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Troubleshooting Deject Functions

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

Deployment

The deployment phase is a frequent source of problems. Many of the problems you might come across 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 Projection Viewer or Cloud Functions Viewer role has read-only access to functions and function details. These roles are not allowed to deploy new functions.

The error message

Cloud panel

              You need permissions for this activity. 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 be)                          

The solution

Assign the user a role that has the appropriate access.

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

In club to deploy a function, a user who has been assigned the Projection Viewer, the Deject Function Developer, or Deject Office Admin office must be assigned an additional role.

The error message

Deject panel

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

Cloud SDK

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

The solution

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

Deployment service account missing the Service Agent role when deploying functions

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

The error message

Cloud console

              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 role. You can practise that by 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'                          

Cloud SDK

              ERROR: (gcloud.functions.deploy) OperationError: lawmaking=seven, message=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 part. You tin do that past running 'gcloud projects add-iam-policy-binding <PROJECT_ID> --fellow member=serviceAccount:service-<PROJECT_NUMBER>@gcf-admin-robot.iam.gserviceaccount.com --office=roles/cloudfunctions.serviceAgent'                          

The solution

Reset this service account to the default role.

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

The Deject Functions service uses the Cloud Functions Service Agent service account (service-<PROJECT_NUMBER>@gcf-admin-robot.iam.gserviceaccount.com) when performing administrative actions. Past default this business relationship is assigned the Deject Functions cloudfunctions.serviceAgent role. To deploy event-driven functions, the Cloud Functions service must admission Deject Pub/Sub to configure topics and subscriptions. If the function assigned to the service business relationship is inverse and the appropriate permissions are non otherwise granted, the Cloud Functions service cannot access Cloud Pub/Sub and the deployment fails.

The error message

Cloud panel

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

Cloud SDK

              Fault: (gcloud.functions.deploy) OperationError: code=13, message=Failed to configure trigger PubSub projects/<PROJECT_ID>/topics/<FUNCTION_NAME>                          

The solution

Y'all can:

• 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 different resources, it is a mutual practice to use per-function identities, with named runtime service accounts rather than the default runtime service account (PROJECT_ID@appspot.gserviceaccount.com).

Still, to use a non-default runtime service account, the deployer must take the iam.serviceAccounts.actAs permission on that non-default account. A user who creates a not-default runtime service business relationship is automatically granted this permission, but other deployers must accept 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 function service business relationship requested: <SERVICE_ACCOUNT_NAME@<PROJECT_ID>.iam.gserviceaccount.com]                  

The solution

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

Runtime service account missing project bucket permissions while deploying a part

Cloud Functions tin can just be triggered past events from Cloud Storage buckets in the aforementioned Google Cloud Platform project. In add-on, the Deject Functions Service Amanuensis service business relationship (service-<PROJECT_NUMBER>@gcf-admin-robot.iam.gserviceaccount.com) needs a cloudfunctions.serviceAgent role on your project.

The mistake 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 role of the saucepan and effort once again.                          

Cloud SDK

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

The solution

Yous can:

• Reset this service account to the default function.

  or

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

  or

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

User with Projection Editor office cannot make a function public

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

The error message

Deject SDK

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

The solution

You can:

• Assign the deployer either the Project Owner or the Cloud Functions Admin role, both of which incorporate the cloudfunctions.functions.setIamPolicy permission.

  or

• Grant the permission manually by creating a custom office.

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

Deject Functions uses Cloud 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 bulletin

Cloud console

One of the below:

              Mistake in the build environment  OR  Unable to build your role due to VPC Service Controls. The Cloud Build service account 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://cloud.google.com/functions/docs/securing/using-vpc-service-controls#grant-build-access"                          

Cloud SDK

Ane of the beneath:

              Mistake: (gcloud.functions.deploy) OperationError: code=13, message=Mistake in the build environs  OR  Unable to build your office due to VPC Service Controls. The Cloud Build service business relationship associated with this office 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://cloud.google.com/functions/docs/securing/using-vpc-service-controls#grant-build-access"                          

The solution

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

Function deployment fails due to incorrectly specified entry point

Cloud Functions deployment can fail if the entry point to your code, that is, the exported function proper noun, is non specified correctly.

