Deploy the first Cloud Run functions

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

1. Create a directory calledrandomgen and change to it:

   mkdir~/randomgencd~/randomgen

2. Create a text file with the filenamemain.py that contains the followingPython code:

   importfunctions_frameworkimportrandomfromflaskimportjsonify@functions_framework.httpdefrandomgen(request):randomNum=random.randint(1,100)output={"random":randomNum}returnjsonify(output)

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

   flask>=1.0.2functions-framework==3.0.0

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

   gcloudfunctionsdeployrandomgen-function\--gen2\--runtimepython310\--entry-point=randomgen\--trigger-http\--allow-unauthenticated

   The function might take a few minutes to deploy. Alternatively, you canuse the Cloud Run functions interfacein the Google Cloud console to deploy the function.

5. Once therandomgen function is deployed, you can confirm thehttpsTrigger.url property:

   gcloudfunctionsdescriberandomgen-function\--gen2\--format="value(serviceConfig.uri)"

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

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

   curl$(gcloudfunctionsdescriberandomgen-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 theinput fromthe 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 calledmultiply and change to it:

   mkdir~/multiplycd~/multiply

3. Create a text file with the filenamemain.py that contains the followingPython code:

   importfunctions_frameworkfromflaskimportjsonify@functions_framework.httpdefmultiply(request):request_json=request.get_json()output={"multiplied":2*request_json['input']}returnjsonify(output)

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

   flask>=1.0.2functions-framework==3.0.0

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

   gcloudfunctionsdeploymultiply-function\--gen2\--runtimepython310\--entry-point=multiply\--trigger-http\--allow-unauthenticated

   The function might take a few minutes to deploy. Alternatively, you canuse the Cloud Run functions interfacein the Google Cloud console to deploy the function.

6. Once themultiply function is deployed, you can confirm thehttpsTrigger.url property:

   gcloudfunctionsdescribemultiply-function\--gen2\--format="value(serviceConfig.uri)"

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

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

   curl-XPOSTMULTIPLY_FUNCTION_URL\-H"Authorization: Bearer$(gcloudauthprint-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 theWorkflows syntax, which can be written in either YAML or JSONformat. This is the workflow's definition. For a detailed explanation, see theSyntax reference page.

1. Navigate back to your home directory:

   cd~

2. Create a text file with the filenameworkflow.yaml that contains the followingcontent:

   -randomgen_function:call:http.getargs:url:RANDOMGEN_FUNCTION_URLresult:randomgen_result-multiply_function:call:http.postargs:url:MULTIPLY_FUNCTION_URLbody:input:${randomgen_result.body.random}result:multiply_result-return_result:return:${multiply_result}

   • ReplaceRANDOMGEN_FUNCTION_URL with the URL of yourrandomgen function.
   • ReplaceMULTIPLY_FUNCTION_URL with the URL of yourmultiply function.

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

   Note: To make a request to a Cloud Run function that accepts onlyauthenticated calls, use OIDC to authenticate. For more information, seeMake authenticated requests from a workflow.

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

   gcloudworkflowsdeployWORKFLOW_NAME\--source=workflow.yaml\--service-account=${SERVICE_ACCOUNT}@PROJECT_ID.iam.gserviceaccount.com

   ReplaceWORKFLOW_NAME with a name for your workflow.

4. Execute the workflow:

   gcloudworkflowsrunWORKFLOW_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 ofWorkflows 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 throughtheWorkflows page in the Google Cloud console.

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

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

   • ReplaceRANDOMGEN_FUNCTION_URL with the URL of yourrandomgen function.
   • ReplaceMULTIPLY_FUNCTION_URL with the URL of yourmultiply function.

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

2. Deploy the modified workflow:

   gcloudworkflowsdeployWORKFLOW_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 HTTPrequest, extractsinput from the JSON body, calculates itsmath.floor, andreturns the result.

1. Create a directory calledfloor and change to it:

   mkdir~/floorcd~/floor

2. Create a text file with the filenameapp.py that contains the followingPython code:

   importjsonimportloggingimportosimportmathfromflaskimportFlask,requestapp=Flask(__name__)@app.route('/',methods=['POST'])defhandle_post():content=json.loads(request.data)input=float(content['input'])returnf"{math.floor(input)}",200if__name__!='__main__':# Redirect Flask logs to Gunicorn logsgunicorn_logger=logging.getLogger('gunicorn.error')app.logger.handlers=gunicorn_logger.handlersapp.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 aDockerfile with the following content:

   # Use an official lightweight Python image.# https://hub.docker.com/_/pythonFROMpython:3.7-slim# Install production dependencies.RUNpipinstallFlaskgunicorn# Copy local code to the container image.WORKDIR/appCOPY..# 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.CMDexecgunicorn--bind0.0.0.0:8080--workers1--threads8app:app

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

   gcloudartifactsrepositoriescreateREPOSITORY\--repository-format=docker\--location=${REGION}

   ReplaceREPOSITORY with a unique name for therepository.

5. Build the container image:

   exportSERVICE_NAME=floorgcloudbuildssubmit--tag${REGION}-docker.pkg.dev/PROJECT_ID/REPOSITORY/${SERVICE_NAME}

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

   gcloudrundeploy${SERVICE_NAME}\--image${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 tospecify 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 Runservice.

1. Navigate back to your home directory:

   cd~

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

   -randomgen_function:call:http.getargs:url:RANDOMGEN_FUNCTION_URLresult:randomgen_result-multiply_function:call:http.postargs:url:MULTIPLY_FUNCTION_URLbody:input:${randomgen_result.body.random}result:multiply_result-log_function:call:http.getargs:url:https://api.mathjs.org/v4/query:expr:${"log(" + string(multiply_result.body.multiplied) + ")"}result:log_result-floor_function:call:http.postargs:url:CLOUD_RUN_SERVICE_URLauth:type:OIDCbody: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}

   • ReplaceRANDOMGEN_FUNCTION_URL with the URL of yourrandomgen function.
   • ReplaceMULTIPLY_FUNCTION_URL with the URL of yourmultiply function.
   • ReplaceCLOUD_RUN_SERVICE_URL with yourCloud Run service URL.

   This connects the Cloud Run service in the workflow. Notethat theauth key ensures that an authentication token is being passed inthe call to the Cloud Run service. For more information,seeMake authenticated requests from a workflow.

3. Deploy the modified workflow:

   gcloudworkflowsdeployWORKFLOW_NAME\--source=workflow.yaml\--service-account=${SERVICE_ACCOUNT}@PROJECT_ID.iam.gserviceaccount.com

4. Execute the final workflow:

   gcloudworkflowsrunWORKFLOW_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 aseries of services together.

To create more complex workflows using expressions, conditional jumps,Base64 encoding or decoding, subworkflows, and more, refer to theWorkflows syntax referenceand theStandard library overview.