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Troubleshooting Cloud Functions

This document shows you some of the mutual bug you might run into and how to deal with them.

Deployment

The deployment phase is a frequent source of bug. Many of the problems you lot might meet during deployment are related to roles and permissions. Others have to do with incorrect configuration.

User with Viewer office cannot deploy a function

A user who has been assigned the Project Viewer or Cloud Functions Viewer function has read-only access to functions and function details. These roles are not allowed to deploy new functions.

The fault message

Cloud console

              You need permissions for this action. Required permission(s): cloudfunctions.functions.create                          

Cloud SDK

              Error: (gcloud.functions.deploy) PERMISSION_DENIED: Permission 'cloudfunctions.functions.sourceCodeSet' denied on resources 'projects/<PROJECT_ID>/locations/<LOCATION>` (or resource may not exist)                          

The solution

Assign the user a role that has the advisable admission.

User with Project Viewer or Deject Function role cannot deploy a function

In order to deploy a office, a user who has been assigned the Project Viewer, the Cloud Role Developer, or Cloud Part Admin role must be assigned an boosted office.

The fault message

Cloud console

              User does not have the iam.serviceAccounts.actAs permission on <PROJECT_ID>@appspot.gserviceaccount.com required to create office. You can prepare this by running 'gcloud iam service-accounts add-iam-policy-binding <PROJECT_ID>@appspot.gserviceaccount.com --member=user: --role=roles/iam.serviceAccountUser'                          

Cloud SDK

              ERROR: (gcloud.functions.deploy) ResponseError: condition=[403], code=[Forbidden], bulletin=[Missing necessary permission iam.serviceAccounts.actAs for <USER> on the service account <PROJECT_ID>@appspot.gserviceaccount.com. Ensure that service account <PROJECT_ID>@appspot.gserviceaccount.com is a member of the project <PROJECT_ID>, and so grant <USER> the role 'roles/iam.serviceAccountUser'. Yous can do that by running 'gcloud iam service-accounts add-iam-policy-bounden <PROJECT_ID>@appspot.gserviceaccount.com --member=<USER> --role=roles/iam.serviceAccountUser' In case the fellow member is a service account please use the prefix 'serviceAccount:' instead of 'user:'.]                          

The solution

Assign the user an additional part, the Service Account User IAM office (roles/iam.serviceAccountUser), scoped to the Cloud Functions runtime service account.

Deployment service account missing the Service Agent role when deploying functions

The Deject Functions service uses the Cloud Functions Service Amanuensis service business relationship (service-<PROJECT_NUMBER>@gcf-admin-robot.iam.gserviceaccount.com) when performing authoritative actions on your project. By default this account is assigned the Deject Functions cloudfunctions.serviceAgent role. This role is required for Cloud Pub/Sub, IAM, Deject Storage and Firebase integrations. If you lot have changed the role for this service account, deployment fails.

The error message

Cloud panel

              Missing necessary permission resourcemanager.projects.getIamPolicy for serviceAccount:service-<PROJECT_NUMBER>@gcf-admin-robot.iam.gserviceaccount.com on project <PROJECT_ID>. Please grant serviceAccount:service-<PROJECT_NUMBER>@gcf-admin-robot.iam.gserviceaccount.com the roles/cloudfunctions.serviceAgent function. You can do that by running 'gcloud projects add together-iam-policy-bounden <PROJECT_ID> --member=serviceAccount:service-<PROJECT_NUMBER>@gcf-admin-robot.iam.gserviceaccount.com --role=roles/cloudfunctions.serviceAgent'                          

Deject SDK

              ERROR: (gcloud.functions.deploy) OperationError: code=vii, bulletin=Missing necessary permission resourcemanager.projects.getIamPolicy for serviceAccount:service-<PROJECT_NUMBER>@gcf-admin-robot.iam.gserviceaccount.com on projection <PROJECT_ID>. Please grant serviceAccount:service-<PROJECT_NUMBER>@gcf-admin-robot.iam.gserviceaccount.com the roles/cloudfunctions.serviceAgent role. You lot tin can exercise that past running 'gcloud projects add together-iam-policy-binding <PROJECT_ID> --member=serviceAccount:service-<PROJECT_NUMBER>@gcf-admin-robot.iam.gserviceaccount.com --role=roles/cloudfunctions.serviceAgent'                          

The solution

Reset this service account to the default function.

