feat: Implement offline_write_batch for BigQuery and Snowflake (fea…

…st-dev#2840)

* Factor out Redshift pyarrow schema inference logic into helper method

Signed-off-by: Felix Wang <wangfelix98@gmail.com>

* Switch file offline store to use offline_utils for offline_write_batch

Signed-off-by: Felix Wang <wangfelix98@gmail.com>

* Implement offline_write_batch for bigquery

Signed-off-by: Felix Wang <wangfelix98@gmail.com>

* Implement offline_write_batch for snowflake

Signed-off-by: Felix Wang <wangfelix98@gmail.com>

* Enable bigquery and snowflake for test_push_features_and_read_from_offline_store test

Signed-off-by: Felix Wang <wangfelix98@gmail.com>

* Rename get_pyarrow_schema

Signed-off-by: Felix Wang <wangfelix98@gmail.com>

sdk/python/feast/infra/offline_stores/bigquery.py

@@ -4,6 +4,7 @@
 from datetime import date, datetime, timedelta
 from pathlib import Path
 from typing import (
+    Any,
     Callable,
     ContextManager,
     Dict,
@@ -303,6 +304,60 @@ def write_logged_features(
                 job_config=job_config,
             )
 
+    @staticmethod
+    def offline_write_batch(
+        config: RepoConfig,
+        feature_view: FeatureView,
+        table: pyarrow.Table,
+        progress: Optional[Callable[[int], Any]],
+    ):
+        if not feature_view.batch_source:
+            raise ValueError(
+                "feature view does not have a batch source to persist offline data"
+            )
+        if not isinstance(config.offline_store, BigQueryOfflineStoreConfig):
+            raise ValueError(
+                f"offline store config is of type {type(config.offline_store)} when bigquery type required"
+            )
+        if not isinstance(feature_view.batch_source, BigQuerySource):
+            raise ValueError(
+                f"feature view batch source is {type(feature_view.batch_source)} not bigquery source"
+            )
+
+        pa_schema, column_names = offline_utils.get_pyarrow_schema_from_batch_source(
+            config, feature_view.batch_source
+        )
+        if column_names != table.column_names:
+            raise ValueError(
+                f"The input pyarrow table has schema {pa_schema} with the incorrect columns {column_names}. "
+                f"The columns are expected to be (in this order): {column_names}."
+            )
+
+        if table.schema != pa_schema:
+            table = table.cast(pa_schema)
+
+        client = _get_bigquery_client(
+            project=config.offline_store.project_id,
+            location=config.offline_store.location,
+        )
+
+        job_config = bigquery.LoadJobConfig(
+            source_format=bigquery.SourceFormat.PARQUET,
+            schema=arrow_schema_to_bq_schema(pa_schema),
+            write_disposition="WRITE_APPEND",  # Default but included for clarity
+        )
+
+        with tempfile.TemporaryFile() as parquet_temp_file:
+            pyarrow.parquet.write_table(table=table, where=parquet_temp_file)
+
+            parquet_temp_file.seek(0)
+
+            client.load_table_from_file(
+                file_obj=parquet_temp_file,
+                destination=feature_view.batch_source.table,
+                job_config=job_config,
+            )
+
 
 class BigQueryRetrievalJob(RetrievalJob):
     def __init__(

sdk/python/feast/infra/offline_stores/file.py

@@ -27,6 +27,7 @@
 )
 from feast.infra.offline_stores.offline_utils import (
     DEFAULT_ENTITY_DF_EVENT_TIMESTAMP_COL,
+    get_pyarrow_schema_from_batch_source,
 )
 from feast.infra.provider import (
     _get_requested_feature_views_to_features_dict,
@@ -408,7 +409,7 @@ def write_logged_features(
     def offline_write_batch(
         config: RepoConfig,
         feature_view: FeatureView,
-        data: pyarrow.Table,
+        table: pyarrow.Table,
         progress: Optional[Callable[[int], Any]],
     ):
         if not feature_view.batch_source:
@@ -423,20 +424,27 @@ def offline_write_batch(
             raise ValueError(
                 f"feature view batch source is {type(feature_view.batch_source)} not file source"
             )
+
+        pa_schema, column_names = get_pyarrow_schema_from_batch_source(
+            config, feature_view.batch_source
+        )
+        if column_names != table.column_names:
+            raise ValueError(
+                f"The input pyarrow table has schema {pa_schema} with the incorrect columns {column_names}. "
+                f"The columns are expected to be (in this order): {column_names}."
+            )
+
         file_options = feature_view.batch_source.file_options
         filesystem, path = FileSource.create_filesystem_and_path(
             file_options.uri, file_options.s3_endpoint_override
         )
-
         prev_table = pyarrow.parquet.read_table(path, memory_map=True)
-        if prev_table.column_names != data.column_names:
-            raise ValueError(
-                f"Input dataframe has incorrect schema or wrong order, expected columns are: {prev_table.column_names}"
-            )
-        if data.schema != prev_table.schema:
-            data = data.cast(prev_table.schema)
-        new_table = pyarrow.concat_tables([data, prev_table])
-        writer = pyarrow.parquet.ParquetWriter(path, data.schema, filesystem=filesystem)
+        if table.schema != prev_table.schema:
+            table = table.cast(prev_table.schema)
+        new_table = pyarrow.concat_tables([table, prev_table])
+        writer = pyarrow.parquet.ParquetWriter(
+            path, table.schema, filesystem=filesystem
+        )
         writer.write_table(new_table)
         writer.close()
 

