# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"). You # may not use this file except in compliance with the License. A copy of # the License is located at # # http://aws.amazon.com/apache2.0/ # # or in the "license" file accompanying this file. This file is # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF # ANY KIND, either express or implied. See the License for the specific # language governing permissions and limitations under the License. from __future__ import absolute_import import json import time from contextlib import contextmanager import boto3 import numpy as np import pandas as pd import pytest from pandas import DataFrame from sagemaker.feature_store.feature_definition import FractionalFeatureDefinition from sagemaker.feature_store.feature_group import FeatureGroup from sagemaker.feature_store.inputs import FeatureValue, FeatureParameter from sagemaker.session import get_execution_role, Session from tests.integ.timeout import timeout BUCKET_POLICY = { "Version": "2012-10-17", "Statement": [ { "Sid": "FeatureStoreOfflineStoreS3BucketPolicy", "Effect": "Allow", "Principal": {"Service": "sagemaker.amazonaws.com"}, "Action": ["s3:PutObject", "s3:PutObjectAcl"], "Resource": "arn:aws:s3:::{bucket_name}-{region_name}/*", "Condition": {"StringEquals": {"s3:x-amz-acl": "bucket-owner-full-control"}}, }, { "Sid": "FeatureStoreOfflineStoreS3BucketPolicy", "Effect": "Allow", "Principal": {"Service": "sagemaker.amazonaws.com"}, "Action": "s3:GetBucketAcl", "Resource": "arn:aws:s3:::{bucket_name}-{region_name}", }, ], } @pytest.fixture(scope="module") def region_name(feature_store_session): return feature_store_session.boto_session.region_name @pytest.fixture(scope="module") def role(feature_store_session): return get_execution_role(feature_store_session) # TODO-reinvent-2020: remove use of specified region and this fixture @pytest.fixture(scope="module") def feature_store_session(): boto_session = boto3.Session(region_name="us-east-2") sagemaker_client = boto_session.client("sagemaker") featurestore_runtime_client = boto_session.client("sagemaker-featurestore-runtime") return Session( boto_session=boto_session, sagemaker_client=sagemaker_client, sagemaker_featurestore_runtime_client=featurestore_runtime_client, ) @pytest.fixture def feature_group_name(): return f"my-feature-group-{int(time.time() * 10**7)}" @pytest.fixture def offline_store_s3_uri(feature_store_session, region_name): bucket = f"sagemaker-test-featurestore-{region_name}-{feature_store_session.account_id()}" feature_store_session._create_s3_bucket_if_it_does_not_exist(bucket, region_name) s3 = feature_store_session.boto_session.client("s3", region_name=region_name) BUCKET_POLICY["Statement"][0]["Resource"] = f"arn:aws:s3:::{bucket}/*" BUCKET_POLICY["Statement"][1]["Resource"] = f"arn:aws:s3:::{bucket}" s3.put_bucket_policy( Bucket=f"{bucket}", Policy=json.dumps(BUCKET_POLICY), ) return f"s3://{bucket}" @pytest.fixture def pandas_data_frame(): df = pd.DataFrame( { "feature1": pd.Series(np.arange(10.0), dtype="float64"), "feature2": pd.Series(np.arange(10), dtype="int64"), "feature3": pd.Series(["2020-10-30T03:43:21Z"] * 10, dtype="string"), "feature4": pd.Series(np.arange(5.0), dtype="float64"), # contains nan } ) return df @pytest.fixture def pandas_data_frame_without_string(): df = pd.DataFrame( { "feature1": pd.Series(np.arange(10), dtype="int64"), "feature2": pd.Series([3141592.6535897] * 10, dtype="float64"), } ) return df @pytest.fixture def record(): return [ FeatureValue(feature_name="feature1", value_as_string="10.0"), FeatureValue(feature_name="feature2", value_as_string="10"), FeatureValue(feature_name="feature3", value_as_string="2020-10-30T03:43:21Z"), ] @pytest.fixture def create_table_ddl(): return ( "CREATE EXTERNAL TABLE IF NOT EXISTS sagemaker_featurestore.