package sqlancer.spark;

import java.util.ArrayList;

import java.util.List;

import sqlancer.common.query.ExpectedErrors;

public final class SparkErrors {

private SparkErrors() {

}

public static List<String> getExpressionErrors() {

ArrayList<String> errors = new ArrayList<>();

errors.add("cannot resolve");

errors.add("AnalysisException");

errors.add("data type mismatch");

errors.add("undefined function");

errors.add("mismatched input");

errors.add("due to data type mismatch");

// --- Invalid Literals

errors.add("The value of the typed literal");

errors.add("DATATYPE_MISMATCH");

errors.add("cannot be cast to");

errors.add("Overflow");

errors.add("Divide by zero"); // Common if spark.sql.ansi.enabled is true

errors.add("division by zero");

// --- Group By / Aggregation errors ---

errors.add("grouping expressions");

errors.add("expression is neither present in the group by");

errors.add("is not a valid grouping expression");

errors.add("is not contained in either an aggregate function or the GROUP BY clause");

return errors;

}

public static void addExpressionErrors(ExpectedErrors errors) {

errors.addAll(getExpressionErrors());

}

public static List<String> getInsertErrors() {

ArrayList<String> errors = new ArrayList<>();

errors.add("not enough data columns");

errors.add("cannot write to");

errors.add("incompatible types");

errors.add("too many data columns");

errors.add("cannot be cast to");

errors.add("Error running query");

errors.add("The value of the typed literal");

errors.add("Cannot safely cast"); // Found in logs: Decimal -> Date

errors.add("AnalysisException"); // Spark throws this for almost all insert failures

return errors;

}

public static void addInsertErrors(ExpectedErrors errors) {

errors.addAll(getInsertErrors());

}

}