The mistake message

Deject console

              Deployment failure: Role failed on loading user code. Error message: Mistake: delight examine your function logs to come across the error crusade: https://deject.google.com/functions/docs/monitoring/logging#viewing_logs                          

Cloud SDK

              Error: (gcloud.functions.deploy) OperationError: lawmaking=iii, bulletin=Role failed on loading user code. Mistake message: Please examine your office logs to see the fault cause: https://deject.google.com/functions/docs/monitoring/logging#viewing_logs                          

The solution

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

Function deployment fails when using Resource Location Constraint organization policy

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

The mistake message

In Cloud Build logs:

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

In Deject Storage logs:

          <REGION>.artifacts.<PROJECT_ID>.appspot.com` storage bucket could not be created.                  

The solution

If you are using constraints/gcp.resourceLocations in your arrangement policy constraints, you should specify the appropriate multi-region location. For example, if yous are deploying in any of the u.s.a. regions, you should utilize us-locations.

Even so, if y'all require more fine grained control and desire to restrict function deployment to a unmarried region (non multiple regions), create the multi-region bucket first:

1. Let the whole multi-region
2. Deploy a test function
3. After the deployment has succeeded, change the organizational policy back to permit but the specific region.

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

Function deployment fails while executing part'due south global scope

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

The error message

In Cloud Logging logs:

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

The solution

For a more than detailed error message, look into your role'southward build logs, also as your function's runtime logs. If it is unclear why your function failed to execute its global telescopic, consider temporarily moving the code into the request invocation, using lazy initialization of the global variables. This allows y'all to add extra log statements effectually your client libraries, which could be timing out on their instantiation (peculiarly if they are calling other services), or crashing/throwing exceptions altogether.

Build

When you deploy your function's source code to Cloud Functions, that source is stored in a Cloud Storage bucket. Deject Build then automatically builds your code into a container epitome and pushes that epitome to Container Registry. Cloud Functions accesses this image 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 equally the deployments depend on these images being present.

The error message

Cloud console

              Build failed: Build error details not bachelor. Please bank check the logs at <CLOUD_CONSOLE_LINK>  CLOUD_CONSOLE_LINK contains an error similar beneath : failed to get Bone from config file for image 'us.gcr.io/<PROJECT_ID>/gcf/the states-central1/<UUID>/worker:latest'"                          

Cloud SDK

              ERROR: (gcloud.functions.deploy) OperationError: code=thirteen, message=Build failed: Build mistake details not available. Delight check the logs at <CLOUD_CONSOLE_LINK>  CLOUD_CONSOLE_LINK contains an error like below : failed to go Os from config file for image 'us.gcr.io/<PROJECT_ID>/gcf/usa-central1/<UUID>/worker:latest'"                          

The solution

1. Disable Lifecycle Direction on the buckets required by Container Registry.
2. Delete all the images of affected functions. You can access build logs to detect the paradigm paths. Reference script to bulk delete the images. Annotation that this does non touch on the functions that are currently deployed.
3. Redeploy the functions.

Serving

The serving phase tin can as well be a source of errors.

Serving permission fault due to the function being individual

Deject Functions allows you to declare functions individual, that is, to restrict access to finish users and service accounts with the advisable permission. By default deployed functions are set as private. This fault message indicates that the caller does not have permission to invoke the role.

The error message

HTTP Error Response code: 403 Forbidden

HTTP Error Response trunk: Error: Forbidden Your customer does non have permission to go URL /<FUNCTION_NAME> from this server.

