Rename batch to historical

feast-dev · Aug 1, 2020 · 721ace4 · 721ace4
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Showing 7 changed files with 15 additions and 15 deletions.
diff --git a/docs/administration/troubleshooting.md b/docs/administration/troubleshooting.md
@@ -15,7 +15,7 @@ docker ps
 ```text
 CONTAINER ID        IMAGE                                  COMMAND                  CREATED             STATUS              PORTS                                            NAMES
 d7447205bced        jupyter/datascience-notebook:latest    "tini -g -- start-no…"   2 minutes ago       Up 2 minutes        0.0.0.0:8888->8888/tcp                           feast_jupyter_1
-8e49dbe81b92        gcr.io/kf-feast/feast-serving:latest   "java -Xms1024m -Xmx…"   2 minutes ago       Up 5 seconds        0.0.0.0:6567->6567/tcp                           feast_batch_serving_1
+8e49dbe81b92        gcr.io/kf-feast/feast-serving:latest   "java -Xms1024m -Xmx…"   2 minutes ago       Up 5 seconds        0.0.0.0:6567->6567/tcp                           feast_historical_serving_1
 b859494bd33a        gcr.io/kf-feast/feast-serving:latest   "java -jar /opt/feas…"   2 minutes ago       Up About a minute   0.0.0.0:6566->6566/tcp                           feast_online_serving_1
 5c4962811767        gcr.io/kf-feast/feast-core:latest      "java -jar /opt/feas…"   2 minutes ago       Up 2 minutes        0.0.0.0:6565->6565/tcp                           feast_core_1
 1ba7239e0ae0        confluentinc/cp-kafka:5.2.1            "/etc/confluent/dock…"   2 minutes ago       Up 2 minutes        0.0.0.0:9092->9092/tcp, 0.0.0.0:9094->9094/tcp   feast_kafka_1
@@ -60,7 +60,7 @@ You will probably need to connect using the hostnames of services and standard F
 ```bash
 export FEAST_CORE_URL=core:6565
 export FEAST_ONLINE_SERVING_URL=online_serving:6566
-export FEAST_BATCH_SERVING_URL=batch_serving:6567
+export FEAST_historical_serving_URL=historical_serving:6567
 ```
 
 ### **Docker Compose \(from outside the docker cluster\)**
@@ -70,7 +70,7 @@ You will probably need to connect using `localhost` and standard ports:
 ```bash
 export FEAST_CORE_URL=localhost:6565
 export FEAST_ONLINE_SERVING_URL=localhost:6566
-export FEAST_BATCH_SERVING_URL=localhost:6567
+export FEAST_historical_serving_URL=localhost:6567
 ```
 
 ### **Google Kubernetes Engine \(GKE\)**
@@ -81,7 +81,7 @@ You will need to find the external IP of one of the nodes as well as the NodePor
 export FEAST_IP=$(kubectl describe nodes | grep ExternalIP | awk '{print $2}' | head -n 1)
 export FEAST_CORE_URL=${FEAST_IP}:32090
 export FEAST_ONLINE_SERVING_URL=${FEAST_IP}:32091
-export FEAST_BATCH_SERVING_URL=${FEAST_IP}:32092
+export FEAST_historical_serving_URL=${FEAST_IP}:32092
 ```
 
 `netcat`, `telnet`, or even `curl` can be used to test whether all services are available and ports are open, but `grpc_cli` is the most powerful. It can be installed from [here](https://github.com/grpc/grpc/blob/master/doc/command_line_tool.md).
@@ -107,7 +107,7 @@ ListProjects
 ### Testing Feast Batch Serving and Online Serving
 
 ```bash
-grpc_cli ls ${FEAST_BATCH_SERVING_URL} feast.serving.ServingService
+grpc_cli ls ${FEAST_historical_serving_URL} feast.serving.ServingService
 ```
 
 ```text
@@ -145,7 +145,7 @@ In order to print the logs from these services, please run the commands below.
 ```
 
 ```text
-docker logs -f feast_batch_serving_1
+docker logs -f feast_historical_serving_1
 ```
 
 ```text

diff --git a/docs/getting-started/deploying-feast/docker-compose.md b/docs/getting-started/deploying-feast/docker-compose.md
@@ -80,7 +80,7 @@ Configure the `.env` file based on your environment. At the very least you have
 | Parameter | Description |
 | :--- | :--- |
 | `FEAST_CORE_GCP_SERVICE_ACCOUNT_KEY` | This should be your service account file name, for example `key.json`. |
-| `FEAST_BATCH_SERVING_GCP_SERVICE_ACCOUNT_KEY` | This should be your service account file name, for example `key.json` |
+| `FEAST_historical_serving_GCP_SERVICE_ACCOUNT_KEY` | This should be your service account file name, for example `key.json` |
 | `FEAST_JUPYTER_GCP_SERVICE_ACCOUNT_KEY` | This should be your service account file name, for example `key.json` |
 
 ### 3.3 Configure Historical Serving

diff --git a/docs/installation/gke.md b/docs/installation/gke.md
@@ -80,7 +80,7 @@ For this guide we will use `NodePort` for exposing Feast services. In order to d
 export FEAST_IP=$(kubectl describe nodes | grep ExternalIP | awk '{print $2}' | head -n 1)
 export FEAST_CORE_URL=${FEAST_IP}:32090
 export FEAST_ONLINE_SERVING_URL=${FEAST_IP}:32091
-export FEAST_BATCH_SERVING_URL=${FEAST_IP}:32092
+export FEAST_historical_serving_URL=${FEAST_IP}:32092
 ```
 
