Deploying Sourcegraph executors using Terraform on Google Cloud

A Terraform module is provided to provision machines running executors on Google Cloud.

See also: Deploying on AWS

Basic Definition

The following is the minimum required definition to deploy an executor on Google Cloud.

TERRAFORM
module "executors" {
  source  = "sourcegraph/executors/google"

  # Find the latest version matching your Sourcegraph version here:
  # https://github.com/sourcegraph/terraform-google-executors/tags
  version = "<version>"

  region = "<region to provision in>"
  zone   = "<zone to provision resources in>"

  executor_sourcegraph_external_url            = "<external url>"
  executor_sourcegraph_executor_proxy_password = "<shared secret>"

  # Either:
  executor_queue_name                          = "<codeintel | batches>"
  # Or:
  executor_queue_names                         = ["codeintel", "batches"]

  executor_instance_tag                        = "<tag to filter in stackdriver monitoring>"
  executor_metrics_environment_label           = "<label to filter custom metrics>"
  executor_use_firecracker                     = true
}
VariableDescription
regionThe Google Cloud region to provision the executor resources in.
zoneThe Google Cloud zone to provision the executor resources in.
executor_sourcegraph_external_urlThe public URL of your Sourcegraph instance. This corresponds to the externalURL value in your Sourcegraph instance's site configuration and must be resolvable from the provisioned executor compute resources.
executor_sourcegraph_executor_proxy_passwordThe access token corresponding to the executors.accessToken in your Sourcegraph instance's site configuration.
executor_queue_nameThe single queue from which the executor should pull jobs - codeintel or batches. Either this or executor_queue_names must be set.
executor_queue_namesThe multiple queues from which the executor should pull jobs - one or more of codeintel and batches. Either this or executor_queue_name must be set.
executor_instance_tagA label tag to add to all the executors; can be used for filtering out the right instances in stackdriver monitoring.
executor_metrics_environment_labelThe value for environment by which to filter the custom metrics.
executor_use_firecrackerWhether to use Firecracker sandboxing for job execution. Requires nested virtualization support. Defaults to true.
private_networkingIf true, the executors and Docker registry mirror will live in a private subnet and communicate with the internet through Cloud NAT. Defaults to false. See the Private Single Executor example.
randomize_resource_namesUse randomized names for resources. Defaults to false. Enabling this on existing deployments will recreate executor resources.
private_ca_cert_pathPath to a private CA certificate file. Use this when executors need to communicate with a Sourcegraph instance that uses a certificate signed by a private/internal CA. Optional.

See the Google Cloud Terraform module variables for additional configurations.

Terraform Version

The executor Terraform modules require Terraform >= 1.1.0, < 2.0.0.

Permissions

Ensure the IAM API is enabled.

Supported Regions

All regions are supported.

Examples

Single Executor

Provisions a single executor to pull from the codeintel queue.

Multiple Executors

Provisions two executors, one to pull from the codeintel queue and the other for the batches queue.

Private Single Executor

Provisions a single executor in a private subnet (no public IP). Cloud NAT is used for outbound internet traffic.

Step-by-step Guide

The following is a step-by-step guide on provisioning a single codeintel executor on Google Cloud.

Provision

  1. Install Terraform.
  2. Install the gcloud CLI
  3. Run gcloud auth application-default login
  4. Set up your Sourcegraph instance's Site configuration for executors
    1. Click on your profile picture in the top right corner
    2. Select Site admin
    3. Expand the Configuration section
    4. Select Site configuration
    5. Set the following,
      • "externalURL": "<URL>"
        • A URL that is accessible from the GCP VM (e.g. a public URL such as https://sourcegraph.example.com)
      • "executors.accessToken": "<new long secret>"
        • Can be generated by running cat /dev/random | base64 | head -c 20
        • The secret will be displayed as REDACTED once it's saved.
      • "codeIntelAutoIndexing.enabled": true
        • This is only for codeintel executors.
  5. Download the example files
  6. Change the following in providers.tf
    • project to the GCP project to provision the executor in
    • region to the GCP region to provision the executor in
    • zone to the GCP zone to provision the executor in
  7. Change the following in main.tf
    • executor_sourcegraph_external_url to the URL configured in your instance's Site configuration
    • executor_sourcegraph_executor_proxy_password to the access token configured in your instance's Site configuration
  8. Run terraform init to download the Sourcegraph executor modules.
  9. Run terraform plan to preview the changes that will occur to your GCP infrastructure.
  10. Run terraform apply and enter "yes" after reviewing the proposed changes to create the executor VM
    • Ensure terraform apply exited with code 0 and did not print any errors
  11. Go back to the site admin page, expand Executors, click Instances, and check to see if your executor shows up in the list with a green dot 🟢

Validation

The following can be done to troubleshoot or double-check that the executor has been properly provisioned.

Ensure the executor is listed in the Compute Engine. Either go to Compute Engine in the GCP Console for your project or run the following command.

SHELL
$ gcloud compute instances list
NAME                                          ZONE           MACHINE_TYPE   PREEMPTIBLE  INTERNAL_IP  EXTERNAL_IP    STATUS
sourcegraph-executor-h0rv                     us-central1-c  c2-standard-8               10.0.1.16                   RUNNING
sourcegraph-executors-docker-registry-mirror  us-central1-c  n2-standard-2               10.0.1.2                    RUNNING

You can ssh into to the instance to ensure the service is running. You can open an ssh connection either via the GCP Console or by running the following command.

SHELL
gcloud compute ssh sourcegraph-executor-h0rv

Then run the following command to check if the service is running.

