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fix: make py4j pyspark api robust #743

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Merged
SemyonSinchenko merged 1 commit into from
Oct 27, 2025
Merged

fix: make py4j pyspark api robust #743

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3 changes: 3 additions & 0 deletions .gitignore
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Original file line number Diff line number Diff line change
Expand Up @@ -76,3 +76,6 @@ connect/project

# Local spark distro
spark-*

# Zed
.zed
95 changes: 45 additions & 50 deletions python/graphframes/classic/graphframe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -108,15 +108,13 @@ def detectingCycles(
use_local_checkpoints: bool = False,
intermediate_storage_level: StorageLevel = StorageLevel.MEMORY_AND_DISK_DESER,
) -> DataFrame:
jdf = (
self._jvm_graph.detectingCycles()
.setUseLocalCheckpoints(use_local_checkpoints)
.setCheckpointInterval(checkpoint_interval)
.setIntermediateStorageLevel(
storage_level_to_jvm(intermediate_storage_level, self._spark)
)
.run()
builder = self._jvm_graph.detectingCycles()
builder.setUseLocalCheckpoints(use_local_checkpoints)
builder.setCheckpointInterval(checkpoint_interval)
builder.setIntermediateStorageLevel(
storage_level_to_jvm(intermediate_storage_level, self._spark)
)
jdf = builder.run()

return DataFrame(jdf, self._spark)

Expand Down Expand Up @@ -147,7 +145,7 @@ def aggregateMessages(
intermediate_storage_level: StorageLevel,
) -> DataFrame:
builder = self._jvm_graph.aggregateMessages()
builder = builder.setIntermediateStorageLevel(
builder.setIntermediateStorageLevel(
storage_level_to_jvm(intermediate_storage_level, self._spark)
)
if len(sendToSrc) == 1:
Expand Down Expand Up @@ -212,17 +210,16 @@ def connectedComponents(
max_iter: int,
storage_level: StorageLevel,
) -> DataFrame:
jdf = (
self._jvm_graph.connectedComponents()
.setAlgorithm(algorithm)
.setCheckpointInterval(checkpointInterval)
.setBroadcastThreshold(broadcastThreshold)
.setUseLabelsAsComponents(useLabelsAsComponents)
.setUseLocalCheckpoints(use_local_checkpoints)
.maxIter(max_iter)
.setIntermediateStorageLevel(storage_level_to_jvm(storage_level, self._spark))
.run()
)
java_cc = self._jvm_graph.connectedComponents()
java_cc.setAlgorithm(algorithm)
java_cc.setCheckpointInterval(checkpointInterval)
java_cc.setBroadcastThreshold(broadcastThreshold)
java_cc.setUseLabelsAsComponents(useLabelsAsComponents)
java_cc.setUseLocalCheckpoints(use_local_checkpoints)
java_cc.maxIter(max_iter)
java_cc.setIntermediateStorageLevel(storage_level_to_jvm(storage_level, self._spark))
jdf = java_cc.run()

return DataFrame(jdf, self._spark)

def labelPropagation(
Expand All @@ -233,15 +230,14 @@ def labelPropagation(
checkpoint_interval: int,
storage_level: StorageLevel,
) -> DataFrame:
jdf = (
self._jvm_graph.labelPropagation()
.maxIter(maxIter)
.setAlgorithm(algorithm)
.setUseLocalCheckpoints(use_local_checkpoints)
.setCheckpointInterval(checkpoint_interval)
.setIntermediateStorageLevel(storage_level_to_jvm(storage_level, self._spark))
.run()
)
java_cdlp = self._jvm_graph.labelPropagation()
java_cdlp.maxIter(maxIter)
java_cdlp.setAlgorithm(algorithm)
java_cdlp.setUseLocalCheckpoints(use_local_checkpoints)
java_cdlp.setCheckpointInterval(checkpoint_interval)
java_cdlp.setIntermediateStorageLevel(storage_level_to_jvm(storage_level, self._spark))
jdf = java_cdlp.run()

return DataFrame(jdf, self._spark)

def pageRank(
Expand All @@ -253,13 +249,13 @@ def pageRank(
) -> "GraphFrame":
builder = self._jvm_graph.pageRank().resetProbability(resetProbability)
if sourceId is not None:
builder = builder.sourceId(sourceId)
builder.sourceId(sourceId)
if maxIter is not None:
builder = builder.maxIter(maxIter)
builder.maxIter(maxIter)
assert tol is None, "Exactly one of maxIter or tol should be set."
else:
assert tol is not None, "Exactly one of maxIter or tol should be set."
builder = builder.tol(tol)
builder.tol(tol)
jgf = builder.run()
return _from_java_gf(jgf, self._spark)

