Workflows Tutorial

Deploy the first Cloud Run functions

After receiving an HTTP request, this HTTP function generates a random number between 1 and 100, and then returns the number in JSON format.

1. Create a directory called randomgen and change to it:

   mkdir ~/randomgen
   cd ~/randomgen

2. Create a text file with the filename main.py that contains the following Python code:

   import functions_framework
   import random
   from flask import jsonify
   
   
   @functions_framework.http
   def randomgen(request):
       randomNum = random.randint(1, 100)
       output = {"random": randomNum}
       return jsonify(output)

3. To support a dependency on Flask for HTTP processing, create a text file for the pip package manager. Give it the filename requirements.txt and add the following:

   flask>=1.0.2
   functions-framework==3.0.0

4. Deploy the function with an HTTP trigger, and allow unauthenticated access:

   gcloud functions deploy randomgen-function \
       --gen2 \
       --runtime python310 \
       --entry-point=randomgen \
       --trigger-http \
       --allow-unauthenticated

   The function might take a few minutes to deploy. Alternatively, you can use the Cloud Run functions interface in the Google Cloud console to deploy the function.

5. Once the randomgen function is deployed, you can confirm the httpsTrigger.url property:

   gcloud functions describe randomgen-function \
       --gen2 \
       --format="value(serviceConfig.uri)"

6. Save the URL. You will need to add it to your Workflow source file in later exercises.

7. You can try out the function with the following curl command:

   curl $(gcloud functions describe randomgen-function \
       --gen2 \
       --format="value(serviceConfig.uri)")

   A number is randomly generated and returned.

Deploy the second Cloud Run functions

After receiving an HTTP request, this HTTP function extracts the input from the JSON body, multiplies it by 2, and returns the result in JSON format.

1. Navigate back to your home directory:

   cd ~

2. Create a directory called multiply and change to it:

   mkdir ~/multiply
   cd ~/multiply

3. Create a text file with the filename main.py that contains the following Python code:

   import functions_framework
   from flask import jsonify
   
   
   @functions_framework.http
   def multiply(request):
       request_json = request.get_json()
       output = {"multiplied": 2 * request_json['input']}
       return jsonify(output)

4. To support a dependency on Flask for HTTP processing, create a text file for the pip package manager. Give it the filename requirements.txt and add the following:

   flask>=1.0.2
   functions-framework==3.0.0

5. Deploy the function with an HTTP trigger, and allow unauthenticated access:

   gcloud functions deploy multiply-function \
       --gen2 \
       --runtime python310 \
       --entry-point=multiply \
       --trigger-http \
       --allow-unauthenticated

   The function might take a few minutes to deploy. Alternatively, you can use the Cloud Run functions interface in the Google Cloud console to deploy the function.

6. Once the multiply function is deployed, you can confirm the httpsTrigger.url property:

   gcloud functions describe multiply-function \
       --gen2\
       --format="value(serviceConfig.uri)"

7. Save the URL. You will need to add it to your Workflow source file in later exercises.

8. You can try out the function with the following curl command:

   curl -X POST MULTIPLY_FUNCTION_URL \
       -H "Authorization: Bearer $(gcloud auth print-identity-token)" \
       -H "Content-Type: application/json" \
       -d '{"input": 5}'

   The number 10 should be returned.

Connect the two Cloud Run functions in a workflow

A workflow is made up of a series of steps described using the Workflows syntax, which can be written in either YAML or JSON format. This is the workflow's definition. For a detailed explanation, see the Syntax reference page.

1. Navigate back to your home directory:

   cd ~

2. Create a text file with the filename workflow.yaml that contains the following content:

   - randomgen_function:
       call: http.get
       args:
           url: RANDOMGEN_FUNCTION_URL
       result: randomgen_result
   - multiply_function:
       call: http.post
       args:
           url: MULTIPLY_FUNCTION_URL
           body:
               input: ${randomgen_result.body.random}
       result: multiply_result
   - return_result:
       return: ${multiply_result}
   

   • Replace RANDOMGEN_FUNCTION_URL with the URL of your randomgen function.
   • Replace MULTIPLY_FUNCTION_URL with the URL of your multiply function.

   This source file links the two HTTP functions together and returns a final result.

3. After creating the workflow, you can deploy it, which makes it ready for execution.

   gcloud workflows deploy WORKFLOW_NAME \
       --source=workflow.yaml \
       --service-account=${SERVICE_ACCOUNT}@PROJECT_ID.iam.gserviceaccount.com

   Replace WORKFLOW_NAME with a name for your workflow.

4. Execute the workflow:

   gcloud workflows run WORKFLOW_NAME

   An execution is a single run of the logic contained in a workflow's definition. All workflow executions are independent, and the rapid scaling of Workflows allows for a high number of concurrent executions.

   After the workflow is executed, the output should resemble the following:

   result: '{"body":{"multiplied":120},"code":200,"headers":{"Alt-Svc":"h3-29=\":443\";
   ...
   startTime: '2021-05-05T14:17:39.135251700Z'
   state: SUCCEEDED
   ...
   

Connect a public REST service in the workflow

Update your existing workflow and connect a public REST API (math.js) that can evaluate mathematical expressions. For example, curl https://api.mathjs.org/v4/?'expr=log(56)'.

Note that since you have deployed your workflow, you can also edit it through the Workflows page in the Google Cloud console.

1. Edit the source file for your workflow and replace it with the following content:

   - randomgen_function:
       call: http.get
       args:
           url: RANDOMGEN_FUNCTION_URL
       result: randomgen_result
   - multiply_function:
       call: http.post
       args:
           url: MULTIPLY_FUNCTION_URL
           body:
               input: ${randomgen_result.body.random}
       result: multiply_result
   - log_function:
       call: http.get
       args:
           url: https://api.mathjs.org/v4/
           query:
               expr: ${"log(" + string(multiply_result.body.multiplied) + ")"}
       result: log_result
   - return_result:
       return: ${log_result}
   

   • Replace RANDOMGEN_FUNCTION_URL with the URL of your randomgen function.
   • Replace MULTIPLY_FUNCTION_URL with the URL of your multiply function.

