFileUri placement.clusterSelector.zoneIt may not be possible to parameterize maps and repeated fields in their entirety since only individual map values and individual items in repeated fields can be referenced. For example, the following field paths are invalid: placement.clusterSelector.clusterLabels jobs'step-id'.sparkJob.args
            "A String",
          ],
          "name": "A String", # Required. Parameter name. The parameter name is used as the key, and paired with the parameter value, which are passed to the template when the template is instantiated. The name must contain only capital letters (A-Z), numbers (0-9), and underscores (_), and must not start with a number. The maximum length is 40 characters.
          "validation": { # Configuration for parameter validation. # Optional. Validation rules to be applied to this parameter's value.
            "regex": { # Validation based on regular expressions. # Validation based on regular expressions.
              "regexes": [ # Required. RE2 regular expressions used to validate the parameter's value. The value must match the regex in its entirety (substring matches are not sufficient).
                "A String",
              ],
            },
            "values": { # Validation based on a list of allowed values. # Validation based on a list of allowed values.
              "values": [ # Required. List of allowed values for the parameter.
                "A String",
              ],
            },
          },
        },
      ],
      "placement": { # Specifies workflow execution target.Either managed_cluster or cluster_selector is required. # Required. WorkflowTemplate scheduling information.
        "clusterSelector": { # A selector that chooses target cluster for jobs based on metadata. # Optional. A selector that chooses target cluster for jobs based on metadata.The selector is evaluated at the time each job is submitted.
          "clusterLabels": { # Required. The cluster labels. Cluster must have all labels to match.
            "a_key": "A String",
          },
          "zone": "A String", # Optional. The zone where workflow process executes. This parameter does not affect the selection of the cluster.If unspecified, the zone of the first cluster matching the selector is used.
        },
        "managedCluster": { # Cluster that is managed by the workflow. # A cluster that is managed by the workflow.
          "clusterName": "A String", # Required. The cluster name prefix. A unique cluster name will be formed by appending a random suffix.The name must contain only lower-case letters (a-z), numbers (0-9), and hyphens (-). Must begin with a letter. Cannot begin or end with hyphen. Must consist of between 2 and 35 characters.
          "config": { # The cluster config. # Required. The cluster configuration.
            "autoscalingConfig": { # Autoscaling Policy config associated with the cluster. # Optional. Autoscaling config for the policy associated with the cluster. Cluster does not autoscale if this field is unset.
              "policyUri": "A String", # Optional. The autoscaling policy used by the cluster.Only resource names including projectid and location (region) are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/locations/[dataproc_region]/autoscalingPolicies/[policy_id] projects/[project_id]/locations/[dataproc_region]/autoscalingPolicies/[policy_id]Note that the policy must be in the same project and Dataproc region.
            },
            "auxiliaryNodeGroups": [ # Optional. The node group settings.
              { # Node group identification and configuration information.
                "nodeGroup": { # Dataproc Node Group. The Dataproc NodeGroup resource is not related to the Dataproc NodeGroupAffinity resource. # Required. Node group configuration.
                  "labels": { # Optional. Node group labels. Label keys must consist of from 1 to 63 characters and conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). Label values can be empty. If specified, they must consist of from 1 to 63 characters and conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). The node group must have no more than 32 labels.
                    "a_key": "A String",
                  },
                  "name": "A String", # The Node group resource name (https://aip.dev/122).
                  "nodeGroupConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The node group instance group configuration.
                    "accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
                      { # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
                        "acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
                        "acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
                      },
                    ],
                    "diskConfig": { # Specifies the config of boot disk and attached disk options for a group of VM instances. # Optional. Disk option config settings.
                      "bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. This field is supported only if boot_disk_type is hyperdisk-balanced.
                      "bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. This field is supported only if boot_disk_type is hyperdisk-balanced.
                      "bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
                      "bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
                      "localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
                      "numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
                    },
                    "imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
                    "instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
                      "instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
                        { # Defines machines types and a rank to which the machines types belong.
                          "machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
                            "A String",
                          ],
                          "rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
                        },
                      ],
                      "instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
                        { # Defines a mapping from machine types to the number of VMs that are created with each machine type.
                          "machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
                          "vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
                        },
                      ],
                      "provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
                        "standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
                        "standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
                      },
                    },
                    "instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
                      "A String",
                    ],
                    "instanceReferences": [ # Output only. List of references to Compute Engine instances.
                      { # A reference to a Compute Engine instance.
                        "instanceId": "A String", # The unique identifier of the Compute Engine instance.
                        "instanceName": "A String", # The user-friendly name of the Compute Engine instance.
                        "publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
                        "publicKey": "A String", # The public RSA key used for sharing data with this instance.
                      },
                    ],
                    "isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
                    "machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
                    "managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
                      "instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
                      "instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
                      "instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
                    },
                    "minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
                    "minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
                    "numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
                    "preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
                    "startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
                      "requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
                    },
                  },
                  "roles": [ # Required. Node group roles.
                    "A String",
                  ],
                },
                "nodeGroupId": "A String", # Optional. A node group ID. Generated if not specified.The ID must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). Cannot begin or end with underscore or hyphen. Must consist of from 3 to 33 characters.
              },
            ],
            "clusterTier": "A String", # Optional. The cluster tier.
            "clusterType": "A String", # Optional. The type of the cluster.
            "configBucket": "A String", # Optional. A Cloud Storage bucket used to stage job dependencies, config files, and job driver console output. If you do not specify a staging bucket, Cloud Dataproc will determine a Cloud Storage location (US, ASIA, or EU) for your cluster's staging bucket according to the Compute Engine zone where your cluster is deployed, and then create and manage this project-level, per-location bucket (see Dataproc staging and temp buckets (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/staging-bucket)). This field requires a Cloud Storage bucket name, not a gs://... URI to a Cloud Storage bucket.
            "dataprocMetricConfig": { # Dataproc metric config. # Optional. The config for Dataproc metrics.
              "metrics": [ # Required. Metrics sources to enable.
                { # A Dataproc custom metric.
                  "metricOverrides": [ # Optional. Specify one or more Custom metrics (https://cloud.google.com/dataproc/docs/guides/dataproc-metrics#custom_metrics) to collect for the metric course (for the SPARK metric source (any Spark metric (https://spark.apache.org/docs/latest/monitoring.html#metrics) can be specified).Provide metrics in the following format: METRIC_SOURCE: INSTANCE:GROUP:METRIC Use camelcase as appropriate.Examples: yarn:ResourceManager:QueueMetrics:AppsCompleted spark:driver:DAGScheduler:job.allJobs sparkHistoryServer:JVM:Memory:NonHeapMemoryUsage.committed hiveserver2:JVM:Memory:NonHeapMemoryUsage.used Notes: Only the specified overridden metrics are collected for the metric source. For example, if one or more spark:executive metrics are listed as metric overrides, other SPARK metrics are not collected. The collection of the metrics for other enabled custom metric sources is unaffected. For example, if both SPARK and YARN metric sources are enabled, and overrides are provided for Spark metrics only, all YARN metrics are collected.
                    "A String",
                  ],
                  "metricSource": "A String", # Required. A standard set of metrics is collected unless metricOverrides are specified for the metric source (see Custom metrics (https://cloud.google.com/dataproc/docs/guides/dataproc-metrics#custom_metrics) for more information).
                },
              ],
            },
            "encryptionConfig": { # Encryption settings for the cluster. # Optional. Encryption settings for the cluster.
              "gcePdKmsKeyName": "A String", # Optional. The Cloud KMS key resource name to use for persistent disk encryption for all instances in the cluster. See Use CMEK with cluster data (https://cloud.google.com//dataproc/docs/concepts/configuring-clusters/customer-managed-encryption#use_cmek_with_cluster_data) for more information.
              "kmsKey": "A String", # Optional. The Cloud KMS key resource name to use for cluster persistent disk and job argument encryption. See Use CMEK with cluster data (https://cloud.google.com//dataproc/docs/concepts/configuring-clusters/customer-managed-encryption#use_cmek_with_cluster_data) for more information.When this key resource name is provided, the following job arguments of the following job types submitted to the cluster are encrypted using CMEK: FlinkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/FlinkJob) HadoopJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/HadoopJob) SparkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkJob) SparkRJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkRJob) PySparkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/PySparkJob) SparkSqlJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkSqlJob) scriptVariables and queryList.queries HiveJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/HiveJob) scriptVariables and queryList.queries PigJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/PigJob) scriptVariables and queryList.queries PrestoJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/PrestoJob) scriptVariables and queryList.queries
            },
            "endpointConfig": { # Endpoint config for this cluster # Optional. Port/endpoint configuration for this cluster
              "enableHttpPortAccess": True or False, # Optional. If true, enable http access to specific ports on the cluster from external sources. Defaults to false.
              "httpPorts": { # Output only. The map of port descriptions to URLs. Will only be populated if enable_http_port_access is true.
                "a_key": "A String",
              },
            },
            "gceClusterConfig": { # Common config settings for resources of Compute Engine cluster instances, applicable to all instances in the cluster. # Optional. The shared Compute Engine config settings for all instances in a cluster.
              "confidentialInstanceConfig": { # Confidential Instance Config for clusters using Confidential VMs (https://cloud.google.com/compute/confidential-vm/docs) # Optional. Confidential Instance Config for clusters using Confidential VMs (https://cloud.google.com/compute/confidential-vm/docs).
                "enableConfidentialCompute": True or False, # Optional. Defines whether the instance should have confidential compute enabled.
              },
              "internalIpOnly": True or False, # Optional. This setting applies to subnetwork-enabled networks. It is set to true by default in clusters created with image versions 2.2.x.When set to true: All cluster VMs have internal IP addresses. Google Private Access (https://cloud.google.com/vpc/docs/private-google-access) must be enabled to access Dataproc and other Google Cloud APIs. Off-cluster dependencies must be configured to be accessible without external IP addresses.When set to false: Cluster VMs are not restricted to internal IP addresses. Ephemeral external IP addresses are assigned to each cluster VM.
              "metadata": { # Optional. The Compute Engine metadata entries to add to all instances (see Project and instance metadata (https://cloud.google.com/compute/docs/storing-retrieving-metadata#project_and_instance_metadata)).
                "a_key": "A String",
              },
              "networkUri": "A String", # Optional. The Compute Engine network to be used for machine communications. Cannot be specified with subnetwork_uri. If neither network_uri nor subnetwork_uri is specified, the "default" network of the project is used, if it exists. Cannot be a "Custom Subnet Network" (see Using Subnetworks (https://cloud.google.com/compute/docs/subnetworks) for more information).A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/networks/default projects/[project_id]/global/networks/default default
              "nodeGroupAffinity": { # Node Group Affinity for clusters using sole-tenant node groups. The Dataproc NodeGroupAffinity resource is not related to the Dataproc NodeGroup resource. # Optional. Node Group Affinity for sole-tenant clusters.
                "nodeGroupUri": "A String", # Required. The URI of a sole-tenant node group resource (https://cloud.google.com/compute/docs/reference/rest/v1/nodeGroups) that the cluster will be created on.A full URL, partial URI, or node group name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/nodeGroups/node-group-1 projects/[project_id]/zones/[zone]/nodeGroups/node-group-1 node-group-1
              },
              "privateIpv6GoogleAccess": "A String", # Optional. The type of IPv6 access for a cluster.
              "reservationAffinity": { # Reservation Affinity for consuming Zonal reservation. # Optional. Reservation Affinity for consuming Zonal reservation.
                "consumeReservationType": "A String", # Optional. Type of reservation to consume
                "key": "A String", # Optional. Corresponds to the label key of reservation resource.
                "values": [ # Optional. Corresponds to the label values of reservation resource.
                  "A String",
                ],
              },
              "resourceManagerTags": { # Optional. Resource manager tags (https://cloud.google.com/resource-manager/docs/tags/tags-creating-and-managing) to add to all instances (see Use secure tags in Dataproc (https://cloud.google.com/dataproc/docs/guides/attach-secure-tags)).
                "a_key": "A String",
              },
              "serviceAccount": "A String", # Optional. The Dataproc service account (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/service-accounts#service_accounts_in_dataproc) (also see VM Data Plane identity (https://cloud.google.com/dataproc/docs/concepts/iam/dataproc-principals#vm_service_account_data_plane_identity)) used by Dataproc cluster VM instances to access Google Cloud Platform services.If not specified, the Compute Engine default service account (https://cloud.google.com/compute/docs/access/service-accounts#default_service_account) is used.
              "serviceAccountScopes": [ # Optional. The URIs of service account scopes to be included in Compute Engine instances. The following base set of scopes is always included: https://www.googleapis.com/auth/cloud.useraccounts.readonly https://www.googleapis.com/auth/devstorage.read_write https://www.googleapis.com/auth/logging.writeIf no scopes are specified, the following defaults are also provided: https://www.googleapis.com/auth/bigquery https://www.googleapis.com/auth/bigtable.admin.table https://www.googleapis.com/auth/bigtable.data https://www.googleapis.com/auth/devstorage.full_control
                "A String",
              ],
              "shieldedInstanceConfig": { # Shielded Instance Config for clusters using Compute Engine Shielded VMs (https://cloud.google.com/security/shielded-cloud/shielded-vm). # Optional. Shielded Instance Config for clusters using Compute Engine Shielded VMs (https://cloud.google.com/security/shielded-cloud/shielded-vm).
                "enableIntegrityMonitoring": True or False, # Optional. Defines whether instances have integrity monitoring enabled.
                "enableSecureBoot": True or False, # Optional. Defines whether instances have Secure Boot enabled.
                "enableVtpm": True or False, # Optional. Defines whether instances have the vTPM enabled.
              },
              "subnetworkUri": "A String", # Optional. The Compute Engine subnetwork to be used for machine communications. Cannot be specified with network_uri.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/regions/[region]/subnetworks/sub0 projects/[project_id]/regions/[region]/subnetworks/sub0 sub0
              "tags": [ # The Compute Engine network tags to add to all instances (see Tagging instances (https://cloud.google.com/vpc/docs/add-remove-network-tags)).
                "A String",
              ],
              "zoneUri": "A String", # Optional. The Compute Engine zone where the Dataproc cluster will be located. If omitted, the service will pick a zone in the cluster's Compute Engine region. On a get request, zone will always be present.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone] projects/[project_id]/zones/[zone] [zone]
            },
            "gkeClusterConfig": { # The cluster's GKE config. # Optional. BETA. The Kubernetes Engine config for Dataproc clusters deployed to The Kubernetes Engine config for Dataproc clusters deployed to Kubernetes. These config settings are mutually exclusive with Compute Engine-based options, such as gce_cluster_config, master_config, worker_config, secondary_worker_config, and autoscaling_config.
              "gkeClusterTarget": "A String", # Optional. A target GKE cluster to deploy to. It must be in the same project and region as the Dataproc cluster (the GKE cluster can be zonal or regional). Format: 'projects/{project}/locations/{location}/clusters/{cluster_id}'
              "namespacedGkeDeploymentTarget": { # Deprecated. Used only for the deprecated beta. A full, namespace-isolated deployment target for an existing GKE cluster. # Optional. Deprecated. Use gkeClusterTarget. Used only for the deprecated beta. A target for the deployment.
                "clusterNamespace": "A String", # Optional. A namespace within the GKE cluster to deploy into.
                "targetGkeCluster": "A String", # Optional. The target GKE cluster to deploy to. Format: 'projects/{project}/locations/{location}/clusters/{cluster_id}'
              },
              "nodePoolTarget": [ # Optional. GKE node pools where workloads will be scheduled. At least one node pool must be assigned the DEFAULT GkeNodePoolTarget.Role. If a GkeNodePoolTarget is not specified, Dataproc constructs a DEFAULT GkeNodePoolTarget. Each role can be given to only one GkeNodePoolTarget. All node pools must have the same location settings.
                { # GKE node pools that Dataproc workloads run on.
                  "nodePool": "A String", # Required. The target GKE node pool. Format: 'projects/{project}/locations/{location}/clusters/{cluster}/nodePools/{node_pool}'
                  "nodePoolConfig": { # The configuration of a GKE node pool used by a Dataproc-on-GKE cluster (https://cloud.google.com/dataproc/docs/concepts/jobs/dataproc-gke#create-a-dataproc-on-gke-cluster). # Input only. The configuration for the GKE node pool.If specified, Dataproc attempts to create a node pool with the specified shape. If one with the same name already exists, it is verified against all specified fields. If a field differs, the virtual cluster creation will fail.If omitted, any node pool with the specified name is used. If a node pool with the specified name does not exist, Dataproc create a node pool with default values.This is an input only field. It will not be returned by the API.
                    "autoscaling": { # GkeNodePoolAutoscaling contains information the cluster autoscaler needs to adjust the size of the node pool to the current cluster usage. # Optional. The autoscaler configuration for this node pool. The autoscaler is enabled only when a valid configuration is present.
                      "maxNodeCount": 42, # The maximum number of nodes in the node pool. Must be >= min_node_count, and must be > 0. Note: Quota must be sufficient to scale up the cluster.
                      "minNodeCount": 42, # The minimum number of nodes in the node pool. Must be >= 0 and <= max_node_count.
                    },
                    "config": { # Parameters that describe cluster nodes. # Optional. The node pool configuration.
                      "accelerators": [ # Optional. A list of hardware accelerators (https://cloud.google.com/compute/docs/gpus) to attach to each node.
                        { # A GkeNodeConfigAcceleratorConfig represents a Hardware Accelerator request for a node pool.
                          "acceleratorCount": "A String", # The number of accelerator cards exposed to an instance.
                          "acceleratorType": "A String", # The accelerator type resource namename (see GPUs on Compute Engine).
                          "gpuPartitionSize": "A String", # Size of partitions to create on the GPU. Valid values are described in the NVIDIA mig user guide (https://docs.nvidia.com/datacenter/tesla/mig-user-guide/#partitioning).
                        },
                      ],
                      "bootDiskKmsKey": "A String", # Optional. The Customer Managed Encryption Key (CMEK) (https://cloud.google.com/kubernetes-engine/docs/how-to/using-cmek) used to encrypt the boot disk attached to each node in the node pool. Specify the key using the following format: projects/{project}/locations/{location}/keyRings/{key_ring}/cryptoKeys/{crypto_key}
                      "localSsdCount": 42, # Optional. The number of local SSD disks to attach to the node, which is limited by the maximum number of disks allowable per zone (see Adding Local SSDs (https://cloud.google.com/compute/docs/disks/local-ssd)).
                      "machineType": "A String", # Optional. The name of a Compute Engine machine type (https://cloud.google.com/compute/docs/machine-types).
                      "minCpuPlatform": "A String", # Optional. Minimum CPU platform (https://cloud.google.com/compute/docs/instances/specify-min-cpu-platform) to be used by this instance. The instance may be scheduled on the specified or a newer CPU platform. Specify the friendly names of CPU platforms, such as "Intel Haswell"` or Intel Sandy Bridge".
                      "preemptible": True or False, # Optional. Whether the nodes are created as legacy preemptible VM instances (https://cloud.google.com/compute/docs/instances/preemptible). Also see Spot VMs, preemptible VM instances without a maximum lifetime. Legacy and Spot preemptible nodes cannot be used in a node pool with the CONTROLLER role or in the DEFAULT node pool if the CONTROLLER role is not assigned (the DEFAULT node pool will assume the CONTROLLER role).
                      "spot": True or False, # Optional. Whether the nodes are created as Spot VM instances (https://cloud.google.com/compute/docs/instances/spot). Spot VMs are the latest update to legacy preemptible VMs. Spot VMs do not have a maximum lifetime. Legacy and Spot preemptible nodes cannot be used in a node pool with the CONTROLLER role or in the DEFAULT node pool if the CONTROLLER role is not assigned (the DEFAULT node pool will assume the CONTROLLER role).
                    },
                    "locations": [ # Optional. The list of Compute Engine zones (https://cloud.google.com/compute/docs/zones#available) where node pool nodes associated with a Dataproc on GKE virtual cluster will be located.Note: All node pools associated with a virtual cluster must be located in the same region as the virtual cluster, and they must be located in the same zone within that region.If a location is not specified during node pool creation, Dataproc on GKE will choose the zone.
                      "A String",
                    ],
                  },
                  "roles": [ # Required. The roles associated with the GKE node pool.
                    "A String",
                  ],
                },
              ],
            },
            "initializationActions": [ # Optional. Commands to execute on each node after config is completed. By default, executables are run on master and all worker nodes. You can test a node's role metadata to run an executable on a master or worker node, as shown below using curl (you can also use wget): ROLE=$(curl -H Metadata-Flavor:Google http://metadata/computeMetadata/v1/instance/attributes/dataproc-role) if [[ "${ROLE}" == 'Master' ]]; then ... master specific actions ... else ... worker specific actions ... fi
              { # Specifies an executable to run on a fully configured node and a timeout period for executable completion.
                "executableFile": "A String", # Required. Cloud Storage URI of executable file.
                "executionTimeout": "A String", # Optional. Amount of time executable has to complete. Default is 10 minutes (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).Cluster creation fails with an explanatory error message (the name of the executable that caused the error and the exceeded timeout period) if the executable is not completed at end of the timeout period.
              },
            ],
            "lifecycleConfig": { # Specifies the cluster auto-delete schedule configuration. # Optional. Lifecycle setting for the cluster.
              "autoDeleteTime": "A String", # Optional. The time when cluster will be auto-deleted (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
              "autoDeleteTtl": "A String", # Optional. The lifetime duration of cluster. The cluster will be auto-deleted at the end of this period. Minimum value is 10 minutes; maximum value is 14 days (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).
              "autoStopTime": "A String", # Optional. The time when cluster will be auto-stopped (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
              "autoStopTtl": "A String", # Optional. The lifetime duration of the cluster. The cluster will be auto-stopped at the end of this period, calculated from the time of submission of the create or update cluster request. Minimum value is 10 minutes; maximum value is 14 days (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).
              "idleDeleteTtl": "A String", # Optional. The duration to keep the cluster alive while idling (when no jobs are running). Passing this threshold will cause the cluster to be deleted. Minimum value is 5 minutes; maximum value is 14 days (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).
              "idleStartTime": "A String", # Output only. The time when cluster became idle (most recent job finished) and became eligible for deletion due to idleness (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
              "idleStopTtl": "A String", # Optional. The duration to keep the cluster started while idling (when no jobs are running). Passing this threshold will cause the cluster to be stopped. Minimum value is 5 minutes; maximum value is 14 days (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).
            },
            "masterConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The Compute Engine config settings for the cluster's master instance.
              "accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
                { # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
                  "acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
                  "acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
                },
              ],
              "diskConfig": { # Specifies the config of boot disk and attached disk options for a group of VM instances. # Optional. Disk option config settings.
                "bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. This field is supported only if boot_disk_type is hyperdisk-balanced.
                "bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. This field is supported only if boot_disk_type is hyperdisk-balanced.
                "bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
                "bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
                "localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
                "numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
              },
              "imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
              "instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
                "instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
                  { # Defines machines types and a rank to which the machines types belong.
                    "machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
                      "A String",
                    ],
                    "rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
                  },
                ],
                "instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
                  { # Defines a mapping from machine types to the number of VMs that are created with each machine type.
                    "machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
                    "vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
                  },
                ],
                "provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
                  "standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
                  "standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
                },
              },
              "instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
                "A String",
              ],
              "instanceReferences": [ # Output only. List of references to Compute Engine instances.
                { # A reference to a Compute Engine instance.
                  "instanceId": "A String", # The unique identifier of the Compute Engine instance.
                  "instanceName": "A String", # The user-friendly name of the Compute Engine instance.
                  "publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
                  "publicKey": "A String", # The public RSA key used for sharing data with this instance.
                },
              ],
              "isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
              "machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
              "managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
                "instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
                "instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
                "instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
              },
              "minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
              "minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
              "numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
              "preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
              "startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
                "requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
              },
            },
            "metastoreConfig": { # Specifies a Metastore configuration. # Optional. Metastore configuration.
              "dataprocMetastoreService": "A String", # Required. Resource name of an existing Dataproc Metastore service.Example: projects/[project_id]/locations/[dataproc_region]/services/[service-name]
            },
            "secondaryWorkerConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The Compute Engine config settings for a cluster's secondary worker instances
              "accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
                { # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
                  "acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
                  "acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
                },
              ],
              "diskConfig": { # Specifies the config of boot disk and attached disk options for a group of VM instances. # Optional. Disk option config settings.
                "bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. This field is supported only if boot_disk_type is hyperdisk-balanced.
                "bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. This field is supported only if boot_disk_type is hyperdisk-balanced.
                "bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
                "bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
                "localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
                "numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
              },
              "imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
              "instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
                "instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
                  { # Defines machines types and a rank to which the machines types belong.
                    "machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
                      "A String",
                    ],
                    "rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
                  },
                ],
                "instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
                  { # Defines a mapping from machine types to the number of VMs that are created with each machine type.
                    "machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
                    "vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
                  },
                ],
                "provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
                  "standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
                  "standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
                },
              },
              "instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
                "A String",
              ],
              "instanceReferences": [ # Output only. List of references to Compute Engine instances.
                { # A reference to a Compute Engine instance.
                  "instanceId": "A String", # The unique identifier of the Compute Engine instance.
                  "instanceName": "A String", # The user-friendly name of the Compute Engine instance.
                  "publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
                  "publicKey": "A String", # The public RSA key used for sharing data with this instance.
                },
              ],
              "isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
              "machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
              "managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
                "instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
                "instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
                "instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
              },
              "minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
              "minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
              "numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
              "preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
              "startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
                "requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
              },
            },
            "securityConfig": { # Security related configuration, including encryption, Kerberos, etc. # Optional. Security settings for the cluster.
              "identityConfig": { # Identity related configuration, including service account based secure multi-tenancy user mappings. # Optional. Identity related configuration, including service account based secure multi-tenancy user mappings.
                "userServiceAccountMapping": { # Required. Map of user to service account.
                  "a_key": "A String",
                },
              },
              "kerberosConfig": { # Specifies Kerberos related configuration. # Optional. Kerberos related configuration.
                "crossRealmTrustAdminServer": "A String", # Optional. The admin server (IP or hostname) for the remote trusted realm in a cross realm trust relationship.
                "crossRealmTrustKdc": "A String", # Optional. The KDC (IP or hostname) for the remote trusted realm in a cross realm trust relationship.
                "crossRealmTrustRealm": "A String", # Optional. The remote realm the Dataproc on-cluster KDC will trust, should the user enable cross realm trust.
                "crossRealmTrustSharedPasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the shared password between the on-cluster Kerberos realm and the remote trusted realm, in a cross realm trust relationship.
                "enableKerberos": True or False, # Optional. Flag to indicate whether to Kerberize the cluster (default: false). Set this field to true to enable Kerberos on a cluster.
                "kdcDbKeyUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the master key of the KDC database.
                "keyPasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the password to the user provided key. For the self-signed certificate, this password is generated by Dataproc.
                "keystorePasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the password to the user provided keystore. For the self-signed certificate, this password is generated by Dataproc.
                "keystoreUri": "A String", # Optional. The Cloud Storage URI of the keystore file used for SSL encryption. If not provided, Dataproc will provide a self-signed certificate.
                "kmsKeyUri": "A String", # Optional. The URI of the KMS key used to encrypt sensitive files.
                "realm": "A String", # Optional. The name of the on-cluster Kerberos realm. If not specified, the uppercased domain of hostnames will be the realm.
                "rootPrincipalPasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the root principal password.
                "tgtLifetimeHours": 42, # Optional. The lifetime of the ticket granting ticket, in hours. If not specified, or user specifies 0, then default value 10 will be used.
                "truststorePasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the password to the user provided truststore. For the self-signed certificate, this password is generated by Dataproc.
                "truststoreUri": "A String", # Optional. The Cloud Storage URI of the truststore file used for SSL encryption. If not provided, Dataproc will provide a self-signed certificate.
              },
            },
            "softwareConfig": { # Specifies the selection and config of software inside the cluster. # Optional. The config settings for cluster software.
              "imageVersion": "A String", # Optional. The version of software inside the cluster. It must be one of the supported Dataproc Versions (https://cloud.google.com/dataproc/docs/concepts/versioning/dataproc-versions#supported-dataproc-image-versions), such as "1.2" (including a subminor version, such as "1.2.29"), or the "preview" version (https://cloud.google.com/dataproc/docs/concepts/versioning/dataproc-versions#other_versions). If unspecified, it defaults to the latest Debian version.
              "optionalComponents": [ # Optional. The set of components to activate on the cluster.
                "A String",
              ],
              "properties": { # Optional. The properties to set on daemon config files.Property keys are specified in prefix:property format, for example core:hadoop.tmp.dir. The following are supported prefixes and their mappings: capacity-scheduler: capacity-scheduler.xml core: core-site.xml distcp: distcp-default.xml hdfs: hdfs-site.xml hive: hive-site.xml mapred: mapred-site.xml pig: pig.properties spark: spark-defaults.conf yarn: yarn-site.xmlFor more information, see Cluster properties (https://cloud.google.com/dataproc/docs/concepts/cluster-properties).
                "a_key": "A String",
              },
            },
            "tempBucket": "A String", # Optional. A Cloud Storage bucket used to store ephemeral cluster and jobs data, such as Spark and MapReduce history files. If you do not specify a temp bucket, Dataproc will determine a Cloud Storage location (US, ASIA, or EU) for your cluster's temp bucket according to the Compute Engine zone where your cluster is deployed, and then create and manage this project-level, per-location bucket. The default bucket has a TTL of 90 days, but you can use any TTL (or none) if you specify a bucket (see Dataproc staging and temp buckets (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/staging-bucket)). This field requires a Cloud Storage bucket name, not a gs://... URI to a Cloud Storage bucket.
            "workerConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The Compute Engine config settings for the cluster's worker instances.
              "accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
                { # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
                  "acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
                  "acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
                },
              ],
              "diskConfig": { # Specifies the config of boot disk and attached disk options for a group of VM instances. # Optional. Disk option config settings.
                "bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. This field is supported only if boot_disk_type is hyperdisk-balanced.
                "bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. This field is supported only if boot_disk_type is hyperdisk-balanced.
                "bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
                "bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
                "localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
                "numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
              },
              "imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
              "instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
                "instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
                  { # Defines machines types and a rank to which the machines types belong.
                    "machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
                      "A String",
                    ],
                    "rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
                  },
                ],
                "instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
                  { # Defines a mapping from machine types to the number of VMs that are created with each machine type.
                    "machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
                    "vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
                  },
                ],
                "provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
                  "standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
                  "standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
                },
              },
              "instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
                "A String",
              ],
              "instanceReferences": [ # Output only. List of references to Compute Engine instances.
                { # A reference to a Compute Engine instance.
                  "instanceId": "A String", # The unique identifier of the Compute Engine instance.
                  "instanceName": "A String", # The user-friendly name of the Compute Engine instance.
                  "publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
                  "publicKey": "A String", # The public RSA key used for sharing data with this instance.
                },
              ],
              "isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
              "machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
              "managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
                "instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
                "instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
                "instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
              },
              "minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
              "minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
              "numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
              "preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
              "startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
                "requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
              },
            },
          },
          "labels": { # Optional. The labels to associate with this cluster.Label keys must be between 1 and 63 characters long, and must conform to the following PCRE regular expression: \p{Ll}\p{Lo}{0,62}Label values must be between 1 and 63 characters long, and must conform to the following PCRE regular expression: \p{Ll}\p{Lo}\p{N}_-{0,63}No more than 32 labels can be associated with a given cluster.
            "a_key": "A String",
          },
        },
      },
      "updateTime": "A String", # Output only. The time template was last updated.
      "version": 42, # Optional. Used to perform a consistent read-modify-write.This field should be left blank for a CreateWorkflowTemplate request. It is required for an UpdateWorkflowTemplate request, and must match the current server version. A typical update template flow would fetch the current template with a GetWorkflowTemplate request, which will return the current template with the version field filled in with the current server version. The user updates other fields in the template, then returns it as part of the UpdateWorkflowTemplate request.
    }

