Getting started with Endpoints for GKE with ESP

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OpenAPI   |  gRPC

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 bookstore-grpc sample. See the What's next section for gRPC samples in other languages.

The tutorial uses prebuilt container images of the sample code and ESP, which are stored in Artifact Registry. If you are unfamiliar with containers, see the following for more information:

• Deploying a containerized web application
• Kubernetes container images

For an overview of Cloud Endpoints, see About Endpoints and Endpoints architecture.

Objectives

Use the following high-level task list as you work through the tutorial. All tasks are required to successfully send requests to the API.

1. Set up a Google Cloud project, and download the required software. See Before you begin.
2. Copy and configure files from the bookstore-grpc sample. See Configuring Endpoints.
3. Deploy the Endpoints configuration to create a Endpoints service. See Deploying the Endpoints configuration.
4. Create a backend to serve the API and deploy the API. See Deploying the API backend.
5. Get the service's external IP address. See Getting the service's external IP address.
6. Send a request to the API. See Sending a request to the API.
7. Avoid incurring charges to your Google Cloud account. See Clean up.

Costs

In this document, you use the following billable components of Google Cloud:

• GKE
• Endpoints

To generate a cost estimate based on your projected usage, use the pricing calculator. New Google Cloud users might be eligible for a free trial.

When you finish the tasks that are described in this document, you can avoid continued billing by deleting the resources that you created. For more information, see Clean up.

Before you begin

1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

3. Make sure that billing is enabled for your Google Cloud project.

4. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

5. Make sure that billing is enabled for your Google Cloud project.

6. Make a note of the Google Cloud project ID because it is needed later.
7. Install and initialize the Google Cloud CLI.
8. Update the gcloud CLI and install the Endpoints components.

   gcloud components update

9. Make sure that the Google Cloud CLI (gcloud) is authorized to access your data and services on Google Cloud:

   gcloud auth login

   A new browser tab opens and you are prompted to choose an account.
10. Set the default project to your project ID.

    gcloud config set project YOUR_PROJECT_ID

    Replace YOUR_PROJECT_ID with your project ID.

    If you have other Google Cloud projects, and you want to use gcloud to manage them, see Managing gcloud CLI configurations.

11. Install kubectl:

    gcloud components install kubectl

12. Acquire new user credentials to use as the application's default credentials. The user credentials are needed to authorize kubectl.

    gcloud auth application-default login

    In the new browser tab that opens, choose an account.
13. Follow the steps in the gRPC Python quickstart to install gRPC and the gRPC tools.

Configuring Endpoints

The bookstore-grpc sample contains the files that you need to copy locally and configure.

1. Create a self-contained protobuf descriptor file from your service .proto file:
   1. Save a copy of bookstore.proto from the example repository. This file defines the Bookstore service's API.
   2. Create the following directory: mkdir generated_pb2
   3. Create the descriptor file, api_descriptor.pb, by using the protoc protocol buffers compiler. Run the following command in the directory where you saved bookstore.proto:

      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_path is set to the current working directory. In your gRPC build environment, if you use a different directory for .proto input files, change --proto_path so the compiler searches the directory where you saved bookstore.proto.

2. Create a gRPC API configuration YAML file:
   1. Save a copy of the api_config.yamlfile. This file defines the gRPC API configuration for the Bookstore service.
   2. Replace MY_PROJECT_ID in your api_config.yaml file with your Google Cloud project ID. For example:

      #
      # Name of the service configuration.
      #
      name: bookstore.endpoints.example-project-12345.cloud.goog
      

      Note that the apis.name field value in this file exactly matches the fully-qualified API name from the .proto file; otherwise deployment won't work. The Bookstore service is defined in bookstore.proto inside package endpoints.examples.bookstore. Its fully-qualified API name is endpoints.examples.bookstore.Bookstore, just as it appears in the api_config.yaml file.

      apis:
        - name: endpoints.examples.bookstore.Bookstore
      

See Configuring Endpoints for more information.

Deploying the Endpoints configuration

To deploy the Endpoints configuration, you use the gcloud endpoints services deploy command. This command uses Service Management to create a managed service.

