Simplify BigQuery load jobs (feast-dev#1935)

* Simplify BigQuery load jobs

Signed-off-by: Judah Rand <[email protected]>

* Ensure `pyarrow` supports `use_compliant_nested_type`

Signed-off-by: Judah Rand <[email protected]>

Author: judahrand

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

@@ -256,8 +256,8 @@ def to_bigquery(
                 job_config = bigquery.QueryJobConfig(destination=path)
 
             if not job_config.dry_run and self.on_demand_feature_views:
-                job = _write_pyarrow_table_to_bq(
-                    self.client, self.to_arrow(), job_config.destination
+                job = self.client.load_table_from_dataframe(
+                    self.to_df(), job_config.destination
                 )
                 job.result()
                 print(f"Done writing to '{job_config.destination}'.")
@@ -366,7 +366,7 @@ def _upload_entity_df_and_get_entity_schema(
     elif isinstance(entity_df, pd.DataFrame):
         # Drop the index so that we dont have unnecessary columns
         entity_df.reset_index(drop=True, inplace=True)
-        job = _write_df_to_bq(client, entity_df, table_name)
+        job = client.load_table_from_dataframe(entity_df, table_name)
         block_until_done(client, job)
         entity_schema = dict(zip(entity_df.columns, entity_df.dtypes))
     else:
@@ -400,44 +400,6 @@ def _get_bigquery_client(project: Optional[str] = None, location: Optional[str]
     return client
 
 
-def _write_df_to_bq(
-    client: bigquery.Client, df: pd.DataFrame, table_name: str
-) -> bigquery.LoadJob:
-    # It is complicated to get BQ to understand that we want an ARRAY<value_type>
-    # https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#parquetoptions
-    # https://github.com/googleapis/python-bigquery/issues/19
-    writer = pyarrow.BufferOutputStream()
-    pyarrow.parquet.write_table(
-        pyarrow.Table.from_pandas(df), writer, use_compliant_nested_type=True
-    )
-    return _write_pyarrow_buffer_to_bq(client, writer.getvalue(), table_name,)
-
-
-def _write_pyarrow_table_to_bq(
-    client: bigquery.Client, table: pyarrow.Table, table_name: str
-) -> bigquery.LoadJob:
-    # It is complicated to get BQ to understand that we want an ARRAY<value_type>
-    # https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#parquetoptions
-    # https://github.com/googleapis/python-bigquery/issues/19
-    writer = pyarrow.BufferOutputStream()
-    pyarrow.parquet.write_table(table, writer, use_compliant_nested_type=True)
-    return _write_pyarrow_buffer_to_bq(client, writer.getvalue(), table_name,)
-
-
-def _write_pyarrow_buffer_to_bq(
-    client: bigquery.Client, buf: pyarrow.Buffer, table_name: str
-) -> bigquery.LoadJob:
-    reader = pyarrow.BufferReader(buf)
-
-    parquet_options = bigquery.format_options.ParquetOptions()
-    parquet_options.enable_list_inference = True
-    job_config = bigquery.LoadJobConfig()
-    job_config.source_format = bigquery.SourceFormat.PARQUET
-    job_config.parquet_options = parquet_options
-
-    return client.load_table_from_file(reader, table_name, job_config=job_config,)
-
-
 # TODO: Optimizations
 #   * Use GENERATE_UUID() instead of ROW_NUMBER(), or join on entity columns directly
 #   * Precompute ROW_NUMBER() so that it doesn't have to be recomputed for every query on entity_dataframe

sdk/python/setup.py

@@ -54,7 +54,7 @@
     "pandas>=1.0.0",
     "pandavro==1.5.*",
     "protobuf>=3.10",
-    "pyarrow>=2.0.0",
+    "pyarrow>=4.0.0",
     "pydantic>=1.0.0",
     "PyYAML>=5.4.*",
     "tabulate==0.8.*",
@@ -66,7 +66,7 @@
 ]
 
 GCP_REQUIRED = [
-    "google-cloud-bigquery>=2.14.0",
+    "google-cloud-bigquery>=2.28.1",
     "google-cloud-bigquery-storage >= 2.0.0",
     "google-cloud-datastore>=2.1.*",
     "google-cloud-storage>=1.34.*",
@@ -112,7 +112,7 @@
     "firebase-admin==4.5.2",
     "pre-commit",
     "assertpy==1.1",
-    "google-cloud-bigquery>=2.14.0",
+    "google-cloud-bigquery>=2.28.1",
     "google-cloud-bigquery-storage >= 2.0.0",
     "google-cloud-datastore>=2.1.*",
     "google-cloud-storage>=1.20.*",

sdk/python/tests/integration/feature_repos/universal/data_sources/bigquery.py

@@ -6,10 +6,7 @@
 
 from feast import BigQuerySource
 from feast.data_source import DataSource
-from feast.infra.offline_stores.bigquery import (
-    BigQueryOfflineStoreConfig,
-    _write_df_to_bq,
-)
+from feast.infra.offline_stores.bigquery import BigQueryOfflineStoreConfig
 from tests.integration.feature_repos.universal.data_source_creator import (
     DataSourceCreator,
 )
@@ -69,7 +66,7 @@ def create_data_source(
                 f"{self.gcp_project}.{self.project_name}.{destination_name}"
             )
 
-        job = _write_df_to_bq(self.client, df, destination_name)
+        job = self.client.load_table_from_dataframe(df, destination_name)
         job.result()
 
         self.tables.append(destination_name)

sdk/python/tests/utils/data_source_utils.py

@@ -7,7 +7,6 @@
 
 from feast import BigQuerySource, FileSource
 from feast.data_format import ParquetFormat
-from feast.infra.offline_stores.bigquery import _write_df_to_bq
 
 
 @contextlib.contextmanager
@@ -39,7 +38,7 @@ def simple_bq_source_using_table_ref_arg(
     client.update_dataset(dataset, ["default_table_expiration_ms"])
     table_ref = f"{gcp_project}.{bigquery_dataset}.table_{random.randrange(100, 999)}"
 
-    job = _write_df_to_bq(client, df, table_ref)
+    job = client.load_table_from_dataframe(df, table_ref)
     job.result()
 
     return BigQuerySource(