Using system packages tutorial

This tutorial shows how to build a custom Cloud Run service that transforms a graph description input parameter into a diagram in the PNG image format. It uses Graphviz and is installed as a system package in the service's container environment. Graphviz is used via command-line utilities to serve requests.

You can use this tutorial with Cloud Run (fully managed) or Cloud Run for Anthos on Google Cloud.


  • Write and build a custom container with a Dockerfile
  • Write, build, and deploy a Cloud Run service
  • Use Graphviz dot utility to generate diagrams
  • Test the service by posting a DOT syntax diagram from the collection or your own creation


This tutorial uses billable components of Cloud Platform, including:

Use the Pricing Calculator to generate a cost estimate based on your projected usage.

New Cloud Platform users might be eligible for a free trial.

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

    Go to the project selector page

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

  4. Enable the Cloud Run API
  5. Install and initialize the Cloud SDK.
  6. For Cloud Run for Anthos on Google Cloud install the gcloud kubectl component:
    gcloud components install kubectl
  7. Update components:
    gcloud components update
  8. Install curl to try out the service
  9. If you are using Cloud Run for Anthos on Google Cloud, create a new cluster using the instructions in Setting up Cloud Run for Anthos on Google Cloud.

Setting up gcloud defaults

To configure gcloud with defaults for your Cloud Run service:

  1. Set your default project:

    gcloud config set project PROJECT_ID

    Replace PROJECT_ID with the name of the project you created for this tutorial.

  2. If you are using Cloud Run (fully managed), configure gcloud for your chosen region:

    gcloud config set run/region REGION

    Replace REGION with the supported Cloud Run region of your choice.

  3. If you are using Cloud Run for Anthos on Google Cloud, configure gcloud for your cluster:

    gcloud config set run/cluster CLUSTER-NAME
    gcloud config set run/cluster_location REGION


    • CLUSTER-NAME with the name you used for your cluster,
    • REGION with the supported cluster location of your choice.

Cloud Run locations

Cloud Run is regional, which means the infrastructure that runs your Cloud Run services is located in a specific region and is managed by Google to be redundantly available across all the zones within that region.

Meeting your latency, availability, or durability requirements are primary factors for selecting the region where your Cloud Run services are run. You can generally select the region nearest to your users but you should consider the location of the other Google Cloud products that are used by your Cloud Run service. Using Google Cloud products together across multiple locations can affect your service's latency as well as cost.

Cloud Run is available in the following regions:

Subject to Tier 1 pricing

  • asia-east1 (Taiwan)
  • asia-northeast1 (Tokyo)
  • asia-northeast2 (Osaka)
  • europe-north1 (Finland)
  • europe-west1 (Belgium)
  • europe-west4 (Netherlands)
  • us-central1 (Iowa)
  • us-east1 (South Carolina)
  • us-east4 (Northern Virginia)
  • us-west1 (Oregon)

Subject to Tier 2 pricing

  • asia-east2 (Hong Kong)
  • asia-northeast3 (Seoul, South Korea)
  • asia-southeast1 (Singapore)
  • asia-southeast2 (Jakarta)
  • asia-south1 (Mumbai, India)
  • australia-southeast1 (Sydney)
  • europe-west2 (London, UK)
  • europe-west3 (Frankfurt, Germany)
  • europe-west6 (Zurich, Switzerland)
  • northamerica-northeast1 (Montreal)
  • southamerica-east1 (Sao Paulo, Brazil)

Note that it is not possible to use the domain mapping feature of Cloud Run (fully managed) for services in these regions:

  • asia-east2
  • asia-northeast2
  • asia-northeast3
  • asia-southeast1
  • asia-southeast2
  • asia-south1
  • australia-southeast1
  • europe-west2
  • europe-west3
  • europe-west6
  • northamerica-northeast1
  • southamerica-east1
You can use Cloud Load Balancing with a serverless NEG to map a custom domain to Cloud Run (fully managed) services in these regions.

If you already created a Cloud Run service, you can view the region in the Cloud Run dashboard in the Cloud Console.

Retrieving the code sample

To retrieve the code sample for use:

  1. Clone the sample app repository to your local machine:


    git clone

    Alternatively, you can download the sample as a zip file and extract it.


    git clone

    Alternatively, you can download the sample as a zip file and extract it.


    git clone

    Alternatively, you can download the sample as a zip file and extract it.


    git clone

    Alternatively, you can download the sample as a zip file and extract it.

