Troubleshoot Cloud Run functions

This document shows you how to troubleshoot error messages and resolve issues when using Cloud Run functions.

Deployment

This section lists issues that you might encounter with deployment and provides suggestions for how to fix each of them. Many of the issues you might encounter during deployment are related to roles and permissions or incorrect configuration.

You use Identity and Access Management to authorize identities to perform administrative actions on functions created using the Cloud Functions v2 API—for example, using gcloud functions, the REST API, or Terraform. Administration actions include creating, updating, and deleting functions. For more information, see Authorize access with IAM.

User missing permissions on runtime service account while deploying a function

Every function is associated with a service account that serves as its identity when the function accesses other resources. This runtime service account can be the default service account or a user-managed service account. In environments where multiple functions are accessing different resources, it's a common practice to use per-function identities with named runtime service accounts rather than the default runtime service account (PROJECT_NUMBER-compute@developer.gserviceaccount.com).

To use a runtime service account, the deployer must have the iam.serviceAccounts.actAs permission on that service account. A user who creates a non-default runtime service account is automatically granted this permission, but other deployers must have this permission granted by a user.

Assign the iam.serviceAccounts.actAs permission on the runtime service account for the user with the Project Viewer, Cloud Functions Developer, or Cloud Functions Admin role.

The error message

  You must have the iam.serviceAccounts.actAs permission on the selected service account. To obtain this permission, you can grant a role that includes it like the Service Account User role, on the project.

ERROR: (gcloud.functions.deploy) ResponseError: status=[403], code=[Ok], message=[Caller is missing permission 'iam.serviceaccounts.actAs' on service account projects/-/serviceAccounts/PROJECT_NUMBER-compute@developer.gserviceaccount.com.Grant the role 'roles/iam.serviceAccountUser' to the caller on the service account projects/-/serviceAccounts/PROJECT_NUMBER-compute@developer.gserviceaccount.com.You can do that by running 'gcloud iam service-accounts add-iam-policy-binding projects/-/serviceAccounts/PROJECT_NUMBER-compute@developer.gserviceaccount.com --member MEMBER --role roles/iam.serviceAccountUser'where MEMBER has a prefix like 'user:' or 'serviceAccount:'

ERROR: (gcloud.functions.deploy) ResponseError: status=[403], code=[Ok], message=[Caller is missing permission 'iam.serviceaccounts.actAs' on service account projects/-/serviceAccounts/
SERVICE_ACCOUNT_NAME@PROJECT_ID
.iam.gserviceaccount.com.Grant the role 'roles/iam.serviceAccountUser' to the caller on the service account projects/-/serviceAccounts/
SERVICE_ACCOUNT_NAME@PROJECT_ID
.iam.gserviceaccount.com.You can do that by running 'gcloud iam service-accounts add-iam-policy-binding projects/-/serviceAccounts/
SERVICE_ACCOUNT_NAME@PROJECT_ID
.iam.gserviceaccount.com --member MEMBER --role roles/iam.serviceAccountUser'where MEMBER has a prefix like 'user:' or 'serviceAccount:'

The solution

Assign the user, the Service Account User role (roles/iam.serviceAccountUser) on the default or the non-default runtime service account. This role includes the iam.serviceAccounts.actAs permission.

Deployment service account missing Pub/Sub permissions when deploying an event-driven function

The Cloud Functions service uses the Cloud Functions Service Agent service account (service-PROJECT_NUMBER@gcf-admin-robot.iam.gserviceaccount.com) when performing administrative actions. By default this account is assigned the Cloud Functions cloudfunctions.serviceAgent role. To deploy event-driven functions, the Cloud Functions service must access Pub/Sub to configure topics and subscriptions. If you change the role assigned to the service account without granting the appropriate permissions, the Cloud Functions service cannot access Pub/Sub and the deployment fails.