      requestId: string, Optional. A tag that prevents multiple concurrent workflow instances with the same tag from running. This mitigates risk of concurrent instances started due to retries.It is recommended to always set this value to a UUID (https://en.wikipedia.org/wiki/Universally_unique_identifier).The tag must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). The maximum length is 40 characters.
      x__xgafv: string, V1 error format.
        Allowed values
          1 - v1 error format
          2 - v2 error format

    Returns:
      An object of the form:

        { # This resource represents a long-running operation that is the result of a network API call.
      "done": True or False, # If the value is false, it means the operation is still in progress. If true, the operation is completed, and either error or response is available.
      "error": { # The Status type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by gRPC (https://github.com/grpc). Each Status message contains three pieces of data: error code, error message, and error details.You can find out more about this error model and how to work with it in the API Design Guide (https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
        "code": 42, # The status code, which should be an enum value of google.rpc.Code.
        "details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
          {
            "a_key": "", # Properties of the object. Contains field @type with type URL.
          },
        ],
        "message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
      },
      "metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
        "a_key": "", # Properties of the object. Contains field @type with type URL.
      },
      "name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the name should be a resource name ending with operations/{unique_id}.
      "response": { # The normal, successful response of the operation. If the original method returns no data on success, such as Delete, the response is google.protobuf.Empty. If the original method is standard Get/Create/Update, the response should be the resource. For other methods, the response should have the type XxxResponse, where Xxx is the original method name. For example, if the original method name is TakeSnapshot(), the inferred response type is TakeSnapshotResponse.
        "a_key": "", # Properties of the object. Contains field @type with type URL.
      },
    }

`list(parent, pageSize=None, pageToken=None, x__xgafv=None)`

    Lists workflows that match the specified filter in the request.

    Args:
      parent: string, Required. The resource name of the region or location, as described in https://cloud.google.com/apis/design/resource_names. For projects.regions.workflowTemplates,list, the resource name of the region has the following format: projects/{project_id}/regions/{region} For projects.locations.workflowTemplates.list, the resource name of the location has the following format: projects/{project_id}/locations/{location} (required)
      pageSize: integer, Optional. The maximum number of results to return in each response.
      pageToken: string, Optional. The page token, returned by a previous call, to request the next page of results.
      x__xgafv: string, V1 error format.
        Allowed values
          1 - v1 error format
          2 - v2 error format

    Returns:
      An object of the form:

        { # A response to a request to list workflow templates in a project.
      "nextPageToken": "A String", # Output only. This token is included in the response if there are more results to fetch. To fetch additional results, provide this value as the page_token in a subsequent ListWorkflowTemplatesRequest.
      "templates": [ # Output only. WorkflowTemplates list.
        { # A Dataproc workflow template resource.
          "createTime": "A String", # Output only. The time template was created.
          "dagTimeout": "A String", # Optional. Timeout duration for the DAG of jobs, expressed in seconds (see JSON representation of duration (https://developers.google.com/protocol-buffers/docs/proto3#json)). The timeout duration must be from 10 minutes ("600s") to 24 hours ("86400s"). The timer begins when the first job is submitted. If the workflow is running at the end of the timeout period, any remaining jobs are cancelled, the workflow is ended, and if the workflow was running on a managed cluster, the cluster is deleted.
          "encryptionConfig": { # Encryption settings for encrypting workflow template job arguments. # Optional. Encryption settings for encrypting workflow template job arguments.
            "kmsKey": "A String", # Optional. The Cloud KMS key name to use for encrypting workflow template job arguments.When this this key is provided, the following workflow template job arguments (https://cloud.google.com/dataproc/docs/concepts/workflows/use-workflows#adding_jobs_to_a_template), if present, are CMEK encrypted (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/customer-managed-encryption#use_cmek_with_workflow_template_data): FlinkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/FlinkJob) HadoopJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/HadoopJob) SparkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkJob) SparkRJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkRJob) PySparkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/PySparkJob) SparkSqlJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkSqlJob) scriptVariables and queryList.queries HiveJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/HiveJob) scriptVariables and queryList.queries PigJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/PigJob) scriptVariables and queryList.queries PrestoJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/PrestoJob) scriptVariables and queryList.queries
          },
          "id": "A String",
          "jobs": [ # Required. The Directed Acyclic Graph of Jobs to submit.
            { # A job executed by the workflow.
              "flinkJob": { # A Dataproc job for running Apache Flink applications on YARN. # Optional. Job is a Flink job.
                "args": [ # Optional. The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision might occur that causes an incorrect job submission.
                  "A String",
                ],
                "jarFileUris": [ # Optional. HCFS URIs of jar files to add to the CLASSPATHs of the Flink driver and tasks.
                  "A String",
                ],
                "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
                  "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                    "a_key": "A String",
                  },
                },
                "mainClass": "A String", # The name of the driver's main class. The jar file that contains the class must be in the default CLASSPATH or specified in jarFileUris.
                "mainJarFileUri": "A String", # The HCFS URI of the jar file that contains the main class.
                "properties": { # Optional. A mapping of property names to values, used to configure Flink. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/flink/conf/flink-defaults.conf and classes in user code.
                  "a_key": "A String",
                },
                "savepointUri": "A String", # Optional. HCFS URI of the savepoint, which contains the last saved progress for starting the current job.
              },
              "hadoopJob": { # A Dataproc job for running Apache Hadoop MapReduce (https://hadoop.apache.org/docs/current/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html) jobs on Apache Hadoop YARN (https://hadoop.apache.org/docs/r2.7.1/hadoop-yarn/hadoop-yarn-site/YARN.html). # Optional. Job is a Hadoop job.
                "archiveUris": [ # Optional. HCFS URIs of archives to be extracted in the working directory of Hadoop drivers and tasks. Supported file types: .jar, .tar, .tar.gz, .tgz, or .zip.
                  "A String",
                ],
                "args": [ # Optional. The arguments to pass to the driver. Do not include arguments, such as -libjars or -Dfoo=bar, that can be set as job properties, since a collision might occur that causes an incorrect job submission.
                  "A String",
                ],
                "fileUris": [ # Optional. HCFS (Hadoop Compatible Filesystem) URIs of files to be copied to the working directory of Hadoop drivers and distributed tasks. Useful for naively parallel tasks.
                  "A String",
                ],
                "jarFileUris": [ # Optional. Jar file URIs to add to the CLASSPATHs of the Hadoop driver and tasks.
                  "A String",
                ],
                "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
                  "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                    "a_key": "A String",
                  },
                },
                "mainClass": "A String", # The name of the driver's main class. The jar file containing the class must be in the default CLASSPATH or specified in jar_file_uris.
                "mainJarFileUri": "A String", # The HCFS URI of the jar file containing the main class. Examples: 'gs://foo-bucket/analytics-binaries/extract-useful-metrics-mr.jar' 'hdfs:/tmp/test-samples/custom-wordcount.jar' 'file:///home/usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar'
                "properties": { # Optional. A mapping of property names to values, used to configure Hadoop. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/hadoop/conf/*-site and classes in user code.
                  "a_key": "A String",
                },
              },
              "hiveJob": { # A Dataproc job for running Apache Hive (https://hive.apache.org/) queries on YARN. # Optional. Job is a Hive job.
                "continueOnFailure": True or False, # Optional. Whether to continue executing queries if a query fails. The default value is false. Setting to true can be useful when executing independent parallel queries.
                "jarFileUris": [ # Optional. HCFS URIs of jar files to add to the CLASSPATH of the Hive server and Hadoop MapReduce (MR) tasks. Can contain Hive SerDes and UDFs.
                  "A String",
                ],
                "properties": { # Optional. A mapping of property names and values, used to configure Hive. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/hadoop/conf/*-site.xml, /etc/hive/conf/hive-site.xml, and classes in user code.
                  "a_key": "A String",
                },
                "queryFileUri": "A String", # The HCFS URI of the script that contains Hive queries.
                "queryList": { # A list of queries to run on a cluster. # A list of queries.
                  "queries": [ # Required. The queries to execute. You do not need to end a query expression with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of a Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } }
                    "A String",
                  ],
                },
                "scriptVariables": { # Optional. Mapping of query variable names to values (equivalent to the Hive command: SET name="value";).
                  "a_key": "A String",
                },
              },
              "labels": { # Optional. The labels to associate with this job.Label keys must be between 1 and 63 characters long, and must conform to the following regular expression: \p{Ll}\p{Lo}{0,62}Label values must be between 1 and 63 characters long, and must conform to the following regular expression: \p{Ll}\p{Lo}\p{N}_-{0,63}No more than 32 labels can be associated with a given job.
                "a_key": "A String",
              },
              "pigJob": { # A Dataproc job for running Apache Pig (https://pig.apache.org/) queries on YARN. # Optional. Job is a Pig job.
                "continueOnFailure": True or False, # Optional. Whether to continue executing queries if a query fails. The default value is false. Setting to true can be useful when executing independent parallel queries.
                "jarFileUris": [ # Optional. HCFS URIs of jar files to add to the CLASSPATH of the Pig Client and Hadoop MapReduce (MR) tasks. Can contain Pig UDFs.
                  "A String",
                ],
                "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
                  "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                    "a_key": "A String",
                  },
                },
                "properties": { # Optional. A mapping of property names to values, used to configure Pig. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/hadoop/conf/*-site.xml, /etc/pig/conf/pig.properties, and classes in user code.
                  "a_key": "A String",
                },
                "queryFileUri": "A String", # The HCFS URI of the script that contains the Pig queries.
                "queryList": { # A list of queries to run on a cluster. # A list of queries.
                  "queries": [ # Required. The queries to execute. You do not need to end a query expression with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of a Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } }
                    "A String",
                  ],
                },
                "scriptVariables": { # Optional. Mapping of query variable names to values (equivalent to the Pig command: name=[value]).
                  "a_key": "A String",
                },
              },
              "prerequisiteStepIds": [ # Optional. The optional list of prerequisite job step_ids. If not specified, the job will start at the beginning of workflow.
                "A String",
              ],
              "prestoJob": { # A Dataproc job for running Presto (https://prestosql.io/) queries. IMPORTANT: The Dataproc Presto Optional Component (https://cloud.google.com/dataproc/docs/concepts/components/presto) must be enabled when the cluster is created to submit a Presto job to the cluster. # Optional. Job is a Presto job.
                "clientTags": [ # Optional. Presto client tags to attach to this query
                  "A String",
                ],
                "continueOnFailure": True or False, # Optional. Whether to continue executing queries if a query fails. The default value is false. Setting to true can be useful when executing independent parallel queries.
                "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
                  "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                    "a_key": "A String",
                  },
                },
                "outputFormat": "A String", # Optional. The format in which query output will be displayed. See the Presto documentation for supported output formats
                "properties": { # Optional. A mapping of property names to values. Used to set Presto session properties (https://prestodb.io/docs/current/sql/set-session.html) Equivalent to using the --session flag in the Presto CLI
                  "a_key": "A String",
                },
                "queryFileUri": "A String", # The HCFS URI of the script that contains SQL queries.
                "queryList": { # A list of queries to run on a cluster. # A list of queries.
                  "queries": [ # Required. The queries to execute. You do not need to end a query expression with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of a Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } }
                    "A String",
                  ],
                },
              },
              "pysparkJob": { # A Dataproc job for running Apache PySpark (https://spark.apache.org/docs/latest/api/python/index.html#pyspark-overview) applications on YARN. # Optional. Job is a PySpark job.
                "archiveUris": [ # Optional. HCFS URIs of archives to be extracted into the working directory of each executor. Supported file types: .jar, .tar, .tar.gz, .tgz, and .zip.
                  "A String",
                ],
                "args": [ # Optional. The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision may occur that causes an incorrect job submission.
                  "A String",
                ],
                "fileUris": [ # Optional. HCFS URIs of files to be placed in the working directory of each executor. Useful for naively parallel tasks.
                  "A String",
                ],
                "jarFileUris": [ # Optional. HCFS URIs of jar files to add to the CLASSPATHs of the Python driver and tasks.
                  "A String",
                ],
                "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
                  "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                    "a_key": "A String",
                  },
                },
                "mainPythonFileUri": "A String", # Required. The HCFS URI of the main Python file to use as the driver. Must be a .py file.
                "properties": { # Optional. A mapping of property names to values, used to configure PySpark. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/spark/conf/spark-defaults.conf and classes in user code.
                  "a_key": "A String",
                },
                "pythonFileUris": [ # Optional. HCFS file URIs of Python files to pass to the PySpark framework. Supported file types: .py, .egg, and .zip.
                  "A String",
                ],
              },
              "scheduling": { # Job scheduling options. # Optional. Job scheduling configuration.
                "maxFailuresPerHour": 42, # Optional. Maximum number of times per hour a driver can be restarted as a result of driver exiting with non-zero code before job is reported failed.A job might be reported as thrashing if the driver exits with a non-zero code four times within a 10-minute window.Maximum value is 10.Note: This restartable job option is not supported in Dataproc workflow templates (https://cloud.google.com/dataproc/docs/concepts/workflows/using-workflows#adding_jobs_to_a_template).
                "maxFailuresTotal": 42, # Optional. Maximum total number of times a driver can be restarted as a result of the driver exiting with a non-zero code. After the maximum number is reached, the job will be reported as failed.Maximum value is 240.Note: Currently, this restartable job option is not supported in Dataproc workflow templates (https://cloud.google.com/dataproc/docs/concepts/workflows/using-workflows#adding_jobs_to_a_template).
              },
              "sparkJob": { # A Dataproc job for running Apache Spark (https://spark.apache.org/) applications on YARN. # Optional. Job is a Spark job.
                "archiveUris": [ # Optional. HCFS URIs of archives to be extracted into the working directory of each executor. Supported file types: .jar, .tar, .tar.gz, .tgz, and .zip.
                  "A String",
                ],
                "args": [ # Optional. The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision may occur that causes an incorrect job submission.
                  "A String",
                ],
                "fileUris": [ # Optional. HCFS URIs of files to be placed in the working directory of each executor. Useful for naively parallel tasks.
                  "A String",
                ],
                "jarFileUris": [ # Optional. HCFS URIs of jar files to add to the CLASSPATHs of the Spark driver and tasks.
                  "A String",
                ],
                "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
                  "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                    "a_key": "A String",
                  },
                },
                "mainClass": "A String", # The name of the driver's main class. The jar file that contains the class must be in the default CLASSPATH or specified in SparkJob.jar_file_uris.
                "mainJarFileUri": "A String", # The HCFS URI of the jar file that contains the main class.
                "properties": { # Optional. A mapping of property names to values, used to configure Spark. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/spark/conf/spark-defaults.conf and classes in user code.
                  "a_key": "A String",
                },
              },
              "sparkRJob": { # A Dataproc job for running Apache SparkR (https://spark.apache.org/docs/latest/sparkr.html) applications on YARN. # Optional. Job is a SparkR job.
                "archiveUris": [ # Optional. HCFS URIs of archives to be extracted into the working directory of each executor. Supported file types: .jar, .tar, .tar.gz, .tgz, and .zip.
                  "A String",
                ],
                "args": [ # Optional. The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision may occur that causes an incorrect job submission.
                  "A String",
                ],
                "fileUris": [ # Optional. HCFS URIs of files to be placed in the working directory of each executor. Useful for naively parallel tasks.
                  "A String",
                ],
                "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
                  "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                    "a_key": "A String",
                  },
                },
                "mainRFileUri": "A String", # Required. The HCFS URI of the main R file to use as the driver. Must be a .R file.
                "properties": { # Optional. A mapping of property names to values, used to configure SparkR. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/spark/conf/spark-defaults.conf and classes in user code.
                  "a_key": "A String",
                },
              },
              "sparkSqlJob": { # A Dataproc job for running Apache Spark SQL (https://spark.apache.org/sql/) queries. # Optional. Job is a SparkSql job.
                "jarFileUris": [ # Optional. HCFS URIs of jar files to be added to the Spark CLASSPATH.
                  "A String",
                ],
                "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
                  "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                    "a_key": "A String",
                  },
                },
                "properties": { # Optional. A mapping of property names to values, used to configure Spark SQL's SparkConf. Properties that conflict with values set by the Dataproc API might be overwritten.
                  "a_key": "A String",
                },
                "queryFileUri": "A String", # The HCFS URI of the script that contains SQL queries.
                "queryList": { # A list of queries to run on a cluster. # A list of queries.
                  "queries": [ # Required. The queries to execute. You do not need to end a query expression with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of a Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } }
                    "A String",
                  ],
                },
                "scriptVariables": { # Optional. Mapping of query variable names to values (equivalent to the Spark SQL command: SET name="value";).
                  "a_key": "A String",
                },
              },
              "stepId": "A String", # Required. The step id. The id must be unique among all jobs within the template.The step id is used as prefix for job id, as job goog-dataproc-workflow-step-id label, and in prerequisiteStepIds field from other steps.The id must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). Cannot begin or end with underscore or hyphen. Must consist of between 3 and 50 characters.
              "trinoJob": { # A Dataproc job for running Trino (https://trino.io/) queries. IMPORTANT: The Dataproc Trino Optional Component (https://cloud.google.com/dataproc/docs/concepts/components/trino) must be enabled when the cluster is created to submit a Trino job to the cluster. # Optional. Job is a Trino job.
                "clientTags": [ # Optional. Trino client tags to attach to this query
                  "A String",
                ],
                "continueOnFailure": True or False, # Optional. Whether to continue executing queries if a query fails. The default value is false. Setting to true can be useful when executing independent parallel queries.
                "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
                  "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                    "a_key": "A String",
                  },
                },
                "outputFormat": "A String", # Optional. The format in which query output will be displayed. See the Trino documentation for supported output formats
                "properties": { # Optional. A mapping of property names to values. Used to set Trino session properties (https://trino.io/docs/current/sql/set-session.html) Equivalent to using the --session flag in the Trino CLI
                  "a_key": "A String",
                },
                "queryFileUri": "A String", # The HCFS URI of the script that contains SQL queries.
                "queryList": { # A list of queries to run on a cluster. # A list of queries.
                  "queries": [ # Required. The queries to execute. You do not need to end a query expression with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of a Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } }
                    "A String",
                  ],
                },
              },
            },
          ],
          "labels": { # Optional. The labels to associate with this template. These labels will be propagated to all jobs and clusters created by the workflow instance.Label keys must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt).Label values may be empty, but, if present, must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt).No more than 32 labels can be associated with a template.
            "a_key": "A String",
          },
          "name": "A String", # Output only. The resource name of the workflow template, as described in https://cloud.google.com/apis/design/resource_names. For projects.regions.workflowTemplates, the resource name of the template has the following format: projects/{project_id}/regions/{region}/workflowTemplates/{template_id} For projects.locations.workflowTemplates, the resource name of the template has the following format: projects/{project_id}/locations/{location}/workflowTemplates/{template_id}
          "parameters": [ # Optional. Template parameters whose values are substituted into the template. Values for parameters must be provided when the template is instantiated.
            { # A configurable parameter that replaces one or more fields in the template. Parameterizable fields: - Labels - File uris - Job properties - Job arguments - Script variables - Main class (in HadoopJob and SparkJob) - Zone (in ClusterSelector)
              "description": "A String", # Optional. Brief description of the parameter. Must not exceed 1024 characters.
              "fields": [ # Required. Paths to all fields that the parameter replaces. A field is allowed to appear in at most one parameter's list of field paths.A field path is similar in syntax to a google.protobuf.FieldMask. For example, a field path that references the zone field of a workflow template's cluster selector would be specified as placement.clusterSelector.zone.Also, field paths can reference fields using the following syntax: Values in maps can be referenced by key: labels'key' placement.clusterSelector.clusterLabels'key' placement.managedCluster.labels'key' placement.clusterSelector.clusterLabels'key' jobs'step-id'.labels'key' Jobs in the jobs list can be referenced by step-id: jobs'step-id'.hadoopJob.mainJarFileUri jobs'step-id'.hiveJob.queryFileUri jobs'step-id'.pySparkJob.mainPythonFileUri jobs'step-id'.hadoopJob.jarFileUris0 jobs'step-id'.hadoopJob.archiveUris0 jobs'step-id'.hadoopJob.fileUris0 jobs'step-id'.pySparkJob.pythonFileUris0 Items in repeated fields can be referenced by a zero-based index: jobs'step-id'.sparkJob.args0 Other examples: jobs'step-id'.hadoopJob.properties'key' jobs'step-id'.hadoopJob.args0 jobs'step-id'.hiveJob.scriptVariables'key' jobs'step-id'.hadoopJob.mainJarFileUri placement.clusterSelector.zoneIt may not be possible to parameterize maps and repeated fields in their entirety since only individual map values and individual items in repeated fields can be referenced. For example, the following field paths are invalid: placement.clusterSelector.clusterLabels jobs'step-id'.sparkJob.args
                "A String",
              ],
              "name": "A String", # Required. Parameter name. The parameter name is used as the key, and paired with the parameter value, which are passed to the template when the template is instantiated. The name must contain only capital letters (A-Z), numbers (0-9), and underscores (_), and must not start with a number. The maximum length is 40 characters.
              "validation": { # Configuration for parameter validation. # Optional. Validation rules to be applied to this parameter's value.
                "regex": { # Validation based on regular expressions. # Validation based on regular expressions.
                  "regexes": [ # Required. RE2 regular expressions used to validate the parameter's value. The value must match the regex in its entirety (substring matches are not sufficient).
                    "A String",
                  ],
                },
                "values": { # Validation based on a list of allowed values. # Validation based on a list of allowed values.
                  "values": [ # Required. List of allowed values for the parameter.
                    "A String",
                  ],
                },
              },
            },
          ],
          "placement": { # Specifies workflow execution target.Either managed_cluster or cluster_selector is required. # Required. WorkflowTemplate scheduling information.
            "clusterSelector": { # A selector that chooses target cluster for jobs based on metadata. # Optional. A selector that chooses target cluster for jobs based on metadata.The selector is evaluated at the time each job is submitted.
              "clusterLabels": { # Required. The cluster labels. Cluster must have all labels to match.
                "a_key": "A String",
              },
              "zone": "A String", # Optional. The zone where workflow process executes. This parameter does not affect the selection of the cluster.If unspecified, the zone of the first cluster matching the selector is used.
            },
            "managedCluster": { # Cluster that is managed by the workflow. # A cluster that is managed by the workflow.
              "clusterName": "A String", # Required. The cluster name prefix. A unique cluster name will be formed by appending a random suffix.The name must contain only lower-case letters (a-z), numbers (0-9), and hyphens (-). Must begin with a letter. Cannot begin or end with hyphen. Must consist of between 2 and 35 characters.
              "config": { # The cluster config. # Required. The cluster configuration.
                "autoscalingConfig": { # Autoscaling Policy config associated with the cluster. # Optional. Autoscaling config for the policy associated with the cluster. Cluster does not autoscale if this field is unset.
                  "policyUri": "A String", # Optional. The autoscaling policy used by the cluster.Only resource names including projectid and location (region) are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/locations/[dataproc_region]/autoscalingPolicies/[policy_id] projects/[project_id]/locations/[dataproc_region]/autoscalingPolicies/[policy_id]Note that the policy must be in the same project and Dataproc region.
                },
                "auxiliaryNodeGroups": [ # Optional. The node group settings.
                  { # Node group identification and configuration information.
                    "nodeGroup": { # Dataproc Node Group. The Dataproc NodeGroup resource is not related to the Dataproc NodeGroupAffinity resource. # Required. Node group configuration.
                      "labels": { # Optional. Node group labels. Label keys must consist of from 1 to 63 characters and conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). Label values can be empty. If specified, they must consist of from 1 to 63 characters and conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). The node group must have no more than 32 labels.
                        "a_key": "A String",
                      },
                      "name": "A String", # The Node group resource name (https://aip.dev/122).
                      "nodeGroupConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The node group instance group configuration.
                        "accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
                          { # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
                            "acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
                            "acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
                          },
                        ],
                        "diskConfig": { # Specifies the config of boot disk and attached disk options for a group of VM instances. # Optional. Disk option config settings.
                          "bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. This field is supported only if boot_disk_type is hyperdisk-balanced.
                          "bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. This field is supported only if boot_disk_type is hyperdisk-balanced.
                          "bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
                          "bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
                          "localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
                          "numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
                        },
                        "imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
                        "instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
                          "instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
                            { # Defines machines types and a rank to which the machines types belong.
                              "machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
                                "A String",
                              ],
                              "rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
                            },
                          ],
                          "instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
                            { # Defines a mapping from machine types to the number of VMs that are created with each machine type.
                              "machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
                              "vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
                            },
                          ],
                          "provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
                            "standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
                            "standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
                          },
                        },
                        "instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
                          "A String",
                        ],
                        "instanceReferences": [ # Output only. List of references to Compute Engine instances.
                          { # A reference to a Compute Engine instance.
                            "instanceId": "A String", # The unique identifier of the Compute Engine instance.
                            "instanceName": "A String", # The user-friendly name of the Compute Engine instance.
                            "publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
                            "publicKey": "A String", # The public RSA key used for sharing data with this instance.
                          },
                        ],
                        "isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
                        "machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
                        "managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
                          "instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
                          "instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
                          "instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
                        },
                        "minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
                        "minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
                        "numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
                        "preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
                        "startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
                          "requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
                        },
                      },
                      "roles": [ # Required. Node group roles.
                        "A String",
                      ],
                    },
                    "nodeGroupId": "A String", # Optional. A node group ID. Generated if not specified.The ID must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). Cannot begin or end with underscore or hyphen. Must consist of from 3 to 33 characters.
                  },
                ],
                "clusterTier": "A String", # Optional. The cluster tier.
                "clusterType": "A String", # Optional. The type of the cluster.
                "configBucket": "A String", # Optional. A Cloud Storage bucket used to stage job dependencies, config files, and job driver console output. If you do not specify a staging bucket, Cloud Dataproc will determine a Cloud Storage location (US, ASIA, or EU) for your cluster's staging bucket according to the Compute Engine zone where your cluster is deployed, and then create and manage this project-level, per-location bucket (see Dataproc staging and temp buckets (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/staging-bucket)). This field requires a Cloud Storage bucket name, not a gs://... URI to a Cloud Storage bucket.
                "dataprocMetricConfig": { # Dataproc metric config. # Optional. The config for Dataproc metrics.
                  "metrics": [ # Required. Metrics sources to enable.
                    { # A Dataproc custom metric.
                      "metricOverrides": [ # Optional. Specify one or more Custom metrics (https://cloud.google.com/dataproc/docs/guides/dataproc-metrics#custom_metrics) to collect for the metric course (for the SPARK metric source (any Spark metric (https://spark.apache.org/docs/latest/monitoring.html#metrics) can be specified).Provide metrics in the following format: METRIC_SOURCE: INSTANCE:GROUP:METRIC Use camelcase as appropriate.Examples: yarn:ResourceManager:QueueMetrics:AppsCompleted spark:driver:DAGScheduler:job.allJobs sparkHistoryServer:JVM:Memory:NonHeapMemoryUsage.committed hiveserver2:JVM:Memory:NonHeapMemoryUsage.used Notes: Only the specified overridden metrics are collected for the metric source. For example, if one or more spark:executive metrics are listed as metric overrides, other SPARK metrics are not collected. The collection of the metrics for other enabled custom metric sources is unaffected. For example, if both SPARK and YARN metric sources are enabled, and overrides are provided for Spark metrics only, all YARN metrics are collected.
                        "A String",
                      ],
                      "metricSource": "A String", # Required. A standard set of metrics is collected unless metricOverrides are specified for the metric source (see Custom metrics (https://cloud.google.com/dataproc/docs/guides/dataproc-metrics#custom_metrics) for more information).
                    },
                  ],
                },
                "encryptionConfig": { # Encryption settings for the cluster. # Optional. Encryption settings for the cluster.
                  "gcePdKmsKeyName": "A String", # Optional. The Cloud KMS key resource name to use for persistent disk encryption for all instances in the cluster. See Use CMEK with cluster data (https://cloud.google.com//dataproc/docs/concepts/configuring-clusters/customer-managed-encryption#use_cmek_with_cluster_data) for more information.
                  "kmsKey": "A String", # Optional. The Cloud KMS key resource name to use for cluster persistent disk and job argument encryption. See Use CMEK with cluster data (https://cloud.google.com//dataproc/docs/concepts/configuring-clusters/customer-managed-encryption#use_cmek_with_cluster_data) for more information.When this key resource name is provided, the following job arguments of the following job types submitted to the cluster are encrypted using CMEK: FlinkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/FlinkJob) HadoopJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/HadoopJob) SparkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkJob) SparkRJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkRJob) PySparkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/PySparkJob) SparkSqlJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkSqlJob) scriptVariables and queryList.queries HiveJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/HiveJob) scriptVariables and queryList.queries PigJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/PigJob) scriptVariables and queryList.queries PrestoJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/PrestoJob) scriptVariables and queryList.queries
