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fix: Rewrite Spark materialization engine to use mapInPandas #3936

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Merged
HaoXuAI merged 2 commits into from
Feb 13, 2024
Merged

fix: Rewrite Spark materialization engine to use mapInPandas #3936

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a45f6a8
rewrite spark materilization engine to use mapInPandas
tokoko Feb 6, 2024
5e3b180
Merge branch 'master' into spark-materialize-map-in-pandas
tokoko Feb 13, 2024
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67 changes: 35 additions & 32 deletions sdk/python/feast/infra/materialization/contrib/spark/spark_materialization_engine.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@
from typing import Callable, List, Literal, Optional, Sequence, Union, cast

import dill
import pandas
import pandas as pd
import pyarrow
from tqdm import tqdm
Expand Down Expand Up @@ -178,9 +179,9 @@ def _materialize_one(
self.repo_config.batch_engine.partitions
)

spark_df.foreachPartition(
lambda x: _process_by_partition(x, spark_serialized_artifacts)
)
spark_df.mapInPandas(
lambda x: _map_by_partition(x, spark_serialized_artifacts), "status int"
).count() # dummy action to force evaluation

return SparkMaterializationJob(
job_id=job_id, status=MaterializationJobStatus.SUCCEEDED
Expand Down Expand Up @@ -225,38 +226,40 @@ def unserialize(self):
return feature_view, online_store, repo_config


def _process_by_partition(rows, spark_serialized_artifacts: _SparkSerializedArtifacts):
"""Load pandas df to online store"""

# convert to pyarrow table
dicts = []
for row in rows:
dicts.append(row.asDict())
def _map_by_partition(iterator, spark_serialized_artifacts: _SparkSerializedArtifacts):
for pdf in iterator:
if pdf.shape[0] == 0:
print("Skipping")
return

df = pd.DataFrame.from_records(dicts)
if df.shape[0] == 0:
print("Skipping")
return
table = pyarrow.Table.from_pandas(pdf)

table = pyarrow.Table.from_pandas(df)
(
feature_view,
online_store,
repo_config,
) = spark_serialized_artifacts.unserialize()

if feature_view.batch_source.field_mapping is not None:
table = _run_pyarrow_field_mapping(
table, feature_view.batch_source.field_mapping
)

# unserialize artifacts
feature_view, online_store, repo_config = spark_serialized_artifacts.unserialize()
join_key_to_value_type = {
entity.name: entity.dtype.to_value_type()
for entity in feature_view.entity_columns
}

if feature_view.batch_source.field_mapping is not None:
table = _run_pyarrow_field_mapping(
table, feature_view.batch_source.field_mapping
rows_to_write = _convert_arrow_to_proto(
table, feature_view, join_key_to_value_type
)
online_store.online_write_batch(
repo_config,
feature_view,
rows_to_write,
lambda x: None,
)

join_key_to_value_type = {
entity.name: entity.dtype.to_value_type()
for entity in feature_view.entity_columns
}

rows_to_write = _convert_arrow_to_proto(table, feature_view, join_key_to_value_type)
online_store.online_write_batch(
repo_config,
feature_view,
rows_to_write,
lambda x: None,
)
yield pd.DataFrame(
[pd.Series(range(1, 2))]
) # dummy result because mapInPandas needs to return something