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chore: Update readme for sparksource #3991

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chore: Update readme for sparksource #3991

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ac2efa4
remove unused parameter when init sparksource
ElliotNguyen68 Mar 2, 2024
8b7d6fb
Merge branch 'feast-dev:master' into master
ElliotNguyen68 Mar 6, 2024
1ef7de9
feat: add entity df to SparkOfflineStore when get_historical_features
ElliotNguyen68 Mar 6, 2024
a5313b5
fix: lint error
ElliotNguyen68 Mar 6, 2024
76ab7bf
update readme spark.md
ElliotNguyen68 Mar 6, 2024
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feat: add entity df to SparkOfflineStore when get_historical_features 
Signed-off-by: tanlocnguyen <tanlocnguyen296@gmail.com>
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@ElliotNguyen68
ElliotNguyen68 committed Mar 6, 2024
commit 1ef7de92c91235c49d1fc4935131ba9be8740cef
26 changes: 16 additions & 10 deletions sdk/python/feast/infra/offline_stores/contrib/spark_offline_store/spark.py
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Expand Up @@ -125,7 +125,7 @@ def get_historical_features(
config: RepoConfig,
feature_views: List[FeatureView],
feature_refs: List[str],
entity_df: Union[pandas.DataFrame, str],
entity_df: Union[pandas.DataFrame, str, pyspark.sql.DataFrame],
registry: Registry,
project: str,
full_feature_names: bool = False,
Expand Down Expand Up @@ -473,15 +473,16 @@ def _get_entity_df_event_timestamp_range(
entity_df_event_timestamp.min().to_pydatetime(),
entity_df_event_timestamp.max().to_pydatetime(),
)
elif isinstance(entity_df, str):
elif isinstance(entity_df, str) or isinstance(entity_df, pyspark.sql.DataFrame):
# If the entity_df is a string (SQL query), determine range
# from table
df = spark_session.sql(entity_df).select(entity_df_event_timestamp_col)

# Checks if executing entity sql resulted in any data
if df.rdd.isEmpty():
raise EntitySQLEmptyResults(entity_df)

if isinstance(entity_df, str):
df = spark_session.sql(entity_df).select(entity_df_event_timestamp_col)
# Checks if executing entity sql resulted in any data
if df.rdd.isEmpty():
raise EntitySQLEmptyResults(entity_df)
else:
df = entity_df
# TODO(kzhang132): need utc conversion here.

entity_df_event_timestamp_range = (
Expand All @@ -499,8 +500,11 @@ def _get_entity_schema(
) -> Dict[str, np.dtype]:
if isinstance(entity_df, pd.DataFrame):
return dict(zip(entity_df.columns, entity_df.dtypes))
elif isinstance(entity_df, str):
entity_spark_df = spark_session.sql(entity_df)
elif isinstance(entity_df, str) or isinstance(entity_df, pyspark.sql.DataFrame):
if isinstance(entity_df, str):
entity_spark_df = spark_session.sql(entity_df)
else:
entity_spark_df = entity_df
return dict(
zip(
entity_spark_df.columns,
Expand All @@ -525,6 +529,8 @@ def _upload_entity_df(
return
elif isinstance(entity_df, str):
spark_session.sql(entity_df).createOrReplaceTempView(table_name)
elif isinstance(entity_df, pyspark.sql.DataFrame):
entity_df.createOrReplaceTempView(table_name)
return
else:
raise InvalidEntityType(type(entity_df))
Expand Down