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handle classic pyspark dataframe #3255

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handle classic pyspark dataframe #3255

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51 changes: 48 additions & 3 deletions plugins/flytekit-spark/flytekitplugins/spark/sd_transformers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,10 +12,12 @@
StructuredDatasetTransformerEngine,
)

import pyspark
from pyspark.sql import dataframe
from pyspark.sql.classic.dataframe import DataFrame as ClassicDataFrame

pd = lazy_module("pandas")
pyspark = lazy_module("pyspark")
ps_dataframe = lazy_module("pyspark.sql.dataframe")
DataFrame = ps_dataframe.DataFrame
DataFrame = dataframe.DataFrame


class SparkDataFrameRenderer:
Expand Down Expand Up @@ -52,6 +54,30 @@ def encode(
return literals.StructuredDataset(uri=path, metadata=StructuredDatasetMetadata(structured_dataset_type))


class ClassicSparkToParquetEncodingHandler(StructuredDatasetEncoder):
def __init__(self):
super().__init__(ClassicDataFrame, None, PARQUET)

def encode(
self,
ctx: FlyteContext,
structured_dataset: StructuredDataset,
structured_dataset_type: StructuredDatasetType,
) -> literals.StructuredDataset:
path = typing.cast(str, structured_dataset.uri)
if not path:
path = ctx.file_access.join(
ctx.file_access.raw_output_prefix,
ctx.file_access.get_random_string(),
)
df = typing.cast(DataFrame, structured_dataset.dataframe)
ss = pyspark.sql.SparkSession.builder.getOrCreate()
# Avoid generating SUCCESS files
ss.conf.set("mapreduce.fileoutputcommitter.marksuccessfuljobs", "false")
df.write.mode("overwrite").parquet(path=path)
return literals.StructuredDataset(uri=path, metadata=StructuredDatasetMetadata(structured_dataset_type))


class ParquetToSparkDecodingHandler(StructuredDatasetDecoder):
def __init__(self):
super().__init__(DataFrame, None, PARQUET)
Expand All @@ -69,6 +95,25 @@ def decode(
return user_ctx.spark_session.read.parquet(flyte_value.uri)


class ClassicParquetToSparkDecodingHandler(StructuredDatasetDecoder):
def __init__(self):
super().__init__(ClassicDataFrame, None, PARQUET)

def decode(
self,
ctx: FlyteContext,
flyte_value: literals.StructuredDataset,
current_task_metadata: StructuredDatasetMetadata,
) -> DataFrame:
user_ctx = FlyteContext.current_context().user_space_params
if current_task_metadata.structured_dataset_type and current_task_metadata.structured_dataset_type.columns:
columns = [c.name for c in current_task_metadata.structured_dataset_type.columns]
return user_ctx.spark_session.read.parquet(flyte_value.uri).select(*columns)
return user_ctx.spark_session.read.parquet(flyte_value.uri)


StructuredDatasetTransformerEngine.register(SparkToParquetEncodingHandler())
StructuredDatasetTransformerEngine.register(ParquetToSparkDecodingHandler())
StructuredDatasetTransformerEngine.register(ClassicSparkToParquetEncodingHandler())
StructuredDatasetTransformerEngine.register(ClassicParquetToSparkDecodingHandler())
StructuredDatasetTransformerEngine.register_renderer(DataFrame, SparkDataFrameRenderer())
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