feat: enable feature store batch serve to BigQuery and GCS for csv and tfrecord

google/cloud/aiplatform/utils/__init__.py

@@ -628,9 +628,11 @@ def get_timestamp_proto(
     """
     if not time:
         time = datetime.datetime.now()
-    t = time.timestamp()
-    seconds = int(t)
-    # must not have higher than millisecond precision.
-    nanos = int((t % 1 * 1e6) * 1e3)
 
-    return timestamp_pb2.Timestamp(seconds=seconds, nanos=nanos)
+    time_str = time.isoformat(sep=" ", timespec="milliseconds")
+    time = datetime.datetime.strptime(time_str, "%Y-%m-%d %H:%M:%S.%f")
+
+    timestamp_proto = timestamp_pb2.Timestamp()
+    timestamp_proto.FromDatetime(time)
+
+    return timestamp_proto

google/cloud/aiplatform/utils/featurestore_utils.py

@@ -29,6 +29,7 @@
 
 RESOURCE_ID_PATTERN_REGEX = r"[a-z_][a-z0-9_]{0,59}"
 GCS_SOURCE_TYPE = {"csv", "avro"}
+GCS_DESTINATION_TYPE = {"csv", "tfrecord"}
 
 _FEATURE_VALUE_TYPE_UNSPECIFIED = "VALUE_TYPE_UNSPECIFIED"
 

tests/system/aiplatform/e2e_base.py

@@ -24,6 +24,7 @@
 
 from google.api_core import exceptions
 from google.cloud import aiplatform
+from google.cloud import bigquery
 from google.cloud import storage
 from google.cloud.aiplatform import initializer
 
@@ -90,6 +91,38 @@ def delete_staging_bucket(self, shared_state: Dict[str, Any]):
         bucket = shared_state["bucket"]
         bucket.delete(force=True)
 
+    @pytest.fixture(scope="class")
+    def prepare_bigquery_dataset(
+        self, shared_state: Dict[str, Any]
+    ) -> Generator[bigquery.dataset.Dataset, None, None]:
+        """Create a bigquery dataset and store bigquery resource object in shared state."""
+
+        bigquery_client = bigquery.Client(project=_PROJECT)
+        shared_state["bigquery_client"] = bigquery_client
+
+        dataset_name = f"{self._temp_prefix.lower()}_{uuid.uuid4()}".replace("-", "_")
+        dataset_id = f"{_PROJECT}.{dataset_name}"
+        shared_state["bigquery_dataset_id"] = dataset_id
+
+        dataset = bigquery.Dataset(dataset_id)
+        dataset.location = _LOCATION
+        shared_state["bigquery_dataset"] = bigquery_client.create_dataset(dataset)
+
+        yield
+
+    @pytest.fixture(scope="class")
+    def delete_bigquery_dataset(self, shared_state: Dict[str, Any]):
+        """Delete the bigquery dataset"""
+
+        yield
+
+        # Get the bigquery dataset id used for testing and wipe it
+        bigquery_dataset = shared_state["bigquery_dataset"]
+        bigquery_client = shared_state["bigquery_client"]
+        bigquery_client.delete_dataset(
+            bigquery_dataset.dataset_id, delete_contents=True, not_found_ok=True
+        )  # Make an API request.
+
     @pytest.fixture(scope="class", autouse=True)
     def teardown(self, shared_state: Dict[str, Any]):
         """Delete every Vertex AI resource created during test"""

tests/system/aiplatform/test_featurestore.py

@@ -16,6 +16,7 @@
 #
 
 import logging
+import pytest
 
 from google.cloud import aiplatform
 from tests.system.aiplatform import e2e_base
@@ -29,6 +30,8 @@
     "gs://cloud-samples-data-us-central1/vertex-ai/feature-store/datasets/movies.avro"
 )
 
+_TEST_READ_INSTANCE_SRC = "gs://cloud-samples-data-us-central1/vertex-ai/feature-store/datasets/movie_prediction.csv"
+
 _TEST_FEATURESTORE_ID = "movie_prediction"
 _TEST_USER_ENTITY_TYPE_ID = "users"
 _TEST_MOVIE_ENTITY_TYPE_ID = "movies"
@@ -42,6 +45,12 @@
 _TEST_MOVIE_AVERAGE_RATING_FEATURE_ID = "average_rating"
 
 
+@pytest.mark.usefixtures(
+    "prepare_staging_bucket",
+    "delete_staging_bucket",
+    "prepare_bigquery_dataset",
+    "delete_bigquery_dataset",
+)
 class TestFeaturestore(e2e_base.TestEndToEnd):
 