sdk/python/feast/infra/offline_stores/offline_store.py

@@ -275,7 +275,7 @@ def write_logged_features(
     def offline_write_batch(
         config: RepoConfig,
         feature_view: FeatureView,
-        data: pyarrow.Table,
+        table: pyarrow.Table,
         progress: Optional[Callable[[int], Any]],
     ):
         """
@@ -286,8 +286,8 @@ def offline_write_batch(
 
         Args:
             config: Repo configuration object
-            table: FeatureView to write the data to.
-            data: pyarrow table containing feature data and timestamp column for historical feature retrieval
+            feature_view: FeatureView to write the data to.
+            table: pyarrow table containing feature data and timestamp column for historical feature retrieval
             progress: Optional function to be called once every mini-batch of rows is written to
             the online store. Can be used to display progress.
         """

sdk/python/feast/infra/offline_stores/offline_utils.py

@@ -5,9 +5,11 @@
 
 import numpy as np
 import pandas as pd
+import pyarrow as pa
 from jinja2 import BaseLoader, Environment
 from pandas import Timestamp
 
+from feast.data_source import DataSource
 from feast.errors import (
     EntityTimestampInferenceException,
     FeastEntityDFMissingColumnsError,
@@ -17,6 +19,8 @@
 from feast.infra.offline_stores.offline_store import OfflineStore
 from feast.infra.provider import _get_requested_feature_views_to_features_dict
 from feast.registry import BaseRegistry
+from feast.repo_config import RepoConfig
+from feast.type_map import feast_value_type_to_pa
 from feast.utils import to_naive_utc
 
 DEFAULT_ENTITY_DF_EVENT_TIMESTAMP_COL = "event_timestamp"
@@ -217,3 +221,25 @@ def get_offline_store_from_config(offline_store_config: Any) -> OfflineStore:
     class_name = qualified_name.replace("Config", "")
     offline_store_class = import_class(module_name, class_name, "OfflineStore")
     return offline_store_class()
+
+
+def get_pyarrow_schema_from_batch_source(
+    config: RepoConfig, batch_source: DataSource
+) -> Tuple[pa.Schema, List[str]]:
+    """Returns the pyarrow schema and column names for the given batch source."""
+    column_names_and_types = batch_source.get_table_column_names_and_types(config)
+
+    pa_schema = []
+    column_names = []
+    for column_name, column_type in column_names_and_types:
+        pa_schema.append(
+            (
+                column_name,
+                feast_value_type_to_pa(
+                    batch_source.source_datatype_to_feast_value_type()(column_type)
+                ),
+            )
+        )
+        column_names.append(column_name)
+
+    return pa.schema(pa_schema), column_names

sdk/python/feast/infra/offline_stores/redshift.py

@@ -42,7 +42,6 @@
 from feast.registry import BaseRegistry
 from feast.repo_config import FeastConfigBaseModel, RepoConfig
 from feast.saved_dataset import SavedDatasetStorage
-from feast.type_map import feast_value_type_to_pa, redshift_to_feast_value_type
 from feast.usage import log_exceptions_and_usage
 
 
@@ -318,33 +317,23 @@ def offline_write_batch(
             raise ValueError(
                 f"feature view batch source is {type(feature_view.batch_source)} not redshift source"
             )
-        redshift_options = feature_view.batch_source.redshift_options
-        redshift_client = aws_utils.get_redshift_data_client(
-            config.offline_store.region
-        )
 