1. Make sure you are in the directory where the api_descriptor.pb and api_config.yaml files are located.
2. Confirm that the default project that the gcloud command-line tool is currently using is the Google Cloud 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
   

3. Deploy the proto descriptor file and the configuration file by using the Google Cloud CLI:

   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 the deployment completes, a message similar to the following is displayed:

   Service Configuration [CONFIG_ID] uploaded for service [bookstore.endpoints.example-project.cloud.goog]

   CONFIG_ID is the unique Endpoints service configuration ID created by the deployment. For example:

   Service Configuration [2017-02-13r0] uploaded for service [bookstore.endpoints.example-project.cloud.goog]
   

   In the previous example, 2017-02-13r0 is the service configuration ID and bookstore.endpoints.example-project.cloud.goog is 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.

Checking required services

At a minimum, Endpoints and ESP require the following Google services to be enabled:

Name	Title
servicemanagement.googleapis.com	Service Management API
servicecontrol.googleapis.com	Service Control API

In most cases, the gcloud endpoints services deploy command enables these required services. However, the gcloud command completes successfully but doesn't enable the required services in the following circumstances:

• If you used a third-party application such as Terraform, and you don't include these services.

• You deployed the Endpoints configuration to an existing Google Cloud project in which these services were explicitly disabled.

Use the following command to confirm that the required services are enabled:

gcloud services list

If you do not see the required services listed, enable them:

gcloud services enable servicemanagement.googleapis.com
gcloud services enable servicecontrol.googleapis.com

Also enable your Endpoints service:

gcloud services enable ENDPOINTS_SERVICE_NAME

To determine the ENDPOINTS_SERVICE_NAME you can either:

• After deploying the Endpoints configuration, go to the Endpoints page in the Cloud console. The list of possible ENDPOINTS_SERVICE_NAME are shown under the Service name column.

• For OpenAPI, the ENDPOINTS_SERVICE_NAME is what you specified in the host field of your OpenAPI spec. For gRPC, the ENDPOINTS_SERVICE_NAME is what you specified in the name field of your gRPC Endpoints configuration.

For more information about the gcloud commands, see gcloud services.

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:

1. In the Google Cloud console, go to the Kubernetes clusters page.
   

   Go to the Kubernetes clusters page

2. Click Create cluster.
3. Accept the default settings and click Create. Make a note of the cluster name and zone, as they are needed later in this tutorial.

Authenticating kubectl to the container cluster

To use 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 name and ZONE with its cluster zone.

gcloud container clusters get-credentials NAME --zone ZONE

Checking required permissions

ESP and ESPv2 calls Google services which use IAM to verify if the calling identity has enough permissions to access the used IAM resources. The calling identity is the attached service account deploying ESP and ESPv2.

When deployed in GKE pod, the attached service account is the node service account. Usually it is the Compute Engine default service account. Please follow this permission recommendation to choose a proper node service account.

If Workload Identity is used, a separate service account other than the node service account can be used to talk to Google services. You can create a Kubernetes service account for the pod to run ESP and ESPv2, create a Google service account and associate the Kubernetes service account to the Google service account.

Follow these steps to associate a Kubernetes service account with a Google service account. This Google service account is the attached service account.

If the attached service account is the Compute Engine default service account of the project and the endpoint service configuration is deployed in the same project, the service account should have enough permissions to access the IAM resources, following IAM roles setup step can be skipped. Otherwise following IAM roles should be added to the attached service account.

Add required IAM roles:

This section describes the IAM resources used by ESP and ESPv2 and the IAM roles required for the attached service account to access these resources.

Endpoint Service Configuration

ESP and ESPv2 call Service Control which uses the endpoint service configuration. The endpoint service configuration is an IAM resource and ESP and ESPv2 need the Service Controller role to access it.

The IAM role is on the endpoint service configuration, not on the project. A project may have multiple endpoint service configurations.

Use the following gcloud command to add the role to the attached service account for the endpoint service configuration.

gcloud endpoints services add-iam-policy-binding SERVICE_NAME \
  --member serviceAccount:SERVICE_ACCOUNT_NAME@DEPLOY_PROJECT_ID.iam.gserviceaccount.com \
  --role roles/servicemanagement.serviceController

Where
* SERVICE_NAME is the endpoint service name
* SERVICE_ACCOUNT_NAME@DEPLOY_PROJECT_ID.iam.gserviceaccount.com is the attached service account.