The solution

You can:

• Let public (unauthenticated) access to all users for the specific role.

  or

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

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

Ingress settings restrict whether an HTTP function tin can be invoked by resource outside of your Google Cloud project or VPC Service Controls service perimeter. When the "permit internal traffic but" setting for ingress networking is configured, this error message indicates that simply requests from VPC networks in the aforementioned projection or VPC Service Controls perimeter are allowed.

The fault message

HTTP Mistake Response code: 403 Forbidden

HTTP Error Response body: Error 403 (Forbidden) 403. That's an error. Access is forbidden. That'south all we know.

The solution

You tin:

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

  or

• Change the ingress settings to let all traffic for the part.

Function invocation lacks valid authentication credentials

Invoking a Cloud Functions function that has been fix upwardly with restricted access requires an ID token. Admission tokens or refresh tokens do not work.

The fault bulletin

HTTP Fault Response code: 401 Unauthorized

HTTP Error Response trunk: Your customer does not accept permission to the requested URL

The solution

Make sure that your requests include an Potency: 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 business relationship's individual key, you must commutation the self-signed JWT token for a Google-signed Identity token, following this guide.

Attempt to invoke function using curl redirects to Google login page

If you attempt to invoke a function that does not be, Cloud Functions responds with an HTTP/2 302 redirect which takes y'all to the Google business relationship login page. This is incorrect. Information technology should answer with an HTTP/2 404 error response code. The trouble is existence addressed.

The solution

Make certain yous specify the name of your function correctly. Yous can always check using gcloud functions call which returns the correct 404 error for a missing function.

Application crashes and function execution fails

This error indicates that the procedure running your function has died. This is usually due to the runtime crashing due to bug in the function code. This may also happen when a deadlock or another condition in your part's code causes the runtime to go unresponsive to incoming requests.

The error bulletin

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

The solution

Different runtimes can crash under dissimilar scenarios. To find the root cause, output detailed debug level logs, check your application logic, and test 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, it can produce this error. Python runtime versions >= 3.8 exercise not have this limitation. We encourage users to drift to a higher version of the Python runtime to avoid this issue.

If you are still uncertain about the cause of the error, bank check out our support folio.

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

Some Cloud Functions runtimes permit users to run asynchronous tasks. If your office creates such tasks, information technology must also explicitly wait for these tasks to complete. Failure to do so may cause your function to stop executing at the incorrect time.

The fault beliefs

Your part exhibits one of the following behaviors:

• Your role terminates while asynchronous tasks are notwithstanding running, but before the specified timeout period has elapsed.
• Your function does not terminate running when these tasks finish, and continues to run until the timeout period has elapsed.

The solution

If your function terminates early, you lot should make sure all your part's asynchronous tasks have been completed before doing any of the following:

• 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 part

If your office fails to stop one time all asynchronous tasks have completed, y'all should verify that your role is correctly signaling Cloud Functions one time information technology has completed. In particular, make sure that you perform 1 of the operations listed above every bit soon as your role has finished its asynchronous tasks.

JavaScript heap out of memory

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

The error message

Deject console

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

The solution

Deploy your Node.js 12+ part, with NODE_OPTIONS configured to take max_old_space_size set to your office'southward memory limit. For example:

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

Function terminated

Y'all may see ane of the following error messages when the procedure running your code exited either due to a runtime error or a deliberate go out. There is besides a small chance that a rare infrastructure error occurred.

The error messages

Function invocation was interrupted. Error: function terminated. Recommended action: inspect logs for termination reason. Additional troubleshooting information tin be institute in Logging.

Asking rejected. Error: function terminated. Recommended action: inspect logs for termination reason. Additional troubleshooting information can be plant in Logging.

Function cannot exist initialized. Mistake: office terminated. Recommended action: inspect logs for termination reason. Additional troubleshooting information can be found in Logging.