 Add firewall rules to open up ports on your Google Cloud Platform project:

diff --git a/examples/basic/basic.ipynb b/examples/basic/basic.ipynb
@@ -43,7 +43,7 @@
     "FEAST_ONLINE_SERVING_URL = os.getenv('FEAST_ONLINE_SERVING_URL', 'localhost:6566')\n",
     "\n",
     "# Feast Batch Serving allows for the retrieval of historical feature data\n",
-    "FEAST_BATCH_SERVING_URL = os.getenv('FEAST_BATCH_SERVING_URL', 'localhost:6567')"
+    "FEAST_historical_serving_URL = os.getenv('FEAST_historical_serving_URL', 'localhost:6567')"
    ]
   },
   {
@@ -592,7 +592,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "batch_client = Client(core_url=FEAST_CORE_URL, serving_url=FEAST_BATCH_SERVING_URL)"
+    "batch_client = Client(core_url=FEAST_CORE_URL, serving_url=FEAST_historical_serving_URL)"
    ]
   },
   {

diff --git a/...hurn-prediction-tutorial/Telecom Customer Churn Prediction (with Feast and XGBoost).ipynb b/...hurn-prediction-tutorial/Telecom Customer Churn Prediction (with Feast and XGBoost).ipynb
@@ -6752,7 +6752,7 @@
     "FEAST_ONLINE_SERVING_URL = os.getenv('FEAST_ONLINE_SERVING_URL', 'localhost:6566')\n",
     "\n",
     "# Feast Batch Serving allows for the retrieval of historical feature data\n",
-    "FEAST_BATCH_SERVING_URL = os.getenv('FEAST_BATCH_SERVING_URL', 'localhost:6567')"
+    "FEAST_historical_serving_URL = os.getenv('FEAST_historical_serving_URL', 'localhost:6567')"
    ]
   },
   {
@@ -7187,7 +7187,7 @@
     "    def __init__(self, features, target, model_path=None):\n",
     "        # Set up Feast clients to retrieve training and online serving data\n",
     "        self._feast_online_client = Client(serving_url=os.environ['FEAST_ONLINE_SERVING_URL'])\n",
-    "        self._feast_batch_client = Client(serving_url=os.environ['FEAST_BATCH_SERVING_URL'],\n",
+    "        self._feast_batch_client = Client(serving_url=os.environ['FEAST_historical_serving_URL'],\n",
     "                                          core_url=os.environ['FEAST_CORE_URL'])\n",
     "        \n",
     "        # Path to either save models after training or load models for serving\n",

diff --git a/infra/charts/feast/charts/feast-jupyter/templates/deployment.yaml b/infra/charts/feast/charts/feast-jupyter/templates/deployment.yaml
@@ -38,7 +38,7 @@ spec:
               value: "{{ .Release.Name }}-feast-core:6565"
             - name: FEAST_ONLINE_SERVING_URL
               value: "{{ .Release.Name }}-feast-online-serving:6566"
-            - name: FEAST_BATCH_SERVING_URL
+            - name: FEAST_historical_serving_URL
               value: "{{ .Release.Name }}-feast-batch-serving:6566"
             {{- if .Values.gcpServiceAccount.enabled }}
             - name: GOOGLE_APPLICATION_CREDENTIALS

diff --git a/infra/charts/feast/templates/tests/test-feast-batch-serving.yaml b/infra/charts/feast/templates/tests/test-feast-batch-serving.yaml
@@ -70,14 +70,14 @@ spec:
       entity_rows_df = df.copy(deep=True).rename(columns={"datetime": "event_timestamp"})[["event_timestamp", "customer_id"]]
       pandavro.to_avro("entity_rows.avro", entity_rows_df)
 
-      batch_serving_job = client.get_historical_features(
+      historical_serving_job = client.get_historical_features(
           entity_rows="file://entity_rows.avro", 
           feature_refs=[
               f"{project}/daily_transactions:1",
               f"{project}/total_transactions:1",
           ]
       )
-      result_df = batch_serving_job.to_dataframe()
+      result_df = historical_serving_job.to_dataframe()
 
       print("Retrieved dataframe: ")
       print(result_df)