                },
                "endpointConfig": { # Endpoint config for this cluster # Optional. Port/endpoint configuration for this cluster
                  "enableHttpPortAccess": True or False, # Optional. If true, enable http access to specific ports on the cluster from external sources. Defaults to false.
                  "httpPorts": { # Output only. The map of port descriptions to URLs. Will only be populated if enable_http_port_access is true.
                    "a_key": "A String",
                  },
                },
                "gceClusterConfig": { # Common config settings for resources of Compute Engine cluster instances, applicable to all instances in the cluster. # Optional. The shared Compute Engine config settings for all instances in a cluster.
                  "confidentialInstanceConfig": { # Confidential Instance Config for clusters using Confidential VMs (https://cloud.google.com/compute/confidential-vm/docs) # Optional. Confidential Instance Config for clusters using Confidential VMs (https://cloud.google.com/compute/confidential-vm/docs).
                    "enableConfidentialCompute": True or False, # Optional. Defines whether the instance should have confidential compute enabled.
                  },
                  "internalIpOnly": True or False, # Optional. This setting applies to subnetwork-enabled networks. It is set to true by default in clusters created with image versions 2.2.x.When set to true: All cluster VMs have internal IP addresses. Google Private Access (https://cloud.google.com/vpc/docs/private-google-access) must be enabled to access Dataproc and other Google Cloud APIs. Off-cluster dependencies must be configured to be accessible without external IP addresses.When set to false: Cluster VMs are not restricted to internal IP addresses. Ephemeral external IP addresses are assigned to each cluster VM.
                  "metadata": { # Optional. The Compute Engine metadata entries to add to all instances (see Project and instance metadata (https://cloud.google.com/compute/docs/storing-retrieving-metadata#project_and_instance_metadata)).
                    "a_key": "A String",
                  },
                  "networkUri": "A String", # Optional. The Compute Engine network to be used for machine communications. Cannot be specified with subnetwork_uri. If neither network_uri nor subnetwork_uri is specified, the "default" network of the project is used, if it exists. Cannot be a "Custom Subnet Network" (see Using Subnetworks (https://cloud.google.com/compute/docs/subnetworks) for more information).A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/networks/default projects/[project_id]/global/networks/default default
                  "nodeGroupAffinity": { # Node Group Affinity for clusters using sole-tenant node groups. The Dataproc NodeGroupAffinity resource is not related to the Dataproc NodeGroup resource. # Optional. Node Group Affinity for sole-tenant clusters.
                    "nodeGroupUri": "A String", # Required. The URI of a sole-tenant node group resource (https://cloud.google.com/compute/docs/reference/rest/v1/nodeGroups) that the cluster will be created on.A full URL, partial URI, or node group name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/nodeGroups/node-group-1 projects/[project_id]/zones/[zone]/nodeGroups/node-group-1 node-group-1
                  },
                  "privateIpv6GoogleAccess": "A String", # Optional. The type of IPv6 access for a cluster.
                  "reservationAffinity": { # Reservation Affinity for consuming Zonal reservation. # Optional. Reservation Affinity for consuming Zonal reservation.
                    "consumeReservationType": "A String", # Optional. Type of reservation to consume
                    "key": "A String", # Optional. Corresponds to the label key of reservation resource.
                    "values": [ # Optional. Corresponds to the label values of reservation resource.
                      "A String",
                    ],
                  },
                  "resourceManagerTags": { # Optional. Resource manager tags (https://cloud.google.com/resource-manager/docs/tags/tags-creating-and-managing) to add to all instances (see Use secure tags in Dataproc (https://cloud.google.com/dataproc/docs/guides/attach-secure-tags)).
                    "a_key": "A String",
                  },
                  "serviceAccount": "A String", # Optional. The Dataproc service account (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/service-accounts#service_accounts_in_dataproc) (also see VM Data Plane identity (https://cloud.google.com/dataproc/docs/concepts/iam/dataproc-principals#vm_service_account_data_plane_identity)) used by Dataproc cluster VM instances to access Google Cloud Platform services.If not specified, the Compute Engine default service account (https://cloud.google.com/compute/docs/access/service-accounts#default_service_account) is used.
                  "serviceAccountScopes": [ # Optional. The URIs of service account scopes to be included in Compute Engine instances. The following base set of scopes is always included: https://www.googleapis.com/auth/cloud.useraccounts.readonly https://www.googleapis.com/auth/devstorage.read_write https://www.googleapis.com/auth/logging.writeIf no scopes are specified, the following defaults are also provided: https://www.googleapis.com/auth/bigquery https://www.googleapis.com/auth/bigtable.admin.table https://www.googleapis.com/auth/bigtable.data https://www.googleapis.com/auth/devstorage.full_control
                    "A String",
                  ],
                  "shieldedInstanceConfig": { # Shielded Instance Config for clusters using Compute Engine Shielded VMs (https://cloud.google.com/security/shielded-cloud/shielded-vm). # Optional. Shielded Instance Config for clusters using Compute Engine Shielded VMs (https://cloud.google.com/security/shielded-cloud/shielded-vm).
                    "enableIntegrityMonitoring": True or False, # Optional. Defines whether instances have integrity monitoring enabled.
                    "enableSecureBoot": True or False, # Optional. Defines whether instances have Secure Boot enabled.
                    "enableVtpm": True or False, # Optional. Defines whether instances have the vTPM enabled.
                  },
                  "subnetworkUri": "A String", # Optional. The Compute Engine subnetwork to be used for machine communications. Cannot be specified with network_uri.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/regions/[region]/subnetworks/sub0 projects/[project_id]/regions/[region]/subnetworks/sub0 sub0
                  "tags": [ # The Compute Engine network tags to add to all instances (see Tagging instances (https://cloud.google.com/vpc/docs/add-remove-network-tags)).
                    "A String",
                  ],
                  "zoneUri": "A String", # Optional. The Compute Engine zone where the Dataproc cluster will be located. If omitted, the service will pick a zone in the cluster's Compute Engine region. On a get request, zone will always be present.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone] projects/[project_id]/zones/[zone] [zone]
                },
                "gkeClusterConfig": { # The cluster's GKE config. # Optional. BETA. The Kubernetes Engine config for Dataproc clusters deployed to The Kubernetes Engine config for Dataproc clusters deployed to Kubernetes. These config settings are mutually exclusive with Compute Engine-based options, such as gce_cluster_config, master_config, worker_config, secondary_worker_config, and autoscaling_config.
                  "gkeClusterTarget": "A String", # Optional. A target GKE cluster to deploy to. It must be in the same project and region as the Dataproc cluster (the GKE cluster can be zonal or regional). Format: 'projects/{project}/locations/{location}/clusters/{cluster_id}'
                  "namespacedGkeDeploymentTarget": { # Deprecated. Used only for the deprecated beta. A full, namespace-isolated deployment target for an existing GKE cluster. # Optional. Deprecated. Use gkeClusterTarget. Used only for the deprecated beta. A target for the deployment.
                    "clusterNamespace": "A String", # Optional. A namespace within the GKE cluster to deploy into.
                    "targetGkeCluster": "A String", # Optional. The target GKE cluster to deploy to. Format: 'projects/{project}/locations/{location}/clusters/{cluster_id}'
                  },
                  "nodePoolTarget": [ # Optional. GKE node pools where workloads will be scheduled. At least one node pool must be assigned the DEFAULT GkeNodePoolTarget.Role. If a GkeNodePoolTarget is not specified, Dataproc constructs a DEFAULT GkeNodePoolTarget. Each role can be given to only one GkeNodePoolTarget. All node pools must have the same location settings.
                    { # GKE node pools that Dataproc workloads run on.
                      "nodePool": "A String", # Required. The target GKE node pool. Format: 'projects/{project}/locations/{location}/clusters/{cluster}/nodePools/{node_pool}'
                      "nodePoolConfig": { # The configuration of a GKE node pool used by a Dataproc-on-GKE cluster (https://cloud.google.com/dataproc/docs/concepts/jobs/dataproc-gke#create-a-dataproc-on-gke-cluster). # Input only. The configuration for the GKE node pool.If specified, Dataproc attempts to create a node pool with the specified shape. If one with the same name already exists, it is verified against all specified fields. If a field differs, the virtual cluster creation will fail.If omitted, any node pool with the specified name is used. If a node pool with the specified name does not exist, Dataproc create a node pool with default values.This is an input only field. It will not be returned by the API.
                        "autoscaling": { # GkeNodePoolAutoscaling contains information the cluster autoscaler needs to adjust the size of the node pool to the current cluster usage. # Optional. The autoscaler configuration for this node pool. The autoscaler is enabled only when a valid configuration is present.
                          "maxNodeCount": 42, # The maximum number of nodes in the node pool. Must be >= min_node_count, and must be > 0. Note: Quota must be sufficient to scale up the cluster.
                          "minNodeCount": 42, # The minimum number of nodes in the node pool. Must be >= 0 and <= max_node_count.
                        },
                        "config": { # Parameters that describe cluster nodes. # Optional. The node pool configuration.
                          "accelerators": [ # Optional. A list of hardware accelerators (https://cloud.google.com/compute/docs/gpus) to attach to each node.
                            { # A GkeNodeConfigAcceleratorConfig represents a Hardware Accelerator request for a node pool.
                              "acceleratorCount": "A String", # The number of accelerator cards exposed to an instance.
                              "acceleratorType": "A String", # The accelerator type resource namename (see GPUs on Compute Engine).
                              "gpuPartitionSize": "A String", # Size of partitions to create on the GPU. Valid values are described in the NVIDIA mig user guide (https://docs.nvidia.com/datacenter/tesla/mig-user-guide/#partitioning).
                            },
                          ],
                          "bootDiskKmsKey": "A String", # Optional. The Customer Managed Encryption Key (CMEK) (https://cloud.google.com/kubernetes-engine/docs/how-to/using-cmek) used to encrypt the boot disk attached to each node in the node pool. Specify the key using the following format: projects/{project}/locations/{location}/keyRings/{key_ring}/cryptoKeys/{crypto_key}
                          "localSsdCount": 42, # Optional. The number of local SSD disks to attach to the node, which is limited by the maximum number of disks allowable per zone (see Adding Local SSDs (https://cloud.google.com/compute/docs/disks/local-ssd)).
                          "machineType": "A String", # Optional. The name of a Compute Engine machine type (https://cloud.google.com/compute/docs/machine-types).
                          "minCpuPlatform": "A String", # Optional. Minimum CPU platform (https://cloud.google.com/compute/docs/instances/specify-min-cpu-platform) to be used by this instance. The instance may be scheduled on the specified or a newer CPU platform. Specify the friendly names of CPU platforms, such as "Intel Haswell"` or Intel Sandy Bridge".
                          "preemptible": True or False, # Optional. Whether the nodes are created as legacy preemptible VM instances (https://cloud.google.com/compute/docs/instances/preemptible). Also see Spot VMs, preemptible VM instances without a maximum lifetime. Legacy and Spot preemptible nodes cannot be used in a node pool with the CONTROLLER role or in the DEFAULT node pool if the CONTROLLER role is not assigned (the DEFAULT node pool will assume the CONTROLLER role).
                          "spot": True or False, # Optional. Whether the nodes are created as Spot VM instances (https://cloud.google.com/compute/docs/instances/spot). Spot VMs are the latest update to legacy preemptible VMs. Spot VMs do not have a maximum lifetime. Legacy and Spot preemptible nodes cannot be used in a node pool with the CONTROLLER role or in the DEFAULT node pool if the CONTROLLER role is not assigned (the DEFAULT node pool will assume the CONTROLLER role).
                        },
                        "locations": [ # Optional. The list of Compute Engine zones (https://cloud.google.com/compute/docs/zones#available) where node pool nodes associated with a Dataproc on GKE virtual cluster will be located.Note: All node pools associated with a virtual cluster must be located in the same region as the virtual cluster, and they must be located in the same zone within that region.If a location is not specified during node pool creation, Dataproc on GKE will choose the zone.
                          "A String",
                        ],
                      },
                      "roles": [ # Required. The roles associated with the GKE node pool.
                        "A String",
                      ],
                    },
                  ],
                },
                "initializationActions": [ # Optional. Commands to execute on each node after config is completed. By default, executables are run on master and all worker nodes. You can test a node's role metadata to run an executable on a master or worker node, as shown below using curl (you can also use wget): ROLE=$(curl -H Metadata-Flavor:Google http://metadata/computeMetadata/v1/instance/attributes/dataproc-role) if [[ "${ROLE}" == 'Master' ]]; then ... master specific actions ... else ... worker specific actions ... fi
                  { # Specifies an executable to run on a fully configured node and a timeout period for executable completion.
                    "executableFile": "A String", # Required. Cloud Storage URI of executable file.
                    "executionTimeout": "A String", # Optional. Amount of time executable has to complete. Default is 10 minutes (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).Cluster creation fails with an explanatory error message (the name of the executable that caused the error and the exceeded timeout period) if the executable is not completed at end of the timeout period.
                  },
                ],
                "lifecycleConfig": { # Specifies the cluster auto-delete schedule configuration. # Optional. Lifecycle setting for the cluster.
                  "autoDeleteTime": "A String", # Optional. The time when cluster will be auto-deleted (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
                  "autoDeleteTtl": "A String", # Optional. The lifetime duration of cluster. The cluster will be auto-deleted at the end of this period. Minimum value is 10 minutes; maximum value is 14 days (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).
                  "autoStopTime": "A String", # Optional. The time when cluster will be auto-stopped (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
                  "autoStopTtl": "A String", # Optional. The lifetime duration of the cluster. The cluster will be auto-stopped at the end of this period, calculated from the time of submission of the create or update cluster request. Minimum value is 10 minutes; maximum value is 14 days (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).
                  "idleDeleteTtl": "A String", # Optional. The duration to keep the cluster alive while idling (when no jobs are running). Passing this threshold will cause the cluster to be deleted. Minimum value is 5 minutes; maximum value is 14 days (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).
                  "idleStartTime": "A String", # Output only. The time when cluster became idle (most recent job finished) and became eligible for deletion due to idleness (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
                  "idleStopTtl": "A String", # Optional. The duration to keep the cluster started while idling (when no jobs are running). Passing this threshold will cause the cluster to be stopped. Minimum value is 5 minutes; maximum value is 14 days (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).
                },
                "masterConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The Compute Engine config settings for the cluster's master instance.
                  "accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
                    { # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
                      "acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
                      "acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
                    },
                  ],
                  "diskConfig": { # Specifies the config of boot disk and attached disk options for a group of VM instances. # Optional. Disk option config settings.
                    "bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. This field is supported only if boot_disk_type is hyperdisk-balanced.
                    "bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. This field is supported only if boot_disk_type is hyperdisk-balanced.
                    "bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
                    "bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
                    "localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
                    "numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
                  },
                  "imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
                  "instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
                    "instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
                      { # Defines machines types and a rank to which the machines types belong.
                        "machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
                          "A String",
                        ],
                        "rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
                      },
                    ],
                    "instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
                      { # Defines a mapping from machine types to the number of VMs that are created with each machine type.
                        "machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
                        "vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
                      },
                    ],
                    "provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
                      "standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
                      "standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
                    },
                  },
                  "instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
                    "A String",
                  ],
                  "instanceReferences": [ # Output only. List of references to Compute Engine instances.
                    { # A reference to a Compute Engine instance.
                      "instanceId": "A String", # The unique identifier of the Compute Engine instance.
                      "instanceName": "A String", # The user-friendly name of the Compute Engine instance.
                      "publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
                      "publicKey": "A String", # The public RSA key used for sharing data with this instance.
                    },
                  ],
                  "isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
                  "machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
                  "managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
                    "instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
                    "instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
                    "instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
                  },
                  "minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
                  "minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
                  "numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
                  "preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
                  "startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
                    "requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
                  },
                },
                "metastoreConfig": { # Specifies a Metastore configuration. # Optional. Metastore configuration.
                  "dataprocMetastoreService": "A String", # Required. Resource name of an existing Dataproc Metastore service.Example: projects/[project_id]/locations/[dataproc_region]/services/[service-name]
                },
                "secondaryWorkerConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The Compute Engine config settings for a cluster's secondary worker instances
                  "accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
                    { # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
                      "acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
                      "acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
                    },
                  ],
                  "diskConfig": { # Specifies the config of boot disk and attached disk options for a group of VM instances. # Optional. Disk option config settings.
                    "bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. This field is supported only if boot_disk_type is hyperdisk-balanced.
                    "bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. This field is supported only if boot_disk_type is hyperdisk-balanced.
                    "bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
                    "bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
                    "localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
                    "numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
                  },
                  "imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
                  "instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
                    "instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
                      { # Defines machines types and a rank to which the machines types belong.
                        "machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
                          "A String",
                        ],
                        "rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
                      },
                    ],
                    "instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
                      { # Defines a mapping from machine types to the number of VMs that are created with each machine type.
                        "machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
                        "vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
                      },
                    ],
                    "provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
                      "standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
                      "standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
                    },
                  },
                  "instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
                    "A String",
                  ],
                  "instanceReferences": [ # Output only. List of references to Compute Engine instances.
                    { # A reference to a Compute Engine instance.
                      "instanceId": "A String", # The unique identifier of the Compute Engine instance.
                      "instanceName": "A String", # The user-friendly name of the Compute Engine instance.
                      "publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
                      "publicKey": "A String", # The public RSA key used for sharing data with this instance.
                    },
                  ],
                  "isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
                  "machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
                  "managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
                    "instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
                    "instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
                    "instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
                  },
                  "minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
                  "minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
                  "numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
                  "preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
                  "startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
                    "requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
                  },
                },
                "securityConfig": { # Security related configuration, including encryption, Kerberos, etc. # Optional. Security settings for the cluster.
                  "identityConfig": { # Identity related configuration, including service account based secure multi-tenancy user mappings. # Optional. Identity related configuration, including service account based secure multi-tenancy user mappings.
                    "userServiceAccountMapping": { # Required. Map of user to service account.
                      "a_key": "A String",
                    },
                  },
                  "kerberosConfig": { # Specifies Kerberos related configuration. # Optional. Kerberos related configuration.
                    "crossRealmTrustAdminServer": "A String", # Optional. The admin server (IP or hostname) for the remote trusted realm in a cross realm trust relationship.
                    "crossRealmTrustKdc": "A String", # Optional. The KDC (IP or hostname) for the remote trusted realm in a cross realm trust relationship.
                    "crossRealmTrustRealm": "A String", # Optional. The remote realm the Dataproc on-cluster KDC will trust, should the user enable cross realm trust.
                    "crossRealmTrustSharedPasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the shared password between the on-cluster Kerberos realm and the remote trusted realm, in a cross realm trust relationship.
                    "enableKerberos": True or False, # Optional. Flag to indicate whether to Kerberize the cluster (default: false). Set this field to true to enable Kerberos on a cluster.
                    "kdcDbKeyUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the master key of the KDC database.
                    "keyPasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the password to the user provided key. For the self-signed certificate, this password is generated by Dataproc.
                    "keystorePasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the password to the user provided keystore. For the self-signed certificate, this password is generated by Dataproc.
                    "keystoreUri": "A String", # Optional. The Cloud Storage URI of the keystore file used for SSL encryption. If not provided, Dataproc will provide a self-signed certificate.
                    "kmsKeyUri": "A String", # Optional. The URI of the KMS key used to encrypt sensitive files.
                    "realm": "A String", # Optional. The name of the on-cluster Kerberos realm. If not specified, the uppercased domain of hostnames will be the realm.
                    "rootPrincipalPasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the root principal password.
                    "tgtLifetimeHours": 42, # Optional. The lifetime of the ticket granting ticket, in hours. If not specified, or user specifies 0, then default value 10 will be used.
                    "truststorePasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the password to the user provided truststore. For the self-signed certificate, this password is generated by Dataproc.
                    "truststoreUri": "A String", # Optional. The Cloud Storage URI of the truststore file used for SSL encryption. If not provided, Dataproc will provide a self-signed certificate.
                  },
                },
                "softwareConfig": { # Specifies the selection and config of software inside the cluster. # Optional. The config settings for cluster software.
                  "imageVersion": "A String", # Optional. The version of software inside the cluster. It must be one of the supported Dataproc Versions (https://cloud.google.com/dataproc/docs/concepts/versioning/dataproc-versions#supported-dataproc-image-versions), such as "1.2" (including a subminor version, such as "1.2.29"), or the "preview" version (https://cloud.google.com/dataproc/docs/concepts/versioning/dataproc-versions#other_versions). If unspecified, it defaults to the latest Debian version.
                  "optionalComponents": [ # Optional. The set of components to activate on the cluster.
                    "A String",
                  ],
                  "properties": { # Optional. The properties to set on daemon config files.Property keys are specified in prefix:property format, for example core:hadoop.tmp.dir. The following are supported prefixes and their mappings: capacity-scheduler: capacity-scheduler.xml core: core-site.xml distcp: distcp-default.xml hdfs: hdfs-site.xml hive: hive-site.xml mapred: mapred-site.xml pig: pig.properties spark: spark-defaults.conf yarn: yarn-site.xmlFor more information, see Cluster properties (https://cloud.google.com/dataproc/docs/concepts/cluster-properties).
                    "a_key": "A String",
                  },
                },
                "tempBucket": "A String", # Optional. A Cloud Storage bucket used to store ephemeral cluster and jobs data, such as Spark and MapReduce history files. If you do not specify a temp bucket, Dataproc will determine a Cloud Storage location (US, ASIA, or EU) for your cluster's temp bucket according to the Compute Engine zone where your cluster is deployed, and then create and manage this project-level, per-location bucket. The default bucket has a TTL of 90 days, but you can use any TTL (or none) if you specify a bucket (see Dataproc staging and temp buckets (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/staging-bucket)). This field requires a Cloud Storage bucket name, not a gs://... URI to a Cloud Storage bucket.
                "workerConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The Compute Engine config settings for the cluster's worker instances.
                  "accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
                    { # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
                      "acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
                      "acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
                    },
                  ],
                  "diskConfig": { # Specifies the config of boot disk and attached disk options for a group of VM instances. # Optional. Disk option config settings.
                    "bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. This field is supported only if boot_disk_type is hyperdisk-balanced.
                    "bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. This field is supported only if boot_disk_type is hyperdisk-balanced.
                    "bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
                    "bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
                    "localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
                    "numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
                  },
                  "imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
                  "instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
                    "instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
                      { # Defines machines types and a rank to which the machines types belong.
                        "machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
                          "A String",
                        ],
                        "rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
                      },
                    ],
                    "instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
                      { # Defines a mapping from machine types to the number of VMs that are created with each machine type.
                        "machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
                        "vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
                      },
                    ],
                    "provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
                      "standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
                      "standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
                    },
                  },
                  "instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
                    "A String",
                  ],
                  "instanceReferences": [ # Output only. List of references to Compute Engine instances.
                    { # A reference to a Compute Engine instance.
                      "instanceId": "A String", # The unique identifier of the Compute Engine instance.
                      "instanceName": "A String", # The user-friendly name of the Compute Engine instance.
                      "publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
                      "publicKey": "A String", # The public RSA key used for sharing data with this instance.
                    },
                  ],
                  "isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
                  "machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
                  "managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
                    "instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
                    "instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
                    "instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
                  },
                  "minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
                  "minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
                  "numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
                  "preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
                  "startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
                    "requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
                  },
                },
              },
              "labels": { # Optional. The labels to associate with this cluster.Label keys must be between 1 and 63 characters long, and must conform to the following PCRE regular expression: \p{Ll}\p{Lo}{0,62}Label values must be between 1 and 63 characters long, and must conform to the following PCRE regular expression: \p{Ll}\p{Lo}\p{N}_-{0,63}No more than 32 labels can be associated with a given cluster.
                "a_key": "A String",
              },
            },
          },
          "updateTime": "A String", # Output only. The time template was last updated.
          "version": 42, # Optional. Used to perform a consistent read-modify-write.This field should be left blank for a CreateWorkflowTemplate request. It is required for an UpdateWorkflowTemplate request, and must match the current server version. A typical update template flow would fetch the current template with a GetWorkflowTemplate request, which will return the current template with the version field filled in with the current server version. The user updates other fields in the template, then returns it as part of the UpdateWorkflowTemplate request.
        },
      ],
      "unreachable": [ # Output only. List of workflow templates that could not be included in the response. Attempting to get one of these resources may indicate why it was not included in the list response.
        "A String",
      ],
    }