Cloud Trace

ESP and ESPv2 call Cloud Trace service to export Trace to a project. This project is called the tracing project. In ESP, the tracing project and the project that owns the endpoint service configuration are the same. In ESPv2, the tracing project can be specified by the flag --tracing_project_id, and defaults to the deploying project.

ESP and ESPv2 require the Cloud Trace Agent role to enable Cloud Trace.

Use the following gcloud command to add the role to the attached service account:

gcloud projects add-iam-policy-binding TRACING_PROJECT_ID \
  --member serviceAccount:SERVICE_ACCOUNT_NAME@DEPLOY_PROJECT_ID.iam.gserviceaccount.com \
  --role roles/cloudtrace.agent

Where
* TRACING_PROJECT_ID is the tracing project ID
* SERVICE_ACCOUNT_NAME@DEPLOY_PROJECT_ID.iam.gserviceaccount.com is the attached service account. For more information, see What are roles and permissions?

Deploying the sample API and ESP to the cluster

To deploy the sample gRPC service to the cluster so that clients can use it:

1. Save and open for editing a copy of the grpc-bookstore.yaml deployment manifest file.
2. Replace SERVICE_NAME with the name of your Endpoints service. This is the same name that you configured in the name field in the api_config.yaml file.

   spec:
     containers:
     - name: esp
       image: gcr.io/endpoints-release/endpoints-runtime:1
       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

   The --rollout_strategy=managed option configures ESP to use the latest deployed service configuration. When you specify this option, up to 5 minutes 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.

   For example:

       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"
           ]
   

   We recommend that you set the imagePullPolicy in the container spec above to Always. This will allow newly created pods to always pull the latest version of ESP. Read about configuration best practices for more details. When a new version of ESP is released, you can perform a rolling restart of all pods to ensure the newest version is used.

3. Start the service:

   kubectl create -f grpc-bookstore.yaml
   

Note: If your GKE cluster is private, you need to use the Compute Engine metadata server as the DNS resolver to resolve Google service host names. Add this ESP flag: --dns=169.254.169.254

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.

1. View the external IP address:

   kubectl get service

2. Make a note of the value for EXTERNAL-IP and save it in a SERVER_IP environment variable. The external IP address is used to send requests to the sample API.

   export SERVER_IP=YOUR_EXTERNAL_IP
   

Sending a request to the API

To send requests to the sample API, you can use a sample gRPC client written in Python.

1. Clone the git repo where the gRPC client code is hosted:

   git clone https://github.com/GoogleCloudPlatform/python-docs-samples.git
      

2. Change your working directory:

   cd python-docs-samples/endpoints/bookstore-grpc/
     

3. Install dependencies:

   pip install virtualenv
   virtualenv env
   source env/bin/activate
   python -m pip install -r requirements.txt
   

4. Send a request to the sample API:

   python bookstore_client.py --host SERVER_IP --port 80
   

   • Look at the activity graphs for your API in the Endpoints > Services page.
     

     Go to the Endpoints Services page

     It may take a few moments for the request to be reflected in the graphs.

   • Look at the request logs for your API in the Logs Explorer page.
     

     Go to the Logs Explorer page

If you don't get a successful response, see Troubleshooting response errors.

You just deployed and tested an API in Endpoints!

Note: If you want to let us know what you liked about this tutorial, feel free to provide feedback at the bottom of this page. Your feedback helps us improve these tutorials.
If you got an error, join the google-cloud-endpoints Google group and let us know what went wrong. This group receives responses from other Endpoints users and from Google engineers, who monitor the group.

Clean up

To avoid incurring charges to your Google Cloud account for the resources used in this tutorial, either delete the project that contains the resources, or keep the project and delete the individual resources.

1. Delete the API:

   gcloud endpoints services delete SERVICE_NAME
   

   Replace SERVICE_NAME with the name of your API.

2. Delete the GKE cluster:

   gcloud container clusters delete NAME --zone ZONE
   

What's next

• Find out how to configure your own gRPC API for Cloud Endpoints.
• Look through the Bookstore sample on GitHub in more detail. Both the client and server are available in Python and Java.
• The getting-started-grpc sample is available on GitHub in the following languages:
  • Java
  • Python
  • Ruby
  • Go
  • Node.js