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

The Cloud Functions service uses the Cloud Functions Service Agent service business relationship (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 result-driven functions, the Cloud Functions service must access Cloud Pub/Sub to configure topics and subscriptions. If the role assigned to the service account is changed and the appropriate permissions are not otherwise granted, the Cloud Functions service cannot access Cloud Pub/Sub and the deployment fails.

The error message

Cloud console

              Failed to configure trigger PubSub projects/<PROJECT_ID>/topics/<FUNCTION_NAME>                          

Cloud SDK

              ERROR: (gcloud.functions.deploy) OperationError: code=thirteen, message=Failed to configure trigger PubSub projects/<PROJECT_ID>/topics/<FUNCTION_NAME>                          

The solution

You lot tin:

• Reset this service account to the default role.

  or

• Grant the pubsub.subscriptions.* and pubsub.topics.* permissions to your service account manually.

User missing permissions for runtime service account while deploying a function

In environments where multiple functions are accessing dissimilar resources, it is a common practice to apply per-part identities, with named runtime service accounts rather than the default runtime service business relationship (PROJECT_ID@appspot.gserviceaccount.com).

However, to apply a non-default runtime service account, the deployer must have the iam.serviceAccounts.actAs permission on that non-default 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 with the correct permissions.

The error message

Cloud SDK

          ERROR: (gcloud.functions.deploy) ResponseError: status=[400], code=[Bad Request], message=[Invalid part service account requested: <SERVICE_ACCOUNT_NAME@<PROJECT_ID>.iam.gserviceaccount.com]                  

The solution

Assign the user the roles/iam.serviceAccountUser role on the non-default <SERVICE_ACCOUNT_NAME> runtime service account. This function includes the iam.serviceAccounts.actAs permission.

Runtime service business relationship missing project bucket permissions while deploying a function

Cloud Functions tin simply exist triggered by events from Cloud Storage buckets in the same Google Cloud Platform project. In improver, the Deject Functions Service Agent service business relationship (service-<PROJECT_NUMBER>@gcf-admin-robot.iam.gserviceaccount.com) needs a cloudfunctions.serviceAgent role on your project.

The error bulletin

Cloud console

              Deployment failure: Insufficient permissions to (re)configure a trigger (permission denied for bucket <BUCKET_ID>). Please, give owner permissions to the editor office of the saucepan and try again.                          

Cloud SDK

              Error: (gcloud.functions.deploy) OperationError: code=7, message=Insufficient permissions to (re)configure a trigger (permission denied for saucepan <BUCKET_ID>). Please, requite possessor permissions to the editor function of the bucket and try again.                          

The solution

You tin can:

• Reset this service account to the default role.

  or

• Grant the runtime service account the cloudfunctions.serviceAgent function.

  or

• Grant the runtime service account the storage.buckets.{get, update} and the resourcemanager.projects.get permissions.

User with Projection Editor role cannot make a function public

To ensure that unauthorized developers cannot modify authentication settings for function invocations, the user or service that is deploying the office must have the cloudfunctions.functions.setIamPolicy permission.

The mistake bulletin

Deject SDK

          ERROR: (gcloud.functions.add together-iam-policy-bounden) ResponseError: status=[403], code=[Forbidden], message=[Permission 'cloudfunctions.functions.setIamPolicy' denied on resources 'projects/<PROJECT_ID>/locations/<LOCATION>/functions/<FUNCTION_NAME> (or resource may not be).]                  

The solution

You can:

• Assign the deployer either the Projection Owner or the Cloud Functions Admin function, both of which contain the cloudfunctions.functions.setIamPolicy permission.

  or

• Grant the permission manually by creating a custom part.