`list_next()`

    Retrieves the next page of results.

            Args:
              previous_request: The request for the previous page. (required)
              previous_response: The response from the request for the previous page. (required)

            Returns:
              A request object that you can call 'execute()' on to request the next
              page. Returns None if there are no more items in the collection.

`setIamPolicy(resource, body=None, x__xgafv=None)`

    Sets the access control policy on the specified resource. Replaces any existing policy.Can return NOT_FOUND, INVALID_ARGUMENT, and PERMISSION_DENIED errors.

    Args:
      resource: string, REQUIRED: The resource for which the policy is being specified. See Resource names (https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. (required)
      body: object, The request body.
        The object takes the form of:

    { # Request message for SetIamPolicy method.
      "policy": { # An Identity and Access Management (IAM) policy, which specifies access controls for Google Cloud resources.A Policy is a collection of bindings. A binding binds one or more members, or principals, to a single role. Principals can be user accounts, service accounts, Google groups, and domains (such as G Suite). A role is a named list of permissions; each role can be an IAM predefined role or a user-created custom role.For some types of Google Cloud resources, a binding can also specify a condition, which is a logical expression that allows access to a resource only if the expression evaluates to true. A condition can add constraints based on attributes of the request, the resource, or both. To learn which resources support conditions in their IAM policies, see the IAM documentation (https://cloud.google.com/iam/help/conditions/resource-policies).JSON example: { "bindings": [ { "role": "roles/resourcemanager.organizationAdmin", "members": [ "user:mike@example.com", "group:admins@example.com", "domain:google.com", "serviceAccount:my-project-id@appspot.gserviceaccount.com" ] }, { "role": "roles/resourcemanager.organizationViewer", "members": [ "user:eve@example.com" ], "condition": { "title": "expirable access", "description": "Does not grant access after Sep 2020", "expression": "request.time < timestamp('2020-10-01T00:00:00.000Z')", } } ], "etag": "BwWWja0YfJA=", "version": 3 } YAML example: bindings: - members: - user:mike@example.com - group:admins@example.com - domain:google.com - serviceAccount:my-project-id@appspot.gserviceaccount.com role: roles/resourcemanager.organizationAdmin - members: - user:eve@example.com role: roles/resourcemanager.organizationViewer condition: title: expirable access description: Does not grant access after Sep 2020 expression: request.time < timestamp('2020-10-01T00:00:00.000Z') etag: BwWWja0YfJA= version: 3 For a description of IAM and its features, see the IAM documentation (https://cloud.google.com/iam/docs/). # REQUIRED: The complete policy to be applied to the resource. The size of the policy is limited to a few 10s of KB. An empty policy is a valid policy but certain Google Cloud services (such as Projects) might reject them.
        "bindings": [ # Associates a list of members, or principals, with a role. Optionally, may specify a condition that determines how and when the bindings are applied. Each of the bindings must contain at least one principal.The bindings in a Policy can refer to up to 1,500 principals; up to 250 of these principals can be Google groups. Each occurrence of a principal counts towards these limits. For example, if the bindings grant 50 different roles to user:alice@example.com, and not to any other principal, then you can add another 1,450 principals to the bindings in the Policy.
          { # Associates members, or principals, with a role.
            "condition": { # Represents a textual expression in the Common Expression Language (CEL) syntax. CEL is a C-like expression language. The syntax and semantics of CEL are documented at https://github.com/google/cel-spec.Example (Comparison): title: "Summary size limit" description: "Determines if a summary is less than 100 chars" expression: "document.summary.size() < 100" Example (Equality): title: "Requestor is owner" description: "Determines if requestor is the document owner" expression: "document.owner == request.auth.claims.email" Example (Logic): title: "Public documents" description: "Determine whether the document should be publicly visible" expression: "document.type != 'private' && document.type != 'internal'" Example (Data Manipulation): title: "Notification string" description: "Create a notification string with a timestamp." expression: "'New message received at ' + string(document.create_time)" The exact variables and functions that may be referenced within an expression are determined by the service that evaluates it. See the service documentation for additional information. # The condition that is associated with this binding.If the condition evaluates to true, then this binding applies to the current request.If the condition evaluates to false, then this binding does not apply to the current request. However, a different role binding might grant the same role to one or more of the principals in this binding.To learn which resources support conditions in their IAM policies, see the IAM documentation (https://cloud.google.com/iam/help/conditions/resource-policies).
              "description": "A String", # Optional. Description of the expression. This is a longer text which describes the expression, e.g. when hovered over it in a UI.
              "expression": "A String", # Textual representation of an expression in Common Expression Language syntax.
              "location": "A String", # Optional. String indicating the location of the expression for error reporting, e.g. a file name and a position in the file.
              "title": "A String", # Optional. Title for the expression, i.e. a short string describing its purpose. This can be used e.g. in UIs which allow to enter the expression.
            },
            "members": [ # Specifies the principals requesting access for a Google Cloud resource. members can have the following values: allUsers: A special identifier that represents anyone who is on the internet; with or without a Google account. allAuthenticatedUsers: A special identifier that represents anyone who is authenticated with a Google account or a service account. Does not include identities that come from external identity providers (IdPs) through identity federation. user:{emailid}: An email address that represents a specific Google account. For example, alice@example.com . serviceAccount:{emailid}: An email address that represents a Google service account. For example, my-other-app@appspot.gserviceaccount.com. serviceAccount:{projectid}.svc.id.goog[{namespace}/{kubernetes-sa}]: An identifier for a Kubernetes service account (https://cloud.google.com/kubernetes-engine/docs/how-to/kubernetes-service-accounts). For example, my-project.svc.id.goog[my-namespace/my-kubernetes-sa]. group:{emailid}: An email address that represents a Google group. For example, admins@example.com. domain:{domain}: The G Suite domain (primary) that represents all the users of that domain. For example, google.com or example.com. principal://iam.googleapis.com/locations/global/workforcePools/{pool_id}/subject/{subject_attribute_value}: A single identity in a workforce identity pool. principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/group/{group_id}: All workforce identities in a group. principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/attribute.{attribute_name}/{attribute_value}: All workforce identities with a specific attribute value. principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/*: All identities in a workforce identity pool. principal://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/subject/{subject_attribute_value}: A single identity in a workload identity pool. principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/group/{group_id}: A workload identity pool group. principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/attribute.{attribute_name}/{attribute_value}: All identities in a workload identity pool with a certain attribute. principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/*: All identities in a workload identity pool. deleted:user:{emailid}?uid={uniqueid}: An email address (plus unique identifier) representing a user that has been recently deleted. For example, alice@example.com?uid=123456789012345678901. If the user is recovered, this value reverts to user:{emailid} and the recovered user retains the role in the binding. deleted:serviceAccount:{emailid}?uid={uniqueid}: An email address (plus unique identifier) representing a service account that has been recently deleted. For example, my-other-app@appspot.gserviceaccount.com?uid=123456789012345678901. If the service account is undeleted, this value reverts to serviceAccount:{emailid} and the undeleted service account retains the role in the binding. deleted:group:{emailid}?uid={uniqueid}: An email address (plus unique identifier) representing a Google group that has been recently deleted. For example, admins@example.com?uid=123456789012345678901. If the group is recovered, this value reverts to group:{emailid} and the recovered group retains the role in the binding. deleted:principal://iam.googleapis.com/locations/global/workforcePools/{pool_id}/subject/{subject_attribute_value}: Deleted single identity in a workforce identity pool. For example, deleted:principal://iam.googleapis.com/locations/global/workforcePools/my-pool-id/subject/my-subject-attribute-value.
              "A String",
            ],
            "role": "A String", # Role that is assigned to the list of members, or principals. For example, roles/viewer, roles/editor, or roles/owner.For an overview of the IAM roles and permissions, see the IAM documentation (https://cloud.google.com/iam/docs/roles-overview). For a list of the available pre-defined roles, see here (https://cloud.google.com/iam/docs/understanding-roles).
          },
        ],
        "etag": "A String", # etag is used for optimistic concurrency control as a way to help prevent simultaneous updates of a policy from overwriting each other. It is strongly suggested that systems make use of the etag in the read-modify-write cycle to perform policy updates in order to avoid race conditions: An etag is returned in the response to getIamPolicy, and systems are expected to put that etag in the request to setIamPolicy to ensure that their change will be applied to the same version of the policy.Important: If you use IAM Conditions, you must include the etag field whenever you call setIamPolicy. If you omit this field, then IAM allows you to overwrite a version 3 policy with a version 1 policy, and all of the conditions in the version 3 policy are lost.
        "version": 42, # Specifies the format of the policy.Valid values are 0, 1, and 3. Requests that specify an invalid value are rejected.Any operation that affects conditional role bindings must specify version 3. This requirement applies to the following operations: Getting a policy that includes a conditional role binding Adding a conditional role binding to a policy Changing a conditional role binding in a policy Removing any role binding, with or without a condition, from a policy that includes conditionsImportant: If you use IAM Conditions, you must include the etag field whenever you call setIamPolicy. If you omit this field, then IAM allows you to overwrite a version 3 policy with a version 1 policy, and all of the conditions in the version 3 policy are lost.If a policy does not include any conditions, operations on that policy may specify any valid version or leave the field unset.To learn which resources support conditions in their IAM policies, see the IAM documentation (https://cloud.google.com/iam/help/conditions/resource-policies).
      },
    }

      x__xgafv: string, V1 error format.
        Allowed values
          1 - v1 error format
          2 - v2 error format

    Returns:
      An object of the form:

        { # An Identity and Access Management (IAM) policy, which specifies access controls for Google Cloud resources.A Policy is a collection of bindings. A binding binds one or more members, or principals, to a single role. Principals can be user accounts, service accounts, Google groups, and domains (such as G Suite). A role is a named list of permissions; each role can be an IAM predefined role or a user-created custom role.For some types of Google Cloud resources, a binding can also specify a condition, which is a logical expression that allows access to a resource only if the expression evaluates to true. A condition can add constraints based on attributes of the request, the resource, or both. To learn which resources support conditions in their IAM policies, see the IAM documentation (https://cloud.google.com/iam/help/conditions/resource-policies).JSON example: { "bindings": [ { "role": "roles/resourcemanager.organizationAdmin", "members": [ "user:mike@example.com", "group:admins@example.com", "domain:google.com", "serviceAccount:my-project-id@appspot.gserviceaccount.com" ] }, { "role": "roles/resourcemanager.organizationViewer", "members": [ "user:eve@example.com" ], "condition": { "title": "expirable access", "description": "Does not grant access after Sep 2020", "expression": "request.time < timestamp('2020-10-01T00:00:00.000Z')", } } ], "etag": "BwWWja0YfJA=", "version": 3 } YAML example: bindings: - members: - user:mike@example.com - group:admins@example.com - domain:google.com - serviceAccount:my-project-id@appspot.gserviceaccount.com role: roles/resourcemanager.organizationAdmin - members: - user:eve@example.com role: roles/resourcemanager.organizationViewer condition: title: expirable access description: Does not grant access after Sep 2020 expression: request.time < timestamp('2020-10-01T00:00:00.000Z') etag: BwWWja0YfJA= version: 3 For a description of IAM and its features, see the IAM documentation (https://cloud.google.com/iam/docs/).
      "bindings": [ # Associates a list of members, or principals, with a role. Optionally, may specify a condition that determines how and when the bindings are applied. Each of the bindings must contain at least one principal.The bindings in a Policy can refer to up to 1,500 principals; up to 250 of these principals can be Google groups. Each occurrence of a principal counts towards these limits. For example, if the bindings grant 50 different roles to user:alice@example.com, and not to any other principal, then you can add another 1,450 principals to the bindings in the Policy.
        { # Associates members, or principals, with a role.
          "condition": { # Represents a textual expression in the Common Expression Language (CEL) syntax. CEL is a C-like expression language. The syntax and semantics of CEL are documented at https://github.com/google/cel-spec.Example (Comparison): title: "Summary size limit" description: "Determines if a summary is less than 100 chars" expression: "document.summary.size() < 100" Example (Equality): title: "Requestor is owner" description: "Determines if requestor is the document owner" expression: "document.owner == request.auth.claims.email" Example (Logic): title: "Public documents" description: "Determine whether the document should be publicly visible" expression: "document.type != 'private' && document.type != 'internal'" Example (Data Manipulation): title: "Notification string" description: "Create a notification string with a timestamp." expression: "'New message received at ' + string(document.create_time)" The exact variables and functions that may be referenced within an expression are determined by the service that evaluates it. See the service documentation for additional information. # The condition that is associated with this binding.If the condition evaluates to true, then this binding applies to the current request.If the condition evaluates to false, then this binding does not apply to the current request. However, a different role binding might grant the same role to one or more of the principals in this binding.To learn which resources support conditions in their IAM policies, see the IAM documentation (https://cloud.google.com/iam/help/conditions/resource-policies).
            "description": "A String", # Optional. Description of the expression. This is a longer text which describes the expression, e.g. when hovered over it in a UI.
            "expression": "A String", # Textual representation of an expression in Common Expression Language syntax.
            "location": "A String", # Optional. String indicating the location of the expression for error reporting, e.g. a file name and a position in the file.
            "title": "A String", # Optional. Title for the expression, i.e. a short string describing its purpose. This can be used e.g. in UIs which allow to enter the expression.
          },
          "members": [ # Specifies the principals requesting access for a Google Cloud resource. members can have the following values: allUsers: A special identifier that represents anyone who is on the internet; with or without a Google account. allAuthenticatedUsers: A special identifier that represents anyone who is authenticated with a Google account or a service account. Does not include identities that come from external identity providers (IdPs) through identity federation. user:{emailid}: An email address that represents a specific Google account. For example, alice@example.com . serviceAccount:{emailid}: An email address that represents a Google service account. For example, my-other-app@appspot.gserviceaccount.com. serviceAccount:{projectid}.svc.id.goog[{namespace}/{kubernetes-sa}]: An identifier for a Kubernetes service account (https://cloud.google.com/kubernetes-engine/docs/how-to/kubernetes-service-accounts). For example, my-project.svc.id.goog[my-namespace/my-kubernetes-sa]. group:{emailid}: An email address that represents a Google group. For example, admins@example.com. domain:{domain}: The G Suite domain (primary) that represents all the users of that domain. For example, google.com or example.com. principal://iam.googleapis.com/locations/global/workforcePools/{pool_id}/subject/{subject_attribute_value}: A single identity in a workforce identity pool. principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/group/{group_id}: All workforce identities in a group. principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/attribute.{attribute_name}/{attribute_value}: All workforce identities with a specific attribute value. principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/*: All identities in a workforce identity pool. principal://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/subject/{subject_attribute_value}: A single identity in a workload identity pool. principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/group/{group_id}: A workload identity pool group. principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/attribute.{attribute_name}/{attribute_value}: All identities in a workload identity pool with a certain attribute. principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/*: All identities in a workload identity pool. deleted:user:{emailid}?uid={uniqueid}: An email address (plus unique identifier) representing a user that has been recently deleted. For example, alice@example.com?uid=123456789012345678901. If the user is recovered, this value reverts to user:{emailid} and the recovered user retains the role in the binding. deleted:serviceAccount:{emailid}?uid={uniqueid}: An email address (plus unique identifier) representing a service account that has been recently deleted. For example, my-other-app@appspot.gserviceaccount.com?uid=123456789012345678901. If the service account is undeleted, this value reverts to serviceAccount:{emailid} and the undeleted service account retains the role in the binding. deleted:group:{emailid}?uid={uniqueid}: An email address (plus unique identifier) representing a Google group that has been recently deleted. For example, admins@example.com?uid=123456789012345678901. If the group is recovered, this value reverts to group:{emailid} and the recovered group retains the role in the binding. deleted:principal://iam.googleapis.com/locations/global/workforcePools/{pool_id}/subject/{subject_attribute_value}: Deleted single identity in a workforce identity pool. For example, deleted:principal://iam.googleapis.com/locations/global/workforcePools/my-pool-id/subject/my-subject-attribute-value.
            "A String",
          ],
          "role": "A String", # Role that is assigned to the list of members, or principals. For example, roles/viewer, roles/editor, or roles/owner.For an overview of the IAM roles and permissions, see the IAM documentation (https://cloud.google.com/iam/docs/roles-overview). For a list of the available pre-defined roles, see here (https://cloud.google.com/iam/docs/understanding-roles).
        },
      ],
      "etag": "A String", # etag is used for optimistic concurrency control as a way to help prevent simultaneous updates of a policy from overwriting each other. It is strongly suggested that systems make use of the etag in the read-modify-write cycle to perform policy updates in order to avoid race conditions: An etag is returned in the response to getIamPolicy, and systems are expected to put that etag in the request to setIamPolicy to ensure that their change will be applied to the same version of the policy.Important: If you use IAM Conditions, you must include the etag field whenever you call setIamPolicy. If you omit this field, then IAM allows you to overwrite a version 3 policy with a version 1 policy, and all of the conditions in the version 3 policy are lost.
      "version": 42, # Specifies the format of the policy.Valid values are 0, 1, and 3. Requests that specify an invalid value are rejected.Any operation that affects conditional role bindings must specify version 3. This requirement applies to the following operations: Getting a policy that includes a conditional role binding Adding a conditional role binding to a policy Changing a conditional role binding in a policy Removing any role binding, with or without a condition, from a policy that includes conditionsImportant: If you use IAM Conditions, you must include the etag field whenever you call setIamPolicy. If you omit this field, then IAM allows you to overwrite a version 3 policy with a version 1 policy, and all of the conditions in the version 3 policy are lost.If a policy does not include any conditions, operations on that policy may specify any valid version or leave the field unset.To learn which resources support conditions in their IAM policies, see the IAM documentation (https://cloud.google.com/iam/help/conditions/resource-policies).
    }

`testIamPermissions(resource, body=None, x__xgafv=None)`

    Returns permissions that a caller has on the specified resource. If the resource does not exist, this will return an empty set of permissions, not a NOT_FOUND error.Note: This operation is designed to be used for building permission-aware UIs and command-line tools, not for authorization checking. This operation may "fail open" without warning.