Expand All @@ -275,9 +271,9 @@ def parallelPersonalizedPageRank(
assert maxIter is not None, "Max number of iterations maxIter must be provided"
sourceIds = self._sc._jvm.PythonUtils.toArray(sourceIds)
builder = self._jvm_graph.parallelPersonalizedPageRank()
builder = builder.resetProbability(resetProbability)
builder = builder.sourceIds(sourceIds)
builder = builder.maxIter(maxIter)
builder.resetProbability(resetProbability)
builder.sourceIds(sourceIds)
builder.maxIter(maxIter)
jgf = builder.run()
return _from_java_gf(jgf, self._spark)

Expand All @@ -289,19 +285,20 @@ def shortestPaths(
checkpoint_interval: int,
storage_level: StorageLevel,
) -> DataFrame:
jdf = (
self._jvm_graph.shortestPaths()
.landmarks(landmarks)
.setAlgorithm(algorithm)
.setUseLocalCheckpoints(use_local_checkpoints)
.setCheckpointInterval(checkpoint_interval)
.setIntermediateStorageLevel(storage_level_to_jvm(storage_level, self._spark))
.run()
)
java_sp = self._jvm_graph.shortestPaths()
java_sp.landmarks(landmarks)
java_sp.setAlgorithm(algorithm)
java_sp.setUseLocalCheckpoints(use_local_checkpoints)
java_sp.setCheckpointInterval(checkpoint_interval)
java_sp.setIntermediateStorageLevel(storage_level_to_jvm(storage_level, self._spark))
jdf = java_sp.run()

return DataFrame(jdf, self._spark)

def stronglyConnectedComponents(self, maxIter: int) -> DataFrame:
jdf = self._jvm_graph.stronglyConnectedComponents().maxIter(maxIter).run()
builder = self._jvm_graph.stronglyConnectedComponents()
builder.maxIter(maxIter)
jdf = builder.run()
return DataFrame(jdf, self._spark)

def svdPlusPlus(
Expand All @@ -325,11 +322,9 @@ def svdPlusPlus(
return (v, loss)

def triangleCount(self, storage_level: StorageLevel) -> DataFrame:
jdf = (
self._jvm_graph.triangleCount()
.setIntermediateStorageLevel(storage_level_to_jvm(storage_level, self._spark))
.run()
)
builder = self._jvm_graph.triangleCount()
builder.setIntermediateStorageLevel(storage_level_to_jvm(storage_level, self._spark))
jdf = builder.run()
return DataFrame(jdf, self._spark)

def powerIterationClustering(
Expand Down
24 changes: 21 additions & 3 deletions python/tests/test_graphframes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -377,9 +377,7 @@ def test_connected_components(
spark: SparkSession, args: PregelArguments, cc_args: tuple[int, bool]
) -> None:
v = spark.createDataFrame([(0, "a", "b")], ["id", "vattr", "gender"])
e = spark.createDataFrame([(0, 0, 1)], ["src", "dst", "test"]).filter("src > 10")
v = spark.createDataFrame([(0, "a", "b")], ["id", "vattr", "gender"])
e = spark.createDataFrame([(0, 0, 1)], ["src", "dst", "test"]).filter("src > 10")
e = spark.createDataFrame([(0, 0, 1)], ["src", "dst", "test"])
g = GraphFrame(v, e)
comps = g.connectedComponents(
algorithm=args.algorithm,
Expand Down Expand Up @@ -419,6 +417,26 @@ def test_connected_components2(
_ = comps.unpersist()


def test_connected_components_example(spark: SparkSession) -> None:
nodes = [(1, "Alice", 30), (2, "Bob", 25), (3, "Charlie", 35)]
nodes_df = spark.createDataFrame(nodes, ["id", "name", "age"])

edges = [
(1, 2, "friend"),
(2, 1, "friend"),
(2, 3, "friend"),
(3, 2, "enemy"), # eek!
]
edges_df = spark.createDataFrame(edges, ["src", "dst", "relationship"])

g = GraphFrame(nodes_df, edges_df)
cc = g.connectedComponents()
cc.write.mode("overwrite").format("noop").save()
res = cc.collect()
assert len(res) == 3
_ = cc.unpersist()


@pytest.mark.parametrize("args", PREGEL_ARGUMENTS, ids=PREGEL_IDS)
def test_shortest_paths(spark: SparkSession, args: PregelArguments) -> None:
edges = [(1, 2), (1, 5), (2, 3), (2, 5), (3, 4), (4, 5), (4, 6)]
Expand Down