feat(ingest): upgrade feast

metadata-ingestion/setup.py

@@ -1,4 +1,5 @@
 import os
+import sys
 from typing import Dict, Set
 
 import setuptools
@@ -95,8 +96,9 @@ def get_long_description():
 kafka_protobuf = {
     "networkx>=2.6.2",
     # Required to generate protobuf python modules from the schema downloaded from the schema registry
-    "grpcio==1.44.0",
-    "grpcio-tools==1.44.0",
+    # NOTE: potential conflict with feast also depending on grpcio
+    "grpcio>=1.44.0,<2",
+    "grpcio-tools>=1.44.0,<2",
 }
 
 sql_common = {
@@ -254,7 +256,7 @@ def get_long_description():
     # https://github.com/elastic/elasticsearch-py/issues/1639#issuecomment-883587433
     "elasticsearch": {"elasticsearch==7.13.4"},
     "feast-legacy": {"docker"},
-    "feast": {"feast==0.18.0", "flask-openid>=1.3.0"},
+    "feast": {"feast~=0.26.0", "flask-openid>=1.3.0"},
     "glue": aws_common,
     # hdbcli is supported officially by SAP, sqlalchemy-hana is built on top but not officially supported
     "hana": sql_common
@@ -397,7 +399,7 @@ def get_long_description():
             "delta-lake",
             "druid",
             "elasticsearch",
-            "feast",
+            "feast" if sys.version_info >= (3, 8) else None,
             "iceberg",
             "ldap",
             "looker",
@@ -425,6 +427,7 @@ def get_long_description():
             "unity-catalog"
             # airflow is added below
         ]
+        if plugin
         for dependency in plugins[plugin]
     ),
 }
@@ -444,7 +447,6 @@ def get_long_description():
             "clickhouse",
             "delta-lake",
             "druid",
-            "feast",
             "feast-legacy",
             "hana",
             "hive",

metadata-ingestion/src/datahub/ingestion/source/feast.py

@@ -1,19 +1,27 @@
+import sys
+
+if sys.version_info < (3, 8):
+    raise ImportError("Feast is only supported on Python 3.8+")
+
 from dataclasses import dataclass
-from typing import Dict, Iterable, List, Tuple, Union
+from typing import Dict, Iterable, List, Optional, Tuple, Union
 
+import feast.types
 from feast import (
     BigQuerySource,
     Entity,
-    Feature,
     FeatureStore,
     FeatureView,
+    Field as FeastField,
     FileSource,
     KafkaSource,
     KinesisSource,
     OnDemandFeatureView,
+    RequestSource,
+    SnowflakeSource,
     ValueType,
 )
-from feast.data_source import DataSource, RequestDataSource
+from feast.data_source import DataSource
 from pydantic import Field
 
 import datahub.emitter.mce_builder as builder
@@ -46,7 +54,7 @@
 )
 
 # FIXME: ValueType module cannot be used as a type
-_field_type_mapping: Dict[ValueType, str] = {
+_field_type_mapping: Dict[Union[ValueType, feast.types.FeastType], str] = {
     ValueType.UNKNOWN: MLFeatureDataType.UNKNOWN,
     ValueType.BYTES: MLFeatureDataType.BYTE,
     ValueType.STRING: MLFeatureDataType.TEXT,
@@ -65,11 +73,26 @@
     ValueType.BOOL_LIST: MLFeatureDataType.SEQUENCE,
     ValueType.UNIX_TIMESTAMP_LIST: MLFeatureDataType.SEQUENCE,
     ValueType.NULL: MLFeatureDataType.UNKNOWN,
+    feast.types.Invalid: MLFeatureDataType.UNKNOWN,
+    feast.types.Bytes: MLFeatureDataType.BYTE,
+    feast.types.String: MLFeatureDataType.TEXT,
+    feast.types.Int32: MLFeatureDataType.ORDINAL,
+    feast.types.Int64: MLFeatureDataType.ORDINAL,
+    feast.types.Float64: MLFeatureDataType.CONTINUOUS,
+    feast.types.Float32: MLFeatureDataType.CONTINUOUS,
+    feast.types.Bool: MLFeatureDataType.BINARY,
+    feast.types.UnixTimestamp: MLFeatureDataType.TIME,
+    feast.types.Array: MLFeatureDataType.SEQUENCE,  # type: ignore
+    feast.types.Invalid: MLFeatureDataType.UNKNOWN,
 }
 
 
 class FeastRepositorySourceConfig(ConfigModel):
     path: str = Field(description="Path to Feast repository")
+    fs_yaml_file: Optional[str] = Field(
+        default=None,
+        description="Path to the `feature_store.yaml` file used to configure the feature store",
+    )
     environment: str = Field(
         default=DEFAULT_ENV, description="Environment to use when constructing URNs"
     )
@@ -85,7 +108,7 @@ class FeastRepositorySource(Source):
     This plugin extracts:
 