The error message

Validation failed for trigger projects/PROJECT_ID/locations/LOCATION/triggers/FUNCTION_NAME-EVENTARC_ID: Permission "iam.serviceAccounts.ActAs" denied on "EndUserCredentials to PROJECT_NUMBER-compute@developer.gserviceaccount.com"

ERROR: (gcloud.functions.deploy) ResponseError: status=[403], code=[Ok], message=[Validation failed for trigger projects/test-project-356312/locations/LOCATION/triggers/FUNCTION_NAME-EVENTARC_ID: Permission "iam.serviceAccounts.ActAs" denied on "EndUserCredentials to PROJECT_NUMBER-compute@developer.gserviceaccount.com"]

The solution

You can reset your service account to the default cloudfunctions.serviceAgent role.

Default runtime service account doesn't exist

When you don't specify a user-managed runtime service account, Cloud Functions uses the default compute service account as the runtime service account. Deployments fail if you delete the default account without specifying a user-managed account.

The error message

ERROR: (gcloud.functions.deploy) ResponseError: status=[404], code=[Ok], message=[Service account projects/-/serviceAccounts/PROJECT_NUMBER-compute@developer.gserviceaccount.com was not found.]

The solution

To resolve this issue, follow any of these solutions:

• Specify a user managed runtime service account before deploying your function.

• Undelete the service account by numeric ID of the default service account.

Cloud Functions Service Agent service account missing project bucket permissions while deploying a function

Cloud Run functions can only be triggered by events from Cloud Storage buckets in the same Google Cloud Platform project. In addition, the Cloud Functions Service Agent service account (service-PROJECT_NUMBER@gcf-admin-robot.iam.gserviceaccount.com) needs a cloudfunctions.serviceAgent role on your project.

The error message

ERROR: (gcloud.functions.deploy) ResponseError: status=[403], code=[Ok], message=[Validation failed for trigger projects/PROJECT_ID/locations/LOCATION/triggers/FUNCTION_NAME-EVENTARC_ID: Permission "iam.serviceAccounts.ActAs" denied on "EndUserCredentials to PROJECT_NUMBER-compute@developer.gserviceaccount.com"]

ERROR: (gcloud.functions.deploy) ResponseError: status=[403], code=[Ok], message=[Validation failed for trigger projects/<project-id>/locations/LOCATION/triggers/FUNCTION_NAME-EVENTARC_ID: Permission "iam.serviceAccounts.ActAs" denied on "EndUserCredentials to PROJECT_NUMBER-compute@developer.gserviceaccount.com"]

The solution

To resolve this issue, reset this service account to the default role.

User with Project Editor role can't make a function public

The Project Editor role has broad permissions to manage resources within a project, but it doesn't inherently grant the ability to make Cloud Functions public. The user or the service deploying the function would require the run.services.setIamPolicy permission.

The error message

ERROR: (gcloud.run.services.add-iam-policy-binding) PERMISSION_DENIED: Permission 'run.services.setIamPolicy' denied on resource 'projects/PROJECT_ID/locations/LOCATION/functions/FUNCTION_NAME' (or resource may not exist).

The solution

You can :

• Assign the deployer either the Project Owner or the Cloud Functions Admin role.

• Grant the permission manually by creating a custom role.

• Check if domain restricted sharing is enforced on the project.

Function deployment fails when using Resource Location Restriction organization policy

If your organization uses a resource location constraint policy, it restricts function deployment in the regions restricted by the policy. In the Google Cloud console, the restricted region won't be available from the region drop-down while deploying a function.

The error message

ResponseError: status=[400], code=[Ok], message=["LOCATION" violates constraint "constraints/gcp.resourceLocations" on the resource "projects/PROJECT_ID/locations/LOCATION/functions/FUNCTION_NAME".]

The solution

You can add or remove locations from the allowed_values or denied_values lists of a resource locations constraint to suit successful deployment.

Function deployment fails while executing function's global scope

This error indicates that there was a problem with your code. The deployment pipeline finished deploying the function, but failed at the last step - sending a health check to the function. This health check is meant to execute a function's global scope, which could be throwing an exception, crashing, or timing out. The global scope is where you commonly load in libraries and initialize clients.