   This links the external REST service to the Cloud Run functions, and returns a final result.

2. Deploy the modified workflow:

   gcloud workflows deploy WORKFLOW_NAME \
       --source=workflow.yaml \
       --service-account=${SERVICE_ACCOUNT}@PROJECT_ID.iam.gserviceaccount.com

Deploy a Cloud Run service

Deploy a Cloud Run service that, after receiving an HTTP request, extracts input from the JSON body, calculates its math.floor, and returns the result.

1. Create a directory called floor and change to it:

   mkdir ~/floor
   cd ~/floor

2. Create a text file with the filename app.py that contains the following Python code:

   import json
   import logging
   import os
   import math
   
   from flask import Flask, request
   
   app = Flask(__name__)
   
   
   @app.route('/', methods=['POST'])
   def handle_post():
       content = json.loads(request.data)
       input = float(content['input'])
       return f"{math.floor(input)}", 200
   
   
   if __name__ != '__main__':
       # Redirect Flask logs to Gunicorn logs
       gunicorn_logger = logging.getLogger('gunicorn.error')
       app.logger.handlers = gunicorn_logger.handlers
       app.logger.setLevel(gunicorn_logger.level)
       app.logger.info('Service started...')
   else:
       app.run(debug=True, host='0.0.0.0', port=int(os.environ.get('PORT', 8080)))

3. In the same directory, create a Dockerfile with the following content:

   # Use an official lightweight Python image.
   # https://hub.docker.com/_/python
   FROM python:3.7-slim
   
   # Install production dependencies.
   RUN pip install Flask gunicorn
   
   # Copy local code to the container image.
   WORKDIR /app
   COPY . .
   
   # Run the web service on container startup. Here we use the gunicorn
   # webserver, with one worker process and 8 threads.
   # For environments with multiple CPU cores, increase the number of workers
   # to be equal to the cores available.
   CMD exec gunicorn --bind 0.0.0.0:8080 --workers 1 --threads 8 app:app

4. Create an Artifact Registry standard repository where you can store your Docker container image:

   gcloud artifacts repositories create REPOSITORY \
       --repository-format=docker \
       --location=${REGION}

   Replace REPOSITORY with a unique name for the repository.

5. Build the container image:

   export SERVICE_NAME=floor
   gcloud builds submit --tag ${REGION}-docker.pkg.dev/PROJECT_ID/REPOSITORY/${SERVICE_NAME}

6. Deploy the container image to Cloud Run, ensuring that it only accepts authenticated calls:

   gcloud run deploy ${SERVICE_NAME} \
       --image ${REGION}-docker.pkg.dev/PROJECT_ID/REPOSITORY/${SERVICE_NAME}:latest \
       --no-allow-unauthenticated

When you see the service URL, the deployment is complete. You will need to specify that URL when updating the workflow definition.

Connect the Cloud Run service in the workflow

Update your existing workflow and specify the URL for the Cloud Run service.

1. Navigate back to your home directory:

   cd ~

2. Edit the source file for your workflow and replace it with the following content:

   - randomgen_function:
       call: http.get
       args:
           url: RANDOMGEN_FUNCTION_URL
       result: randomgen_result
   - multiply_function:
       call: http.post
       args:
           url: MULTIPLY_FUNCTION_URL
           body:
               input: ${randomgen_result.body.random}
       result: multiply_result
   - log_function:
       call: http.get
       args:
           url: https://api.mathjs.org/v4/
           query:
               expr: ${"log(" + string(multiply_result.body.multiplied) + ")"}
       result: log_result
   - floor_function:
       call: http.post
       args:
           url: CLOUD_RUN_SERVICE_URL
           auth:
               type: OIDC
           body:
               input: ${log_result.body}
       result: floor_result
   - create_output_map:
       assign:
         - outputMap:
             randomResult: ${randomgen_result}
             multiplyResult: ${multiply_result}
             logResult: ${log_result}
             floorResult: ${floor_result}
   - return_output:
       return: ${outputMap}
   

   • Replace RANDOMGEN_FUNCTION_URL with the URL of your randomgen function.
   • Replace MULTIPLY_FUNCTION_URL with the URL of your multiply function.
   • Replace CLOUD_RUN_SERVICE_URL with your Cloud Run service URL.

   This connects the Cloud Run service in the workflow. Note that the auth key ensures that an authentication token is being passed in the call to the Cloud Run service. For more information, see Make authenticated requests from a workflow.

3. Deploy the modified workflow:

   gcloud workflows deploy WORKFLOW_NAME \
       --source=workflow.yaml \
       --service-account=${SERVICE_ACCOUNT}@PROJECT_ID.iam.gserviceaccount.com

4. Execute the final workflow:

   gcloud workflows run WORKFLOW_NAME

   The output should resemble the following:

   result: '{"floorResult":{"body":"4","code":200
     ...
     "logResult":{"body":"4.02535169073515","code":200
     ...
     "multiplyResult":{"body":{"multiplied":56},"code":200
     ...
     "randomResult":{"body":{"random":28},"code":200
     ...
   startTime: '2023-11-13T21:22:56.782669001Z'
   state: SUCCEEDED
   

Congratulations! You have deployed and executed a workflow that connects a series of services together.

To create more complex workflows using expressions, conditional jumps, Base64 encoding or decoding, subworkflows, and more, refer to the Workflows syntax reference and the Standard library overview.