{feature_group_name} (\n" " feature1 FLOAT\n" " feature2 INT\n" " feature3 STRING\n" " feature4 FLOAT\n" " write_time TIMESTAMP\n" " event_time TIMESTAMP\n" " is_deleted BOOLEAN\n" ")\n" "ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'\n" " STORED AS\n" " INPUTFORMAT 'parquet.hive.DeprecatedParquetInputFormat'\n" " OUTPUTFORMAT 'parquet.hive.DeprecatedParquetOutputFormat'\n" "LOCATION '{resolved_output_s3_uri}'" ) def test_create_feature_store_online_only( feature_store_session, role, feature_group_name, pandas_data_frame, ): feature_group = FeatureGroup(name=feature_group_name, sagemaker_session=feature_store_session) feature_group.load_feature_definitions(data_frame=pandas_data_frame) with cleanup_feature_group(feature_group): output = feature_group.create( s3_uri=False, record_identifier_name="feature1", event_time_feature_name="feature3", role_arn=role, enable_online_store=True, ) _wait_for_feature_group_create(feature_group) assert output["FeatureGroupArn"].endswith(f"feature-group/{feature_group_name}") def test_create_feature_store( feature_store_session, role, feature_group_name, offline_store_s3_uri, pandas_data_frame, record, create_table_ddl, ): feature_group = FeatureGroup(name=feature_group_name, sagemaker_session=feature_store_session) feature_group.load_feature_definitions(data_frame=pandas_data_frame) with cleanup_feature_group(feature_group): output = feature_group.create( s3_uri=offline_store_s3_uri, record_identifier_name="feature1", event_time_feature_name="feature3", role_arn=role, enable_online_store=True, ) _wait_for_feature_group_create(feature_group) resolved_output_s3_uri = ( feature_group.describe() .get("OfflineStoreConfig") .get("S3StorageConfig") .get("ResolvedOutputS3Uri") ) # Ingest data feature_group.put_record(record=record) ingestion_manager = feature_group.ingest( data_frame=pandas_data_frame, max_workers=3, wait=False ) ingestion_manager.wait() assert 0 == len(ingestion_manager.failed_rows) # Query the integrated Glue table. athena_query = feature_group.athena_query() df = DataFrame() with timeout(minutes=10): while df.shape[0] < 11: athena_query.run( query_string=f'SELECT * FROM "{athena_query.table_name}"', output_location=f"{offline_store_s3_uri}/query_results", ) athena_query.wait() assert "SUCCEEDED" == athena_query.get_query_execution().get("QueryExecution").get( "Status" ).get("State") df = athena_query.as_dataframe() print(f"Found {df.shape[0]} records.") time.sleep(60) assert df.shape[0] == 11 nans = pd.isna(df.loc[df["feature1"].isin([5, 6, 7, 8, 9])]["feature4"]) for is_na in nans.items(): assert is_na assert ( create_table_ddl.format( feature_group_name=feature_group_name, region=feature_store_session.boto_session.region_name, account=feature_store_session.account_id(), resolved_output_s3_uri=resolved_output_s3_uri, ) == feature_group.as_hive_ddl() ) assert output["FeatureGroupArn"].endswith(f"feature-group/{feature_group_name}") def test_update_feature_group( feature_store_session, role, feature_group_name, offline_store_s3_uri, pandas_data_frame, ): feature_group = FeatureGroup(name=feature_group_name, sagemaker_session=feature_store_session) feature_group.load_feature_definitions(data_frame=pandas_data_frame) with cleanup_feature_group(feature_group): feature_group.create( s3_uri=offline_store_s3_uri, record_identifier_name="feature1", event_time_feature_name="feature3", role_arn=role, enable_online_store=True, ) _wait_for_feature_group_create(feature_group) new_feature_name = "new_feature" new_features = [FractionalFeatureDefinition(feature_name=new_feature_name)] feature_group.update(new_features) _wait_for_feature_group_update(feature_group) feature_definitions = feature_group.describe().get("FeatureDefinitions") assert any([True for elem in feature_definitions if new_feature_name in elem.values()]) def test_feature_metadata( feature_store_session, role, feature_group_name, offline_store_s3_uri, pandas_data_frame, ): feature_group = FeatureGroup(name=feature_group_name, sagemaker_session=feature_store_session) feature_group.load_feature_definitions(data_frame=pandas_data_frame) with