Cloud Function Metrics

Prerequisites

• A Google Cloud account with administrative privileges or Cloud Functions Admin privileges.
• Access to a project in Google Cloud Platform (GCP).
• The Google Cloud Functions APIs must be enabled. You can follow this guide to learn how to enable an API in Google Cloud.

Setup

Get started with Cloud Function Configuration

Follow these steps to create the Cloud Function:

Step 1: Go to your GCP console and search for Cloud Functions, go to Functions, and click on CREATE FUNCTION.

Step 2: Enter the following information to create a Cloud Function:

1. Environment: Select "2nd gen."
2. Function name: Enter a name for your Cloud Function.
3. Region: The default region of your GCP account will be used.
4. Trigger: Specify how the Cloud Function will be triggered.
5. Trigger Type: Select "HTTPS" to enable triggering the Cloud Function via a URL.
6. Authentication: Decide if the invocation needs to be authenticated or unauthenticated. For this demonstration, we have chosen unauthenticated invocation.

Step 3: Click on the NEXT button, which will bring us to the page where we can add our code. Here we are using a simple python Hello World! script.

Add code to the Google Cloud Function

For this demonstration, we will be using python 3.11.

Here is the complete code for the main.py file, along with a summary of its functionality.

import functions_framework

@functions_framework.http
def hello_http(request):
    """HTTP Cloud Function.
    Args:
        request (flask.Request): The request object.
        <https://flask.palletsprojects.com/en/1.1.x/api/#incoming-request-data>
    Returns:
        The response text, or any set of values that can be turned into a
        Response object using `make_response`
        <https://flask.palletsprojects.com/en/1.1.x/api/#flask.make_response>.
    """
    request_json = request.get_json(silent=True)
    request_args = request.args

    if request_json and 'name' in request_json:
        name = request_json['name']
    elif request_args and 'name' in request_args:
        name = request_args['name']
    else:
        name = 'World'
    return 'Hello {}!'.format(name)

1. Import the Framework: The functions_framework module is imported to handle HTTP requests.

2. Define the Function: The hello_http function is defined to process HTTP requests. This function is triggered whenever the Cloud Function receives an HTTP request.

3. Handle the Request:

   • The function checks if the incoming request contains JSON data (request_json).
   • It also checks for URL query parameters (request_args).

4. Determine the Name to Greet:

   • If the JSON body contains a key name, it uses its value.
   • If the query parameters contain a key name, it uses its value.
   • If neither is provided, it defaults to 'World'.

5. Return the Response:

   • The function returns a greeting message: 'Hello {name}!', where '{name}'' is replaced with the determined name.

Testing your cloud function

Step 1: After completing the deployment, navigate to the TRIGGER section to obtain the URL to invoke the function.

Step 2: Hit the URL that you have obtained, and you should see the function output.

Deploy OpenTelemetry to scrape the metrics from Google Cloud Monitoring

Step 1: Install and configure OpenTelemetry for scraping the metrics from Google Cloud Monitoring. Follow OpenTelemetry Binary Usage in Virtual Machine guide for detailed instructions.

Step 2: Create config.yaml.

Under the googlecloudmonitoring receiver, you need to specify the metrics that you want to capture in the metrics_list. You can include all the metrics that you would like to capture for Cloud Function, see the available metrics for Cloud Function.

Here is the config.yaml file for capturing Cloud Function metrics:

receivers:
  googlecloudmonitoring:
    collection_interval: 5m # Can be specified in seconds (s), minutes (m), or hours (h)
    project_id: my-project-id
    metrics_list:
      - metric_name: "cloudfunctions.googleapis.com/function/execution_count"
      - metric_name: "cloudfunctions.googleapis.com/function/execution_times"
      - metric_name: "cloudfunctions.googleapis.com/function/instance_count"
      - metric_name: "cloudfunctions.googleapis.com/function/network_egress"
      - metric_name: "cloudfunctions.googleapis.com/function/user_memory_bytes"
      - metric_name: "cloudfunctions.googleapis.com/function/active_instances"
processors:
  resource/env:
    attributes:
    - key: deployment.environment
      value: prod
      action: upsert
  batch: {}
exporters:
  otlp:
    endpoint: "ingest.{region}.signoz.cloud:443"
    tls:
      insecure: false
    headers:
      "signoz-ingestion-key": "<SIGNOZ_INGESTION_KEY>"
service:
  pipelines:
    metrics:
      receivers: [googlecloudmonitoring]
      processors: [resource/env,batch]
      exporters: [otlp]

Depending on the choice of your region for SigNoz cloud, the otlp endpoint will vary according to this table.