  2. Change to the directory that contains the Cloud Run sample code:


    cd nodejs-docs-samples/run/system-package/


    cd python-docs-samples/run/system-package/


    cd golang-samples/run/system_package/


    cd java-docs-samples/run/system-package/

Visualizing the architecture

The basic architecture looks like this:

Diagram showing request flow from user to web service to graphviz dot
For the diagram source, see the DOT Description

The user makes an HTTP request to the Cloud Run service which executes a Graphviz utility to transform the request into an image. That image is delivered to the user as the HTTP response.

Understanding the code

Defining your environment configuration with the Dockerfile

Your Dockerfile is specific to the language and base operating environment, such as Ubuntu, that your service will use.

The Build and Deploy Quickstart shows various Dockerfiles that can be used as a starting point to build a Dockerfile for other services.

This service requires one or more additional system packages not available by default.

  1. Open the Dockerfile in an editor.

  2. Look for a Dockerfile RUN statement. This statement allows running arbitrary shell commands to modify the environment. If the Dockerfile has multiple stages, identified by finding multiple FROM statements, it will be found in the last stage.

    The specific packages required and the mechanism to install them varies by the operating system declared inside the container.

    To get instructions for your operating system or base image, click the appropriate tab.

    To determine the operating system of your container image, check the name in the FROM statement or a README associated with your base image. For example, if you extend from node, you can find documentation and the parent Dockerfile on Docker Hub.

  3. Test your customization by building the image, using docker build locally or Cloud Build.

Handling incoming requests

The sample service uses parameters from the incoming HTTP request to invoke a system call that executes the appropriate dot utility command.

In the HTTP handler below, a graph description input parameter is extracted from the dot querystring variable.

Graph descriptions can include characters which must be URL encoded for use in a querystring.


app.get('/diagram.png', (req, res) => {
  try {
    const image = createDiagram(;
    res.setHeader('Content-Type', 'image/png');
    res.setHeader('Content-Length', image.length);
    res.setHeader('Cache-Control', 'public, max-age=86400');
  } catch (err) {
    console.error(`error: ${err.message}`);
    const errDetails = (err.stderr || err.message).toString();
    if (errDetails.includes('syntax')) {
      res.status(400).send(`Bad Request: ${err.message}`);
    } else {
      res.status(500).send('Internal Server Error');


@app.route("/diagram.png", methods=["GET"])
def index():
    # Takes an HTTP GET request with query param dot and
    # returns a png with the rendered DOT diagram in a HTTP response.
        image = create_diagram(request.args.get("dot"))
        response = make_response(image)
        response.headers.set("Content-Type", "image/png")
        return response

    except Exception as e:
        print("error: {}".format(e))

        # If no graphviz definition or bad graphviz def, return 400
        if "syntax" in str(e):
            return "Bad Request: {}".format(e), 400

        return "Internal Server Error", 500


// diagramHandler renders a diagram using HTTP request parameters and the dot command.
func diagramHandler(w http.ResponseWriter, r *http.Request) {
	if r.Method != http.MethodGet {
		log.Printf("method not allowed: %s", r.Method)
		http.Error(w, fmt.Sprintf("HTTP Method %s Not Allowed", r.Method), http.StatusMethodNotAllowed)

	q := r.URL.Query()
	dot := q.Get("dot")
	if dot == "" {
		log.Print("no graphviz definition provided")
		http.Error(w, "Bad Request", http.StatusBadRequest)

	// Cache header must be set before writing a response.
	w.Header().Set("Cache-Control", "public, max-age=86400")

	input := strings.NewReader(dot)
	if err := createDiagram(w, input); err != nil {
		log.Printf("createDiagram: %v", err)
		// Do not cache error responses.
		if strings.Contains(err.Error(), "syntax") {
			http.Error(w, "Bad Request: DOT syntax error", http.StatusBadRequest)
		} else {
			http.Error(w, "Internal Server Error", http.StatusInternalServerError)


    (req, res) -> {
      InputStream image = null;
      try {
        String dot = req.queryParams("dot");
        image = createDiagram(dot);
        res.header("Content-Type", "image/png");
        res.header("Content-Length", Integer.toString(image.available()));
        res.header("Cache-Control", "public, max-age=86400");
      } catch (Exception e) {
        if (e.getMessage().contains("syntax")) {
          return String.format("Bad Request: %s", e.getMessage());
        } else {
          return "Internal Server Error";
      return image;

You'll need to differentiate between internal server errors and invalid user input. This sample service returns an Internal Server Error for all dot command-line errors unless the error message contains the string syntax, which indicates a user input problem.