    Args:
      resource: string, REQUIRED: The resource for which the policy detail is being requested. See Resource names (https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. (required)
      body: object, The request body.
        The object takes the form of:

    { # Request message for TestIamPermissions method.
      "permissions": [ # The set of permissions to check for the resource. Permissions with wildcards (such as * or storage.*) are not allowed. For more information see IAM Overview (https://cloud.google.com/iam/docs/overview#permissions).
        "A String",
      ],
    }

      x__xgafv: string, V1 error format.
        Allowed values
          1 - v1 error format
          2 - v2 error format

    Returns:
      An object of the form:

        { # Response message for TestIamPermissions method.
      "permissions": [ # A subset of TestPermissionsRequest.permissions that the caller is allowed.
        "A String",
      ],
    }

`update(name, body=None, x__xgafv=None)`

    Updates (replaces) workflow template. The updated template must contain version that matches the current server version.

    Args:
      name: string, Output only. The resource name of the workflow template, as described in https://cloud.google.com/apis/design/resource_names. For projects.regions.workflowTemplates, the resource name of the template has the following format: projects/{project_id}/regions/{region}/workflowTemplates/{template_id} For projects.locations.workflowTemplates, the resource name of the template has the following format: projects/{project_id}/locations/{location}/workflowTemplates/{template_id} (required)
      body: object, The request body.
        The object takes the form of:

    { # A Dataproc workflow template resource.
      "createTime": "A String", # Output only. The time template was created.
      "dagTimeout": "A String", # Optional. Timeout duration for the DAG of jobs, expressed in seconds (see JSON representation of duration (https://developers.google.com/protocol-buffers/docs/proto3#json)). The timeout duration must be from 10 minutes ("600s") to 24 hours ("86400s"). The timer begins when the first job is submitted. If the workflow is running at the end of the timeout period, any remaining jobs are cancelled, the workflow is ended, and if the workflow was running on a managed cluster, the cluster is deleted.
      "encryptionConfig": { # Encryption settings for encrypting workflow template job arguments. # Optional. Encryption settings for encrypting workflow template job arguments.
        "kmsKey": "A String", # Optional. The Cloud KMS key name to use for encrypting workflow template job arguments.When this this key is provided, the following workflow template job arguments (https://cloud.google.com/dataproc/docs/concepts/workflows/use-workflows#adding_jobs_to_a_template), if present, are CMEK encrypted (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/customer-managed-encryption#use_cmek_with_workflow_template_data): FlinkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/FlinkJob) HadoopJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/HadoopJob) SparkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkJob) SparkRJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkRJob) PySparkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/PySparkJob) SparkSqlJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkSqlJob) scriptVariables and queryList.queries HiveJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/HiveJob) scriptVariables and queryList.queries PigJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/PigJob) scriptVariables and queryList.queries PrestoJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/PrestoJob) scriptVariables and queryList.queries
      },
      "id": "A String",
      "jobs": [ # Required. The Directed Acyclic Graph of Jobs to submit.
        { # A job executed by the workflow.
          "flinkJob": { # A Dataproc job for running Apache Flink applications on YARN. # Optional. Job is a Flink job.
            "args": [ # Optional. The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision might occur that causes an incorrect job submission.
              "A String",
            ],
            "jarFileUris": [ # Optional. HCFS URIs of jar files to add to the CLASSPATHs of the Flink driver and tasks.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "mainClass": "A String", # The name of the driver's main class. The jar file that contains the class must be in the default CLASSPATH or specified in jarFileUris.
            "mainJarFileUri": "A String", # The HCFS URI of the jar file that contains the main class.
            "properties": { # Optional. A mapping of property names to values, used to configure Flink. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/flink/conf/flink-defaults.conf and classes in user code.
              "a_key": "A String",
            },
            "savepointUri": "A String", # Optional. HCFS URI of the savepoint, which contains the last saved progress for starting the current job.
          },
          "hadoopJob": { # A Dataproc job for running Apache Hadoop MapReduce (https://hadoop.apache.org/docs/current/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html) jobs on Apache Hadoop YARN (https://hadoop.apache.org/docs/r2.7.1/hadoop-yarn/hadoop-yarn-site/YARN.html). # Optional. Job is a Hadoop job.
            "archiveUris": [ # Optional. HCFS URIs of archives to be extracted in the working directory of Hadoop drivers and tasks. Supported file types: .jar, .tar, .tar.gz, .tgz, or .zip.
              "A String",
            ],
            "args": [ # Optional. The arguments to pass to the driver. Do not include arguments, such as -libjars or -Dfoo=bar, that can be set as job properties, since a collision might occur that causes an incorrect job submission.
              "A String",
            ],
            "fileUris": [ # Optional. HCFS (Hadoop Compatible Filesystem) URIs of files to be copied to the working directory of Hadoop drivers and distributed tasks. Useful for naively parallel tasks.
              "A String",
            ],
            "jarFileUris": [ # Optional. Jar file URIs to add to the CLASSPATHs of the Hadoop driver and tasks.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "mainClass": "A String", # The name of the driver's main class. The jar file containing the class must be in the default CLASSPATH or specified in jar_file_uris.
            "mainJarFileUri": "A String", # The HCFS URI of the jar file containing the main class. Examples: 'gs://foo-bucket/analytics-binaries/extract-useful-metrics-mr.jar' 'hdfs:/tmp/test-samples/custom-wordcount.jar' 'file:///home/usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar'
            "properties": { # Optional. A mapping of property names to values, used to configure Hadoop. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/hadoop/conf/*-site and classes in user code.
              "a_key": "A String",
            },
          },
          "hiveJob": { # A Dataproc job for running Apache Hive (https://hive.apache.org/) queries on YARN. # Optional. Job is a Hive job.
            "continueOnFailure": True or False, # Optional. Whether to continue executing queries if a query fails. The default value is false. Setting to true can be useful when executing independent parallel queries.
            "jarFileUris": [ # Optional. HCFS URIs of jar files to add to the CLASSPATH of the Hive server and Hadoop MapReduce (MR) tasks. Can contain Hive SerDes and UDFs.
              "A String",
            ],
            "properties": { # Optional. A mapping of property names and values, used to configure Hive. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/hadoop/conf/*-site.xml, /etc/hive/conf/hive-site.xml, and classes in user code.
              "a_key": "A String",
            },
            "queryFileUri": "A String", # The HCFS URI of the script that contains Hive queries.
            "queryList": { # A list of queries to run on a cluster. # A list of queries.
              "queries": [ # Required. The queries to execute. You do not need to end a query expression with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of a Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } }
                "A String",
              ],
            },
            "scriptVariables": { # Optional. Mapping of query variable names to values (equivalent to the Hive command: SET name="value";).
              "a_key": "A String",
            },
          },
          "labels": { # Optional. The labels to associate with this job.Label keys must be between 1 and 63 characters long, and must conform to the following regular expression: \p{Ll}\p{Lo}{0,62}Label values must be between 1 and 63 characters long, and must conform to the following regular expression: \p{Ll}\p{Lo}\p{N}_-{0,63}No more than 32 labels can be associated with a given job.
            "a_key": "A String",
          },
          "pigJob": { # A Dataproc job for running Apache Pig (https://pig.apache.org/) queries on YARN. # Optional. Job is a Pig job.
            "continueOnFailure": True or False, # Optional. Whether to continue executing queries if a query fails. The default value is false. Setting to true can be useful when executing independent parallel queries.
            "jarFileUris": [ # Optional. HCFS URIs of jar files to add to the CLASSPATH of the Pig Client and Hadoop MapReduce (MR) tasks. Can contain Pig UDFs.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "properties": { # Optional. A mapping of property names to values, used to configure Pig. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/hadoop/conf/*-site.xml, /etc/pig/conf/pig.properties, and classes in user code.
              "a_key": "A String",
            },
            "queryFileUri": "A String", # The HCFS URI of the script that contains the Pig queries.
            "queryList": { # A list of queries to run on a cluster. # A list of queries.
              "queries": [ # Required. The queries to execute. You do not need to end a query expression with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of a Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } }
                "A String",
              ],
            },
            "scriptVariables": { # Optional. Mapping of query variable names to values (equivalent to the Pig command: name=[value]).
              "a_key": "A String",
            },
          },
          "prerequisiteStepIds": [ # Optional. The optional list of prerequisite job step_ids. If not specified, the job will start at the beginning of workflow.
            "A String",
          ],
          "prestoJob": { # A Dataproc job for running Presto (https://prestosql.io/) queries. IMPORTANT: The Dataproc Presto Optional Component (https://cloud.google.com/dataproc/docs/concepts/components/presto) must be enabled when the cluster is created to submit a Presto job to the cluster. # Optional. Job is a Presto job.
            "clientTags": [ # Optional. Presto client tags to attach to this query
              "A String",
            ],
            "continueOnFailure": True or False, # Optional. Whether to continue executing queries if a query fails. The default value is false. Setting to true can be useful when executing independent parallel queries.
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "outputFormat": "A String", # Optional. The format in which query output will be displayed. See the Presto documentation for supported output formats
            "properties": { # Optional. A mapping of property names to values. Used to set Presto session properties (https://prestodb.io/docs/current/sql/set-session.html) Equivalent to using the --session flag in the Presto CLI
              "a_key": "A String",
            },
            "queryFileUri": "A String", # The HCFS URI of the script that contains SQL queries.
            "queryList": { # A list of queries to run on a cluster. # A list of queries.
              "queries": [ # Required. The queries to execute. You do not need to end a query expression with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of a Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } }
                "A String",
              ],
            },
          },
          "pysparkJob": { # A Dataproc job for running Apache PySpark (https://spark.apache.org/docs/latest/api/python/index.html#pyspark-overview) applications on YARN. # Optional. Job is a PySpark job.
            "archiveUris": [ # Optional. HCFS URIs of archives to be extracted into the working directory of each executor. Supported file types: .jar, .tar, .tar.gz, .tgz, and .zip.
              "A String",
            ],
            "args": [ # Optional. The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision may occur that causes an incorrect job submission.
              "A String",
            ],
            "fileUris": [ # Optional. HCFS URIs of files to be placed in the working directory of each executor. Useful for naively parallel tasks.
              "A String",
            ],
            "jarFileUris": [ # Optional. HCFS URIs of jar files to add to the CLASSPATHs of the Python driver and tasks.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "mainPythonFileUri": "A String", # Required. The HCFS URI of the main Python file to use as the driver. Must be a .py file.
            "properties": { # Optional. A mapping of property names to values, used to configure PySpark. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/spark/conf/spark-defaults.conf and classes in user code.
              "a_key": "A String",
            },
            "pythonFileUris": [ # Optional. HCFS file URIs of Python files to pass to the PySpark framework. Supported file types: .py, .egg, and .zip.
              "A String",
            ],
          },
          "scheduling": { # Job scheduling options. # Optional. Job scheduling configuration.
            "maxFailuresPerHour": 42, # Optional. Maximum number of times per hour a driver can be restarted as a result of driver exiting with non-zero code before job is reported failed.A job might be reported as thrashing if the driver exits with a non-zero code four times within a 10-minute window.Maximum value is 10.Note: This restartable job option is not supported in Dataproc workflow templates (https://cloud.google.com/dataproc/docs/concepts/workflows/using-workflows#adding_jobs_to_a_template).
            "maxFailuresTotal": 42, # Optional. Maximum total number of times a driver can be restarted as a result of the driver exiting with a non-zero code. After the maximum number is reached, the job will be reported as failed.Maximum value is 240.Note: Currently, this restartable job option is not supported in Dataproc workflow templates (https://cloud.google.com/dataproc/docs/concepts/workflows/using-workflows#adding_jobs_to_a_template).
          },
          "sparkJob": { # A Dataproc job for running Apache Spark (https://spark.apache.org/) applications on YARN. # Optional. Job is a Spark job.
            "archiveUris": [ # Optional. HCFS URIs of archives to be extracted into the working directory of each executor. Supported file types: .jar, .tar, .tar.gz, .tgz, and .zip.
              "A String",
            ],
            "args": [ # Optional. The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision may occur that causes an incorrect job submission.
              "A String",
            ],
            "fileUris": [ # Optional. HCFS URIs of files to be placed in the working directory of each executor. Useful for naively parallel tasks.
              "A String",
            ],
            "jarFileUris": [ # Optional. HCFS URIs of jar files to add to the CLASSPATHs of the Spark driver and tasks.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "mainClass": "A String", # The name of the driver's main class. The jar file that contains the class must be in the default CLASSPATH or specified in SparkJob.jar_file_uris.
            "mainJarFileUri": "A String", # The HCFS URI of the jar file that contains the main class.
            "properties": { # Optional. A mapping of property names to values, used to configure Spark. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/spark/conf/spark-defaults.conf and classes in user code.
              "a_key": "A String",
            },
          },
          "sparkRJob": { # A Dataproc job for running Apache SparkR (https://spark.apache.org/docs/latest/sparkr.html) applications on YARN. # Optional. Job is a SparkR job.
            "archiveUris": [ # Optional. HCFS URIs of archives to be extracted into the working directory of each executor. Supported file types: .jar, .tar, .tar.gz, .tgz, and .zip.
              "A String",
            ],
            "args": [ # Optional. The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision may occur that causes an incorrect job submission.
              "A String",
            ],
            "fileUris": [ # Optional. HCFS URIs of files to be placed in the working directory of each executor. Useful for naively parallel tasks.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "mainRFileUri": "A String", # Required. The HCFS URI of the main R file to use as the driver. Must be a .R file.
            "properties": { # Optional. A mapping of property names to values, used to configure SparkR. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/spark/conf/spark-defaults.conf and classes in user code.
              "a_key": "A String",
            },
          },
          "sparkSqlJob": { # A Dataproc job for running Apache Spark SQL (https://spark.apache.org/sql/) queries. # Optional. Job is a SparkSql job.
            "jarFileUris": [ # Optional. HCFS URIs of jar files to be added to the Spark CLASSPATH.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "properties": { # Optional. A mapping of property names to values, used to configure Spark SQL's SparkConf. Properties that conflict with values set by the Dataproc API might be overwritten.
              "a_key": "A String",
            },
            "queryFileUri": "A String", # The HCFS URI of the script that contains SQL queries.
            "queryList": { # A list of queries to run on a cluster. # A list of queries.
              "queries": [ # Required. The queries to execute. You do not need to end a query expression with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of a Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } }
                "A String",
              ],
            },
            "scriptVariables": { # Optional. Mapping of query variable names to values (equivalent to the Spark SQL command: SET name="value";).
              "a_key": "A String",
            },
          },
          "stepId": "A String", # Required. The step id. The id must be unique among all jobs within the template.The step id is used as prefix for job id, as job goog-dataproc-workflow-step-id label, and in prerequisiteStepIds field from other steps.The id must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). Cannot begin or end with underscore or hyphen. Must consist of between 3 and 50 characters.
          "trinoJob": { # A Dataproc job for running Trino (https://trino.io/) queries. IMPORTANT: The Dataproc Trino Optional Component (https://cloud.google.com/dataproc/docs/concepts/components/trino) must be enabled when the cluster is created to submit a Trino job to the cluster. # Optional. Job is a Trino job.
            "clientTags": [ # Optional. Trino client tags to attach to this query
              "A String",
            ],
            "continueOnFailure": True or False, # Optional. Whether to continue executing queries if a query fails. The default value is false. Setting to true can be useful when executing independent parallel queries.
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "outputFormat": "A String", # Optional. The format in which query output will be displayed. See the Trino documentation for supported output formats
            "properties": { # Optional. A mapping of property names to values. Used to set Trino session properties (https://trino.io/docs/current/sql/set-session.html) Equivalent to using the --session flag in the Trino CLI
              "a_key": "A String",
            },
            "queryFileUri": "A String", # The HCFS URI of the script that contains SQL queries.
            "queryList": { # A list of queries to run on a cluster. # A list of queries.
              "queries": [ # Required. The queries to execute. You do not need to end a query expression with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of a Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } }
                "A String",
              ],
            },
          },
        },
      ],
      "labels": { # Optional. The labels to associate with this template. These labels will be propagated to all jobs and clusters created by the workflow instance.Label keys must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt).Label values may be empty, but, if present, must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt).No more than 32 labels can be associated with a template.
        "a_key": "A String",
      },
      "name": "A String", # Output only. The resource name of the workflow template, as described in https://cloud.google.com/apis/design/resource_names. For projects.regions.workflowTemplates, the resource name of the template has the following format: projects/{project_id}/regions/{region}/workflowTemplates/{template_id} For projects.locations.workflowTemplates, the resource name of the template has the following format: projects/{project_id}/locations/{location}/workflowTemplates/{template_id}
      "parameters": [ # Optional. Template parameters whose values are substituted into the template. Values for parameters must be provided when the template is instantiated.
        { # A configurable parameter that replaces one or more fields in the template. Parameterizable fields: - Labels - File uris - Job properties - Job arguments - Script variables - Main class (in HadoopJob and SparkJob) - Zone (in ClusterSelector)
          "description": "A String", # Optional. Brief description of the parameter. Must not exceed 1024 characters.
          "fields": [ # Required. Paths to all fields that the parameter replaces. A field is allowed to appear in at most one parameter's list of field paths.A field path is similar in syntax to a google.protobuf.FieldMask. For example, a field path that references the zone field of a workflow template's cluster selector would be specified as placement.clusterSelector.zone.Also, field paths can reference fields using the following syntax: Values in maps can be referenced by key: labels'key' placement.clusterSelector.clusterLabels'key' placement.managedCluster.labels'key' placement.clusterSelector.clusterLabels'key' jobs'step-id'.labels'key' Jobs in the jobs list can be referenced by step-id: jobs'step-id'.hadoopJob.mainJarFileUri jobs'step-id'.hiveJob.queryFileUri jobs'step-id'.pySparkJob.mainPythonFileUri jobs'step-id'.hadoopJob.jarFileUris0 jobs'step-id'.hadoopJob.archiveUris0 jobs'step-id'.hadoopJob.fileUris0 jobs'step-id'.pySparkJob.pythonFileUris0 Items in repeated fields can be referenced by a zero-based index: jobs'step-id'.sparkJob.args0 Other examples: jobs'step-id'.hadoopJob.properties'key' jobs'step-id'.hadoopJob.args0 jobs'step-id'.hiveJob.scriptVariables'key' jobs'step-id'.hadoopJob.mainJarFileUri placement.clusterSelector.zoneIt may not be possible to parameterize maps and repeated fields in their entirety since only individual map values and individual items in repeated fields can be referenced. For example, the following field paths are invalid: placement.clusterSelector.clusterLabels jobs'step-id'.sparkJob.args
            "A String",
          ],
          "name": "A String", # Required. Parameter name. The parameter name is used as the key, and paired with the parameter value, which are passed to the template when the template is instantiated. The name must contain only capital letters (A-Z), numbers (0-9), and underscores (_), and must not start with a number. The maximum length is 40 characters.
          "validation": { # Configuration for parameter validation. # Optional. Validation rules to be applied to this parameter's value.
            "regex": { # Validation based on regular expressions. # Validation based on regular expressions.
              "regexes": [ # Required. RE2 regular expressions used to validate the parameter's value. The value must match the regex in its entirety (substring matches are not sufficient).
                "A String",
              ],
            },
            "values": { # Validation based on a list of allowed values. # Validation based on a list of allowed values.
              "values": [ # Required. List of allowed values for the parameter.
                "A String",
              ],
            },
          },
        },
      ],
      "placement": { # Specifies workflow execution target.Either managed_cluster or cluster_selector is required. # Required. WorkflowTemplate scheduling information.
        "clusterSelector": { # A selector that chooses target cluster for jobs based on metadata. # Optional. A selector that chooses target cluster for jobs based on metadata.The selector is evaluated at the time each job is submitted.
          "clusterLabels": { # Required. The cluster labels. Cluster must have all labels to match.
            "a_key": "A String",
          },
          "zone": "A String", # Optional. The zone where workflow process executes. This parameter does not affect the selection of the cluster.If unspecified, the zone of the first cluster matching the selector is used.
        },
        "managedCluster": { # Cluster that is managed by the workflow. # A cluster that is managed by the workflow.
          "clusterName": "A String", # Required. The cluster name prefix. A unique cluster name will be formed by appending a random suffix.The name must contain only lower-case letters (a-z), numbers (0-9), and hyphens (-). Must begin with a letter. Cannot begin or end with hyphen. Must consist of between 2 and 35 characters.
          "config": { # The cluster config. # Required. The cluster configuration.
            "autoscalingConfig": { # Autoscaling Policy config associated with the cluster. # Optional. Autoscaling config for the policy associated with the cluster. Cluster does not autoscale if this field is unset.
              "policyUri": "A String", # Optional. The autoscaling policy used by the cluster.Only resource names including projectid and location (region) are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/locations/[dataproc_region]/autoscalingPolicies/[policy_id] projects/[project_id]/locations/[dataproc_region]/autoscalingPolicies/[policy_id]Note that the policy must be in the same project and Dataproc region.
            },
            "auxiliaryNodeGroups": [ # Optional. The node group settings.
              { # Node group identification and configuration information.
                "nodeGroup": { # Dataproc Node Group. The Dataproc NodeGroup resource is not related to the Dataproc NodeGroupAffinity resource. # Required. Node group configuration.
                  "labels": { # Optional. Node group labels. Label keys must consist of from 1 to 63 characters and conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). Label values can be empty. If specified, they must consist of from 1 to 63 characters and conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). The node group must have no more than 32 labels.
                    "a_key": "A String",
                  },
                  "name": "A String", # The Node group resource name (https://aip.dev/122).
                  "nodeGroupConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The node group instance group configuration.
                    "accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
                      { # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
                        "acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
                        "acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
                      },
                    ],
                    "diskConfig": { # Specifies the config of boot disk and attached disk options for a group of VM instances. # Optional. Disk option config settings.
                      "bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. This field is supported only if boot_disk_type is hyperdisk-balanced.
                      "bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. This field is supported only if boot_disk_type is hyperdisk-balanced.
                      "bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
                      "bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
                      "localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
                      "numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
                    },
                    "imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
                    "instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
                      "instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
                        { # Defines machines types and a rank to which the machines types belong.
                          "machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
                            "A String",
                          ],
                          "rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
                        },
                      ],
                      "instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
                        { # Defines a mapping from machine types to the number of VMs that are created with each machine type.
                          "machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
                          "vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
                        },
                      ],
                      "provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
                        "standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
                        "standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
                      },
                    },
                    "instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
                      "A String",
                    ],
                    "instanceReferences": [ # Output only. List of references to Compute Engine instances.
                      { # A reference to a Compute Engine instance.
                        "instanceId": "A String", # The unique identifier of the Compute Engine instance.
                        "instanceName": "A String", # The user-friendly name of the Compute Engine instance.
                        "publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
                        "publicKey": "A String", # The public RSA key used for sharing data with this instance.
                      },
                    ],
                    "isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
                    "machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
                    "managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
                      "instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
                      "instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
                      "instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
                    },
                    "minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
                    "minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
                    "numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
                    "preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
                    "startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
                      "requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
                    },
                  },
                  "roles": [ # Required. Node group roles.
                    "A String",
                  ],
                },
                "nodeGroupId": "A String", # Optional. A node group ID. Generated if not specified.The ID must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). Cannot begin or end with underscore or hyphen. Must consist of from 3 to 33 characters.
              },
            ],
            "clusterTier": "A String", # Optional. The cluster tier.
            "clusterType": "A String", # Optional. The type of the cluster.
            "configBucket": "A String", # Optional. A Cloud Storage bucket used to stage job dependencies, config files, and job driver console output. If you do not specify a staging bucket, Cloud Dataproc will determine a Cloud Storage location (US, ASIA, or EU) for your cluster's staging bucket according to the Compute Engine zone where your cluster is deployed, and then create and manage this project-level, per-location bucket (see Dataproc staging and temp buckets (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/staging-bucket)). This field requires a Cloud Storage bucket name, not a gs://... URI to a Cloud Storage bucket.
            "dataprocMetricConfig": { # Dataproc metric config. # Optional. The config for Dataproc metrics.
              "metrics": [ # Required. Metrics sources to enable.
                { # A Dataproc custom metric.
                  "metricOverrides": [ # Optional. Specify one or more Custom metrics (https://cloud.google.com/dataproc/docs/guides/dataproc-metrics#custom_metrics) to collect for the metric course (for the SPARK metric source (any Spark metric (https://spark.apache.org/docs/latest/monitoring.html#metrics) can be specified).Provide metrics in the following format: METRIC_SOURCE: INSTANCE:GROUP:METRIC Use camelcase as appropriate.Examples: yarn:ResourceManager:QueueMetrics:AppsCompleted spark:driver:DAGScheduler:job.allJobs sparkHistoryServer:JVM:Memory:NonHeapMemoryUsage.committed hiveserver2:JVM:Memory:NonHeapMemoryUsage.used Notes: Only the specified overridden metrics are collected for the metric source. For example, if one or more spark:executive metrics are listed as metric overrides, other SPARK metrics are not collected. The collection of the metrics for other enabled custom metric sources is unaffected. For example, if both SPARK and YARN metric sources are enabled, and overrides are provided for Spark metrics only, all YARN metrics are collected.
                    "A String",
                  ],
                  "metricSource": "A String", # Required. A standard set of metrics is collected unless metricOverrides are specified for the metric source (see Custom metrics (https://cloud.google.com/dataproc/docs/guides/dataproc-metrics#custom_metrics) for more information).
                },
              ],
            },
            "encryptionConfig": { # Encryption settings for the cluster. # Optional. Encryption settings for the cluster.
              "gcePdKmsKeyName": "A String", # Optional. The Cloud KMS key resource name to use for persistent disk encryption for all instances in the cluster. See Use CMEK with cluster data (https://cloud.google.com//dataproc/docs/concepts/configuring-clusters/customer-managed-encryption#use_cmek_with_cluster_data) for more information.
              "kmsKey": "A String", # Optional. The Cloud KMS key resource name to use for cluster persistent disk and job argument encryption. See Use CMEK with cluster data (https://cloud.google.com//dataproc/docs/concepts/configuring-clusters/customer-managed-encryption#use_cmek_with_cluster_data) for more information.When this key resource name is provided, the following job arguments of the following job types submitted to the cluster are encrypted using CMEK: FlinkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/FlinkJob) HadoopJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/HadoopJob) SparkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkJob) SparkRJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkRJob) PySparkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/PySparkJob) SparkSqlJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkSqlJob) scriptVariables and queryList.queries HiveJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/HiveJob) scriptVariables and queryList.queries PigJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/PigJob) scriptVariables and queryList.queries PrestoJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/PrestoJob) scriptVariables and queryList.queries
            },
            "endpointConfig": { # Endpoint config for this cluster # Optional. Port/endpoint configuration for this cluster
              "enableHttpPortAccess": True or False, # Optional. If true, enable http access to specific ports on the cluster from external sources. Defaults to false.
              "httpPorts": { # Output only. The map of port descriptions to URLs. Will only be populated if enable_http_port_access is true.
                "a_key": "A String",
              },
            },
            "gceClusterConfig": { # Common config settings for resources of Compute Engine cluster instances, applicable to all instances in the cluster. # Optional. The shared Compute Engine config settings for all instances in a cluster.
              "confidentialInstanceConfig": { # Confidential Instance Config for clusters using Confidential VMs (https://cloud.google.com/compute/confidential-vm/docs) # Optional. Confidential Instance Config for clusters using Confidential VMs (https://cloud.google.com/compute/confidential-vm/docs).
                "enableConfidentialCompute": True or False, # Optional. Defines whether the instance should have confidential compute enabled.
              },
              "internalIpOnly": True or False, # Optional. This setting applies to subnetwork-enabled networks. It is set to true by default in clusters created with image versions 2.2.x.When set to true: All cluster VMs have internal IP addresses. Google Private Access (https://cloud.google.com/vpc/docs/private-google-access) must be enabled to access Dataproc and other Google Cloud APIs. Off-cluster dependencies must be configured to be accessible without external IP addresses.When set to false: Cluster VMs are not restricted to internal IP addresses. Ephemeral external IP addresses are assigned to each cluster VM.
              "metadata": { # Optional. The Compute Engine metadata entries to add to all instances (see Project and instance metadata (https://cloud.google.com/compute/docs/storing-retrieving-metadata#project_and_instance_metadata)).
                "a_key": "A String",
              },
              "networkUri": "A String", # Optional. The Compute Engine network to be used for machine communications. Cannot be specified with subnetwork_uri. If neither network_uri nor subnetwork_uri is specified, the "default" network of the project is used, if it exists. Cannot be a "Custom Subnet Network" (see Using Subnetworks (https://cloud.google.com/compute/docs/subnetworks) for more information).A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/networks/default projects/[project_id]/global/networks/default default
              "nodeGroupAffinity": { # Node Group Affinity for clusters using sole-tenant node groups. The Dataproc NodeGroupAffinity resource is not related to the Dataproc NodeGroup resource. # Optional. Node Group Affinity for sole-tenant clusters.
                "nodeGroupUri": "A String", # Required. The URI of a sole-tenant node group resource (https://cloud.google.com/compute/docs/reference/rest/v1/nodeGroups) that the cluster will be created on.A full URL, partial URI, or node group name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/nodeGroups/node-group-1 projects/[project_id]/zones/[zone]/nodeGroups/node-group-1 node-group-1
              },
              "privateIpv6GoogleAccess": "A String", # Optional. The type of IPv6 access for a cluster.
              "reservationAffinity": { # Reservation Affinity for consuming Zonal reservation. # Optional. Reservation Affinity for consuming Zonal reservation.
                "consumeReservationType": "A String", # Optional. Type of reservation to consume
                "key": "A String", # Optional. Corresponds to the label key of reservation resource.
                "values": [ # Optional. Corresponds to the label values of reservation resource.
                  "A String",
                ],
              },
              "resourceManagerTags": { # Optional. Resource manager tags (https://cloud.google.com/resource-manager/docs/tags/tags-creating-and-managing) to add to all instances (see Use secure tags in Dataproc (https://cloud.google.com/dataproc/docs/guides/attach-secure-tags)).
                "a_key": "A String",
              },
              "serviceAccount": "A String", # Optional. The Dataproc service account (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/service-accounts#service_accounts_in_dataproc) (also see VM Data Plane identity (https://cloud.google.com/dataproc/docs/concepts/iam/dataproc-principals#vm_service_account_data_plane_identity)) used by Dataproc cluster VM instances to access Google Cloud Platform services.If not specified, the Compute Engine default service account (https://cloud.google.com/compute/docs/access/service-accounts#default_service_account) is used.
              "serviceAccountScopes": [ # Optional. The URIs of service account scopes to be included in Compute Engine instances. The following base set of scopes is always included: https://www.googleapis.com/auth/cloud.useraccounts.readonly https://www.googleapis.com/auth/devstorage.read_write https://www.googleapis.com/auth/logging.writeIf no scopes are specified, the following defaults are also provided: https://www.googleapis.com/auth/bigquery https://www.googleapis.com/auth/bigtable.admin.table https://www.googleapis.com/auth/bigtable.data https://www.googleapis.com/auth/devstorage.full_control
                "A String",
              ],
              "shieldedInstanceConfig": { # Shielded Instance Config for clusters using Compute Engine Shielded VMs (https://cloud.google.com/security/shielded-cloud/shielded-vm). # Optional. Shielded Instance Config for clusters using Compute Engine Shielded VMs (https://cloud.google.com/security/shielded-cloud/shielded-vm).
                "enableIntegrityMonitoring": True or False, # Optional. Defines whether instances have integrity monitoring enabled.
                "enableSecureBoot": True or False, # Optional. Defines whether instances have Secure Boot enabled.
                "enableVtpm": True or False, # Optional. Defines whether instances have the vTPM enabled.
              },
              "subnetworkUri": "A String", # Optional. The Compute Engine subnetwork to be used for machine communications. Cannot be specified with network_uri.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/regions/[region]/subnetworks/sub0 projects/[project_id]/regions/[region]/subnetworks/sub0 sub0
              "tags": [ # The Compute Engine network tags to add to all instances (see Tagging instances (https://cloud.google.com/vpc/docs/add-remove-network-tags)).
                "A String",
              ],
              "zoneUri": "A String", # Optional. The Compute Engine zone where the Dataproc cluster will be located. If omitted, the service will pick a zone in the cluster's Compute Engine region. On a get request, zone will always be present.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone] projects/[project_id]/zones/[zone] [zone]
            },
            "gkeClusterConfig": { # The cluster's GKE config. # Optional. BETA. The Kubernetes Engine config for Dataproc clusters deployed to The Kubernetes Engine config for Dataproc clusters deployed to Kubernetes. These config settings are mutually exclusive with Compute Engine-based options, such as gce_cluster_config, master_config, worker_config, secondary_worker_config, and autoscaling_config.
              "gkeClusterTarget": "A String", # Optional. A target GKE cluster to deploy to. It must be in the same project and region as the Dataproc cluster (the GKE cluster can be zonal or regional). Format: 'projects/{project}/locations/{location}/clusters/{cluster_id}'
              "namespacedGkeDeploymentTarget": { # Deprecated. Used only for the deprecated beta. A full, namespace-isolated deployment target for an existing GKE cluster. # Optional. Deprecated. Use gkeClusterTarget. Used only for the deprecated beta. A target for the deployment.
                "clusterNamespace": "A String", # Optional. A namespace within the GKE cluster to deploy into.
                "targetGkeCluster": "A String", # Optional. The target GKE cluster to deploy to. Format: 'projects/{project}/locations/{location}/clusters/{cluster_id}'
              },
              "nodePoolTarget": [ # Optional. GKE node pools where workloads will be scheduled. At least one node pool must be assigned the DEFAULT GkeNodePoolTarget.Role. If a GkeNodePoolTarget is not specified, Dataproc constructs a DEFAULT GkeNodePoolTarget. Each role can be given to only one GkeNodePoolTarget. All node pools must have the same location settings.
                { # GKE node pools that Dataproc workloads run on.
                  "nodePool": "A String", # Required. The target GKE node pool. Format: 'projects/{project}/locations/{location}/clusters/{cluster}/nodePools/{node_pool}'
                  "nodePoolConfig": { # The configuration of a GKE node pool used by a Dataproc-on-GKE cluster (https://cloud.google.com/dataproc/docs/concepts/jobs/dataproc-gke#create-a-dataproc-on-gke-cluster). # Input only. The configuration for the GKE node pool.If specified, Dataproc attempts to create a node pool with the specified shape. If one with the same name already exists, it is verified against all specified fields. If a field differs, the virtual cluster creation will fail.If omitted, any node pool with the specified name is used. If a node pool with the specified name does not exist, Dataproc create a node pool with default values.This is an input only field. It will not be returned by the API.
                    "autoscaling": { # GkeNodePoolAutoscaling contains information the cluster autoscaler needs to adjust the size of the node pool to the current cluster usage. # Optional. The autoscaler configuration for this node pool. The autoscaler is enabled only when a valid configuration is present.
                      "maxNodeCount": 42, # The maximum number of nodes in the node pool. Must be >= min_node_count, and must be > 0. Note: Quota must be sufficient to scale up the cluster.
                      "minNodeCount": 42, # The minimum number of nodes in the node pool. Must be >= 0 and <= max_node_count.
                    },
                    "config": { # Parameters that describe cluster nodes. # Optional. The node pool configuration.
                      "accelerators": [ # Optional. A list of hardware accelerators (https://cloud.google.com/compute/docs/gpus) to attach to each node.
                        { # A GkeNodeConfigAcceleratorConfig represents a Hardware Accelerator request for a node pool.
                          "acceleratorCount": "A String", # The number of accelerator cards exposed to an instance.
                          "acceleratorType": "A String", # The accelerator type resource namename (see GPUs on Compute Engine).
                          "gpuPartitionSize": "A String", # Size of partitions to create on the GPU. Valid values are described in the NVIDIA mig user guide (https://docs.nvidia.com/datacenter/tesla/mig-user-guide/#partitioning).
                        },
                      ],
                      "bootDiskKmsKey": "A String", # Optional. The Customer Managed Encryption Key (CMEK) (https://cloud.google.com/kubernetes-engine/docs/how-to/using-cmek) used to encrypt the boot disk attached to each node in the node pool. Specify the key using the following format: projects/{project}/locations/{location}/keyRings/{key_ring}/cryptoKeys/{crypto_key}
                      "localSsdCount": 42, # Optional. The number of local SSD disks to attach to the node, which is limited by the maximum number of disks allowable per zone (see Adding Local SSDs (https://cloud.google.com/compute/docs/disks/local-ssd)).
                      "machineType": "A String", # Optional. The name of a Compute Engine machine type (https://cloud.google.com/compute/docs/machine-types).
                      "minCpuPlatform": "A String", # Optional. Minimum CPU platform (https://cloud.google.com/compute/docs/instances/specify-min-cpu-platform) to be used by this instance. The instance may be scheduled on the specified or a newer CPU platform. Specify the friendly names of CPU platforms, such as "Intel Haswell"` or Intel Sandy Bridge".
                      "preemptible": True or False, # Optional. Whether the nodes are created as legacy preemptible VM instances (https://cloud.google.com/compute/docs/instances/preemptible). Also see Spot VMs, preemptible VM instances without a maximum lifetime. Legacy and Spot preemptible nodes cannot be used in a node pool with the CONTROLLER role or in the DEFAULT node pool if the CONTROLLER role is not assigned (the DEFAULT node pool will assume the CONTROLLER role).
                      "spot": True or False, # Optional. Whether the nodes are created as Spot VM instances (https://cloud.google.com/compute/docs/instances/spot). Spot VMs are the latest update to legacy preemptible VMs. Spot VMs do not have a maximum lifetime. Legacy and Spot preemptible nodes cannot be used in a node pool with the CONTROLLER role or in the DEFAULT node pool if the CONTROLLER role is not assigned (the DEFAULT node pool will assume the CONTROLLER role).
                    },
                    "locations": [ # Optional. The list of Compute Engine zones (https://cloud.google.com/compute/docs/zones#available) where node pool nodes associated with a Dataproc on GKE virtual cluster will be located.Note: All node pools associated with a virtual cluster must be located in the same region as the virtual cluster, and they must be located in the same zone within that region.If a location is not specified during node pool creation, Dataproc on GKE will choose the zone.
                      "A String",
                    ],
                  },
                  "roles": [ # Required. The roles associated with the GKE node pool.
                    "A String",
                  ],
                },
              ],
            },
            "initializationActions": [ # Optional. Commands to execute on each node after config is completed. By default, executables are run on master and all worker nodes. You can test a node's role metadata to run an executable on a master or worker node, as shown below using curl (you can also use wget): ROLE=$(curl -H Metadata-Flavor:Google http://metadata/computeMetadata/v1/instance/attributes/dataproc-role) if [[ "${ROLE}" == 'Master' ]]; then ... master specific actions ... else ... worker specific actions ... fi
              { # Specifies an executable to run on a fully configured node and a timeout period for executable completion.
                "executableFile": "A String", # Required. Cloud Storage URI of executable file.
                "executionTimeout": "A String", # Optional. Amount of time executable has to complete. Default is 10 minutes (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).Cluster creation fails with an explanatory error message (the name of the executable that caused the error and the exceeded timeout period) if the executable is not completed at end of the timeout period.
              },
            ],
            "lifecycleConfig": { # Specifies the cluster auto-delete schedule configuration. # Optional. Lifecycle setting for the cluster.
              "autoDeleteTime": "A String", # Optional. The time when cluster will be auto-deleted (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
              "autoDeleteTtl": "A String", # Optional. The lifetime duration of cluster. The cluster will be auto-deleted at the end of this period. Minimum value is 10 minutes; maximum value is 14 days (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).
              "autoStopTime": "A String", # Optional. The time when cluster will be auto-stopped (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
              "autoStopTtl": "A String", # Optional. The lifetime duration of the cluster. The cluster will be auto-stopped at the end of this period, calculated from the time of submission of the create or update cluster request. Minimum value is 10 minutes; maximum value is 14 days (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).
              "idleDeleteTtl": "A String", # Optional. The duration to keep the cluster alive while idling (when no jobs are running). Passing this threshold will cause the cluster to be deleted. Minimum value is 5 minutes; maximum value is 14 days (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).
              "idleStartTime": "A String", # Output only. The time when cluster became idle (most recent job finished) and became eligible for deletion due to idleness (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
              "idleStopTtl": "A String", # Optional. The duration to keep the cluster started while idling (when no jobs are running). Passing this threshold will cause the cluster to be stopped. Minimum value is 5 minutes; maximum value is 14 days (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).
            },
            "masterConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The Compute Engine config settings for the cluster's master instance.
              "accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
                { # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
                  "acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
                  "acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
                },
              ],
              "diskConfig": { # Specifies the config of boot disk and attached disk options for a group of VM instances. # Optional. Disk option config settings.
                "bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. This field is supported only if boot_disk_type is hyperdisk-balanced.
                "bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. This field is supported only if boot_disk_type is hyperdisk-balanced.
                "bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
                "bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
                "localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
                "numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
              },
              "imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
              "instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
                "instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
                  { # Defines machines types and a rank to which the machines types belong.
                    "machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
                      "A String",
                    ],
                    "rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
                  },
                ],
                "instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
                  { # Defines a mapping from machine types to the number of VMs that are created with each machine type.
                    "machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
                    "vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
                  },
                ],
                "provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
                  "standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
                  "standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
                },
              },
              "instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
                "A String",
              ],
              "instanceReferences": [ # Output only. List of references to Compute Engine instances.
                { # A reference to a Compute Engine instance.
                  "instanceId": "A String", # The unique identifier of the Compute Engine instance.
                  "instanceName": "A String", # The user-friendly name of the Compute Engine instance.
                  "publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
                  "publicKey": "A String", # The public RSA key used for sharing data with this instance.
                },
              ],
              "isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
              "machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
              "managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
                "instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
                "instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
                "instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
              },
              "minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
              "minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
              "numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
              "preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
              "startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
                "requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
              },
            },
            "metastoreConfig": { # Specifies a Metastore configuration. # Optional. Metastore configuration.
              "dataprocMetastoreService": "A String", # Required. Resource name of an existing Dataproc Metastore service.Example: projects/[project_id]/locations/[dataproc_region]/services/[service-name]
            },
            "secondaryWorkerConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The Compute Engine config settings for a cluster's secondary worker instances
              "accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
                { # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
                  "acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
                  "acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
                },
              ],
              "diskConfig": { # Specifies the config of boot disk and attached disk options for a group of VM instances. # Optional. Disk option config settings.
                "bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. This field is supported only if boot_disk_type is hyperdisk-balanced.
                "bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. This field is supported only if boot_disk_type is hyperdisk-balanced.
                "bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
                "bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
                "localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
                "numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
              },
              "imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
              "instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
                "instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
                  { # Defines machines types and a rank to which the machines types belong.
                    "machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
                      "A String",
                    ],
                    "rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
                  },
                ],
                "instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
                  { # Defines a mapping from machine types to the number of VMs that are created with each machine type.
                    "machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
                    "vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
                  },
                ],
                "provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
                  "standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
                  "standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
                },
              },
              "instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
                "A String",
              ],
              "instanceReferences": [ # Output only. List of references to Compute Engine instances.
                { # A reference to a Compute Engine instance.
                  "instanceId": "A String", # The unique identifier of the Compute Engine instance.
                  "instanceName": "A String", # The user-friendly name of the Compute Engine instance.
                  "publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
                  "publicKey": "A String", # The public RSA key used for sharing data with this instance.
                },
              ],
              "isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
              "machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
              "managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
                "instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
                "instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
                "instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
              },
              "minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
              "minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
              "numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
              "preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
              "startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
                "requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
              },
            },
            "securityConfig": { # Security related configuration, including encryption, Kerberos, etc. # Optional. Security settings for the cluster.
              "identityConfig": { # Identity related configuration, including service account based secure multi-tenancy user mappings. # Optional. Identity related configuration, including service account based secure multi-tenancy user mappings.
                "userServiceAccountMapping": { # Required. Map of user to service account.
                  "a_key": "A String",
                },
              },
              "kerberosConfig": { # Specifies Kerberos related configuration. # Optional. Kerberos related configuration.
                "crossRealmTrustAdminServer": "A String", # Optional. The admin server (IP or hostname) for the remote trusted realm in a cross realm trust relationship.
                "crossRealmTrustKdc": "A String", # Optional. The KDC (IP or hostname) for the remote trusted realm in a cross realm trust relationship.
                "crossRealmTrustRealm": "A String", # Optional. The remote realm the Dataproc on-cluster KDC will trust, should the user enable cross realm trust.
                "crossRealmTrustSharedPasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the shared password between the on-cluster Kerberos realm and the remote trusted realm, in a cross realm trust relationship.
                "enableKerberos": True or False, # Optional. Flag to indicate whether to Kerberize the cluster (default: false). Set this field to true to enable Kerberos on a cluster.
                "kdcDbKeyUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the master key of the KDC database.
                "keyPasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the password to the user provided key. For the self-signed certificate, this password is generated by Dataproc.
                "keystorePasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the password to the user provided keystore. For the self-signed certificate, this password is generated by Dataproc.
                "keystoreUri": "A String", # Optional. The Cloud Storage URI of the keystore file used for SSL encryption. If not provided, Dataproc will provide a self-signed certificate.
                "kmsKeyUri": "A String", # Optional. The URI of the KMS key used to encrypt sensitive files.
                "realm": "A String", # Optional. The name of the on-cluster Kerberos realm. If not specified, the uppercased domain of hostnames will be the realm.
                "rootPrincipalPasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the root principal password.
                "tgtLifetimeHours": 42, # Optional. The lifetime of the ticket granting ticket, in hours. If not specified, or user specifies 0, then default value 10 will be used.
                "truststorePasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the password to the user provided truststore. For the self-signed certificate, this password is generated by Dataproc.
                "truststoreUri": "A String", # Optional. The Cloud Storage URI of the truststore file used for SSL encryption. If not provided, Dataproc will provide a self-signed certificate.
              },
            },
            "softwareConfig": { # Specifies the selection and config of software inside the cluster. # Optional. The config settings for cluster software.
              "imageVersion": "A String", # Optional. The version of software inside the cluster. It must be one of the supported Dataproc Versions (https://cloud.google.com/dataproc/docs/concepts/versioning/dataproc-versions#supported-dataproc-image-versions), such as "1.2" (including a subminor version, such as "1.2.29"), or the "preview" version (https://cloud.google.com/dataproc/docs/concepts/versioning/dataproc-versions#other_versions). If unspecified, it defaults to the latest Debian version.
              "optionalComponents": [ # Optional. The set of components to activate on the cluster.
                "A String",
              ],
              "properties": { # Optional. The properties to set on daemon config files.Property keys are specified in prefix:property format, for example core:hadoop.tmp.dir. The following are supported prefixes and their mappings: capacity-scheduler: capacity-scheduler.xml core: core-site.xml distcp: distcp-default.xml hdfs: hdfs-site.xml hive: hive-site.xml mapred: mapred-site.xml pig: pig.properties spark: spark-defaults.conf yarn: yarn-site.xmlFor more information, see Cluster properties (https://cloud.google.com/dataproc/docs/concepts/cluster-properties).
                "a_key": "A String",
              },
            },
            "tempBucket": "A String", # Optional. A Cloud Storage bucket used to store ephemeral cluster and jobs data, such as Spark and MapReduce history files. If you do not specify a temp bucket, Dataproc will determine a Cloud Storage location (US, ASIA, or EU) for your cluster's temp bucket according to the Compute Engine zone where your cluster is deployed, and then create and manage this project-level, per-location bucket. The default bucket has a TTL of 90 days, but you can use any TTL (or none) if you specify a bucket (see Dataproc staging and temp buckets (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/staging-bucket)). This field requires a Cloud Storage bucket name, not a gs://... URI to a Cloud Storage bucket.
            "workerConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The Compute Engine config settings for the cluster's worker instances.
              "accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
                { # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
                  "acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
                  "acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
                },
              ],
              "diskConfig": { # Specifies the config of boot disk and attached disk options for a group of VM instances. # Optional. Disk option config settings.
                "bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. This field is supported only if boot_disk_type is hyperdisk-balanced.
                "bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. This field is supported only if boot_disk_type is hyperdisk-balanced.
                "bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
                "bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
                "localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
                "numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
              },
              "imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
              "instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
                "instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
                  { # Defines machines types and a rank to which the machines types belong.
                    "machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
                      "A String",
                    ],
                    "rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
                  },
                ],
                "instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
                  { # Defines a mapping from machine types to the number of VMs that are created with each machine type.
                    "machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
                    "vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
                  },
                ],
                "provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
                  "standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
                  "standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
                },
              },
              "instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
                "A String",
              ],
              "instanceReferences": [ # Output only. List of references to Compute Engine instances.
                { # A reference to a Compute Engine instance.
                  "instanceId": "A String", # The unique identifier of the Compute Engine instance.
                  "instanceName": "A String", # The user-friendly name of the Compute Engine instance.
                  "publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
                  "publicKey": "A String", # The public RSA key used for sharing data with this instance.
                },
              ],
              "isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
              "machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
              "managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
                "instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
                "instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
                "instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
              },
              "minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
              "minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
              "numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
              "preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
              "startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
                "requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
              },
            },
          },
          "labels": { # Optional. The labels to associate with this cluster.Label keys must be between 1 and 63 characters long, and must conform to the following PCRE regular expression: \p{Ll}\p{Lo}{0,62}Label values must be between 1 and 63 characters long, and must conform to the following PCRE regular expression: \p{Ll}\p{Lo}\p{N}_-{0,63}No more than 32 labels can be associated with a given cluster.
            "a_key": "A String",
          },
        },
      },
      "updateTime": "A String", # Output only. The time template was last updated.
      "version": 42, # Optional. Used to perform a consistent read-modify-write.This field should be left blank for a CreateWorkflowTemplate request. It is required for an UpdateWorkflowTemplate request, and must match the current server version. A typical update template flow would fetch the current template with a GetWorkflowTemplate request, which will return the current template with the version field filled in with the current server version. The user updates other fields in the template, then returns it as part of the UpdateWorkflowTemplate request.
    }

      x__xgafv: string, V1 error format.
        Allowed values
          1 - v1 error format
          2 - v2 error format