The error message

In Cloud Logging logs:

Could not create or update Cloud Run service FUNCTION_NAME, Container Healthcheck failed. Revision REVISION_NAMEE is not ready and cannot serve traffic. The user-provided container failed to start and listen on the port defined provided by the PORT=8080 environment variable. Logs for this revision might contain more information.

The solution

To resolve this issue, follow any of these solutions:

• For a more detailed error message, review your function's build logs, and your function's runtime logs.

• If it is unclear why your function failed to execute its global scope, consider temporarily moving the code into the request invocation, using lazy initialization of the global variables. This lets you add extra log statements around your client libraries, which could be timing out on their instantiation (especially if they are calling other services), crashing, or throwing exceptions altogether.

• Additionally, try increasing the function timeout. Longer timeout limits in Cloud Run functions provides more breathing room for initialization and it offers more scalable resource allocation in a Cloud Run environment, potentially mitigating this issue if caused by resource exhaustion.

• The source code must contain an entry point function that has been correctly specified in the deployment, either through the console or gcloud.

User with Viewer role cannot deploy a function

Users with either the Project Viewer or Cloud Functions Viewer role have read-only access to functions and function details, and can't deploy new functions. The Create function feature is grayed out in the Google Cloud console with the following error:

The error message

ERROR: (gcloud.functions.deploy) ResponseError: status=[403], code=[Ok], message=[Permission 'cloudfunctions.functions.generateUploadUrl' denied on 'projects/PROJECT_ID/locations/LOCATION/functions']

The solution

Assign the user Cloud Functions Developer role.

Build service account missing permissions

The error message

In the function deploy error or in the build logs, you may see one of the following errors:

The service account running this build does not have permission to write logs. To fix this, grant the Logs Writer (roles/logging.logWriter) role to the service account.

Step #0 - "fetch": failed to Fetch: failed to download archive gs://gcf-v2-sources-PROJECT_NUMBER-LOCATION/FUNCTION_NAME/version-VERSION_NUMBER/function-source.zip: Access to bucket gcf-v2-sources-PROJECT_NUMBER-LOCATION denied. You must grant Storage Object Viewer permission to PROJECT_NUMBER-compute@developer.gserviceaccount.com.

Step #2 - "build": ERROR: failed to create image cache: accessing cache image "LOCATION-docker.pkg.dev/PROJECT/gcf-artifacts/FUNCTION_NAME/cache:latest": connect to repo store "LOCATION-docker.pkg.dev/PROJECT/gcf-artifacts/FUNCTION_NAME/cache:latest": GET https://LOCATION-docker.pkg.dev/v2/token?scope=repository%3APROJECT%2Fgcf-artifacts%2FFUNCTION_NAME%2Fcache%3Apull&service=: DENIED: Permission "artifactregistry.repositories.downloadArtifacts" denied on resource "projects/PROJECT/locations/LOCATION/repositories/gcf-artifacts" (or it may not exist)

Could not build the function due to a missing permission on the build service account. If  you didn't revoke that permission explicitly, this could be caused by a change in the organization policies.

The solution

The build service account needs permission to read from the source bucket and read and write permissions for the Artifact Deployment repository. You may encounter this error due to a change in the default behavior for how Cloud Build uses service accounts, detailed in Cloud Build Service Account Change.

To resolve this issue, use any of these solutions:

• Create a custom build service account for function deployments.
• Add the Cloud Build Service Account role (roles/cloudbuild.builds.builder) to the default Compute Service Account.
• Review the Cloud Build guidance on changes to the default service account and opt out of these changes.

Build service account disabled

The error message

Could not build the function due to disabled service account used by Cloud Build. Please make sure that the service account is active.

The solution

The build service account needs to be enabled in order to deploy a function. You may encounter this error due to a change in the default behavior for how Cloud Build uses service accounts, detailed in Cloud Build Service Account Change.

To resolve this issue, use any of these solutions:

• Create a custom build service account for function deployments.
• Enable the default Compute Service Account.
• Review the Cloud Build guidance on changes to the default service account and opt out of these changes.

Serving

This section lists serving issues that you might encounter, and provides suggestions for how to fix each of them.