     - Entities as [`MLPrimaryKey`](https://datahubproject.io/docs/graphql/objects#mlprimarykey)
-    - Features as [`MLFeature`](https://datahubproject.io/docs/graphql/objects#mlfeature)
+    - Fields as [`MLFeature`](https://datahubproject.io/docs/graphql/objects#mlfeature)
     - Feature views and on-demand feature views as [`MLFeatureTable`](https://datahubproject.io/docs/graphql/objects#mlfeaturetable)
     - Batch and stream source details as [`Dataset`](https://datahubproject.io/docs/graphql/objects#dataset)
     - Column types associated with each entity and feature
@@ -97,12 +120,16 @@ class FeastRepositorySource(Source):
 
     def __init__(self, config: FeastRepositorySourceConfig, ctx: PipelineContext):
         super().__init__(ctx)
-
         self.source_config = config
         self.report = SourceReport()
-        self.feature_store = FeatureStore(self.source_config.path)
+        self.feature_store = FeatureStore(
+            repo_path=self.source_config.path,
+            fs_yaml_file=self.source_config.fs_yaml_file,
+        )
 
-    def _get_field_type(self, field_type: ValueType, parent_name: str) -> str:
+    def _get_field_type(
+        self, field_type: Union[ValueType, feast.types.FeastType], parent_name: str
+    ) -> str:
         """
         Maps types encountered in Feast to corresponding schema types.
         """
@@ -128,26 +155,24 @@ def _get_data_source_details(self, source: DataSource) -> Tuple[str, str]:
 
         if isinstance(source, FileSource):
             platform = "file"
-
             name = source.path.replace("://", ".").replace("/", ".")
-
-        if isinstance(source, BigQuerySource):
+        elif isinstance(source, BigQuerySource):
             platform = "bigquery"
             name = source.table
-
-        if isinstance(source, KafkaSource):
+        elif isinstance(source, KafkaSource):
             platform = "kafka"
             name = source.kafka_options.topic
-
-        if isinstance(source, KinesisSource):
+        elif isinstance(source, KinesisSource):
             platform = "kinesis"
             name = (
                 f"{source.kinesis_options.region}:{source.kinesis_options.stream_name}"
             )
-
-        if isinstance(source, RequestDataSource):
+        elif isinstance(source, RequestSource):
             platform = "request"
             name = source.name
+        elif isinstance(source, SnowflakeSource):
+            platform = "snowflake"
+            name = source.table
 
         return platform, name
 
@@ -214,25 +239,24 @@ def _get_feature_workunit(
         self,
         # FIXME: FeatureView and OnDemandFeatureView cannot be used as a type
         feature_view: Union[FeatureView, OnDemandFeatureView],
-        feature: Feature,
+        field: FeastField,
     ) -> MetadataWorkUnit:
         """
         Generate an MLFeature work unit for a Feast feature.
         """
         feature_view_name = f"{self.feature_store.project}.{feature_view.name}"
 
         feature_snapshot = MLFeatureSnapshot(
-            urn=builder.make_ml_feature_urn(feature_view_name, feature.name),
+            urn=builder.make_ml_feature_urn(feature_view_name, field.name),
             aspects=[StatusClass(removed=False)],
         )
 
         feature_sources = []
-
         if isinstance(feature_view, FeatureView):
             feature_sources = self._get_data_sources(feature_view)
         elif isinstance(feature_view, OnDemandFeatureView):
-            if feature_view.input_request_data_sources is not None:
-                for request_source in feature_view.input_request_data_sources.values():
+            if feature_view.source_request_sources is not None:
+                for request_source in feature_view.source_request_sources.values():
                     source_platform, source_name = self._get_data_source_details(
                         request_source
                     )
@@ -245,10 +269,10 @@ def _get_feature_workunit(
                         )
                     )
 
-            if feature_view.input_feature_view_projections is not None:
+            if feature_view.source_feature_view_projections is not None:
                 for (
                     feature_view_projection
-                ) in feature_view.input_feature_view_projections.values():
+                ) in feature_view.source_feature_view_projections.values():
                     feature_view_source = self.feature_store.get_feature_view(
                         feature_view_projection.name
                     )
@@ -257,15 +281,15 @@ def _get_feature_workunit(
 
         feature_snapshot.aspects.append(
             MLFeaturePropertiesClass(
-                description=feature.labels.get("description"),
-                dataType=self._get_field_type(feature.dtype, feature.name),
+                description=field.tags.get("description"),
+                dataType=self._get_field_type(field.dtype, field.name),
                 sources=feature_sources,
             )
         )
 
         mce = MetadataChangeEvent(proposedSnapshot=feature_snapshot)
 
