Getting started with Endpoints for Compute Engine with ESP

This page shows you how to deploy a simple example gRPC service with the Extensible Service Proxy (ESP) in a Docker container in Compute Engine.

This page uses the Python version of the bookstore-grpc sample. See the What's next section for gRPC samples in other languages.

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


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 required software. See Before you begin.
  2. Create a Compute Engine VM instance. See Creating a Compute Engine instance.
  3. Copy and configure files from the bookstore-grpc sample. See Configuring Endpoints.
  4. Deploy the Endpoints configuration to create an Endpoints service. See Deploying the Endpoints configuration.
  5. Deploy the API and ESP on the Compute Engine VM. See Deploying the API backend.
  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.


This tutorial uses the following billable components of Google Cloud:

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 this tutorial, you can avoid continued billing by deleting the resources you created. For more information, see Cleaning up.

Before you begin

  1. Sign in to your Google Account.

    If you don't already have one, sign up for a new account.

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

    Go to the project selector page

  3. Make sure that billing is enabled for your Cloud project. Learn how to confirm that billing is enabled for your project.

  4. Make a note of the project ID because it's needed later.
  5. Install and initialize the Cloud SDK.
  6. Update the Cloud SDK and install the Endpoints components:
    gcloud components update
  7. Make sure that the Cloud SDK (gcloud) is authorized to access your data and services on Google Cloud:
    gcloud auth login
    In the new browser tab that opens, select an account.
  8. 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 Cloud SDK Configurations.

  9. Follow the steps in the gRPC Python quickstart to install gRPC and the gRPC tools.

Creating a Compute Engine instance

    To create a Compute Engine instance:

    1. In the Cloud Console, go to the VM instances page.

      Go to VM instances

    2. Click Create instance.
    3. In the Firewall section, select Allow HTTP traffic and Allow HTTPS traffic.
    4. Click Create to create the instance.
    5. Screenshot of the VM instance creation window with the required options set

      Allow a short time for the instance to start up. Once ready, it is listed on the VM Instances page with a green status icon.

    6. Make sure you that you can connect to your VM instance.
      1. In the list of virtual machine instances, click SSH in the row of the instance that you want to connect to.
      2. You can now use the terminal to run Linux commands on your Debian instance.
      3. Enter exit to disconnect from the instance.
    7. Make a note the instance name, zone, and external IP address because they are needed later.

Configuring Endpoints

Clone the bookstore-grpc sample repository from GitHub.

To configure Endpoints:

  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 \

      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.

      Note that the 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.

        - 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 gcloud command-line tool:
    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 []

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

    Service Configuration [2017-02-13r0] uploaded for service []

    In the previous example, 2017-02-13r0 is the service configuration ID and 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 Service Management API Service Control API Google Cloud Endpoints

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
gcloud services enable
gcloud services enable

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 API configuration to Service Management, but you haven't yet deployed the code that serves the API backend. This section walks you through getting Docker set up on your VM instance and running the API backend code and the ESP in a Docker container.

Install Docker on the VM instance

To install Docker on the VM instance:

  1. Set the zone for your project by running the command:
    gcloud config set compute/zone YOUR_INSTANCE_ZONE

    Replace YOUR_INSTANCE_ZONE with the zone where your instance is running.

  2. Connect to your instance by using the following command:
    gcloud compute ssh INSTANCE_NAME

    Replace INSTANCE_NAME with your VM instance name.

  3. See the Docker documentation to set up the Docker repository. Make sure to follow the steps that match the version and architecture of your VM instance:
    • Jessie or newer
    • x86_64 / amd64

Run the sample API and ESP in a Docker container

To run the sample gRPC service with ESP in a Docker container so that clients can use it:

  1. On the VM instance, create your own container network called esp_net.
    sudo docker network create --driver bridge esp_net
  2. Run the sample Bookstore server that serves the sample API:
    sudo docker run \
        --detach \
        --name=bookstore \
        --net=esp_net \
  3. Run the pre-packaged ESP Docker container. In the ESP startup options, replace SERVICE_NAME with the name of your service. This is the same name that you configured in the name field in the api_config.yaml file. For example:
    sudo docker run \
        --detach \
        --name=esp \
        --publish=80:9000 \
        --net=esp_net \ \
        --service=SERVICE_NAME \
        --rollout_strategy=managed \
        --http2_port=9000 \

    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.

If you have Transcoding enabled, make sure to configure a port for HTTP1.1 or SSL traffic.

If you get an error message, see Troubleshooting Endpoints on Compute Engine.

Sending a request to the API

If you’re sending the request from the same instance in which the Docker containers are running, you can replace $SERVER_IP with localhost. Otherwise replace $SERVER_IP with the external IP of the instance.

You can find the external IP address by running:

gcloud compute instances list

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

  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 --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!

Cleaning 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 service.

  2. In the Cloud Console, go to the VM instances page.

    Go to VM instances

  3. Click the checkbox for the instance you want to delete.
  4. Click Delete to delete the instance.

What's next