Generating a diagram

The core logic of diagram generation uses the dot command-line tool to process the graph description input parameter into a diagram in the PNG image format.


// Generate a diagram based on a graphviz DOT diagram description.
const createDiagram = (dot) => {
  if (!dot) {
    throw new Error('syntax: no graphviz definition provided');

  // Adds a watermark to the dot graphic.
  const dotFlags = [
    '-Glabel="Made on Cloud Run"',
  ].join(' ');

  const image = execSync(`/usr/bin/dot ${dotFlags} -Tpng`, {
    input: dot,
  return image;


def create_diagram(dot):
    # Generates a diagram based on a graphviz DOT diagram description.
    if not dot:
        raise Exception("syntax: no graphviz definition provided")

    dot_args = [  # These args add a watermark to the dot graphic.
        "-Glabel=Made on Cloud Run",

    # Uses local `dot` binary from Graphviz:
    image =
        ["dot"] + dot_args, input=dot.encode("utf-8"), stdout=subprocess.PIPE

    if not image:
        raise Exception("syntax: bad graphviz definition provided")
    return image


// createDiagram generates a diagram image from the provided io.Reader written to the io.Writer.
func createDiagram(w io.Writer, r io.Reader) error {
	stderr := new(bytes.Buffer)
	args := []string{
		"-Glabel=Made on Cloud Run",
	cmd := exec.Command("/usr/bin/dot", args...)
	cmd.Stdin = r
	cmd.Stdout = w
	cmd.Stderr = stderr

	if err := cmd.Run(); err != nil {
		return fmt.Errorf("exec(%s) failed (%v): %s", cmd.Path, err, stderr.String())

	return nil


// Generate a diagram based on a graphviz DOT diagram description.
public static InputStream createDiagram(String dot) {
  if (dot == null || dot.isEmpty()) {
    throw new NullPointerException("syntax: no graphviz definition provided");
  // Adds a watermark to the dot graphic.
  List<String> args = new ArrayList<String>();
  args.add("-Glabel=\"Made on Cloud Run\"");

  StringBuilder output = new StringBuilder();
  InputStream stdout = null;
  try {
    ProcessBuilder pb = new ProcessBuilder(args);
    Process process = pb.start();
    OutputStream stdin = process.getOutputStream();
    stdout = process.getInputStream();
    // The Graphviz dot program reads from stdin.
    Writer writer = new OutputStreamWriter(stdin, "UTF-8");
  } catch (Exception e) {
  return stdout;

Designing a secure service

Any vulnerabilities in the dot tool are potential vulnerabilities of the web service. You can mitigate this by using up-to-date versions of the graphviz package through re-building the container image on a regular basis.

If you extend the current sample to accept user input as command-line parameters, you should protect against command-injection attacks. Some of the ways to prevent injection attacks include:

  • Mapping inputs to a dictionary of supported parameters
  • Validating inputs match a range of known-safe values, perhaps using regular expressions
  • Escaping inputs to ensure shell syntax is not evaluated

Shipping the code

To ship your code, you build with Cloud Build, and upload to Container Registry, and deploy to Cloud Run or Cloud Run for Anthos on Google Cloud:

  1. Run the following command to build your container and publish on Container Registry.


    gcloud builds submit --tag

    Where PROJECT_ID is your GCP project ID, and graphviz is the name you want to give your service.

    Upon success, you will see a SUCCESS message containing the ID, creation time, and image name. The image is stored in Container Registry and can be re-used if desired.


    gcloud builds submit --tag

    Where PROJECT_ID is your GCP project ID, and graphviz is the name you want to give your service.

    Upon success, you will see a SUCCESS message containing the ID, creation time, and image name. The image is stored in Container Registry and can be re-used if desired.


    gcloud builds submit --tag

    Where PROJECT_ID is your GCP project ID, and graphviz is the name you want to give your service.

