gcp-doctor - Diagnostics for Google Cloud Platform
gcp-doctor is a command-line diagnostics tool for GCP customers. It finds and helps to fix common issues in Google Cloud Platform projects. It is used to test projects against a wide range of best-practices and frequent mistakes, based on the troubleshooting experience of the Google Cloud Support team.
gcp-doctor is open-source and contributions are welcome! Note that this is not an officially supported Google product, but a community effort. The Google Cloud Support team maintains this code and we do our best to avoid causing any problems in your projects, but we give no guarantees to that end.
Installation
You can run gcp-doctor using a shell wrapper that starts gcp-doctor in a Docker container. This should work on any machine with Docker installed, including Cloud Shell.
curl https://storage.googleapis.com/gcp-doctor/gcp-doctor.sh >gcp-doctor
chmod +x gcp-doctor
gcloud auth login --update-adc
./gcp-doctor lint --auth-adc --project=[*MYPROJECT*]
Note: the gcloud auth step is not required in Cloud Shell.
Usage
Currently gcp-doctor mainly supports one subcommand: lint, which is used to run diagnostics on one or more GCP projects.
usage: gcp-doctor lint --project P [OPTIONS]
Run diagnostics in GCP projects.
optional arguments:
-h, --help show this help message and exit
--auth-adc Authenticate using Application Default Credentials
--auth-key FILE Authenticate using a service account private key file
--project P Project ID of project that should be inspected (can be specified multiple times)
--billing-project P Project used for billing/quota of API calls done by gcp-doctor
(default is the inspected project, requires 'serviceusage.services.use' permission)
--show-skipped Show skipped rules
--hide-ok Hide rules with result OK
-v, --verbose Increase log verbosity
--within-days D How far back to search logs and metrics (default: 3)
Authentication
gcp-doctor supports authentication using multiple mechanisms:
-
Oauth user consent flow
By default gcp-doctor can use a Oauth user authentication flow, similarly to what gcloud does. It will print a URL that you need to access with a browser, and ask you to enter the token that you receive after you authenticate there. Note that this currently doesn't work for people outside of google.com, because gcp-doctor is not approved for external Oauth authentication yet.
The credentials will be cached on disk, so that you can keep running it for 1 hour. To remove cached authentication credentials, you can delete the
$HOME/.cache/gcp-doctordirectory. -
Application default credentials
If you supply
--auth-adc, gcp-doctor will use Application Default Credentials to authenticate. For example this works out of the box in Cloud Shell and you don't need to re-authenticate, or you can usegcloud auth login --update-adcto refresh the credentials using gcloud. -
Service account key
You can also use the
--auth-keyparameter to specify the private key of a service account.
The authenticated user will need as minimum the following roles granted (both of them):
Vieweron the inspected projectService Usage Consumeron the project used for billing/quota enforcement, which is per default the project being inspected, but can be explicitely set using the--billing-projectoption
The Editor and Owner roles include all the required permissions, but we recommend that if you use service account authentication (--auth-key), you only grant the Viewer+Service Usage Consumer on that service account.
Test Products, Classes, and IDs
Tests are organized by product, class, and ID.
The product is the GCP service that is being tested. Examples: GKE or GCE.
The class is what kind of test it is, currently we have:
| Class name | Description |
|---|---|
| BP | Best practice, opinionated recommendations |
| WARN | Warnings: things that are possibly wrong |
| ERR | Errors: things that are very likely to be wrong |
| SEC | Potential security issues |
The ID is currently formatted as YYYY_NNN, where YYYY is the year the test was written, and NNN is a counter. The ID must be unique per product/class combination.
Each test also has a short_description and a long_description. The short description is a statement about the good state that is being verified to be true (i.e. we don't test for errors, we test for compliance, i.e. an problem not to be present).