Function deployment fails due to Cloud Build not supporting VPC-SC

Cloud Functions uses Deject Build to build your source code into a runnable container. In order to use Cloud Functions with VPC Service Controls, you must configure an access level for the Cloud Build service account in your service perimeter.

The error message

Cloud console

One of the beneath:

              Error in the build surroundings  OR  Unable to build your function due to VPC Service Controls. The Cloud Build service business relationship associated with this function needs an appropriate access level on the service perimeter. Please grant access to the Cloud Build service account: '{PROJECT_NUMBER}@cloudbuild.gserviceaccount.com' by following the instructions at https://deject.google.com/functions/docs/securing/using-vpc-service-controls#grant-build-access"                          

Cloud SDK

I of the below:

              ERROR: (gcloud.functions.deploy) OperationError: code=thirteen, message=Error in the build environment  OR  Unable to build your function due to VPC Service Controls. The Cloud Build service business relationship associated with this function needs an appropriate access level on the service perimeter. Delight grant access to the Cloud Build service account: '{PROJECT_NUMBER}@cloudbuild.gserviceaccount.com' past post-obit the instructions at https://deject.google.com/functions/docs/securing/using-vpc-service-controls#grant-build-access"                          

The solution

If your project's Audited Resource logs mention "Request is prohibited past organisation's policy" in the VPC Service Controls section and have a Deject Storage label, you demand to grant the Cloud Build Service Account admission to the VPC Service Controls perimeter.

Office deployment fails due to incorrectly specified entry indicate

Deject Functions deployment can neglect if the entry point to your lawmaking, that is, the exported function proper name, is non specified correctly.

The error message

Deject console

              Deployment failure: Function failed on loading user code. Error message: Error: please examine your function logs to see the error crusade: https://cloud.google.com/functions/docs/monitoring/logging#viewing_logs                          

Cloud SDK

              Mistake: (gcloud.functions.deploy) OperationError: code=3, message=Function failed on loading user code. Error bulletin: Delight examine your function logs to see the error cause: https://cloud.google.com/functions/docs/monitoring/logging#viewing_logs                          

The solution

Your source code must comprise an entry point part that has been correctly specified in your deployment, either via Deject console or Cloud SDK.

Office deployment fails when using Resource Location Constraint organisation policy

If your organization uses a Resource Location Constraint policy, you may see this error in your logs. It indicates that the deployment pipeline failed to create a multi-regional storage bucket.

The error message

In Cloud Build logs:

          Token exchange failed for projection '<PROJECT_ID>'. Org Policy Violated: '<REGION>' violates constraint 'constraints/gcp.resourceLocations'                  

In Cloud Storage logs:

          <REGION>.artifacts.<PROJECT_ID>.appspot.com` storage saucepan could non be created.                  

The solution

If you lot are using constraints/gcp.resourceLocations in your organization policy constraints, you should specify the appropriate multi-region location. For example, if you are deploying in any of the us regions, you lot should use us-locations.

However, if you require more fine grained control and desire to restrict function deployment to a single region (not multiple regions), create the multi-region saucepan commencement:

1. Allow the whole multi-region
2. Deploy a examination part
3. After the deployment has succeeded, change the organizational policy back to let only the specific region.

The multi-region storage bucket stays available for that region, so that subsequent deployments can succeed. If you afterwards determine to allowlist a region outside of the i where the multi-region storage bucket was created, you must repeat the process.

Function deployment fails while executing function'due south global telescopic

This error indicates that there was a problem with your code. The deployment pipeline finished deploying the function, simply failed at the final step - sending a health cheque to the part. This health check is meant to execute a function'southward global scope, which could be throwing an exception, crashing, or timing out. The global scope is where y'all ordinarily load in libraries and initialize clients.

The error message

In Deject Logging logs:

          "Part failed on loading user code. This is likely due to a bug in the user code."                  