Serving permission error due to the function requiring authentication

HTTP functions without Allow unauthenticated invocations enabled, restrict access to end users and service accounts that don't have appropriate permissions. This error message indicates that the caller does not have permission to invoke the function.

The error message

HTTP Error Response code: 403 Forbidden

HTTP Error Response body:

Error: Forbidden Your client does not have permission
to get URL /FUNCTION_NAME from this server.

The solution

To resolve this issue, follow any of these solutions:

• Assign the user the Cloud Run Invoker Cloud IAM role for the specific function.

• Redeploy your function to allow unauthenticated invocations if this is supported by your organization. This is useful for testing.

Serving error due to allow internal traffic only configuration

Ingress settings restrict whether an HTTP function can be invoked by resources outside of your Google Cloud project or VPC Service Controls service perimeter. When you configure allow internal traffic only setting for ingress networking, this error message indicates that only requests from VPC networks in the same project or VPC Service Controls perimeter are allowed. This can also be a 404 error for default functions.net URL.

The error message

HTTP Error Response code: 404 NOT FOUND

The solution

To resolve this error, follow any of these solutions:

• Ensure that the request is coming from your Google Cloud project or VPC Service Controls service perimeter.

• Change the ingress settings to allow all traffic for the function.

• Cloud Run functions source code can also result in a 404 due to incorrect function url, http methods, logic errors etc.

Function invocation lacks valid authentication credentials

Invoking a Cloud Run functions function that has been set up with restricted access requires an ID token. Access tokens or refresh tokens don't work.

The error message

HTTP Error Response code: 401 Unauthorized

HTTP Error Response body:

Your client does not have permission to the requested URL 'FUNCTION_NAME'

The solution

To resolve this error, follow either of these solutions:

• Make sure that your requests include an Authorization: Bearer ID_TOKEN header, and that the token is an ID token, not an access or refresh token. If you generate this token manually with a service account's private key, you must exchange the self-signed JWT token for a Google-signed Identity token. For more information, see Authenticate for invocation.

  Invoke your HTTP function using authentication credentials in your request header. For example, you can get an identity token using gcloud as follows:

    curl  -H "Authorization: Bearer $(gcloud auth print-identity-token)" 
  
      https://REGION-PROJECT_ID.cloudfunctions.net/FUNCTION_NAME
    

  To learn more, see Authenticating for invocation .

• Redeploy your function to allow unauthenticated invocations if this is supported by your organization. This is useful for testing.

Function stops mid-execution, or continues running after your code finishes

Some Cloud Run functions runtimes allow users to run asynchronous tasks. If your function creates such tasks, it must also explicitly wait for these tasks to complete. Failure to do so might cause your function to stop executing at the wrong time.

The error behavior

Your function exhibits one of the following behaviors:

• Your function terminates while asynchronous tasks are still running, but before the specified timeout period has elapsed.
• Your function doesn't stop running when these tasks finish, and continues to run until the timeout period has elapsed.

The solution

If your function terminates early, you should make sure all your function's asynchronous tasks are complete before your function performs any of the following actions:

• Returning a value
• Resolving or rejecting a returned Promise object (Node.js functions only)
• Throwing uncaught exceptions or errors
• Sending an HTTP response
• Calling a callback function

If your function fails to terminate after completing asynchronous tasks, you should verify that your function is correctly signaling Cloud Run functions after it has completed. In particular, make sure that you perform one of the operations listed earlier as soon as your function has finished its asynchronous tasks.

Runtime error when accessing resources protected by VPC Service Controls

By default, Cloud Run functions uses public IP addresses to make outbound requests to other services. If your functions are not inside a VPC Service Controls perimeter, this might cause them to receive HTTP 403 responses when attempting to access Google Cloud services protected by VPC Service Controls, due to service perimeter denials.