-        return MetadataWorkUnit(id=feature.name, mce=mce)
+        return MetadataWorkUnit(id=field.name, mce=mce)
 
     def _get_feature_view_workunit(self, feature_view: FeatureView) -> MetadataWorkUnit:
         """
@@ -359,8 +383,8 @@ def get_workunits(self) -> Iterable[MetadataWorkUnit]:
 
                 yield work_unit
 
-            for feature in feature_view.features:
-                work_unit = self._get_feature_workunit(feature_view, feature)
+            for field in feature_view.features:
+                work_unit = self._get_feature_workunit(feature_view, field)
                 self.report.report_workunit(work_unit)
 
                 yield work_unit

metadata-ingestion/tests/integration/feast/feature_store/feature_store.yaml

@@ -5,6 +5,4 @@ online_store:
   path: data/online_store.db
 offline_store:
   type: file
-flags:
-  alpha_features: true
-  on_demand_transforms: true
+entity_key_serialization_version: 2

metadata-ingestion/tests/integration/feast/feature_store/features.py

@@ -1,63 +1,74 @@
 from datetime import timedelta
 
+import feast.types
 import pandas as pd
-from feast import Entity, Feature, FeatureView, FileSource, ValueType
-from feast.data_source import RequestDataSource
+from feast import Entity, FeatureView, Field, FileSource, RequestSource, ValueType
 from feast.on_demand_feature_view import on_demand_feature_view
 
 driver_hourly_stats_source = FileSource(
+    name="driver_hourly_stats_source",
     path="data/driver_stats_with_string.parquet",
-    event_timestamp_column="event_timestamp",
+    timestamp_field="event_timestamp",
     created_timestamp_column="created",
 )
 
-driver_entity = Entity(
-    name="driver_id", value_type=ValueType.INT64, description="Driver ID"
+driver = Entity(
+    # It would be the modern Feast pattern to call this `driver`, but the
+    # golden tests have the name as `driver_id`
+    name="driver_id",
+    join_keys=["driver_id"],
+    value_type=ValueType.INT64,
+    description="Driver ID",
 )
 
 driver_hourly_stats_view = FeatureView(
     name="driver_hourly_stats",
-    entities=["driver_id"],
+    entities=[driver],
     ttl=timedelta(days=7),
-    features=[
-        Feature(
+    schema=[
+        Field(
             name="conv_rate",
-            dtype=ValueType.FLOAT,
-            labels=dict(description="Conv rate"),
+            dtype=feast.types.Float64,
+            tags=dict(description="Conv rate"),
         ),
-        Feature(
-            name="acc_rate", dtype=ValueType.FLOAT, labels=dict(description="Acc rate")
+        Field(
+            name="acc_rate",
+            dtype=feast.types.Float64,
+            tags=dict(description="Acc rate"),
         ),
-        Feature(
+        Field(
             name="avg_daily_trips",
-            dtype=ValueType.INT64,
-            labels=dict(description="Avg daily trips"),
+            dtype=feast.types.Int64,
+            tags=dict(description="Avg daily trips"),
         ),
-        Feature(
+        Field(
             name="string_feature",
-            dtype=ValueType.STRING,
-            labels=dict(description="String feature"),
+            dtype=feast.types.String,
+            tags=dict(description="String feature"),
         ),
     ],
     online=True,
-    batch_source=driver_hourly_stats_source,
+    source=driver_hourly_stats_source,
     tags={},
 )
 
-input_request = RequestDataSource(
+input_request = RequestSource(
     name="vals_to_add",
-    schema={"val_to_add": ValueType.INT64, "val_to_add_2": ValueType.INT64},
+    schema=[
+        Field(name="val_to_add", dtype=feast.types.Int64),
+        Field(name="val_to_add_2", dtype=feast.types.Int64),
+    ],
 )
 
 
 @on_demand_feature_view(  # type: ignore
-    inputs={
-        "driver_hourly_stats": driver_hourly_stats_view,
-        "vals_to_add": input_request,
-    },
-    features=[
-        Feature(name="conv_rate_plus_val1", dtype=ValueType.DOUBLE),
-        Feature(name="conv_rate_plus_val2", dtype=ValueType.DOUBLE),
+    sources=[
+        driver_hourly_stats_view,
+        input_request,
+    ],
+    schema=[
+        Field(name="conv_rate_plus_val1", dtype=feast.types.Float64),
+        Field(name="conv_rate_plus_val2", dtype=feast.types.Float64),
     ],
 )
 def transformed_conv_rate(inputs: pd.DataFrame) -> pd.DataFrame:

metadata-ingestion/tests/integration/feast/test_feast_repository.py

@@ -1,4 +1,4 @@
-import os
+import sys
 
 import pytest
 from freezegun import freeze_time
@@ -8,11 +8,12 @@
 
 FROZEN_TIME = "2020-04-14 07:00:00"
 
+pytestmark = pytest.mark.skipif(
+    sys.version_info < (3, 8), reason="requires python 3.8 or higher"
+)
+
 
 @freeze_time(FROZEN_TIME)
-@pytest.mark.skipif(
-    os.getenv("AIRFLOW1_TEST") == "true", reason="feast requires Airflow 2.0 or newer"
-)
 def test_feast_repository_ingest(pytestconfig, tmp_path, mock_time):
     test_resources_dir = pytestconfig.rootpath / "tests/integration/feast"
     output_path = tmp_path / "feast_repository_mces.json"