fix: Fix java server after odfv update (#2602)

kevjumba · web-flow · commit 0ca62970dd6b · 2022-04-22T14:28:03.000-07:00
Signed-off-by: Kevin Zhang &lt;kzhang@tecton.ai&gt;
diff --git a/docs/getting-started/concepts/feature-view.md b/docs/getting-started/concepts/feature-view.md
@@ -157,10 +157,10 @@ input_request = RequestSource(
 
 # Use the input data and feature view features to create new features
 @on_demand_feature_view(
-   sources={
-       'driver_hourly_stats': driver_hourly_stats_view,
-       'vals_to_add': input_request
-   },
+   sources=[
+       driver_hourly_stats_view,
+       input_request
+   ],
    schema=[
      Field(name='conv_rate_plus_val1', dtype=Float64),
      Field(name='conv_rate_plus_val2', dtype=Float64)
diff --git a/docs/reference/alpha-on-demand-feature-view.md b/docs/reference/alpha-on-demand-feature-view.md
@@ -21,7 +21,7 @@ There are new CLI commands:
 
 ## Example
 
-See [https://github.com/feast-dev/on-demand-feature-views-demo](https://github.com/feast-dev/on-demand-feature-views-demo) for an example on how to use on demand feature views. 
+See [https://github.com/feast-dev/on-demand-feature-views-demo](https://github.com/feast-dev/on-demand-feature-views-demo) for an example on how to use on demand feature views.
 
 ### **Registering transformations**
 
@@ -32,7 +32,7 @@ from feast import Field, RequestSource
 from feast.types import Float64, Int64
 import pandas as pd
 
-# Define a request data source which encodes features / information only 
+# Define a request data source which encodes features / information only
 # available at request time (e.g. part of the user initiated HTTP request)
 input_request = RequestSource(
     name="vals_to_add",
@@ -44,10 +44,10 @@ input_request = RequestSource(
 
 # Use the input data and feature view features to create new features
 @on_demand_feature_view(
-   sources={
-       'driver_hourly_stats': driver_hourly_stats_view,
-       'vals_to_add': input_request
-   },
+   sources=[
+       driver_hourly_stats_view,
+       input_request
+   ],
    schema=[
      Field(name='conv_rate_plus_val1', dtype=Float64),
      Field(name='conv_rate_plus_val2', dtype=Float64)
@@ -63,7 +63,7 @@ def transformed_conv_rate(features_df: pd.DataFrame) -> pd.DataFrame:
 ### **Feature retrieval**
 
 {% hint style="info" %}
-The on demand feature view's name is the function name \(i.e. `transformed_conv_rate`\). 
+The on demand feature view's name is the function name \(i.e. `transformed_conv_rate`\).
 {% endhint %}
 
 And then to retrieve historical or online features, we can call this in a feature service or reference individual features:
diff --git a/examples/java-demo/feature_repo/driver_repo.py b/examples/java-demo/feature_repo/driver_repo.py
@@ -43,7 +43,7 @@
 # Define an on demand feature view which can generate new features based on
 # existing feature views and RequestSource features
 @on_demand_feature_view(
-    inputs=[
+    sources=[
         driver_hourly_stats_view,
         input_request,
     ],
diff --git a/java/serving/src/test/resources/docker-compose/feast10/definitions.py b/java/serving/src/test/resources/docker-compose/feast10/definitions.py
@@ -50,10 +50,10 @@
 
 
 @on_demand_feature_view(
-    sources={
-        "driver_hourly_stats": driver_hourly_stats_view,
-        "vals_to_add": input_request,
-    },
+    sources=[
+        driver_hourly_stats_view,
+        input_request,
+    ],
     schema=[
         Field(name="conv_rate_plus_val1", dtype=Float64),
         Field(name="conv_rate_plus_val2", dtype=Float64),
diff --git a/ui/feature_repo/features.py b/ui/feature_repo/features.py
@@ -140,7 +140,7 @@
 # Define an on demand feature view which can generate new features based on
 # existing feature views and RequestSource features
 @on_demand_feature_view(
-    sources={"credit_history": credit_history, "transaction": input_request,},
+    sources=[credit_history, input_request],
     schema=[
         Field(name="transaction_gt_last_credit_card_due", dtype=Bool),
     ],
