Feast 0.26.0 support

example.py

@@ -1,39 +1,41 @@
 # This is an example feature definition file
 
-from google.protobuf.duration_pb2 import Duration
+import datetime
 
-from feast import Entity, Feature, FeatureView, ValueType
+from feast import Entity, FeatureView, Field, ValueType
+from feast.types import PrimitiveFeastType
 from feast_hive import HiveSource
 
 # Read data from Hive table
 # Here we use a Query to reuse the original parquet data,
 # but you can replace to your own Table or Query.
 driver_hourly_stats = HiveSource(
     # table='driver_stats',
-    query = """
+    query="""
     SELECT from_unixtime(cast(event_timestamp / 1000000 as bigint)) AS event_timestamp, 
            driver_id, conv_rate, acc_rate, avg_daily_trips, 
            from_unixtime(cast(created / 1000000 as bigint)) AS created 
     FROM driver_stats
     """,
-    event_timestamp_column="event_timestamp",
+    name="driver_stats",
+    timestamp_field="event_timestamp",
     created_timestamp_column="created",
 )
 
 # Define an entity for the driver.
-driver = Entity(name="driver_id", value_type=ValueType.INT64, description="driver id", )
+driver = Entity(name="driver_id", join_keys=["driver_id"], value_type=ValueType.INT64, description="driver id", )
 
 # Define FeatureView
 driver_hourly_stats_view = FeatureView(
     name="driver_hourly_stats",
-    entities=["driver_id"],
-    ttl=Duration(seconds=86400 * 1),
-    features=[
-        Feature(name="conv_rate", dtype=ValueType.FLOAT),
-        Feature(name="acc_rate", dtype=ValueType.FLOAT),
-        Feature(name="avg_daily_trips", dtype=ValueType.INT64),
+    entities=[driver],
+    ttl=datetime.timedelta(seconds=86400 * 1),
+    schema=[
+        Field(name="conv_rate", dtype=PrimitiveFeastType.FLOAT32),
+        Field(name="acc_rate", dtype=PrimitiveFeastType.FLOAT32),
+        Field(name="avg_daily_trips", dtype=PrimitiveFeastType.INT32),
     ],
     online=True,
-    input=driver_hourly_stats,
+    source=driver_hourly_stats,
     tags={},
-)
+)