From ecf2c18896ec84e427328f581e36407b06c1973b Mon Sep 17 00:00:00 2001
From: Leonid <lenin133@yandex.ru>
Date: Wed, 21 Apr 2021 01:21:51 +0000
Subject: [PATCH] fix template

---
 sdk/python/feast/infra/aws_dynamo_provider.py | 16 -----------
 .../templates/aws_dynamo/feature_store.yaml   |  1 -
 sdk/python/feast/templates/aws_dynamo/test.py | 27 +------------------
 3 files changed, 1 insertion(+), 43 deletions(-)

diff --git a/sdk/python/feast/infra/aws_dynamo_provider.py b/sdk/python/feast/infra/aws_dynamo_provider.py
index 846514ce4a..57444404bd 100644
--- a/sdk/python/feast/infra/aws_dynamo_provider.py
+++ b/sdk/python/feast/infra/aws_dynamo_provider.py
@@ -46,7 +46,6 @@ def update_infra(
         dynamodb = self._initialize_dynamodb()
 
         for table_name in tables_to_keep:
-            # TODO: add table creation to dynamo.
             table = None
             try:
                 table = dynamodb.create_table(
@@ -82,16 +81,6 @@ def update_infra(
                 if ce.response['Error']['Code'] == 'ResourceNotFoundException':
                     table = dynamodb.Table(table_name.name)
 
-        #             table.update_item(
-        #                 Key={
-        #                     "Project": project
-        #                 },
-        #                 UpdateExpression='SET created_ts = :val1',
-        #                 ExpressionAttributeValues={
-        #                     ':val1': datetime.utcnow().strftime("")
-        #                 }
-        #             )
-
         for table_name in tables_to_delete:
             table = dynamodb.Table(table_name.name)
             table.delete()
@@ -144,17 +133,12 @@ def online_read(
         for entity_key in entity_keys:
             table_instace = dynamodb.Table(table.name)
             document_id = compute_datastore_entity_id(entity_key)  # TODO check id
-            print("entity")
-            print(entity_key)
-            print("id")
-            print(document_id)
             response = table_instace.get_item(
                 Key={
                     "Row": document_id,
                     "Project": project
                 }
             )
-            print(response)
             value = response['Item']
 
             if value is not None:
diff --git a/sdk/python/feast/templates/aws_dynamo/feature_store.yaml b/sdk/python/feast/templates/aws_dynamo/feature_store.yaml
index 1a4363e349..7d2dab35b5 100644
--- a/sdk/python/feast/templates/aws_dynamo/feature_store.yaml
+++ b/sdk/python/feast/templates/aws_dynamo/feature_store.yaml
@@ -1,4 +1,3 @@
 project: my_project
 registry: s3://feast-provectus-demo/data/registry.db
 provider: aws_dynamo
-online_store:
diff --git a/sdk/python/feast/templates/aws_dynamo/test.py b/sdk/python/feast/templates/aws_dynamo/test.py
index e5d3e327a1..7ba3cee584 100644
--- a/sdk/python/feast/templates/aws_dynamo/test.py
+++ b/sdk/python/feast/templates/aws_dynamo/test.py
@@ -1,7 +1,7 @@
 from datetime import datetime, timedelta
 
 import pandas as pd
-from drivers_example import driver, driver_hourly_stats_view
+from driver_example import driver, driver_hourly_stats_view
 
 from feast import FeatureStore
 
@@ -20,31 +20,6 @@ def main():
     # Select features
     feature_refs = ["driver_hourly_stats:conv_rate", "driver_hourly_stats:acc_rate"]
 
-    #     # Create an entity dataframe. This is the dataframe that will be enriched with historical features
-    #     entity_df = pd.DataFrame(
-    #         {
-    #             "event_timestamp": [
-    #                 pd.Timestamp(dt, unit="ms", tz="UTC").round("ms")
-    #                 for dt in pd.date_range(
-    #                     start=datetime.now() - timedelta(days=3),
-    #                     end=datetime.now(),
-    #                     periods=3,
-    #                 )
-    #             ],
-    #             "driver_id": [1001, 1002, 1003],
-    #         }
-    #     )
-
-    #     print("Retrieving training data...")
-
-    #     # Retrieve historical features by joining the entity dataframe to the BigQuery table source
-    #     training_df = fs.get_historical_features(
-    #         feature_refs=feature_refs, entity_df=entity_df
-    #     ).to_df()
-
-    #     print()
-    #     print(training_df)
-
     #     print()
     print("Loading features into the online store...")
     fs.materialize_incremental(end_date=datetime.now())