After successful configuration start the OTel Collector using following command:

./otelcol-contrib --config ./config.yaml &> otelcol-output.log & echo "$!" > otel-pid

Step 3: If the configurations are configured correctly, you can see the output logs from OpenTelemtry as follows:

Send and Visualize the metrics obtained by OpenTelemetry in SigNoz

Step 1: Go to the SigNoz Cloud URL and head over to the dashboard.

Step 2: If not already created, create a new dashboard. You can create the dashboard and multiple panel under it by following the instructions here.

Step 3: While creating the panel, select metric for Cloud Function.

All metrics starting with cloudfunctions_googleapis_com_ have been collected from Cloud Function.

For example, cloudfunctions_googleapis_com_function_execution_times is one of the metrics which is collected.

Here is the sample dashboard for Cloud Function:

Troubleshooting

If you run into any problems while setting up monitoring for your Cloud Function's metrics with SigNoz, consider these troubleshooting steps:

• Verify Configuration: Double-check your config.yaml file to ensure all settings, including the ingestion key and endpoint, are correct.
• Review Logs: Look at the logs of both your Cloud Function and the OpenTelemetry Collector to identify any error messages or warnings that might provide insights into what’s going wrong.
• Update Dependencies: Ensure all relevant packages and dependencies are up-to-date to avoid compatibility issues.
• Consult Documentation: Review the SigNoz and OpenTelemetry documentation for any additional troubleshooting of the common issues.s

Prerequisites

• A Google Cloud account with administrative privileges or Cloud Functions Admin privileges.
• Access to a project in Google Cloud Platform (GCP).
• The Google Cloud Functions APIs must be enabled. You can follow this guide to learn how to enable an API in Google Cloud.

Setup

Get started with Cloud Function Configuration

Follow these steps to create the Cloud Function:

Step 1: Go to your GCP console and search for Cloud Functions, go to Functions, and click on CREATE FUNCTION.

Step 2: Enter the following information to create a Cloud Function:

1. Environment: Select "2nd gen."
2. Function name: Enter a name for your Cloud Function.
3. Region: The default region of your GCP account will be used.
4. Trigger: Specify how the Cloud Function will be triggered.
5. Trigger Type: Select "HTTPS" to enable triggering the Cloud Function via a URL.
6. Authentication: Decide if the invocation needs to be authenticated or unauthenticated. For this demonstration, we have chosen unauthenticated invocation.

Step 3: Click on the NEXT button, which will bring us to the page where we can add our code. Here we are using a simple python Hello World! script.

Add code to the Google Cloud Function

For this demonstration, we will be using python 3.11.

Here is the complete code for the main.py file, along with a summary of its functionality.

import functions_framework

@functions_framework.http
def hello_http(request):
    """HTTP Cloud Function.
    Args:
        request (flask.Request): The request object.
        <https://flask.palletsprojects.com/en/1.1.x/api/#incoming-request-data>
    Returns:
        The response text, or any set of values that can be turned into a
        Response object using `make_response`
        <https://flask.palletsprojects.com/en/1.1.x/api/#flask.make_response>.
    """
    request_json = request.get_json(silent=True)
    request_args = request.args

    if request_json and 'name' in request_json:
        name = request_json['name']
    elif request_args and 'name' in request_args:
        name = request_args['name']
    else:
        name = 'World'
    return 'Hello {}!'.format(name)

1. Import the Framework: The functions_framework module is imported to handle HTTP requests.

2. Define the Function: The hello_http function is defined to process HTTP requests. This function is triggered whenever the Cloud Function receives an HTTP request.