Available Rules
| Product | Class | ID | Short description | Long description |
|---|---|---|---|---|
| gce | BP | 2021_001 | Serial port logging is enabled. | Serial port output can be often useful for troubleshooting, and enabling serial logging makes sure that you don't lose the information when the VM is restarted. Additionally, serial port logs are timestamped, which is useful to determine when a particular serial output line was printed. Reference: https://cloud.google.com/compute/docs/instances/viewing-serial-port-output |
| gce | ERR | 2021_001 | Managed instance groups are not reporting scaleup failures. | Suggested Cloud Logging query: resource.type="gce_instance" AND log_id(cloudaudit.googleapis.com/activity) AND severity=ERROR AND protoPayload.methodName="v1.compute.instances.insert" AND protoPayload.requestMetadata.callerSuppliedUserAgent="GCE Managed Instance Group" |
| gce | WARN | 2021_001 | GCE instance service account permissions for logging. | The service account used by GCE instance should have the logging.logWriter permission, otherwise, if you install the logging agent, it won't be able to send the logs to Cloud Logging. |
| gce | WARN | 2021_002 | GCE nodes have good disk performance. | Verify that the persistent disks used by the GCE instances provide a "good" performance, where good is defined to be less than 100ms IO queue time. If it's more than that, it probably means that the instance would benefit from a faster disk (changing the type or making it larger). |
| gke | BP | 2021_001 | GKE system logging and monitoring enabled. | Disabling system logging and monitoring (aka "GKE Cloud Operations") severly impacts the ability of Google Cloud Support to troubleshoot any issues that you might have. |
| gke | ERR | 2021_001 | GKE nodes service account permissions for logging. | The service account used by GKE nodes should have the logging.logWriter role, otherwise ingestion of logs won't work. |
| gke | ERR | 2021_002 | GKE nodes service account permissions for monitoring. | The service account used by GKE nodes should have the monitoring.metricWriter role, otherwise ingestion of metrics won't work. |
| gke | ERR | 2021_003 | App-layer secrets encryption is activated and Cloud KMS key is enabled. | GKE's default service account cannot use a disabled Cloud KMS key for application-level secrets encryption. |
| gke | ERR | 2021_004 | GKE nodes aren't reporting connection issues to apiserver. | GKE nodes need to connect to the control plane to register and to report status regularly. If connection errors are found in the logs, possibly there is a connectivity issue, like a firewall rule blocking access. The following log line is searched: "Failed to connect to apiserver" |
| gke | ERR | 2021_005 | GKE nodes aren't reporting connection issues to storage.google.com. | GKE node need to download artifacts from storage.google.com:443 when booting. If a node reports that it can't connect to storage.google.com, it probably means that it can't boot correctly. The following log line is searched in the GCE serial logs: "Failed to connect to storage.googleapis.com" |
| gke | ERR | 2021_006 | GKE Autoscaler isn't reporting scaleup failures. | If the GKE autoscaler reported a problem when trying to add nodes to a cluster, it could mean that you don't have enough resources to accomodate for new nodes. E.g. you might not have enough free IP addresses in the GKE cluster network. Suggested Cloud Logging query: resource.type="gce_instance" AND log_id(cloudaudit.googleapis.com/activity) AND severity=ERROR AND protoPayload.methodName="v1.compute.instances.insert" AND protoPayload.requestMetadata.callerSuppliedUserAgent="GCE Managed Instance Group for GKE" |
| gke | ERR | 2021_007 | Service Account used by the cluster exists and not disabled | Disabling or deleting service account used by the nodepool will render this nodepool not functional. To fix - restore the default compute account or service account that was specified when nodepool was created. |
| gke | SEC | 2021_001 | GKE nodes don't use the GCE default service account. | The GCE default service account has more permissions than are required to run your Kubernetes Engine cluster. You should either use GKE Workload Identity or create and use a minimally privileged service account. Reference: Hardening your cluster's security https://cloud.google.com/kubernetes-engine/docs/how-to/hardening-your-cluster#use_least_privilege_sa |
| gke | WARN | 2021_001 | GKE master version available for new clusters. | The GKE master version should be a version that is available for new clusters. If a version is not available it could mean that it is deprecated, or possibly retired due to issues with it. |
| gke | WARN | 2021_002 | GKE nodes version available for new clusters. | The GKE nodes version should be a version that is available for new clusters. If a version is not available it could mean that it is deprecated, or possibly retired due to issues with it. |
| gke | WARN | 2021_003 | GKE cluster size close to maximum allowed by pod range | The maximum amount of nodes in a GKE cluster is limited based on its pod CIDR range. This test checks if the cluster is approaching the maximum amount of nodes allowed by the pod range. Users may end up blocked in production if they are not able to scale their cluster due to this hard limit imposed by the pod CIDR. |
| gke | WARN | 2021_004 | GKE system workloads are running stable. | GKE includes some system workloads running in the user-managed nodes which are essential for the correct operation of the cluster. We verify that restart count of containers in one of the system namespaces (kube-system, istio-system, custom-metrics) stayed stable in the last 24 hours. |
| gke | WARN | 2021_005 | GKE nodes have good disk performance. | Disk performance is essential for the proper operation of GKE nodes. If too much IO is done and the disk latency gets too high, system components can start to misbehave. Often the boot disk is a bottleneck because it is used for multiple things: the operating system, docker images, container filesystems (usually including /tmp, etc.), and EmptyDir volumes. |
| gke | WARN | 2021_006 | GKE nodes aren't reporting conntrack issues. | The following string was found in the serial logs: nf_conntrack: table full See also: https://cloud.google.com/kubernetes-engine/docs/troubleshooting |
![[gcs/BP/2022_001] KeyError: 'iamConfiguration'](https://avatars.githubusercontent.com/u/76945466?v=4)
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