fix: Updated AWS Athena template (#3322)

* Update the template on how to use AWS Athena

Signed-off-by: Youngkyu OH <toping4445@gmail.com>

* Remove unnecessary imports

Signed-off-by: Youngkyu OH <toping4445@gmail.com>

* lint & format

Signed-off-by: Youngkyu OH <toping4445@gmail.com>

Signed-off-by: Youngkyu OH <toping4445@gmail.com>

sdk/python/feast/templates/athena/feature_repo/feature_store.yaml

@@ -6,8 +6,9 @@ online_store:
     path: online_store.db
 offline_store:
     type: athena
-    region: ap-northeast-2
-    database: sampledb
+    region: {AWS region}
+    database: {The database in the data catalog to be used by Athena}
     data_source: AwsDataCatalog
-    s3_staging_location: s3://sagemaker-yelo-test
+    s3_staging_location: s3://{S3 bucket to be used by Feast}
+    workgroup: {Workgroup for Athena}
 entity_key_serialization_version: 2

sdk/python/feast/templates/athena/feature_repo/test_workflow.py

@@ -3,16 +3,28 @@
 
 import pandas as pd
 
-from feast import Entity, Feature, FeatureStore, FeatureView, ValueType
+from feast import Entity, FeatureStore, FeatureView, Field
 from feast.infra.offline_stores.contrib.athena_offline_store.athena_source import (
     AthenaSource,
 )
+from feast.types import Float64, Int64
 
 
 def test_end_to_end():
 
     try:
-        fs = FeatureStore(".")
+
+        # Before running this test method
+        # 1. Upload the driver_stats.parquet file to your S3 bucket.
+        # (https://github.com/feast-dev/feast-custom-offline-store-demo/tree/main/feature_repo/data)
+        # 2. Using AWS Glue Crawler, create a table in the data catalog. The generated table can be queried through Athena.
+        # 3. Specify the S3 bucket name, data source(AwsDataCatalog), database name, Athena's workgroup, etc. in feature_store.yaml
+
+        fs = FeatureStore("./feature_repo")
+
+        # Partition pruning has a significant impact on Athena's query performance and cost.
+        # If offline feature dataset is large, it is highly recommended to create partitions using date columns such as ('created','event_timestamp')
+        # The date_partition_column must be in form of YYYY-MM-DD(string) as in the beginning of the date column.
 
         driver_hourly_stats = AthenaSource(
             timestamp_field="event_timestamp",
@@ -21,31 +33,29 @@ def test_end_to_end():
             database="sampledb",
             data_source="AwsDataCatalog",
             created_timestamp_column="created",
-            # date_partition_column="std_date"
+            # date_partition_column="std_date"  #YYYY-MM-DD
         )
 
         driver = Entity(
             name="driver_id",
-            value_type=ValueType.INT64,
             description="driver id",
         )
 
         driver_hourly_stats_view = FeatureView(
             name="driver_hourly_stats",
-            entities=["driver_id"],
-            ttl=timedelta(days=365),
-            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=timedelta(days=500),
+            schema=[
+                Field(name="conv_rate", dtype=Float64),
+                Field(name="acc_rate", dtype=Float64),
+                Field(name="avg_daily_trips", dtype=Int64),
             ],
             online=True,
-            batch_source=driver_hourly_stats,
+            source=driver_hourly_stats,
         )
 
         # apply repository
         fs.apply([driver_hourly_stats, driver, driver_hourly_stats_view])
-
         print(fs.list_data_sources())
         print(fs.list_feature_views())
 
@@ -54,7 +64,6 @@ def test_end_to_end():
         )
 
         # Read features from offline store
-
         feature_vector = (
             fs.get_historical_features(
                 features=["driver_hourly_stats:conv_rate"], entity_df=entity_df