The solution

For a more detailed error message, look into your function'south build logs, likewise as your function's runtime logs. If information technology is unclear why your part failed to execute its global scope, consider temporarily moving the code into the asking invocation, using lazy initialization of the global variables. This allows you to add actress log statements effectually your client libraries, which could be timing out on their instantiation (particularly if they are calling other services), or crashing/throwing exceptions altogether.

Build

When you deploy your office's source code to Cloud Functions, that source is stored in a Cloud Storage saucepan. Deject Build so automatically builds your code into a container image and pushes that image to Container Registry. Cloud Functions accesses this epitome when information technology needs to run the container to execute your function.

Build failed due to missing Container Registry Images

Cloud Functions uses Container Registry to manage images of the functions. Container Registry uses Cloud Storage to store the layers of the images in buckets named STORAGE-REGION.artifacts.PROJECT-ID.appspot.com. Using Object Lifecycle Management on these buckets breaks the deployment of the functions as the deployments depend on these images being nowadays.

The error message

Cloud console

              Build failed: Build error details not available. Please check the logs at <CLOUD_CONSOLE_LINK>  CLOUD_CONSOLE_LINK contains an error like beneath : failed to get Bone from config file for image 'usa.gcr.io/<PROJECT_ID>/gcf/u.s.-central1/<UUID>/worker:latest'"                          

Cloud SDK

              ERROR: (gcloud.functions.deploy) OperationError: code=13, message=Build failed: Build fault details not available. Please check the logs at <CLOUD_CONSOLE_LINK>  CLOUD_CONSOLE_LINK contains an mistake like beneath : failed to get OS from config file for prototype 'us.gcr.io/<PROJECT_ID>/gcf/us-central1/<UUID>/worker:latest'"                          

The solution

1. Disable Lifecycle Management on the buckets required past Container Registry.
2. Delete all the images of afflicted functions. You tin access build logs to find the epitome paths. Reference script to bulk delete the images. Note that this does non bear on the functions that are currently deployed.
3. Redeploy the functions.

Serving

The serving phase can also be a source of errors.

Serving permission error due to the office being individual

Cloud Functions allows you to declare functions private, that is, to restrict access to cease users and service accounts with the appropriate permission. By default deployed functions are ready equally private. 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 Mistake Response body: Error: Forbidden Your client does not accept permission to get URL /<FUNCTION_NAME> from this server.

The solution

You can:

• Allow public (unauthenticated) admission to all users for the specific function.

  or

• Assign the user the Cloud Functions Invoker Cloud IAM role for all functions.

Serving permission error due to "allow internal traffic only" configuration

Ingress settings restrict whether an HTTP office can be invoked by resources outside of your Google Deject projection or VPC Service Controls service perimeter. When the "let internal traffic only" setting for ingress networking is configured, this error message indicates that merely requests from VPC networks in the aforementioned project or VPC Service Controls perimeter are allowed.

The mistake message

HTTP Error Response code: 403 Forbidden

HTTP Error Response trunk: Error 403 (Forbidden) 403. That's an fault. Access is forbidden. That's all we know.

The solution

You can:

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

  or

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

Function invocation lacks valid authentication credentials

Invoking a Cloud Functions part that has been set up with restricted access requires an ID token. Access tokens or refresh tokens exercise not piece of work.

The mistake message

HTTP Fault Response lawmaking: 401 Unauthorized

HTTP Mistake Response torso: Your client does not have permission to the requested URL

The solution

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 are generating this token manually with a service account's private cardinal, you must exchange the cocky-signed JWT token for a Google-signed Identity token, following this guide.

Effort to invoke function using curl redirects to Google login folio

If you attempt to invoke a function that does not be, Cloud Functions responds with an HTTP/2 302 redirect which takes you to the Google business relationship login page. This is incorrect. It should respond with an HTTP/ii 404 error response code. The problem is being addressed.

The solution

Make certain you specify the proper noun of your function correctly. You can always check using gcloud functions call which returns the correct 404 error for a missing role.