The error message

In Audited Resources logs, an entry like the following:

"protoPayload": {
  "@type": "type.googleapis.com/google.cloud.audit.AuditLog",
  "status": {
    "code": 7,
    "details": [
      {
        "@type": "type.googleapis.com/google.rpc.PreconditionFailure",
        "violations": [
          {
            "type": "VPC_SERVICE_CONTROLS",
  ...
  "authenticationInfo": {
    "principalEmail": "CLOUD_FUNCTION_RUNTIME_SERVICE_ACCOUNT",
  ...
  "metadata": {
    "violationReason": "NO_MATCHING_ACCESS_LEVEL",
    "securityPolicyInfo": {
      "organizationId": "ORGANIZATION_ID",
      "servicePerimeterName": "accessPolicies/NUMBER/servicePerimeters/SERVICE_PERIMETER_NAME"
  ...

The solution

To resolve this error, follow either of these solutions:

• The function should route all outgoing traffic through the VPC network. See Deploy functions compliant with VPC Service Controls section for more information.

• Alternatively grant the function's runtime service account access to the perimeter. You can do this either by creating an access level and adding the access level to the service perimeter, or by creating an ingress policy on the perimeter. See Using VPC Service Controls with functions outside a perimeter for more information.

Scalability

This section lists scalability issues and provides suggestions for how to fix each of them.

Cloud Logging errors related to pending queue request aborts

Scaling failures can occur from the following scenarios:

• A huge sudden increase in traffic.
• Long cold start time.
• Long request processing time.
• High function error rate.
• Reaching the maximum instance limit and preventing the system from scaling.
• Transient factors attributed to the Cloud Run functions service.

In each case Cloud Run functions might not scale up fast enough to manage the traffic.

The error message

• The request was aborted because there was no available instance
  • severity=WARNING ( Response code: 429 ) Cloud Run functions cannot scale due to the max-instances limit you set during configuration.
  • severity=ERROR ( Response code: 500 ) Cloud Run functions intrinsically cannot manage the rate of traffic.

The solution

• To resolve this issue, address the previously listed causes.

• For HTTP trigger-based functions, have the client implement exponential backoff and retries for requests that must not be dropped. If you are triggering Cloud Run functions from Workflows, you can use the try/retry syntax to achieve this.

• For background or event-driven functions, Cloud Run functions supports at-least-once delivery. Even without explicitly enabling retry, the event is automatically re-delivered and the function execution will be retried. See Retrying Event-Driven Functions for more information.

• When the root cause of the issue is a period of heightened transient errors attributed solely to Cloud Run functions or if you need assistance with your issue, contact support.

• For issues pertaining to cold starts, configure minimum instances to reduce the amount of cold starts with a higher billing implication.

Logging

The following section covers issues with logging and how to fix them.

Logs entries have none or incorrect log severity levels

Cloud Run functions includes runtime logging by default. Logs written to stdout or stderr appear automatically in the Cloud Logging. But these log entries by default, contain only string messages.

The error message

No or incorrect severity levels in logs.

The solution

To include log severities, you must send a structured log entry.

Handle or log exceptions differently in the event of a crash

You might want to customize how you manage and log crash information.

The solution

Wrap your function in a try block to customize handling exceptions and logging stack traces.

Example

import logging
import traceback
def try_catch_log(wrapped_func):
  def wrapper(*args, **kwargs):
    try:
      response = wrapped_func(*args, **kwargs)
    except Exception:
      # Replace new lines with spaces so as to prevent several entries which
      # would trigger several errors.
      error_message = traceback.format_exc().replace('\n', '  ')
      logging.error(error_message)
      return 'Error';
    return response;
  return wrapper;

#Example hello world function
@try_catch_log
def python_hello_world(request):
  request_args = request.args

  if request_args and 'name' in request_args:
    1 + 's'
  return 'Hello World!'

Logs too large in Node.js 10+, Python 3.8, Go 1.13, and Java 11

The maximum size for a regular log entry in these runtimes is 105 KiB.

The solution

Send log entries smaller than this limit.

Missing logs despite Cloud Run functions returning errors

Cloud Run functions streams Cloud Run function logs to a default bucket. When you create a project, Cloud Run functions creates and enables the default bucket. If the default bucket is disabled or if Cloud Run function logs are in the exclusion filter, the logs won't appear in Logs Explorer.

