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[SkipRecovery] Restore empty partitioned Parquet write tests [reduced-it] [databricks] #15949

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[SkipRecovery] Restore empty partitioned Parquet write tests [reduced-it] [databricks] #15949

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38 changes: 23 additions & 15 deletions integration_tests/src/main/python/parquet_write_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -752,28 +752,36 @@ def test_fallback_to_single_writer_from_concurrent_writer(spark_tmp_path, aqe_en
))


@pytest.mark.skipif(True, reason="currently not support write emtpy data: https://github.com/NVIDIA/spark-rapids/issues/6453")
def test_write_empty_data_concurrent_writer(spark_tmp_path):
def _assert_write_empty_partitioned_data(spark_tmp_path, max_concurrent_writers):
schema = StructType(
[StructField("c1", StringType()), StructField("c2", IntegerType()), StructField("c3", IntegerType())])
data = [] # empty data
data_path = spark_tmp_path + '/PARQUET_DATA'
with_gpu_session(lambda spark: spark.createDataFrame(spark.sparkContext.parallelize(data), schema)
.write.mode("overwrite").partitionBy('c1', 'c2').parquet(data_path),
# concurrent writer
{"spark.sql.maxConcurrentOutputFileWriters": 10})
with_cpu_session(lambda spark: spark.read.parquet(data_path).collect())

def read_empty_data(spark, path):
# Empty partitioned output has no data files from which either writer can infer a schema.
with pytest.raises(pyspark.sql.utils.AnalysisException, match="Unable to infer schema for Parquet"):
spark.read.parquet(path)
result = spark.read.schema(schema).parquet(path)
assert result.schema == schema
return result

@pytest.mark.skipif(True, reason="currently not support write emtpy data: https://github.com/NVIDIA/spark-rapids/issues/6453")
assert_gpu_and_cpu_writes_are_equal_collect(
lambda spark, path: spark.createDataFrame(spark.sparkContext.parallelize(data), schema)
.write.mode("overwrite").partitionBy('c1', 'c2').parquet(path),
read_empty_data,
data_path,
conf={"spark.sql.maxConcurrentOutputFileWriters": max_concurrent_writers})
Comment on lines +769 to +774


@validate_execs_in_gpu_plan("GpuDataWritingCommandExec")
def test_write_empty_data_concurrent_writer(spark_tmp_path):
_assert_write_empty_partitioned_data(spark_tmp_path, 10)


@validate_execs_in_gpu_plan("GpuDataWritingCommandExec")
def test_write_empty_data_single_writer(spark_tmp_path):
schema = StructType(
[StructField("c1", StringType()), StructField("c2", IntegerType()), StructField("c3", IntegerType())])
data = [] # empty data
data_path = spark_tmp_path + '/PARQUET_DATA'
with_gpu_session(lambda spark: spark.createDataFrame(spark.sparkContext.parallelize(data), schema)
.write.mode("overwrite").partitionBy('c1', 'c2').parquet(data_path))
with_cpu_session(lambda spark: spark.read.parquet(data_path).collect())
_assert_write_empty_partitioned_data(spark_tmp_path, 0)


PartitionWriteMode = Enum('PartitionWriteMode', ['Static', 'Dynamic'])
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