Awarding crashes and part execution fails

This error indicates that the procedure running your role has died. This is commonly due to the runtime crashing due to problems in the role code. This may also happen when a deadlock or some other condition in your function's code causes the runtime to become unresponsive to incoming requests.

The error message

In Cloud Logging logs: "Infrastructure cannot communicate with function. There was likely a crash or deadlock in the user-provided code."

The solution

Different runtimes can crash under unlike scenarios. To notice the root cause, output detailed debug level logs, check your application logic, and examination for edge cases.

The Cloud Functions Python37 runtime currently has a known limitation on the rate that it tin can handle logging. If log statements from a Python37 runtime instance are written at a sufficiently loftier rate, information technology tin produce this error. Python runtime versions >= 3.eight do not have this limitation. We encourage users to migrate to a higher version of the Python runtime to avert this issue.

If you are still uncertain almost the cause of the error, check out our support page.

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

Some Cloud Functions runtimes allow users to run asynchronous tasks. If your office creates such tasks, it must likewise explicitly wait for these tasks to complete. Failure to do then may cause your function to end executing at the incorrect fourth dimension.

The error behavior

Your function exhibits one of the post-obit behaviors:

• Your part terminates while asynchronous tasks are however running, merely earlier the specified timeout period has elapsed.
• Your role does not cease running when these tasks cease, and continues to run until the timeout catamenia has elapsed.

The solution

If your office terminates early on, yous should make sure all your function's asynchronous tasks have been completed before doing any of the post-obit:

• returning a value
• resolving or rejecting a returned Promise object (Node.js functions merely)
• throwing uncaught exceptions and/or errors
• sending an HTTP response
• calling a callback function

If your function fails to terminate once all asynchronous tasks have completed, you lot should verify that your function is correctly signaling Cloud Functions once information technology has completed. In particular, make sure that you perform 1 of the operations listed above as soon as your function has finished its asynchronous tasks.

JavaScript heap out of memory

For Node.js 12+ functions with memory limits greater than 2GiB, users demand to configure NODE_OPTIONS to have max_old_space_size so that the JavaScript heap limit is equivalent to the function's memory limit.

The fault message

Cloud console

            FATAL ERROR: CALL_AND_RETRY_LAST Allocation failed - JavaScript heap out of retentivity                      

The solution

Deploy your Node.js 12+ function, with NODE_OPTIONS configured to have max_old_space_size set to your office's memory limit. For case:

          gcloud functions deploy envVarMemory \ --runtime nodejs16 \ --set-env-vars NODE_OPTIONS="--max_old_space_size=8192" \ --retention 8Gi \ --trigger-http                  

Part terminated

You may see one of the post-obit error messages when the process running your code exited either due to a runtime error or a deliberate leave. There is besides a small chance that a rare infrastructure error occurred.

The mistake letters

Function invocation was interrupted. Fault: role terminated. Recommended action: inspect logs for termination reason. Additional troubleshooting information can be plant in Logging.

Request rejected. Mistake: role terminated. Recommended activity: inspect logs for termination reason. Additional troubleshooting information can be found in Logging.

Function cannot be initialized. Error: office terminated. Recommended action: audit logs for termination reason. Additional troubleshooting information can be plant in Logging.

The solution

• For a background (Pub/Sub triggered) function when an executionID is associated with the request that ended upward in error, try enabling retry on failure. This allows the retrying of part execution when a retriable exception is raised. For more information for how to employ this choice safely, including mitigations for avoiding infinite retry loops and managing retriable/fatal errors differently, come across All-time Practices.

• Background activity (anything that happens later on your office has terminated) tin can cause issues, then bank check your lawmaking. Cloud Functions does non guarantee whatsoever actions other than those that run during the execution period of the office, and so even if an action runs in the background, information technology might be terminated by the cleanup process.

• In cases when at that place is a sudden traffic spike, try spreading the workload over a little more time. Also test your functions locally using the Functions Framework before you deploy to Cloud Functions to ensure that the error is not due to missing or conflicting dependencies.