    Returns:
      An object of the form:

        { # A Dataproc workflow template resource.
      "createTime": "A String", # Output only. The time template was created.
      "dagTimeout": "A String", # Optional. Timeout duration for the DAG of jobs, expressed in seconds (see JSON representation of duration (https://developers.google.com/protocol-buffers/docs/proto3#json)). The timeout duration must be from 10 minutes ("600s") to 24 hours ("86400s"). The timer begins when the first job is submitted. If the workflow is running at the end of the timeout period, any remaining jobs are cancelled, the workflow is ended, and if the workflow was running on a managed cluster, the cluster is deleted.
      "encryptionConfig": { # Encryption settings for encrypting workflow template job arguments. # Optional. Encryption settings for encrypting workflow template job arguments.
        "kmsKey": "A String", # Optional. The Cloud KMS key name to use for encrypting workflow template job arguments.When this this key is provided, the following workflow template job arguments (https://cloud.google.com/dataproc/docs/concepts/workflows/use-workflows#adding_jobs_to_a_template), if present, are CMEK encrypted (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/customer-managed-encryption#use_cmek_with_workflow_template_data): FlinkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/FlinkJob) HadoopJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/HadoopJob) SparkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkJob) SparkRJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkRJob) PySparkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/PySparkJob) SparkSqlJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkSqlJob) scriptVariables and queryList.queries HiveJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/HiveJob) scriptVariables and queryList.queries PigJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/PigJob) scriptVariables and queryList.queries PrestoJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/PrestoJob) scriptVariables and queryList.queries
      },
      "id": "A String",
      "jobs": [ # Required. The Directed Acyclic Graph of Jobs to submit.
        { # A job executed by the workflow.
          "flinkJob": { # A Dataproc job for running Apache Flink applications on YARN. # Optional. Job is a Flink job.
            "args": [ # Optional. The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision might occur that causes an incorrect job submission.
              "A String",
            ],
            "jarFileUris": [ # Optional. HCFS URIs of jar files to add to the CLASSPATHs of the Flink driver and tasks.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "mainClass": "A String", # The name of the driver's main class. The jar file that contains the class must be in the default CLASSPATH or specified in jarFileUris.
            "mainJarFileUri": "A String", # The HCFS URI of the jar file that contains the main class.
            "properties": { # Optional. A mapping of property names to values, used to configure Flink. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/flink/conf/flink-defaults.conf and classes in user code.
              "a_key": "A String",
            },
            "savepointUri": "A String", # Optional. HCFS URI of the savepoint, which contains the last saved progress for starting the current job.
          },
          "hadoopJob": { # A Dataproc job for running Apache Hadoop MapReduce (https://hadoop.apache.org/docs/current/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html) jobs on Apache Hadoop YARN (https://hadoop.apache.org/docs/r2.7.1/hadoop-yarn/hadoop-yarn-site/YARN.html). # Optional. Job is a Hadoop job.
            "archiveUris": [ # Optional. HCFS URIs of archives to be extracted in the working directory of Hadoop drivers and tasks. Supported file types: .jar, .tar, .tar.gz, .tgz, or .zip.
              "A String",
            ],
            "args": [ # Optional. The arguments to pass to the driver. Do not include arguments, such as -libjars or -Dfoo=bar, that can be set as job properties, since a collision might occur that causes an incorrect job submission.
              "A String",
            ],
            "fileUris": [ # Optional. HCFS (Hadoop Compatible Filesystem) URIs of files to be copied to the working directory of Hadoop drivers and distributed tasks. Useful for naively parallel tasks.
              "A String",
            ],
            "jarFileUris": [ # Optional. Jar file URIs to add to the CLASSPATHs of the Hadoop driver and tasks.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "mainClass": "A String", # The name of the driver's main class. The jar file containing the class must be in the default CLASSPATH or specified in jar_file_uris.
            "mainJarFileUri": "A String", # The HCFS URI of the jar file containing the main class. Examples: 'gs://foo-bucket/analytics-binaries/extract-useful-metrics-mr.jar' 'hdfs:/tmp/test-samples/custom-wordcount.jar' 'file:///home/usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar'
            "properties": { # Optional. A mapping of property names to values, used to configure Hadoop. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/hadoop/conf/*-site and classes in user code.
              "a_key": "A String",
            },
          },
          "hiveJob": { # A Dataproc job for running Apache Hive (https://hive.apache.org/) queries on YARN. # Optional. Job is a Hive job.
            "continueOnFailure": True or False, # Optional. Whether to continue executing queries if a query fails. The default value is false. Setting to true can be useful when executing independent parallel queries.
            "jarFileUris": [ # Optional. HCFS URIs of jar files to add to the CLASSPATH of the Hive server and Hadoop MapReduce (MR) tasks. Can contain Hive SerDes and UDFs.
              "A String",
            ],
            "properties": { # Optional. A mapping of property names and values, used to configure Hive. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/hadoop/conf/*-site.xml, /etc/hive/conf/hive-site.xml, and classes in user code.
              "a_key": "A String",
            },
            "queryFileUri": "A String", # The HCFS URI of the script that contains Hive queries.
            "queryList": { # A list of queries to run on a cluster. # A list of queries.
              "queries": [ # Required. The queries to execute. You do not need to end a query expression with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of a Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } }
                "A String",
              ],
            },
            "scriptVariables": { # Optional. Mapping of query variable names to values (equivalent to the Hive command: SET name="value";).
              "a_key": "A String",
            },
          },
          "labels": { # Optional. The labels to associate with this job.Label keys must be between 1 and 63 characters long, and must conform to the following regular expression: \p{Ll}\p{Lo}{0,62}Label values must be between 1 and 63 characters long, and must conform to the following regular expression: \p{Ll}\p{Lo}\p{N}_-{0,63}No more than 32 labels can be associated with a given job.
            "a_key": "A String",
          },
          "pigJob": { # A Dataproc job for running Apache Pig (https://pig.apache.org/) queries on YARN. # Optional. Job is a Pig job.
            "continueOnFailure": True or False, # Optional. Whether to continue executing queries if a query fails. The default value is false. Setting to true can be useful when executing independent parallel queries.
            "jarFileUris": [ # Optional. HCFS URIs of jar files to add to the CLASSPATH of the Pig Client and Hadoop MapReduce (MR) tasks. Can contain Pig UDFs.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "properties": { # Optional. A mapping of property names to values, used to configure Pig. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/hadoop/conf/*-site.xml, /etc/pig/conf/pig.properties, and classes in user code.
              "a_key": "A String",
            },
            "queryFileUri": "A String", # The HCFS URI of the script that contains the Pig queries.
            "queryList": { # A list of queries to run on a cluster. # A list of queries.
              "queries": [ # Required. The queries to execute. You do not need to end a query expression with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of a Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } }
                "A String",
              ],
            },
            "scriptVariables": { # Optional. Mapping of query variable names to values (equivalent to the Pig command: name=[value]).
              "a_key": "A String",
            },
          },
          "prerequisiteStepIds": [ # Optional. The optional list of prerequisite job step_ids. If not specified, the job will start at the beginning of workflow.
            "A String",
          ],
          "prestoJob": { # A Dataproc job for running Presto (https://prestosql.io/) queries. IMPORTANT: The Dataproc Presto Optional Component (https://cloud.google.com/dataproc/docs/concepts/components/presto) must be enabled when the cluster is created to submit a Presto job to the cluster. # Optional. Job is a Presto job.
            "clientTags": [ # Optional. Presto client tags to attach to this query
              "A String",
            ],
            "continueOnFailure": True or False, # Optional. Whether to continue executing queries if a query fails. The default value is false. Setting to true can be useful when executing independent parallel queries.
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "outputFormat": "A String", # Optional. The format in which query output will be displayed. See the Presto documentation for supported output formats
            "properties": { # Optional. A mapping of property names to values. Used to set Presto session properties (https://prestodb.io/docs/current/sql/set-session.html) Equivalent to using the --session flag in the Presto CLI
              "a_key": "A String",
            },
            "queryFileUri": "A String", # The HCFS URI of the script that contains SQL queries.
            "queryList": { # A list of queries to run on a cluster. # A list of queries.
              "queries": [ # Required. The queries to execute. You do not need to end a query expression with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of a Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } }
                "A String",
              ],
            },
          },
          "pysparkJob": { # A Dataproc job for running Apache PySpark (https://spark.apache.org/docs/latest/api/python/index.html#pyspark-overview) applications on YARN. # Optional. Job is a PySpark job.
            "archiveUris": [ # Optional. HCFS URIs of archives to be extracted into the working directory of each executor. Supported file types: .jar, .tar, .tar.gz, .tgz, and .zip.
              "A String",
            ],
            "args": [ # Optional. The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision may occur that causes an incorrect job submission.
              "A String",
            ],
            "fileUris": [ # Optional. HCFS URIs of files to be placed in the working directory of each executor. Useful for naively parallel tasks.
              "A String",
            ],
            "jarFileUris": [ # Optional. HCFS URIs of jar files to add to the CLASSPATHs of the Python driver and tasks.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "mainPythonFileUri": "A String", # Required. The HCFS URI of the main Python file to use as the driver. Must be a .py file.
            "properties": { # Optional. A mapping of property names to values, used to configure PySpark. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/spark/conf/spark-defaults.conf and classes in user code.
              "a_key": "A String",
            },
            "pythonFileUris": [ # Optional. HCFS file URIs of Python files to pass to the PySpark framework. Supported file types: .py, .egg, and .zip.
              "A String",
            ],
          },
          "scheduling": { # Job scheduling options. # Optional. Job scheduling configuration.
            "maxFailuresPerHour": 42, # Optional. Maximum number of times per hour a driver can be restarted as a result of driver exiting with non-zero code before job is reported failed.A job might be reported as thrashing if the driver exits with a non-zero code four times within a 10-minute window.Maximum value is 10.Note: This restartable job option is not supported in Dataproc workflow templates (https://cloud.google.com/dataproc/docs/concepts/workflows/using-workflows#adding_jobs_to_a_template).
            "maxFailuresTotal": 42, # Optional. Maximum total number of times a driver can be restarted as a result of the driver exiting with a non-zero code. After the maximum number is reached, the job will be reported as failed.Maximum value is 240.Note: Currently, this restartable job option is not supported in Dataproc workflow templates (https://cloud.google.com/dataproc/docs/concepts/workflows/using-workflows#adding_jobs_to_a_template).
          },
          "sparkJob": { # A Dataproc job for running Apache Spark (https://spark.apache.org/) applications on YARN. # Optional. Job is a Spark job.
            "archiveUris": [ # Optional. HCFS URIs of archives to be extracted into the working directory of each executor. Supported file types: .jar, .tar, .tar.gz, .tgz, and .zip.
              "A String",
            ],
            "args": [ # Optional. The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision may occur that causes an incorrect job submission.
              "A String",
            ],
            "fileUris": [ # Optional. HCFS URIs of files to be placed in the working directory of each executor. Useful for naively parallel tasks.
              "A String",
            ],
            "jarFileUris": [ # Optional. HCFS URIs of jar files to add to the CLASSPATHs of the Spark driver and tasks.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "mainClass": "A String", # The name of the driver's main class. The jar file that contains the class must be in the default CLASSPATH or specified in SparkJob.jar_file_uris.
            "mainJarFileUri": "A String", # The HCFS URI of the jar file that contains the main class.
            "properties": { # Optional. A mapping of property names to values, used to configure Spark. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/spark/conf/spark-defaults.conf and classes in user code.
              "a_key": "A String",
            },
          },
          "sparkRJob": { # A Dataproc job for running Apache SparkR (https://spark.apache.org/docs/latest/sparkr.html) applications on YARN. # Optional. Job is a SparkR job.
            "archiveUris": [ # Optional. HCFS URIs of archives to be extracted into the working directory of each executor. Supported file types: .jar, .tar, .tar.gz, .tgz, and .zip.
              "A String",
            ],
            "args": [ # Optional. The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision may occur that causes an incorrect job submission.
              "A String",
            ],
            "fileUris": [ # Optional. HCFS URIs of files to be placed in the working directory of each executor. Useful for naively parallel tasks.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "mainRFileUri": "A String", # Required. The HCFS URI of the main R file to use as the driver. Must be a .R file.
            "properties": { # Optional. A mapping of property names to values, used to configure SparkR. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/spark/conf/spark-defaults.conf and classes in user code.
              "a_key": "A String",
            },
          },
          "sparkSqlJob": { # A Dataproc job for running Apache Spark SQL (https://spark.apache.org/sql/) queries. # Optional. Job is a SparkSql job.
            "jarFileUris": [ # Optional. HCFS URIs of jar files to be added to the Spark CLASSPATH.
              "A String",
            ],
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "properties": { # Optional. A mapping of property names to values, used to configure Spark SQL's SparkConf. Properties that conflict with values set by the Dataproc API might be overwritten.
              "a_key": "A String",
            },
            "queryFileUri": "A String", # The HCFS URI of the script that contains SQL queries.
            "queryList": { # A list of queries to run on a cluster. # A list of queries.
              "queries": [ # Required. The queries to execute. You do not need to end a query expression with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of a Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } }
                "A String",
              ],
            },
            "scriptVariables": { # Optional. Mapping of query variable names to values (equivalent to the Spark SQL command: SET name="value";).
              "a_key": "A String",
            },
          },
          "stepId": "A String", # Required. The step id. The id must be unique among all jobs within the template.The step id is used as prefix for job id, as job goog-dataproc-workflow-step-id label, and in prerequisiteStepIds field from other steps.The id must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). Cannot begin or end with underscore or hyphen. Must consist of between 3 and 50 characters.
          "trinoJob": { # A Dataproc job for running Trino (https://trino.io/) queries. IMPORTANT: The Dataproc Trino Optional Component (https://cloud.google.com/dataproc/docs/concepts/components/trino) must be enabled when the cluster is created to submit a Trino job to the cluster. # Optional. Job is a Trino job.
            "clientTags": [ # Optional. Trino client tags to attach to this query
              "A String",
            ],
            "continueOnFailure": True or False, # Optional. Whether to continue executing queries if a query fails. The default value is false. Setting to true can be useful when executing independent parallel queries.
            "loggingConfig": { # The runtime logging config of the job. # Optional. The runtime log config for job execution.
              "driverLogLevels": { # The per-package log levels for the driver. This can include "root" package name to configure rootLogger. Examples: - 'com.google = FATAL' - 'root = INFO' - 'org.apache = DEBUG'
                "a_key": "A String",
              },
            },
            "outputFormat": "A String", # Optional. The format in which query output will be displayed. See the Trino documentation for supported output formats
            "properties": { # Optional. A mapping of property names to values. Used to set Trino session properties (https://trino.io/docs/current/sql/set-session.html) Equivalent to using the --session flag in the Trino CLI
              "a_key": "A String",
            },
            "queryFileUri": "A String", # The HCFS URI of the script that contains SQL queries.
            "queryList": { # A list of queries to run on a cluster. # A list of queries.
              "queries": [ # Required. The queries to execute. You do not need to end a query expression with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of a Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } }
                "A String",
              ],
            },
          },
        },
      ],
      "labels": { # Optional. The labels to associate with this template. These labels will be propagated to all jobs and clusters created by the workflow instance.Label keys must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt).Label values may be empty, but, if present, must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt).No more than 32 labels can be associated with a template.
        "a_key": "A String",
      },
      "name": "A String", # Output only. The resource name of the workflow template, as described in https://cloud.google.com/apis/design/resource_names. For projects.regions.workflowTemplates, the resource name of the template has the following format: projects/{project_id}/regions/{region}/workflowTemplates/{template_id} For projects.locations.workflowTemplates, the resource name of the template has the following format: projects/{project_id}/locations/{location}/workflowTemplates/{template_id}
      "parameters": [ # Optional. Template parameters whose values are substituted into the template. Values for parameters must be provided when the template is instantiated.
        { # A configurable parameter that replaces one or more fields in the template. Parameterizable fields: - Labels - File uris - Job properties - Job arguments - Script variables - Main class (in HadoopJob and SparkJob) - Zone (in ClusterSelector)
          "description": "A String", # Optional. Brief description of the parameter. Must not exceed 1024 characters.
          "fields": [ # Required. Paths to all fields that the parameter replaces. A field is allowed to appear in at most one parameter's list of field paths.A field path is similar in syntax to a google.protobuf.FieldMask. For example, a field path that references the zone field of a workflow template's cluster selector would be specified as placement.clusterSelector.zone.Also, field paths can reference fields using the following syntax: Values in maps can be referenced by key: labels'key' placement.clusterSelector.clusterLabels'key' placement.managedCluster.labels'key' placement.clusterSelector.clusterLabels'key' jobs'step-id'.labels'key' Jobs in the jobs list can be referenced by step-id: jobs'step-id'.hadoopJob.mainJarFileUri jobs'step-id'.hiveJob.queryFileUri jobs'step-id'.pySparkJob.mainPythonFileUri jobs'step-id'.hadoopJob.jarFileUris0 jobs'step-id'.hadoopJob.archiveUris0 jobs'step-id'.hadoopJob.fileUris0 jobs'step-id'.pySparkJob.pythonFileUris0 Items in repeated fields can be referenced by a zero-based index: jobs'step-id'.sparkJob.args0 Other examples: jobs'step-id'.hadoopJob.properties'key' jobs'step-id'.hadoopJob.args0 jobs'step-id'.hiveJob.scriptVariables'key' jobs'step-id'.hadoopJob.mainJarFileUri placement.clusterSelector.zoneIt may not be possible to parameterize maps and repeated fields in their entirety since only individual map values and individual items in repeated fields can be referenced. For example, the following field paths are invalid: placement.clusterSelector.clusterLabels jobs'step-id'.sparkJob.args
            "A String",
          ],
          "name": "A String", # Required. Parameter name. The parameter name is used as the key, and paired with the parameter value, which are passed to the template when the template is instantiated. The name must contain only capital letters (A-Z), numbers (0-9), and underscores (_), and must not start with a number. The maximum length is 40 characters.
          "validation": { # Configuration for parameter validation. # Optional. Validation rules to be applied to this parameter's value.
            "regex": { # Validation based on regular expressions. # Validation based on regular expressions.
              "regexes": [ # Required. RE2 regular expressions used to validate the parameter's value. The value must match the regex in its entirety (substring matches are not sufficient).
                "A String",
              ],
            },
            "values": { # Validation based on a list of allowed values. # Validation based on a list of allowed values.
              "values": [ # Required. List of allowed values for the parameter.
                "A String",
              ],
            },
          },
        },
      ],
      "placement": { # Specifies workflow execution target.Either managed_cluster or cluster_selector is required. # Required. WorkflowTemplate scheduling information.
        "clusterSelector": { # A selector that chooses target cluster for jobs based on metadata. # Optional. A selector that chooses target cluster for jobs based on metadata.The selector is evaluated at the time each job is submitted.
          "clusterLabels": { # Required. The cluster labels. Cluster must have all labels to match.
            "a_key": "A String",
          },
          "zone": "A String", # Optional. The zone where workflow process executes. This parameter does not affect the selection of the cluster.If unspecified, the zone of the first cluster matching the selector is used.
        },
        "managedCluster": { # Cluster that is managed by the workflow. # A cluster that is managed by the workflow.
          "clusterName": "A String", # Required. The cluster name prefix. A unique cluster name will be formed by appending a random suffix.The name must contain only lower-case letters (a-z), numbers (0-9), and hyphens (-). Must begin with a letter. Cannot begin or end with hyphen. Must consist of between 2 and 35 characters.
          "config": { # The cluster config. # Required. The cluster configuration.
            "autoscalingConfig": { # Autoscaling Policy config associated with the cluster. # Optional. Autoscaling config for the policy associated with the cluster. Cluster does not autoscale if this field is unset.
              "policyUri": "A String", # Optional. The autoscaling policy used by the cluster.Only resource names including projectid and location (region) are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/locations/[dataproc_region]/autoscalingPolicies/[policy_id] projects/[project_id]/locations/[dataproc_region]/autoscalingPolicies/[policy_id]Note that the policy must be in the same project and Dataproc region.
            },
            "auxiliaryNodeGroups": [ # Optional. The node group settings.
              { # Node group identification and configuration information.
                "nodeGroup": { # Dataproc Node Group. The Dataproc NodeGroup resource is not related to the Dataproc NodeGroupAffinity resource. # Required. Node group configuration.
                  "labels": { # Optional. Node group labels. Label keys must consist of from 1 to 63 characters and conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). Label values can be empty. If specified, they must consist of from 1 to 63 characters and conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). The node group must have no more than 32 labels.
                    "a_key": "A String",
                  },
                  "name": "A String", # The Node group resource name (https://aip.dev/122).
                  "nodeGroupConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The node group instance group configuration.
                    "accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
                      { # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
                        "acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
                        "acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
                      },
                    ],
                    "diskConfig": { # Specifies the config of boot disk and attached disk options for a group of VM instances. # Optional. Disk option config settings.
                      "bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. This field is supported only if boot_disk_type is hyperdisk-balanced.
                      "bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. This field is supported only if boot_disk_type is hyperdisk-balanced.
                      "bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
                      "bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
                      "localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
                      "numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
                    },
                    "imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
                    "instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
                      "instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
                        { # Defines machines types and a rank to which the machines types belong.
                          "machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
                            "A String",
                          ],
                          "rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
                        },
                      ],
                      "instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
                        { # Defines a mapping from machine types to the number of VMs that are created with each machine type.
                          "machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
                          "vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
                        },
                      ],
                      "provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
                        "standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
                        "standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
                      },
                    },
                    "instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
                      "A String",
                    ],
                    "instanceReferences": [ # Output only. List of references to Compute Engine instances.
                      { # A reference to a Compute Engine instance.
                        "instanceId": "A String", # The unique identifier of the Compute Engine instance.
                        "instanceName": "A String", # The user-friendly name of the Compute Engine instance.
                        "publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
                        "publicKey": "A String", # The public RSA key used for sharing data with this instance.
                      },
                    ],
                    "isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
                    "machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
                    "managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
                      "instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
                      "instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
                      "instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
                    },
                    "minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
                    "minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
                    "numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
                    "preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
                    "startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
                      "requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
                    },
                  },
                  "roles": [ # Required. Node group roles.
                    "A String",
                  ],
                },
                "nodeGroupId": "A String", # Optional. A node group ID. Generated if not specified.The ID must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). Cannot begin or end with underscore or hyphen. Must consist of from 3 to 33 characters.
              },
            ],
            "clusterTier": "A String", # Optional. The cluster tier.
            "clusterType": "A String", # Optional. The type of the cluster.
            "configBucket": "A String", # Optional. A Cloud Storage bucket used to stage job dependencies, config files, and job driver console output. If you do not specify a staging bucket, Cloud Dataproc will determine a Cloud Storage location (US, ASIA, or EU) for your cluster's staging bucket according to the Compute Engine zone where your cluster is deployed, and then create and manage this project-level, per-location bucket (see Dataproc staging and temp buckets (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/staging-bucket)). This field requires a Cloud Storage bucket name, not a gs://... URI to a Cloud Storage bucket.
            "dataprocMetricConfig": { # Dataproc metric config. # Optional. The config for Dataproc metrics.
              "metrics": [ # Required. Metrics sources to enable.
                { # A Dataproc custom metric.
                  "metricOverrides": [ # Optional. Specify one or more Custom metrics (https://cloud.google.com/dataproc/docs/guides/dataproc-metrics#custom_metrics) to collect for the metric course (for the SPARK metric source (any Spark metric (https://spark.apache.org/docs/latest/monitoring.html#metrics) can be specified).Provide metrics in the following format: METRIC_SOURCE: INSTANCE:GROUP:METRIC Use camelcase as appropriate.Examples: yarn:ResourceManager:QueueMetrics:AppsCompleted spark:driver:DAGScheduler:job.allJobs sparkHistoryServer:JVM:Memory:NonHeapMemoryUsage.committed hiveserver2:JVM:Memory:NonHeapMemoryUsage.used Notes: Only the specified overridden metrics are collected for the metric source. For example, if one or more spark:executive metrics are listed as metric overrides, other SPARK metrics are not collected. The collection of the metrics for other enabled custom metric sources is unaffected. For example, if both SPARK and YARN metric sources are enabled, and overrides are provided for Spark metrics only, all YARN metrics are collected.
                    "A String",
                  ],
                  "metricSource": "A String", # Required. A standard set of metrics is collected unless metricOverrides are specified for the metric source (see Custom metrics (https://cloud.google.com/dataproc/docs/guides/dataproc-metrics#custom_metrics) for more information).
                },
              ],
            },
            "encryptionConfig": { # Encryption settings for the cluster. # Optional. Encryption settings for the cluster.
              "gcePdKmsKeyName": "A String", # Optional. The Cloud KMS key resource name to use for persistent disk encryption for all instances in the cluster. See Use CMEK with cluster data (https://cloud.google.com//dataproc/docs/concepts/configuring-clusters/customer-managed-encryption#use_cmek_with_cluster_data) for more information.
              "kmsKey": "A String", # Optional. The Cloud KMS key resource name to use for cluster persistent disk and job argument encryption. See Use CMEK with cluster data (https://cloud.google.com//dataproc/docs/concepts/configuring-clusters/customer-managed-encryption#use_cmek_with_cluster_data) for more information.When this key resource name is provided, the following job arguments of the following job types submitted to the cluster are encrypted using CMEK: FlinkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/FlinkJob) HadoopJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/HadoopJob) SparkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkJob) SparkRJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkRJob) PySparkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/PySparkJob) SparkSqlJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkSqlJob) scriptVariables and queryList.queries HiveJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/HiveJob) scriptVariables and queryList.queries PigJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/PigJob) scriptVariables and queryList.queries PrestoJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/PrestoJob) scriptVariables and queryList.queries
            },
            "endpointConfig": { # Endpoint config for this cluster # Optional. Port/endpoint configuration for this cluster
              "enableHttpPortAccess": True or False, # Optional. If true, enable http access to specific ports on the cluster from external sources. Defaults to false.
              "httpPorts": { # Output only. The map of port descriptions to URLs. Will only be populated if enable_http_port_access is true.
                "a_key": "A String",
              },
            },
            "gceClusterConfig": { # Common config settings for resources of Compute Engine cluster instances, applicable to all instances in the cluster. # Optional. The shared Compute Engine config settings for all instances in a cluster.
              "confidentialInstanceConfig": { # Confidential Instance Config for clusters using Confidential VMs (https://cloud.google.com/compute/confidential-vm/docs) # Optional. Confidential Instance Config for clusters using Confidential VMs (https://cloud.google.com/compute/confidential-vm/docs).
                "enableConfidentialCompute": True or False, # Optional. Defines whether the instance should have confidential compute enabled.
              },
              "internalIpOnly": True or False, # Optional. This setting applies to subnetwork-enabled networks. It is set to true by default in clusters created with image versions 2.2.x.When set to true: All cluster VMs have internal IP addresses. Google Private Access (https://cloud.google.com/vpc/docs/private-google-access) must be enabled to access Dataproc and other Google Cloud APIs. Off-cluster dependencies must be configured to be accessible without external IP addresses.When set to false: Cluster VMs are not restricted to internal IP addresses. Ephemeral external IP addresses are assigned to each cluster VM.
              "metadata": { # Optional. The Compute Engine metadata entries to add to all instances (see Project and instance metadata (https://cloud.google.com/compute/docs/storing-retrieving-metadata#project_and_instance_metadata)).
                "a_key": "A String",
              },
              "networkUri": "A String", # Optional. The Compute Engine network to be used for machine communications. Cannot be specified with subnetwork_uri. If neither network_uri nor subnetwork_uri is specified, the "default" network of the project is used, if it exists. Cannot be a "Custom Subnet Network" (see Using Subnetworks (https://cloud.google.com/compute/docs/subnetworks) for more information).A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/networks/default projects/[project_id]/global/networks/default default
              "nodeGroupAffinity": { # Node Group Affinity for clusters using sole-tenant node groups. The Dataproc NodeGroupAffinity resource is not related to the Dataproc NodeGroup resource. # Optional. Node Group Affinity for sole-tenant clusters.
                "nodeGroupUri": "A String", # Required. The URI of a sole-tenant node group resource (https://cloud.google.com/compute/docs/reference/rest/v1/nodeGroups) that the cluster will be created on.A full URL, partial URI, or node group name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/nodeGroups/node-group-1 projects/[project_id]/zones/[zone]/nodeGroups/node-group-1 node-group-1
              },
              "privateIpv6GoogleAccess": "A String", # Optional. The type of IPv6 access for a cluster.
              "reservationAffinity": { # Reservation Affinity for consuming Zonal reservation. # Optional. Reservation Affinity for consuming Zonal reservation.
                "consumeReservationType": "A String", # Optional. Type of reservation to consume
                "key": "A String", # Optional. Corresponds to the label key of reservation resource.
                "values": [ # Optional. Corresponds to the label values of reservation resource.
                  "A String",
                ],
              },
              "resourceManagerTags": { # Optional. Resource manager tags (https://cloud.google.com/resource-manager/docs/tags/tags-creating-and-managing) to add to all instances (see Use secure tags in Dataproc (https://cloud.google.com/dataproc/docs/guides/attach-secure-tags)).
                "a_key": "A String",
              },
              "serviceAccount": "A String", # Optional. The Dataproc service account (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/service-accounts#service_accounts_in_dataproc) (also see VM Data Plane identity (https://cloud.google.com/dataproc/docs/concepts/iam/dataproc-principals#vm_service_account_data_plane_identity)) used by Dataproc cluster VM instances to access Google Cloud Platform services.If not specified, the Compute Engine default service account (https://cloud.google.com/compute/docs/access/service-accounts#default_service_account) is used.
              "serviceAccountScopes": [ # Optional. The URIs of service account scopes to be included in Compute Engine instances. The following base set of scopes is always included: https://www.googleapis.com/auth/cloud.useraccounts.readonly https://www.googleapis.com/auth/devstorage.read_write https://www.googleapis.com/auth/logging.writeIf no scopes are specified, the following defaults are also provided: https://www.googleapis.com/auth/bigquery https://www.googleapis.com/auth/bigtable.admin.table https://www.googleapis.com/auth/bigtable.data https://www.googleapis.com/auth/devstorage.full_control
                "A String",
              ],
              "shieldedInstanceConfig": { # Shielded Instance Config for clusters using Compute Engine Shielded VMs (https://cloud.google.com/security/shielded-cloud/shielded-vm). # Optional. Shielded Instance Config for clusters using Compute Engine Shielded VMs (https://cloud.google.com/security/shielded-cloud/shielded-vm).
                "enableIntegrityMonitoring": True or False, # Optional. Defines whether instances have integrity monitoring enabled.
                "enableSecureBoot": True or False, # Optional. Defines whether instances have Secure Boot enabled.
                "enableVtpm": True or False, # Optional. Defines whether instances have the vTPM enabled.
              },
              "subnetworkUri": "A String", # Optional. The Compute Engine subnetwork to be used for machine communications. Cannot be specified with network_uri.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/regions/[region]/subnetworks/sub0 projects/[project_id]/regions/[region]/subnetworks/sub0 sub0
              "tags": [ # The Compute Engine network tags to add to all instances (see Tagging instances (https://cloud.google.com/vpc/docs/add-remove-network-tags)).
                "A String",
              ],
              "zoneUri": "A String", # Optional. The Compute Engine zone where the Dataproc cluster will be located. If omitted, the service will pick a zone in the cluster's Compute Engine region. On a get request, zone will always be present.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone] projects/[project_id]/zones/[zone] [zone]
            },
            "gkeClusterConfig": { # The cluster's GKE config. # Optional. BETA. The Kubernetes Engine config for Dataproc clusters deployed to The Kubernetes Engine config for Dataproc clusters deployed to Kubernetes. These config settings are mutually exclusive with Compute Engine-based options, such as gce_cluster_config, master_config, worker_config, secondary_worker_config, and autoscaling_config.
              "gkeClusterTarget": "A String", # Optional. A target GKE cluster to deploy to. It must be in the same project and region as the Dataproc cluster (the GKE cluster can be zonal or regional). Format: 'projects/{project}/locations/{location}/clusters/{cluster_id}'
              "namespacedGkeDeploymentTarget": { # Deprecated. Used only for the deprecated beta. A full, namespace-isolated deployment target for an existing GKE cluster. # Optional. Deprecated. Use gkeClusterTarget. Used only for the deprecated beta. A target for the deployment.
                "clusterNamespace": "A String", # Optional. A namespace within the GKE cluster to deploy into.
                "targetGkeCluster": "A String", # Optional. The target GKE cluster to deploy to. Format: 'projects/{project}/locations/{location}/clusters/{cluster_id}'
              },
              "nodePoolTarget": [ # Optional. GKE node pools where workloads will be scheduled. At least one node pool must be assigned the DEFAULT GkeNodePoolTarget.Role. If a GkeNodePoolTarget is not specified, Dataproc constructs a DEFAULT GkeNodePoolTarget. Each role can be given to only one GkeNodePoolTarget. All node pools must have the same location settings.
                { # GKE node pools that Dataproc workloads run on.
                  "nodePool": "A String", # Required. The target GKE node pool. Format: 'projects/{project}/locations/{location}/clusters/{cluster}/nodePools/{node_pool}'
                  "nodePoolConfig": { # The configuration of a GKE node pool used by a Dataproc-on-GKE cluster (https://cloud.google.com/dataproc/docs/concepts/jobs/dataproc-gke#create-a-dataproc-on-gke-cluster). # Input only. The configuration for the GKE node pool.If specified, Dataproc attempts to create a node pool with the specified shape. If one with the same name already exists, it is verified against all specified fields. If a field differs, the virtual cluster creation will fail.If omitted, any node pool with the specified name is used. If a node pool with the specified name does not exist, Dataproc create a node pool with default values.This is an input only field. It will not be returned by the API.
                    "autoscaling": { # GkeNodePoolAutoscaling contains information the cluster autoscaler needs to adjust the size of the node pool to the current cluster usage. # Optional. The autoscaler configuration for this node pool. The autoscaler is enabled only when a valid configuration is present.
                      "maxNodeCount": 42, # The maximum number of nodes in the node pool. Must be >= min_node_count, and must be > 0. Note: Quota must be sufficient to scale up the cluster.
                      "minNodeCount": 42, # The minimum number of nodes in the node pool. Must be >= 0 and <= max_node_count.
                    },
                    "config": { # Parameters that describe cluster nodes. # Optional. The node pool configuration.
                      "accelerators": [ # Optional. A list of hardware accelerators (https://cloud.google.com/compute/docs/gpus) to attach to each node.
                        { # A GkeNodeConfigAcceleratorConfig represents a Hardware Accelerator request for a node pool.
                          "acceleratorCount": "A String", # The number of accelerator cards exposed to an instance.
                          "acceleratorType": "A String", # The accelerator type resource namename (see GPUs on Compute Engine).
                          "gpuPartitionSize": "A String", # Size of partitions to create on the GPU. Valid values are described in the NVIDIA mig user guide (https://docs.nvidia.com/datacenter/tesla/mig-user-guide/#partitioning).
                        },
                      ],
                      "bootDiskKmsKey": "A String", # Optional. The Customer Managed Encryption Key (CMEK) (https://cloud.google.com/kubernetes-engine/docs/how-to/using-cmek) used to encrypt the boot disk attached to each node in the node pool. Specify the key using the following format: projects/{project}/locations/{location}/keyRings/{key_ring}/cryptoKeys/{crypto_key}
                      "localSsdCount": 42, # Optional. The number of local SSD disks to attach to the node, which is limited by the maximum number of disks allowable per zone (see Adding Local SSDs (https://cloud.google.com/compute/docs/disks/local-ssd)).
                      "machineType": "A String", # Optional. The name of a Compute Engine machine type (https://cloud.google.com/compute/docs/machine-types).
                      "minCpuPlatform": "A String", # Optional. Minimum CPU platform (https://cloud.google.com/compute/docs/instances/specify-min-cpu-platform) to be used by this instance. The instance may be scheduled on the specified or a newer CPU platform. Specify the friendly names of CPU platforms, such as "Intel Haswell"` or Intel Sandy Bridge".
                      "preemptible": True or False, # Optional. Whether the nodes are created as legacy preemptible VM instances (https://cloud.google.com/compute/docs/instances/preemptible). Also see Spot VMs, preemptible VM instances without a maximum lifetime. Legacy and Spot preemptible nodes cannot be used in a node pool with the CONTROLLER role or in the DEFAULT node pool if the CONTROLLER role is not assigned (the DEFAULT node pool will assume the CONTROLLER role).
                      "spot": True or False, # Optional. Whether the nodes are created as Spot VM instances (https://cloud.google.com/compute/docs/instances/spot). Spot VMs are the latest update to legacy preemptible VMs. Spot VMs do not have a maximum lifetime. Legacy and Spot preemptible nodes cannot be used in a node pool with the CONTROLLER role or in the DEFAULT node pool if the CONTROLLER role is not assigned (the DEFAULT node pool will assume the CONTROLLER role).
                    },
                    "locations": [ # Optional. The list of Compute Engine zones (https://cloud.google.com/compute/docs/zones#available) where node pool nodes associated with a Dataproc on GKE virtual cluster will be located.Note: All node pools associated with a virtual cluster must be located in the same region as the virtual cluster, and they must be located in the same zone within that region.If a location is not specified during node pool creation, Dataproc on GKE will choose the zone.
                      "A String",
                    ],
                  },
                  "roles": [ # Required. The roles associated with the GKE node pool.
                    "A String",
                  ],
                },
              ],
            },
            "initializationActions": [ # Optional. Commands to execute on each node after config is completed. By default, executables are run on master and all worker nodes. You can test a node's role metadata to run an executable on a master or worker node, as shown below using curl (you can also use wget): ROLE=$(curl -H Metadata-Flavor:Google http://metadata/computeMetadata/v1/instance/attributes/dataproc-role) if [[ "${ROLE}" == 'Master' ]]; then ... master specific actions ... else ... worker specific actions ... fi
              { # Specifies an executable to run on a fully configured node and a timeout period for executable completion.
                "executableFile": "A String", # Required. Cloud Storage URI of executable file.
                "executionTimeout": "A String", # Optional. Amount of time executable has to complete. Default is 10 minutes (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).Cluster creation fails with an explanatory error message (the name of the executable that caused the error and the exceeded timeout period) if the executable is not completed at end of the timeout period.
              },
            ],
            "lifecycleConfig": { # Specifies the cluster auto-delete schedule configuration. # Optional. Lifecycle setting for the cluster.
              "autoDeleteTime": "A String", # Optional. The time when cluster will be auto-deleted (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
              "autoDeleteTtl": "A String", # Optional. The lifetime duration of cluster. The cluster will be auto-deleted at the end of this period. Minimum value is 10 minutes; maximum value is 14 days (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).
              "autoStopTime": "A String", # Optional. The time when cluster will be auto-stopped (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
              "autoStopTtl": "A String", # Optional. The lifetime duration of the cluster. The cluster will be auto-stopped at the end of this period, calculated from the time of submission of the create or update cluster request. Minimum value is 10 minutes; maximum value is 14 days (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).
              "idleDeleteTtl": "A String", # Optional. The duration to keep the cluster alive while idling (when no jobs are running). Passing this threshold will cause the cluster to be deleted. Minimum value is 5 minutes; maximum value is 14 days (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).
              "idleStartTime": "A String", # Output only. The time when cluster became idle (most recent job finished) and became eligible for deletion due to idleness (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
              "idleStopTtl": "A String", # Optional. The duration to keep the cluster started while idling (when no jobs are running). Passing this threshold will cause the cluster to be stopped. Minimum value is 5 minutes; maximum value is 14 days (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).
            },
            "masterConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The Compute Engine config settings for the cluster's master instance.
              "accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
                { # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
                  "acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
                  "acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
                },
              ],
              "diskConfig": { # Specifies the config of boot disk and attached disk options for a group of VM instances. # Optional. Disk option config settings.
                "bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. This field is supported only if boot_disk_type is hyperdisk-balanced.
                "bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. This field is supported only if boot_disk_type is hyperdisk-balanced.
                "bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
                "bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
                "localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
                "numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
              },
              "imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
              "instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
                "instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
                  { # Defines machines types and a rank to which the machines types belong.
                    "machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
                      "A String",
                    ],
                    "rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
                  },
                ],
                "instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
                  { # Defines a mapping from machine types to the number of VMs that are created with each machine type.
                    "machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
                    "vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
                  },
                ],
                "provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
                  "standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
                  "standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
                },
              },
              "instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
                "A String",
              ],
              "instanceReferences": [ # Output only. List of references to Compute Engine instances.
                { # A reference to a Compute Engine instance.
                  "instanceId": "A String", # The unique identifier of the Compute Engine instance.
                  "instanceName": "A String", # The user-friendly name of the Compute Engine instance.
                  "publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
                  "publicKey": "A String", # The public RSA key used for sharing data with this instance.
                },
              ],
              "isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
              "machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
              "managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
                "instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
                "instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
                "instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
              },
              "minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
              "minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
              "numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
              "preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
              "startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
                "requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
              },
            },
            "metastoreConfig": { # Specifies a Metastore configuration. # Optional. Metastore configuration.
              "dataprocMetastoreService": "A String", # Required. Resource name of an existing Dataproc Metastore service.Example: projects/[project_id]/locations/[dataproc_region]/services/[service-name]
            },
            "secondaryWorkerConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The Compute Engine config settings for a cluster's secondary worker instances
              "accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
                { # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
                  "acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
                  "acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
                },
              ],
              "diskConfig": { # Specifies the config of boot disk and attached disk options for a group of VM instances. # Optional. Disk option config settings.
                "bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. This field is supported only if boot_disk_type is hyperdisk-balanced.
                "bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. This field is supported only if boot_disk_type is hyperdisk-balanced.
                "bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
                "bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
                "localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
                "numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
              },
              "imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
              "instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
                "instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
                  { # Defines machines types and a rank to which the machines types belong.
                    "machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
                      "A String",
                    ],
                    "rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
                  },
                ],
                "instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
                  { # Defines a mapping from machine types to the number of VMs that are created with each machine type.
                    "machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
                    "vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
                  },
                ],
                "provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
                  "standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
                  "standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
                },
              },
              "instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
                "A String",
              ],
              "instanceReferences": [ # Output only. List of references to Compute Engine instances.
                { # A reference to a Compute Engine instance.
                  "instanceId": "A String", # The unique identifier of the Compute Engine instance.
                  "instanceName": "A String", # The user-friendly name of the Compute Engine instance.
                  "publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
                  "publicKey": "A String", # The public RSA key used for sharing data with this instance.
                },
              ],
              "isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
              "machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
              "managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
                "instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
                "instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
                "instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
              },
              "minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
              "minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
              "numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
              "preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
              "startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
                "requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
              },
            },
            "securityConfig": { # Security related configuration, including encryption, Kerberos, etc. # Optional. Security settings for the cluster.
              "identityConfig": { # Identity related configuration, including service account based secure multi-tenancy user mappings. # Optional. Identity related configuration, including service account based secure multi-tenancy user mappings.
                "userServiceAccountMapping": { # Required. Map of user to service account.
                  "a_key": "A String",
                },
              },
              "kerberosConfig": { # Specifies Kerberos related configuration. # Optional. Kerberos related configuration.
                "crossRealmTrustAdminServer": "A String", # Optional. The admin server (IP or hostname) for the remote trusted realm in a cross realm trust relationship.
                "crossRealmTrustKdc": "A String", # Optional. The KDC (IP or hostname) for the remote trusted realm in a cross realm trust relationship.
                "crossRealmTrustRealm": "A String", # Optional. The remote realm the Dataproc on-cluster KDC will trust, should the user enable cross realm trust.
                "crossRealmTrustSharedPasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the shared password between the on-cluster Kerberos realm and the remote trusted realm, in a cross realm trust relationship.
                "enableKerberos": True or False, # Optional. Flag to indicate whether to Kerberize the cluster (default: false). Set this field to true to enable Kerberos on a cluster.
                "kdcDbKeyUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the master key of the KDC database.
                "keyPasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the password to the user provided key. For the self-signed certificate, this password is generated by Dataproc.
                "keystorePasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the password to the user provided keystore. For the self-signed certificate, this password is generated by Dataproc.
                "keystoreUri": "A String", # Optional. The Cloud Storage URI of the keystore file used for SSL encryption. If not provided, Dataproc will provide a self-signed certificate.
                "kmsKeyUri": "A String", # Optional. The URI of the KMS key used to encrypt sensitive files.
                "realm": "A String", # Optional. The name of the on-cluster Kerberos realm. If not specified, the uppercased domain of hostnames will be the realm.
                "rootPrincipalPasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the root principal password.
                "tgtLifetimeHours": 42, # Optional. The lifetime of the ticket granting ticket, in hours. If not specified, or user specifies 0, then default value 10 will be used.
                "truststorePasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the password to the user provided truststore. For the self-signed certificate, this password is generated by Dataproc.
                "truststoreUri": "A String", # Optional. The Cloud Storage URI of the truststore file used for SSL encryption. If not provided, Dataproc will provide a self-signed certificate.
              },
            },
            "softwareConfig": { # Specifies the selection and config of software inside the cluster. # Optional. The config settings for cluster software.
              "imageVersion": "A String", # Optional. The version of software inside the cluster. It must be one of the supported Dataproc Versions (https://cloud.google.com/dataproc/docs/concepts/versioning/dataproc-versions#supported-dataproc-image-versions), such as "1.2" (including a subminor version, such as "1.2.29"), or the "preview" version (https://cloud.google.com/dataproc/docs/concepts/versioning/dataproc-versions#other_versions). If unspecified, it defaults to the latest Debian version.
              "optionalComponents": [ # Optional. The set of components to activate on the cluster.
                "A String",
              ],
              "properties": { # Optional. The properties to set on daemon config files.Property keys are specified in prefix:property format, for example core:hadoop.tmp.dir. The following are supported prefixes and their mappings: capacity-scheduler: capacity-scheduler.xml core: core-site.xml distcp: distcp-default.xml hdfs: hdfs-site.xml hive: hive-site.xml mapred: mapred-site.xml pig: pig.properties spark: spark-defaults.conf yarn: yarn-site.xmlFor more information, see Cluster properties (https://cloud.google.com/dataproc/docs/concepts/cluster-properties).
                "a_key": "A String",
              },
            },
            "tempBucket": "A String", # Optional. A Cloud Storage bucket used to store ephemeral cluster and jobs data, such as Spark and MapReduce history files. If you do not specify a temp bucket, Dataproc will determine a Cloud Storage location (US, ASIA, or EU) for your cluster's temp bucket according to the Compute Engine zone where your cluster is deployed, and then create and manage this project-level, per-location bucket. The default bucket has a TTL of 90 days, but you can use any TTL (or none) if you specify a bucket (see Dataproc staging and temp buckets (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/staging-bucket)). This field requires a Cloud Storage bucket name, not a gs://... URI to a Cloud Storage bucket.
            "workerConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The Compute Engine config settings for the cluster's worker instances.
              "accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
                { # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
                  "acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
                  "acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
                },
              ],
              "diskConfig": { # Specifies the config of boot disk and attached disk options for a group of VM instances. # Optional. Disk option config settings.
                "bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. This field is supported only if boot_disk_type is hyperdisk-balanced.
                "bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. This field is supported only if boot_disk_type is hyperdisk-balanced.
                "bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
                "bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
                "localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
                "numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
              },
              "imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
              "instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
                "instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
                  { # Defines machines types and a rank to which the machines types belong.
                    "machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
                      "A String",
                    ],
                    "rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
                  },
                ],
                "instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
                  { # Defines a mapping from machine types to the number of VMs that are created with each machine type.
                    "machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
                    "vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
                  },
                ],
                "provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
                  "standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
                  "standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
                },
              },
              "instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
                "A String",
              ],
              "instanceReferences": [ # Output only. List of references to Compute Engine instances.
                { # A reference to a Compute Engine instance.
                  "instanceId": "A String", # The unique identifier of the Compute Engine instance.
                  "instanceName": "A String", # The user-friendly name of the Compute Engine instance.
                  "publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
                  "publicKey": "A String", # The public RSA key used for sharing data with this instance.
                },
              ],
              "isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
              "machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
              "managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
                "instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
                "instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
                "instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
              },
              "minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
              "minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
              "numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
              "preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
              "startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
                "requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
              },
            },
          },
          "labels": { # Optional. The labels to associate with this cluster.Label keys must be between 1 and 63 characters long, and must conform to the following PCRE regular expression: \p{Ll}\p{Lo}{0,62}Label values must be between 1 and 63 characters long, and must conform to the following PCRE regular expression: \p{Ll}\p{Lo}\p{N}_-{0,63}No more than 32 labels can be associated with a given cluster.
            "a_key": "A String",
          },
        },
      },
      "updateTime": "A String", # Output only. The time template was last updated.
      "version": 42, # Optional. Used to perform a consistent read-modify-write.This field should be left blank for a CreateWorkflowTemplate request. It is required for an UpdateWorkflowTemplate request, and must match the current server version. A typical update template flow would fetch the current template with a GetWorkflowTemplate request, which will return the current template with the version field filled in with the current server version. The user updates other fields in the template, then returns it as part of the UpdateWorkflowTemplate request.
    }