This tutorial shows you how to deploy a simple example gRPC
service with the
Extensible Service Proxy
(ESP) on Google Kubernetes Engine
(GKE). This tutorial
uses the Python version of the
sample. See the
What's next section for gRPC samples in other
The tutorial uses prebuilt container images of the sample code and ESP, which are stored in Container Registry. If you are unfamiliar with containers, see the following for more information:
Use the following high-level task list as you work through the tutorial. All tasks are required to successfully send requests to the API.
- Set up a Google Cloud Platform (GCP) project, and download the required software. See Before you begin.
- Copy and configure files from the
bookstore-grpcsample. See Configuring Endpoints.
- Deploy the Endpoints configuration to create a Endpoints service. See Deploying the Endpoints configuration.
- Create a backend to serve the API and deploy the API. See Deploying the API backend.
- Get the service's external IP address. See Getting the service's external IP address.
- Send a request to the API. See Sending a request to the API.
- Avoid incurring charges to your GCP account. See Clean up.
Before you begin
Sign in to your Google Account.
If you don't already have one, sign up for a new account.
Select or create a GCP project.
Make sure that billing is enabled for your project.
- Make a note of the GCP project ID because it is needed later.
- Install and initialize the Cloud SDK.
- Update the Cloud SDK and install the Endpoints
gcloud components update
- Make sure that the Cloud SDK (
gcloud) is authorized to access your data and services on GCP:
gcloud auth loginA new browser tab opens and you are prompted to choose an account.
- Set the default project to your project ID.
gcloud config set project YOUR_PROJECT_ID
YOUR_PROJECT_IDwith your project ID.
If you have other GCP projects, and you want to use
gcloudto manage them, see Managing Cloud SDK configurations.
gcloud components install kubectl
- Acquire new user credentials to use as the application's default
credentials. The user credentials are needed to authorize
gcloud auth application-default loginIn the new browser tab that opens, choose an account.
- Follow the steps in the gRPC Python quickstart to install gRPC and the gRPC tools.
sample contains the files that you need to copy locally and configure.
- Create a self-contained protobuf descriptor file from your service
- Save a copy of
bookstore.protofrom the example repository. This file defines the Bookstore service's API.
- Create the following directory:
- Create the descriptor file,
api_descriptor.pb, by using the
protocprotocol buffers compiler. Run the following command in the directory where you saved
python -m grpc_tools.protoc \ --include_imports \ --include_source_info \ --proto_path=. \ --descriptor_set_out=api_descriptor.pb \ --python_out=generated_pb2 \ --grpc_python_out=generated_pb2 \ bookstore.proto
In the preceding command,
--proto_pathis set to the current working directory. In your gRPC build environment, if you use a different directory for
.protoinput files, change
--proto_pathso the compiler searches the directory where you saved
- Save a copy of
- Create a gRPC API configuration YAML file:
- Save a copy of the
api_config.yamlfile. This file defines the gRPC API configuration for the Bookstore service.
api_config.yamlfile with your GCP project ID. For example:
# # Name of the service configuration. # name: bookstore.endpoints.example-project-12345.cloud.goog
Note that the
apis.namefield value in this file exactly matches the fully-qualified API name from the
.protofile; otherwise deployment won't work. The Bookstore service is defined in
endpoints.examples.bookstore. Its fully-qualified API name is
endpoints.examples.bookstore.Bookstore, just as it appears in the
apis: - name: endpoints.examples.bookstore.Bookstore
- Save a copy of the
See Configuring Endpoints for more information.
Deploying the Endpoints configuration
- Make sure you are in the directory where the
api_config.yamlfiles are located.