     _temp_prefix = "temp_vertex_sdk_e2e_featurestore_test"
@@ -131,7 +140,7 @@ def test_create_get_list_features(self, shared_state):
         user_age_feature = user_entity_type.create_feature(
             feature_id=_TEST_USER_AGE_FEATURE_ID, value_type="INT64"
         )
-
+        shared_state["user_age_feature_resource_name"] = user_age_feature.resource_name
         get_user_age_feature = user_entity_type.get_feature(
             feature_id=_TEST_USER_AGE_FEATURE_ID
         )
@@ -142,6 +151,9 @@ def test_create_get_list_features(self, shared_state):
             value_type="STRING",
             entity_type_name=user_entity_type_name,
         )
+        shared_state[
+            "user_gender_feature_resource_name"
+        ] = user_gender_feature.resource_name
 
         get_user_gender_feature = aiplatform.Feature(
             feature_name=user_gender_feature.resource_name
@@ -153,6 +165,9 @@ def test_create_get_list_features(self, shared_state):
         user_liked_genres_feature = user_entity_type.create_feature(
             feature_id=_TEST_USER_LIKED_GENRES_FEATURE_ID, value_type="STRING_ARRAY",
         )
+        shared_state[
+            "user_liked_genres_feature_resource_name"
+        ] = user_liked_genres_feature.resource_name
 
         get_user_liked_genres_feature = aiplatform.Feature(
             feature_name=user_liked_genres_feature.resource_name
@@ -250,6 +265,105 @@ def test_search_features(self, shared_state):
             len(list_searched_features) - shared_state["base_list_searched_features"]
         ) == 6
 
+    def test_batch_serve_to_gcs(self, shared_state, caplog):
+
+        assert shared_state["featurestore"]
+        assert shared_state["bucket"]
+        assert shared_state["user_age_feature_resource_name"]
+        assert shared_state["user_gender_feature_resource_name"]
+        assert shared_state["user_liked_genres_feature_resource_name"]
+
+        featurestore = shared_state["featurestore"]
+        bucket_name = shared_state["staging_bucket_name"]
+        user_age_feature_resource_name = shared_state["user_age_feature_resource_name"]
+        user_gender_feature_resource_name = shared_state[
+            "user_gender_feature_resource_name"
+        ]
+        user_liked_genres_feature_resource_name = shared_state[
+            "user_liked_genres_feature_resource_name"
+        ]
+
+        aiplatform.init(
+            project=e2e_base._PROJECT, location=e2e_base._LOCATION,
+        )
+
+        caplog.set_level(logging.INFO)
+
+        featurestore.batch_serve_to_gcs(
+            serving_feature_ids={
+                _TEST_USER_ENTITY_TYPE_ID: [
+                    _TEST_USER_AGE_FEATURE_ID,
+                    _TEST_USER_GENDER_FEATURE_ID,
+                    _TEST_USER_LIKED_GENRES_FEATURE_ID,
+                ],
+                _TEST_MOVIE_ENTITY_TYPE_ID: [
+                    _TEST_MOVIE_TITLE_FEATURE_ID,
+                    _TEST_MOVIE_GENRES_FEATURE_ID,
+                    _TEST_MOVIE_AVERAGE_RATING_FEATURE_ID,
+                ],
+            },
+            feature_destination_fields={
+                user_age_feature_resource_name: "user_age_dest",
+                user_gender_feature_resource_name: "user_gender_dest",
+                user_liked_genres_feature_resource_name: "user_liked_genres_dest",
+            },
+            read_instances=_TEST_READ_INSTANCE_SRC,
+            gcs_destination_output_uri_prefix=f"gs://{bucket_name}/featurestore_test/tfrecord",
+            gcs_destination_type="tfrecord",
+        )
+        assert "Featurestore feature values served." in caplog.text
+
+        caplog.clear()
+
+    def test_batch_serve_to_bq(self, shared_state, caplog):
+
+        assert shared_state["featurestore"]
+        assert shared_state["bigquery_dataset"]
+        assert shared_state["user_age_feature_resource_name"]
+        assert shared_state["user_gender_feature_resource_name"]
+        assert shared_state["user_liked_genres_feature_resource_name"]
+
+        featurestore = shared_state["featurestore"]
+        bigquery_dataset_id = shared_state["bigquery_dataset_id"]
+        user_age_feature_resource_name = shared_state["user_age_feature_resource_name"]
+        user_gender_feature_resource_name = shared_state[
+            "user_gender_feature_resource_name"
+        ]
+        user_liked_genres_feature_resource_name = shared_state[
+            "user_liked_genres_feature_resource_name"
+        ]
+
+        aiplatform.init(
+            project=e2e_base._PROJECT, location=e2e_base._LOCATION,
+        )
+
+        caplog.set_level(logging.INFO)
+
+        featurestore.batch_serve_to_bq(
+            serving_feature_ids={
+                _TEST_USER_ENTITY_TYPE_ID: [
+                    _TEST_USER_AGE_FEATURE_ID,
+                    _TEST_USER_GENDER_FEATURE_ID,
+                    _TEST_USER_LIKED_GENRES_FEATURE_ID,
+                ],
+                _TEST_MOVIE_ENTITY_TYPE_ID: [
+                    _TEST_MOVIE_TITLE_FEATURE_ID,
+                    _TEST_MOVIE_GENRES_FEATURE_ID,
+                    _TEST_MOVIE_AVERAGE_RATING_FEATURE_ID,
+                ],
+            },
+            feature_destination_fields={
+                user_age_feature_resource_name: "user_age_dest",
+                user_gender_feature_resource_name: "user_gender_dest",
+                user_liked_genres_feature_resource_name: "user_liked_genres_dest",
+            },
+            read_instances=_TEST_READ_INSTANCE_SRC,
+            bq_destination_output_uri=f"bq://{bigquery_dataset_id}.test_table",
+        )
+
+        assert "Featurestore feature values served." in caplog.text
+        caplog.clear()
+
     def test_online_reads(self, shared_state):
         assert shared_state["user_entity_type"]
         assert shared_state["movie_entity_type"]