Add to_bigquery() function to BigQueryRetrievalJob

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

@@ -1,11 +1,13 @@
 import time
+import uuid
 from dataclasses import asdict, dataclass
-from datetime import datetime, timedelta
+from datetime import date, datetime, timedelta
 from typing import List, Optional, Set, Union
 
 import pandas
 import pyarrow
 from jinja2 import BaseLoader, Environment
+from tenacity import retry, stop_after_delay, wait_fixed
 
 from feast import errors
 from feast.data_source import BigQuerySource, DataSource
@@ -118,7 +120,7 @@ def get_historical_features(
             entity_df_event_timestamp_col=entity_df_event_timestamp_col,
         )
 
-        job = BigQueryRetrievalJob(query=query, client=client)
+        job = BigQueryRetrievalJob(query=query, client=client, config=config)
         return job
 
 
@@ -206,15 +208,41 @@ def _infer_event_timestamp_from_dataframe(entity_df: pandas.DataFrame) -> str:
 
 
 class BigQueryRetrievalJob(RetrievalJob):
-    def __init__(self, query, client):
+    def __init__(self, query, client, config):
         self.query = query
         self.client = client
+        self.config = config
 
     def to_df(self):
         # TODO: Ideally only start this job when the user runs "get_historical_features", not when they run to_df()
         df = self.client.query(self.query).to_dataframe(create_bqstorage_client=True)
         return df
 
+    def to_bigquery(self, dry_run=False) -> Optional[str]:
+        @retry(wait=wait_fixed(10), stop=stop_after_delay(1800), reraise=True)
+        def _block_until_done():
+            return self.client.get_job(bq_job.job_id).state in ["PENDING", "RUNNING"]
+
+        today = date.today().strftime("%Y%m%d")
+        rand_id = str(uuid.uuid4())[:7]
+        path = f"{self.client.project}.{self.config.offline_store.dataset}.historical_{today}_{rand_id}"
+        job_config = bigquery.QueryJobConfig(destination=path, dry_run=dry_run)
+        bq_job = self.client.query(self.query, job_config=job_config)
+
+        if dry_run:
+            print(
+                "This query will process {} bytes.".format(bq_job.total_bytes_processed)
+            )
+            return None
+
+        _block_until_done()
+
+        if bq_job.exception():
+            raise bq_job.exception()
+
+        print(f"Done writing to '{path}'.")
+        return path
+
 
 @dataclass(frozen=True)
 class FeatureViewQueryContext:

sdk/python/setup.py

@@ -54,6 +54,7 @@
     "pydantic>=1.0.0",
     "PyYAML==5.3.*",
     "tabulate==0.8.*",
+    "tenacity>=7.*",
     "toml==0.10.*",
     "tqdm==4.*",
 ]
@@ -92,7 +93,6 @@
     "pytest-mock==1.10.4",
     "Sphinx!=4.0.0",
     "sphinx-rtd-theme",
-    "tenacity",
     "adlfs==0.5.9",
     "firebase-admin==4.5.2",
     "pre-commit",

sdk/python/tests/test_historical_retrieval.py

@@ -441,6 +441,22 @@ def test_historical_features_from_bigquery_sources(
             ],
         )
 
+        # Just a dry run, should not create table
+        bq_dry_run = job_from_sql.to_bigquery(dry_run=True)
+        assert bq_dry_run is None
+
+        bq_temp_table_path = job_from_sql.to_bigquery()
+        assert bq_temp_table_path.split(".")[0] == gcp_project
+
+        if provider_type == "gcp_custom_offline_config":
+            assert bq_temp_table_path.split(".")[1] == "foo"
+        else:
+            assert bq_temp_table_path.split(".")[1] == bigquery_dataset
+
+        # Check that this table actually exists
+        actual_bq_temp_table = bigquery.Client().get_table(bq_temp_table_path)
+        assert actual_bq_temp_table.table_id == bq_temp_table_path.split(".")[-1]
+
         start_time = datetime.utcnow()
         actual_df_from_sql_entities = job_from_sql.to_df()
         end_time = datetime.utcnow()