- Confirm that the default project that the
gcloudcommand-line tool is currently using is the GCP project that you want to deploy the Endpoints configuration to. Validate the project ID returned from the following command to make sure that the service doesn't get created in the wrong project.
gcloud config list project
If you need to change the default project, run the following command:
gcloud config set project YOUR_PROJECT_ID
- Deploy the
proto descriptorfile and the configuration file by using the
gcloud endpoints services deploy api_descriptor.pb api_config.yaml
As it is creating and configuring the service, Service Management outputs information to the terminal. When it finishes configuring the service, Service Management outputs the service configuration ID and the service name, similar to the following:
Service Configuration [2017-02-13r0] uploaded for service [bookstore.endpoints.example-project.cloud.goog]
In the previous example,
2017-02-13r0is the service configuration ID and
bookstore.endpoints.example-project.cloud.googis the service name. The service configuration ID consists of a date stamp followed by a revision number. If you deploy the Endpoints configuration again on the same day, the revision number is incremented in the service configuration ID.
If you get an error message, see Troubleshooting Endpoints configuration deployment.
See Deploying the Endpoints configuration for additional information.
Deploying the API backend
So far you have deployed the service configuration to Service Management, but you haven't yet deployed the code that serves the API backend. This section walks you through creating a GKE cluster to host the API backend and deploying the API.
Creating a container cluster
To create a container cluster for our example:
- In the Google Cloud Platform Console, go to the Kubernetes clusters page.
- Click Create cluster.
- Accept the default settings and click Create. Make a note of the cluster name and zone, as they are needed later in this tutorial.
kubectl to the container cluster
kubectl to create and manager cluster resources, you need to get
cluster credentials and make them available to
kubectl. To do this, run the
following command, replacing
NAME with your new cluster
ZONEwith its cluster zone.
gcloud container clusters get-credentials NAME --zone ZONE
Deploying the sample API and ESP to the cluster
To deploy the sample gRPC service to the cluster so that clients can use it:
- Save and open for editing a copy of the grpc-bookstore.yaml deployment manifest file.
SERVICE_NAMEwith the name of your Endpoints service. This is the same name that you configured in the
namefield in the
spec: containers: - name: esp image: gcr.io/endpoints-release/endpoints-runtime:1.16.0 args: [ "--http2_port=9000", "--service=SERVICE_NAME", "--rollout_strategy=managed", "--backend=grpc://127.0.0.1:8000" ] ports: - containerPort: 9000 - name: bookstore image: gcr.io/endpointsv2/python-grpc-bookstore-server:1 ports: - containerPort: 8000
--rollout_strategy=managed"option configures ESP to use the latest deployed service configuration. When you specify this option, within a minute after you deploy a new service configuration, ESP detects the change and automatically begins using it. We recommend that you specify this option instead of a specific configuration ID for ESP to use. For more details on the ESP arguments, see ESP startup options.
spec: containers: - name: esp image: gcr.io/endpoints-release/endpoints-runtime:1 args: [ "--http2_port=9000", "--service=bookstore.endpoints.example-project-12345.cloud.goog", "--rollout_strategy=managed", "--backend=grpc://127.0.0.1:8000" ]
- Start the service:
kubectl create -f grpc-bookstore.yaml
If you get an error message, see Troubleshooting Endpoints in GKE.
Getting the service's external IP address
You need the service's external IP address to send requests to the sample API. It can take a few minutes after you start your service in the container before the external IP address is ready.
View the external IP address:
kubectl get service
Make a note of the value for
EXTERNAL-IPand save it in a
SERVER_IPenvironment variable. The external IP address is used to send requests to the sample API.
Sending a request to the API
To send requests to the sample API, you can use a sample gRPC client written in Python.
Clone the git repo where the gRPC client code is hosted:
git clone https://github.com/GoogleCloudPlatform/python-docs-samples.git
Change your working directory:
pip install virtualenv virtualenv env source env/bin/activate python -m pip install -r requirements.txt
Send a request to the sample API:
python bookstore_client.py --host $SERVER_IP --port 80
If you don't get a successful response, see Troubleshooting response errors.
You just deployed and tested an API in Endpoints!
To avoid incurring charges to your GCP account for the resources used in this quickstart:
Delete the API:
gcloud endpoints services delete SERVICE_NAME
SERVICE_NAMEwith the name of your API.
Delete the GKE cluster:
gcloud container clusters delete NAME --zone ZONE