-        column_name_to_type = feature_view.batch_source.get_table_column_names_and_types(
-            config
+        pa_schema, column_names = offline_utils.get_pyarrow_schema_from_batch_source(
+            config, feature_view.batch_source
         )
-        pa_schema_list = []
-        column_names = []
-        for column_name, redshift_type in column_name_to_type:
-            pa_schema_list.append(
-                (
-                    column_name,
-                    feast_value_type_to_pa(redshift_to_feast_value_type(redshift_type)),
-                )
-            )
-            column_names.append(column_name)
-        pa_schema = pa.schema(pa_schema_list)
         if column_names != table.column_names:
             raise ValueError(
-                f"Input dataframe has incorrect schema or wrong order, expected columns are: {column_names}"
+                f"The input pyarrow table has schema {pa_schema} with the incorrect columns {column_names}. "
+                f"The columns are expected to be (in this order): {column_names}."
             )
 
         if table.schema != pa_schema:
             table = table.cast(pa_schema)
 
+        redshift_options = feature_view.batch_source.redshift_options
+        redshift_client = aws_utils.get_redshift_data_client(
+            config.offline_store.region
+        )
         s3_resource = aws_utils.get_s3_resource(config.offline_store.region)
 
         aws_utils.upload_arrow_table_to_redshift(

sdk/python/feast/infra/offline_stores/snowflake.py

@@ -3,6 +3,7 @@
 from datetime import datetime
 from pathlib import Path
 from typing import (
+    Any,
     Callable,
     ContextManager,
     Dict,
@@ -306,6 +307,47 @@ def write_logged_features(
                 auto_create_table=True,
             )
 
+    @staticmethod
+    def offline_write_batch(
+        config: RepoConfig,
+        feature_view: FeatureView,
+        table: pyarrow.Table,
+        progress: Optional[Callable[[int], Any]],
+    ):
+        if not feature_view.batch_source:
+            raise ValueError(
+                "feature view does not have a batch source to persist offline data"
+            )
+        if not isinstance(config.offline_store, SnowflakeOfflineStoreConfig):
+            raise ValueError(
+                f"offline store config is of type {type(config.offline_store)} when snowflake type required"
+            )
+        if not isinstance(feature_view.batch_source, SnowflakeSource):
+            raise ValueError(
+                f"feature view batch source is {type(feature_view.batch_source)} not snowflake source"
+            )
+
+        pa_schema, column_names = offline_utils.get_pyarrow_schema_from_batch_source(
+            config, feature_view.batch_source
+        )
+        if column_names != table.column_names:
+            raise ValueError(
+                f"The input pyarrow table has schema {pa_schema} with the incorrect columns {column_names}. "
+                f"The columns are expected to be (in this order): {column_names}."
+            )
+
+        if table.schema != pa_schema:
+            table = table.cast(pa_schema)
+
+        snowflake_conn = get_snowflake_conn(config.offline_store)
+
+        write_pandas(
+            snowflake_conn,
+            table.to_pandas(),
+            table_name=feature_view.batch_source.table,
+            auto_create_table=True,
+        )
+
 
 class SnowflakeRetrievalJob(RetrievalJob):
     def __init__(

sdk/python/tests/integration/feature_repos/repo_configuration.py

@@ -76,7 +76,7 @@
 
 OFFLINE_STORE_TO_PROVIDER_CONFIG: Dict[str, DataSourceCreator] = {
     "file": ("local", FileDataSourceCreator),
-    "gcp": ("gcp", BigQueryDataSourceCreator),
+    "bigquery": ("gcp", BigQueryDataSourceCreator),
     "redshift": ("aws", RedshiftDataSourceCreator),
     "snowflake": ("aws", RedshiftDataSourceCreator),
 }

sdk/python/tests/integration/offline_store/test_offline_write.py

@@ -109,7 +109,7 @@ def test_writing_incorrect_schema_fails(environment, universal_data_sources):
 
 
 @pytest.mark.integration
-@pytest.mark.universal_offline_stores(only=["file", "redshift"])
+@pytest.mark.universal_offline_stores
 @pytest.mark.universal_online_stores(only=["sqlite"])
 def test_writing_consecutively_to_offline_store(environment, universal_data_sources):
     store = environment.feature_store

sdk/python/tests/integration/offline_store/test_push_offline_retrieval.py

@@ -16,7 +16,7 @@
 
 
 @pytest.mark.integration
-@pytest.mark.universal_offline_stores(only=["file", "redshift"])
+@pytest.mark.universal_offline_stores
 @pytest.mark.universal_online_stores(only=["sqlite"])
 def test_push_features_and_read_from_offline_store(environment, universal_data_sources):
     store = environment.feature_store