cleanup_feature_group(feature_group): feature_group.create( s3_uri=offline_store_s3_uri, record_identifier_name="feature1", event_time_feature_name="feature3", role_arn=role, enable_online_store=True, ) _wait_for_feature_group_create(feature_group) parameter_additions = [ FeatureParameter(key="key1", value="value1"), FeatureParameter(key="key2", value="value2"), ] description = "test description" feature_name = "feature1" feature_group.update_feature_metadata( feature_name=feature_name, description=description, parameter_additions=parameter_additions, ) describe_feature_metadata = feature_group.describe_feature_metadata( feature_name=feature_name ) print(describe_feature_metadata) assert description == describe_feature_metadata.get("Description") assert 2 == len(describe_feature_metadata.get("Parameters")) parameter_removals = ["key1"] feature_group.update_feature_metadata( feature_name=feature_name, parameter_removals=parameter_removals ) describe_feature_metadata = feature_group.describe_feature_metadata( feature_name=feature_name ) assert description == describe_feature_metadata.get("Description") assert 1 == len(describe_feature_metadata.get("Parameters")) def test_ingest_without_string_feature( feature_store_session, role, feature_group_name, offline_store_s3_uri, pandas_data_frame_without_string, ): feature_group = FeatureGroup(name=feature_group_name, sagemaker_session=feature_store_session) feature_group.load_feature_definitions(data_frame=pandas_data_frame_without_string) with cleanup_feature_group(feature_group): output = feature_group.create( s3_uri=offline_store_s3_uri, record_identifier_name="feature1", event_time_feature_name="feature2", role_arn=role, enable_online_store=True, ) _wait_for_feature_group_create(feature_group) ingestion_manager = feature_group.ingest( data_frame=pandas_data_frame_without_string, max_workers=3, wait=False ) ingestion_manager.wait() assert output["FeatureGroupArn"].endswith(f"feature-group/{feature_group_name}") def test_ingest_multi_process( feature_store_session, role, feature_group_name, offline_store_s3_uri, pandas_data_frame, ): feature_group = FeatureGroup(name=feature_group_name, sagemaker_session=feature_store_session) feature_group.load_feature_definitions(data_frame=pandas_data_frame) with cleanup_feature_group(feature_group): output = feature_group.create( s3_uri=offline_store_s3_uri, record_identifier_name="feature1", event_time_feature_name="feature3", role_arn=role, enable_online_store=True, ) _wait_for_feature_group_create(feature_group) feature_group.ingest( data_frame=pandas_data_frame, max_workers=3, max_processes=2, wait=True ) assert output["FeatureGroupArn"].endswith(f"feature-group/{feature_group_name}") def _wait_for_feature_group_create(feature_group: FeatureGroup): status = feature_group.describe().get("FeatureGroupStatus") while status == "Creating": print("Waiting for Feature Group Creation") time.sleep(5) status = feature_group.describe().get("FeatureGroupStatus") if status != "Created": print(feature_group.describe()) raise RuntimeError(f"Failed to create feature group {feature_group.name}") print(f"FeatureGroup {feature_group.name} successfully created.") def _wait_for_feature_group_update(feature_group: FeatureGroup): status = feature_group.describe().get("LastUpdateStatus").get("Status") while status == "InProgress": print("Waiting for Feature Group Update") time.sleep(5) status = feature_group.describe().get("LastUpdateStatus").get("Status") if status != "Successful": print(feature_group.describe()) raise RuntimeError(f"Failed to update feature group {feature_group.name}") print(f"FeatureGroup {feature_group.name} successfully updated.") @contextmanager def cleanup_feature_group(feature_group: FeatureGroup): try: yield finally: try: feature_group.delete() except Exception: raise RuntimeError(f"Failed to delete feature group with name {feature_group.name}")