3. Handle the Request:

   • The function checks if the incoming request contains JSON data (request_json).
   • It also checks for URL query parameters (request_args).

4. Determine the Name to Greet:

   • If the JSON body contains a key name, it uses its value.
   • If the query parameters contain a key name, it uses its value.
   • If neither is provided, it defaults to 'World'.

5. Return the Response:

   • The function returns a greeting message: 'Hello {name}!', where '{name}' is replaced with the determined name.

Testing your Cloud Function

Step 1: After completing the deployment, navigate to the TRIGGER section to obtain the URL to invoke the function.

Step 2: Hit the URL that you have obtained, and you should see the function output.

Deploying a Self-Hosted SigNoz in a Docker container

Step 1: The deployment process of the Self-Hosted SigNoz can be found in official documentation.

Step 2: Make sure all the containers are running in a Healthy state.

exporters:
  otlp:
	endpoint: "<endpoint-ip>:4317"
	tls:
  	   insecure: true
  debug:
	verbosity: normal

Configuring config.yaml

Here is the config.yaml file for capturing Cloud Function metrics:

receivers:
  googlecloudmonitoring:
    collection_interval: 5m # Can be specified in seconds (s), minutes (m), or hours (h)
    project_id: my-project-id
    metrics_list:
      - metric_name: "cloudfunctions.googleapis.com/function/execution_count"
      - metric_name: "cloudfunctions.googleapis.com/function/execution_times"
      - metric_name: "cloudfunctions.googleapis.com/function/instance_count"
      - metric_name: "cloudfunctions.googleapis.com/function/network_egress"
      - metric_name: "cloudfunctions.googleapis.com/function/user_memory_bytes"
      - metric_name: "cloudfunctions.googleapis.com/function/active_instances"
processors:
  resource/env:
    attributes:
    - key: deployment.environment
      value: prod
      action: upsert
  batch: {}
exporters:
  otlp:
	endpoint: "<endpoint-ip>:4317"
	tls:
  	   insecure: true
  debug:
	verbosity: normal
service:
  pipelines:
    metrics:
      receivers: [googlecloudmonitoring]
      processors: [resource/env,batch]
      exporters: [otlp]

You can include all the metrics that you would like to capture for Cloud Function, see the available metrics for Cloud Function.

If the configurations are configured correctly, you can see the output logs from OpenTelemtry as follows:

Visualize the metrics in Self-Hosted SigNoz

Step 1: Go to your Self-Hosted SigNoz UI, and navigate to the Self-Hosted SigNoz dashboard. Click on the Dashboards section to view the metrics. Create a new dashboard (If not already present ). The default Self-Hosted SigNoz dashboard endpoint would be http://<Self-Hosted SigNoz-host-ip>:3301, however, the URL can be different based on how you have set up the infrastructure.

Step 2: If not already created, create a new dashboard. You can create the dashboard and multiple panel under it by following the instructions here.

Step 3: While creating the panel, select metric for Cloud Function.

All metrics starting with cloudfunctions_googleapis_com_ have been collected from Cloud Function.

For example, cloudfunctions_googleapis_com_function_execution_times is one of the metrics which is collected.

Here is the sample dashboard for Cloud Function:

Troubleshooting

If you run into any problems while setting up monitoring for your Cloud Function's metrics with SigNoz, consider these troubleshooting steps:

• Verify Configuration: Double-check your config.yaml file to ensure all settings, including the ingestion key and endpoint, are correct.
• Verify Docker container logs: Check if all the containers are running properly, and not unexpectedly crashed.
• Review Logs: Look at the logs of both your Cloud Function and the OpenTelemetry Collector to identify any error messages or warnings that might provide insights into what’s going wrong.
• Update Dependencies: Ensure all relevant packages and dependencies are up-to-date to avoid compatibility issues.
• Consult Documentation: Review the SigNoz and OpenTelemetry documentation for any additional troubleshooting of the common issues.