The solution

• For a groundwork (Pub/Sub triggered) office when an executionID is associated with the asking that ended upward in fault, try enabling retry on failure. This allows the retrying of function execution when a retriable exception is raised. For more data for how to apply this choice safely, including mitigations for avoiding infinite retry loops and managing retriable/fatal errors differently, see All-time Practices.

• Background activity (anything that happens afterward your part has terminated) can cause issues, so check your code. Cloud Functions does not guarantee any deportment other than those that run during the execution period of the part, so even if an activeness runs in the background, it might be terminated by the cleanup procedure.

• In cases when there is a sudden traffic fasten, effort spreading the workload over a little more than time. Also test your functions locally using the Functions Framework before you deploy to Deject Functions to ensure that the error is not due to missing or conflicting dependencies.

Runtime error when accessing resources protected by VPC-SC

By default, Deject Functions uses public IP addresses to make outbound requests to other services. If your functions are not within a VPC Service Controls perimeter, this might crusade them to receive HTTP 403 responses when attempting to access Google Deject services protected past VPC-SC, due to service perimeter denials.

The fault message

In Audited Resource logs, an entry similar the following:

"protoPayload": {   "@type": "blazon.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 together Cloud Functions in your Google Cloud projection as a protected resource in the service perimeter and deploy VPC-SC compliant functions. See Using VPC Service Controls for more information.

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

Scalability

Scaling issues related to Cloud Functions infrastructure can arise in several circumstances.

The following weather can be associated with scaling failures.

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

In each example Cloud Functions might non scale up fast enough to manage the traffic.

The error message

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

The solution

• For HTTP trigger-based functions, take the client implement exponential backoff and retries for requests that must not be dropped.
• For groundwork / result-driven functions, Cloud Functions supports at least once delivery. Even without explicitly enabling retry, the event is automatically re-delivered and the function execution will exist retried. See Retrying Result-Driven Functions for more information.
• When the root cause of the upshot is a period of heightened transient errors attributed solely to Cloud Functions or if y'all need aid with your issue, please contact back up

Logging

Setting upwards logging to assist you lot rails down bug tin cause bug of its ain.

Logs entries take no, or incorrect, log severity levels

Cloud Functions includes unproblematic runtime logging by default. Logs written to stdout or stderr appear automatically in the Cloud console. But these log entries, by default, contain only uncomplicated string messages.

The error message

No or wrong severity levels in logs.

The solution

To include log severities, you must transport a structured log entry instead.

Handle or log exceptions differently in the event of a crash

You may want to customize how you manage and log crash data.

The solution

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

Example

                      import logging import traceback def try_catch_log(wrapped_func):   def wrapper(*args, **kwargs):     attempt:       response = wrapped_func(*args, **kwargs)     except Exception:       # Replace new lines with spaces then as to prevent several entries which       # would trigger several errors.       error_message = traceback.format_exc().replace('\north', '  ')       logging.error(error_message)       return 'Fault';     return response;   return wrapper;   #Example hello globe function @try_catch_log def python_hello_world(asking):   request_args = request.args    if request_args and 'name' in request_args:     one + 's'   return 'How-do-you-do World!'                  

Logs besides large in Node.js 10+, Python 3.8, Become 1.thirteen, and Java 11

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

The solution

Make sure y'all send log entries smaller than this limit.

Cloud Functions logs are not appearing in Log Explorer

Some Cloud Logging client libraries apply an asynchronous procedure to write log entries. If a function crashes, or otherwise terminates, it is possible that some log entries have not been written yet and may appear later. It is also possible that some logs will be lost and cannot be seen in Log Explorer.

The solution

Apply the client library interface to flush buffered log entries before exiting the role or use the library to write log entries synchronously. You can too synchronously write logs directly to stdout or stderr.

Cloud Functions logs are not appearing 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 be discarded.

The solution

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

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 run into a Cloud SQL alarm in your logs, for case, "context deadline exceeded", you might need to suit your connection configuration. Meet the Cloud SQL docs for additional details.

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

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