Runtime fault when accessing resources protected by VPC-SC

Past default, Cloud Functions uses public IP addresses to brand 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-SC, 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": vii,     "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

Add Cloud Functions in your Google Cloud project as a protected resources in the service perimeter and deploy VPC-SC compliant functions. Run across Using VPC Service Controls for more information.

Alternatively, if your Cloud Functions project cannot exist added to the service perimeter, see Using VPC Service Controls with functions outside a perimeter.

Scalability

Scaling problems related to Deject Functions infrastructure can arise in several circumstances.

The post-obit conditions tin be associated with scaling failures.

• A huge sudden increase in traffic.
• A long cold beginning time.
• A long request processing time.
• High function error rate.
• Reaching the maximum instance limit and hence the system cannot scale whatsoever further.
• Transient factors attributed to the Cloud Functions service.

In each case Cloud Functions might not scale upwards fast plenty to manage the traffic.

The fault message

• The request was aborted because there was no available instance
  • severity=WARNING ( Response lawmaking: 429 ) Deject Functions cannot calibration due to the max-instances limit y'all set during configuration.
  • severity=Mistake ( Response lawmaking: 500 ) Cloud Functions intrinsically cannot manage the rate of traffic.

The solution

• For HTTP trigger-based functions, have the client implement exponential backoff and retries for requests that must not be dropped.
• For groundwork / event-driven functions, Cloud Functions supports at least one time 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 than information.
• When the root cause of the effect is a menstruation of heightened transient errors attributed solely to Cloud Functions or if yous need aid with your issue, delight contact back up

Logging

Setting up logging to help y'all track downward issues can cause problems of its own.

Logs entries accept no, or wrong, log severity levels

Deject Functions includes uncomplicated runtime logging by default. Logs written to stdout or stderr appear automatically in the Cloud console. Merely these log entries, by default, comprise just simple 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 instead.

Handle or log exceptions differently in the event of a crash

Y'all may want to customize how you manage and log crash information.

The solution

Wrap your function is a try/catch block to customize treatment exceptions and logging stack traces.

Example

                      import logging import traceback def try_catch_log(wrapped_func):   def wrapper(*args, **kwargs):     endeavour:       response = wrapped_func(*args, **kwargs)     except Exception:       # Replace new lines with spaces and then as to prevent several entries which       # would trigger several errors.       error_message = traceback.format_exc().supervene upon('\northward', '  ')       logging.fault(error_message)       render 'Error';     return response;   render wrapper;   #Example hello world office @try_catch_log def python_hello_world(request):   request_args = asking.args    if request_args and 'name' in request_args:     one + 'south'   render 'Hello World!'                  

Logs also big in Node.js 10+, Python 3.8, Get i.13, and Java xi

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

The solution

Brand sure you send log entries smaller than this limit.

Cloud Functions logs are non appearing in Log Explorer

Some Deject Logging client libraries apply an asynchronous process to write log entries. If a function crashes, or otherwise terminates, it is possible that some log entries have not been written even so and may appear later on. It is too possible that some logs volition be lost and cannot be seen in Log Explorer.

The solution

Use the client library interface to affluent buffered log entries before exiting the part or use the library to write log entries synchronously. Yous can also synchronously write logs directly to stdout or stderr.

Cloud Functions logs are not actualization via Log Router Sink

Log entries are routed to their various destinations using Log Router Sinks.

Included in the settings are Exclusion filters, which define entries that can simply exist discarded.

The solution

Make sure no exclusion filter is set for resource.type="cloud_functions"

Database connections

There are a number of bug that can arise when connecting to a database, many associated with exceeding connection limits or timing out. If you run into a Cloud SQL alert in your logs, for case, "context deadline exceeded", yous might need to accommodate your connection configuration. See the Cloud SQL docs for additional details.

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Source: https://cloud.google.com/functions/docs/troubleshooting

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