The solution

Enable default logs.

Cloud Run functions logs aren't appearing in Logs Explorer

Some Cloud Logging client libraries use an asynchronous process to write log entries. If a function crashes, or otherwise terminates, it is possible that some log entries haven't been written yet, and might appear later. Some logs may be lost and can't be seen in the Logs Explorer.

The solution

Use the client library interface to flush buffered log entries before exiting the function, or use the library to write log entries synchronously. You can also synchronously write logs directly to stdout or stderr.

Missing Cloud Run functions logs when using Log Router sink

Log Router sinks route log entries to various destinations.

The Exclusion filters define entries that you can discard.

The solution

Remove exclusion filter set for resource.type = "cloud_run_revision".

Database connections

There are a number of issues that can arise when connecting to a database, many associated with exceeding connection limits or timing out. If you see a Cloud SQL warning in your logs, for example, Context deadline exceeded, you might need to adjust your connection configuration. For more information, see Cloud SQL best practices.

Networking

This section lists networking issues and provides suggestions for how to fix each of them.

Network connectivity

If all outbound requests from a Cloud Run function fail even after you configure egress settings, you can run Connectivity Tests to identify any underlying network connectivity issues. For more information, see Create and run Connectivity Tests.

Serverless VPC Access connector is not ready or does not exist

If a Serverless VPC Access connector fails, it might not be using a /28 subnet mask dedicated to the connector as required.

The error message

Problem connecting to VPC Connector projects/xxxxx/locations/REGION/connectors/xxxx: Serverless VPC Access is not found.

When Cloud Run functions is deployed with a connector in a bad state due to missing permission on the Google APIs Service Agent service account PROJECT_NUMBER@cloudservices.gserviceaccount.com, it results in the following error:

The error message

Failed to prepare VPC connector. Please try again later.

The solution

List your subnets to check whether your connector uses a /28 subnet mask. If your connector doesn't use the /28 subnet mask, recreate or create a new connector.

To resolve this issue, follow any of these solutions:

• If you recreate the connector, you don't need to redeploy other functions. You might experience a network interruption as the connector is recreated.

• If you create a new alternate connector, redeploy your functions to use the new connector and then delete the original connector. This method avoids network interruption.

• Ensure the Cloud Run functions and its associated connector are deployed in the same region.

• For the Shared VPC configuration:

  • Ensure that the service accounts SERVICE_PROJECT_NUMBER@cloudservices.gserviceaccount.com and service-SERVICE_PROJECT_NUMBER@gcp-sa-vpcaccess.iam.gserviceaccount.com used by VPC Connector to provision resources in the project are not missing permissions. These service accounts should have the roles/compute.networkUser role in the host project of Shared VPC configuration when the connector is in the service project.

  • If the connector is created in the host project, ensure that the Serverless VPC Access User role is granted on Cloud Run Service Agent in your host project.

• If the connector status shows Connector is in a bad state, manual deletion recommended error and the Google APIs Service Agent is missing the required permissions to provision compute resources in the connector's project, grant roles/compute.admin to the PROJECT_NUMBER@cloudservices.gserviceaccount.com service account. In some cases the connector may need to be recreated after adding these permissions.

SMTP traffic to external destination IP addresses using TCP port 25 is blocked

For added security, Google Cloud blocks connections to TCP destination port 25 when sending emails from functions.

The solution

To unblock these connections, follow any of these solutions:

• Connect to your SMTP server on a different port, such as TCP port 587 or 465.

• Use a third-party email service.

404 error for default functions.net URL

Disabling the run.app URL in Cloud Run also prevents access to the default cloudfunctions.net URL for Cloud Run functions (2nd gen). This error can also be caused by a Serving error due to allow internal traffic only configuration.

The error message

HTTP Error Response code: 404 NOT FOUND

The solution

To re-enable the default cloudfunctions.net URL for Cloud Run functions (2nd gen), you must re-enable the run.app URL in Cloud Run by replacing the service.yaml file with a new configuration where annotations:run.googleapis.com/default-url-disabled: false