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feat(spark): SparkSource query+path and pre-computed offline read for BatchFeatureView by abhijeet-dhumal · Pull Request #6440 · feast-dev/feast · GitHub
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feat(spark): SparkSource query+path and pre-computed offline read for BatchFeatureView #6440

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Merged
ntkathole merged 8 commits into from
Jul 20, 2026
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feat(spark): SparkSource query+path and pre-computed offline read for BatchFeatureView #6440

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0055589
feat: allow query + path in SparkSource for offline materialization
abhijeet-dhumal May 7, 2026
3535a8c
feat: read from offline path in get_historical_features for BFVs
abhijeet-dhumal May 7, 2026
5dc7b6d
fix: graceful fallback when offline path is not readable
abhijeet-dhumal May 7, 2026
7639c2a
style: ruff format spark.py and spark_source.py
abhijeet-dhumal May 27, 2026
b2c76ff
fix: allow offline-only BatchFeatureView to skip online validation in…
abhijeet-dhumal May 27, 2026
17405f3
fix(spark): narrow exception handling in offline path fallback
abhijeet-dhumal Jun 2, 2026
de321f8
fix(test): lower performance benchmark threshold from 1.5x to 1.2x
abhijeet-dhumal Jun 9, 2026
16798e4
refactor: fold pre-computed path into _apply_bfv_transformations per …
abhijeet-dhumal Jul 19, 2026
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feat: read from offline path in get_historical_features for BFVs 
Signed-off-by: abhijeet-dhumal <abhijeetdhumal652@gmail.com>
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@abhijeet-dhumal @ntkathole
abhijeet-dhumal authored and ntkathole committed Jul 20, 2026
commit 3535a8cea700c72103a71b91c8edf4fbfbd609f3
72 changes: 70 additions & 2 deletions sdk/python/feast/infra/offline_stores/contrib/spark_offline_store/spark.py
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Original file line number Diff line number Diff line change
Expand Up @@ -92,6 +92,72 @@ class SparkFeatureViewQueryContext(offline_utils.FeatureViewQueryContext):
max_date_partition: Optional[str]


def _apply_bfv_transformations_for_historical(

@ntkathole ntkathole Jun 3, 2026

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I think instead of writing new helper, better approach is to extend the existing _apply_bfv_transformations with a pre-computed-path shortcut (adding the "read from parquet if offline=True and path exists" branch at the top), rather than creating a parallel function that reimplements UDF execution.

@abhijeet-dhumal abhijeet-dhumal Jul 19, 2026

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Agreed 🙌
Refactored exactly as you suggested. _apply_bfv_transformations_for_historical is deleted. _apply_bfv_transformations now has two branches:

  1. Pre-computed path shortcut (offline=True + batch_source.path) which reads parquet directly, falls back on error
  2. UDF execution via has_transformation() / get_transformation_function() which is unchanged from upstream

No duplication now.. Please review !

spark_session: SparkSession,
feature_views: List[FeatureView],
query_context: List[offline_utils.FeatureViewQueryContext],
) -> List[offline_utils.FeatureViewQueryContext]:
"""
For BatchFeatureViews, redirect get_historical_features to read from the
pre-materialized offline store (batch_source.path) when available, avoiding
expensive UDF re-execution on raw data.

Precedence:
1. offline=True + batch_source.path set -> read pre-computed parquet
2. Python/pandas UDF present -> execute UDF on raw source (fallback)
3. Otherwise -> pass through unchanged
"""
from dataclasses import replace

fv_by_name = {fv.projection.name_to_use(): fv for fv in feature_views}
new_contexts = []

for ctx in query_context:
fv = fv_by_name.get(ctx.name)
if fv is None or not isinstance(fv, BatchFeatureView):
new_contexts.append(ctx)
continue

if (
getattr(fv, "offline", False)
and isinstance(fv.batch_source, SparkSource)
and fv.batch_source.path
):
tmp_view = f"__feast_offline_{ctx.name}_{uuid.uuid4().hex[:8]}"
file_format = fv.batch_source.file_format or "parquet"
df = spark_session.read.format(file_format).load(fv.batch_source.path)
df.createOrReplaceTempView(tmp_view)
ctx = replace(ctx, table_subquery=tmp_view)
elif (
hasattr(fv, "feature_transformation")

@ntkathole ntkathole Jun 3, 2026

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Use has_transformation() and get_transformation_function() instead

@abhijeet-dhumal abhijeet-dhumal Jul 19, 2026

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Done ✅
The UDF detection now uses has_transformation() and get_transformation_function() from feast.feature_view_utils, same as the existing _apply_bfv_transformations already did. The raw hasattr/getattr chains for feature_transformation are gone.

and fv.feature_transformation is not None
and (
getattr(fv.feature_transformation, "mode", None)
in ("python", "pandas")
or getattr(
getattr(fv.feature_transformation, "mode", None), "value", None
)
in ("python", "pandas")
)
):
udf = getattr(fv.feature_transformation, "udf", None) or getattr(
fv, "udf", None
)
if udf is not None:
temp_view_name = f"__feast_bfv_{ctx.name}_{uuid.uuid4().hex[:8]}"
spark_session.conf.set("spark.sql.runSQLOnFiles", "true")
raw_df = spark_session.sql(
f"SELECT * FROM {ctx.table_subquery}"
)
transformed_df = udf(raw_df)
transformed_df.createOrReplaceTempView(temp_view_name)
ctx = replace(ctx, table_subquery=temp_view_name)

new_contexts.append(ctx)

return new_contexts


class SparkOfflineStore(OfflineStore):
@staticmethod
def pull_latest_from_table_or_query(
Expand Down Expand Up @@ -298,8 +364,10 @@ def get_historical_features(
entity_df_event_timestamp_range,
)

query_context = _apply_bfv_transformations(
spark_session, feature_views, query_context
query_context = _apply_bfv_transformations_for_historical(

@ntkathole ntkathole Jun 3, 2026 •
edited
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so previous _apply_bfv_transformations helper not removed from code?

@abhijeet-dhumal abhijeet-dhumal Jul 19, 2026

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Good catch 🙌
the previous version had a separate _apply_bfv_transformations_for_historical that duplicated UDF execution logic. Removed it and folded the pre-computed path shortcut directly into _apply_bfv_transformations as the first branch. The call site now uses the single function.
Please review !

spark_session=spark_session,
feature_views=feature_views,
query_context=query_context,
)

spark_query_context = [
Expand Down