    Upon success, you will see a SUCCESS message containing the ID, creation time, and image name. The image is stored in Container Registry and can be re-used if desired.


    This sample uses Jib to build Docker images using common Java tools. Jib optimizes container builds without the need for a Dockerfile or having Docker installed. Learn more about building Java containers with Jib.

    1. Using the Dockerfile, configure and build a base image with the system packages installed to override Jib's default base image:

      # Use the Official OpenJDK image for a lean production stage of our multi-stage build.
      FROM adoptopenjdk/openjdk11:alpine
      RUN apk --no-cache add graphviz ttf-ubuntu-font-family
      gcloud builds submit --tag

      Where PROJECT_ID is your GCP project ID.

    2. Build your final container with Jib and publish on Container Registry:

      mvn compile jib:build \ \

      Where PROJECT_ID is your GCP project ID.

  2. Deploy using the following command:

    gcloud run deploy graphviz-web --image

    Where PROJECT_ID is your GCP project ID, and graphviz is the name of the container from above and graphviz-web is the name of the service.

    If deploying to Cloud Run, answer Y to the "allow unauthenticated" prompt. See Managing Access for more details on IAM-based authentication.

    Wait until the deployment is complete: this can take about half a minute. On success, the command line displays the service URL.

  3. If you want to deploy a code update to the service, repeat the previous steps. Each deployment to a service creates a new revision and automatically starts serving traffic when ready.

Try it out

Try out your service by sending HTTP POST requests with DOT syntax descriptions in the request payload.

  1. Send an HTTP request to your service.

    Copy the URL into your browser URL bar and update [SERVICE_DOMAIN]:

    https://SERVICE_DOMAIN/diagram.png?dot=digraph Run { rankdir=LR Code -> Build -> Deploy -> Run }

    You can embed the diagram in a web page:

    <img src="https://SERVICE_DOMAIN/diagram.png?dot=digraph Run { rankdir=LR Code -> Build -> Deploy -> Run }" />

    Services deployed on Cloud Run for Anthos on Google Cloud without a custom domain will need to modify this command.

    1. If you do not already have it, determine the ingress gateway of your cluster.

      export GATEWAY_IP="$(kubectl get svc istio-ingressgateway \
         --namespace istio-system \
         --output 'jsonpath={.status.loadBalancer.ingress[0].ip}')"
    2. Run a curl command using this GATEWAY_IP address in the URL.

      curl -G -H "Host: SERVICE_DOMAIN" https://$GATEWAY_IP/diagram.png \
         --data-urlencode "dot=digraph Run { rankdir=LR Code -> Build -> Deploy -> Run }" \
         > diagram.png
  2. Open the resulting diagram.png file in any application that supports PNG files, such as Chrome.

    It should look like this:

    Diagram showing the stage flow
  of Code to Build to Deploy to 'Run'.
    Source: DOT Description

You can explore a small collection of ready-made diagram descriptions.

  1. Copy the contents of the selected .dot file
  2. Paste it into a curl command similar to the above:


Cleaning up

If you created a new project for this tutorial, delete the project. If you used an existing project and wish to keep it without the changes added in this tutorial, delete resources created for the tutorial.

Deleting the project

The easiest way to eliminate billing is to delete the project that you created for the tutorial.

To delete the project:

  1. In the Cloud Console, go to the Manage resources page.

    Go to the Manage resources page

  2. In the project list, select the project that you want to delete and then click Delete .
  3. In the dialog, type the project ID and then click Shut down to delete the project.

Deleting tutorial resources

  1. Delete the Cloud Run service you deployed in this tutorial:

    gcloud run services delete SERVICE-NAME

    Where SERVICE-NAME is your chosen service name.

    You can also delete Cloud Run services from the Google Cloud Console.

  2. Remove the gcloud default configurations you added during tutorial setup.

    If you use Cloud Run (fully managed), remove the region setting:

     gcloud config unset run/region

    If you use Cloud Run for Anthos on Google Cloud, remove the cluster configuration:

     gcloud config unset run/cluster run/cluster
     gcloud config unset run/cluster run/cluster_location
  3. Remove the project configuration:

     gcloud config unset project
  4. Delete other Google Cloud resources created in this tutorial:

What's next

  • Experiment with your graphviz app:
  • Try out other Google Cloud features for yourself. Have a look at our tutorials.