SHELL
you@sourcegraph-executor-h0rv:~$ systemctl status executor
🟢 executor.service - User code executor
     Loaded: loaded (/etc/systemd/system/executor.service; enabled; vendor preset: enabled)
     Active: active (running) since Thu 2021-11-18 02:28:48 UTC; 19s ago

To check the logs, you can either query the Log Explorer in the GCP Console or by running the following command while connected to the instance.

SHELL
you@sourcegraph-executor-h0rv:~$ journalctl -u executor | less
Nov 18 02:31:01 sourcegraph-executor-h0rv executor[2465]: t=2021-11-18T02:31:01+0000 lvl=dbug msg="TRACE internal" host=... path=/.executors/queue/codeintel/dequeue code=204 duration=92.131237ms
Nov 18 02:31:01 sourcegraph-executor-h0rv executor[2465]: t=2021-11-18T02:31:01+0000 lvl=dbug msg="TRACE internal" host=... path=/.executors/queue/codeintel/canceled code=200 duration=90.630467ms
Nov 18 02:31:02 sourcegraph-executor-h0rv executor[2465]: t=2021-11-18T02:31:02+0000 lvl=dbug msg="TRACE internal" host=... path=/.executors/queue/codeintel/dequeue code=204 duration=91.269106ms
Nov 18 02:31:02 sourcegraph-executor-h0rv executor[2465]: t=2021-11-18T02:31:02+0000 lvl=dbug msg="TRACE internal" host=... path=/.executors/queue/codeintel/canceled code=200 duration=161.469685ms

Ensure the EXECUTOR_FRONTEND_URL and EXECUTOR_FRONTEND_PASSWORD in /etc/systemd/system/executor.env are correct

SHELL
cat /etc/systemd/system/executor.env

Ensure the VM can hit your externalURL:

SHELL
you@sourcegraph-executor-h0rv:~$ curl <your externalURL here>
<a href="/sign-in?returnTo=%2F">Found</a>

Configure Auto-indexing

  1. Go to the Site admin page
  2. Expand Code graph,
  3. Select Configuration
  4. Click Create new policy, and fill in:
    • Name: TEST
    • Click add a repository pattern
    • Repository pattern #1: set this to an existing repository on your Sourcegraph instance ( e.g. github.com/gorilla/mux)
    • Type: HEAD
    • Retention: Disabled
    • Auto-indexing: Enabled
  5. Expand Code graph
  6. Select Auto-indexing, and check to see if an indexing job has appeared. If nothing is there:
    • Try clicking Enqueue
    • Try setting a higher update frequency: PRECISE_CODE_INTEL_AUTO_INDEXING_TASK_INTERVAL=10s
    • Try setting a lower delay: PRECISE_CODE_INTEL_AUTO_INDEXING_REPOSITORY_PROCESS_DELAY=10s
  7. Once you have a completed indexing job, click Uploads and check to see that an index has been uploaded.
  8. Once the index has been uploaded, you should see the PRECISE badge in the hover! 🎉

Auto-scaling

NOTE: Auto scaling is currently not supported when downloading and running executor binaries yourself, and on managed instances when using self-hosted executors, since it requires deployment adjustments.

Auto-scaling of executor instances can help to increase concurrency of jobs, without paying for unused resources. With auto-scaling, you can scale down to 0 instances when no workload exist and scale up as far as you like and your cloud provider can support. Auto-scaling needs to be configured separately.

Auto-scaling makes use of Instance Groups on Google Cloud. Sourcegraph's worker service publishes a scaling metric (that is, the number of jobs in queue) to Cloud Monitoring. Then, based on that reported value, the auto-scaler adds and removes compute resources to match the required amount of compute. The autoscaler will attempt to hold 1 instance running per each executor_jobs_per_instance_scaling items in queue.

For example, if executor_jobs_per_instance_scaling is set to 20 and the queue size is currently 400, then 20 instances would be determined as required to handle the load. You might want to tweak this number based on the machine_type, maximum_num_jobs (concurrency per machine), and desired processing speed. See the Google Cloud variable definitions for details.

With the Terraform variables executor_min_replicas and executor_max_replicas, you can configure the minimum and maximum number of compute machines to be run at a given time.

For auto-scaling to work, two things must be true:

  1. executor_min_replicas must be >= 0 and executor_max_replicas must be > executor_min_replicas.
  2. The Sourcegraph instance (its worker service, specifically) needs to publish scaling metrics to Cloud Monitoring.

For the latter to work, the Sourcegraph instance needs to be configured with the correct credentials that allow it to access Google Cloud.

The credentials submodule in the Google Cloud executor module exists for that purpose. When used, the credentials module sets up a service account with permission to write Cloud Monitoring metrics and returns the credentials in the Terraform outputs.

Here's an example of how one would configure auto-scaling.

TERRAFORM
module "executors" {
  source  = "sourcegraph/executors/google"
  version = "<version>"

  # Basic configuration...

  # Auto-scaling
  executor_min_replicas              = 0 # Spin down when not in use
  executor_max_replicas              = 30
  executor_jobs_per_instance_scaling = 20
}

module "my-credentials" {
  source  = "sourcegraph/executors/google//modules/credentials"
  version = "<version>"

  resource_prefix = "<optional prefix to add to created resources>"
}

output "metric_writer_credentials_file" {
  value     = module.my-credentials.metric_writer_credentials_file
  sensitive = true
}

After running terraform apply, retrieve the credentials by running the following command.

SHELL
$ terraform output metric_writer_credentials_file

Configuring the Sourcegraph instance

The Google Compute Engine auto-scaling groups configured by the Sourcegraph Terraform module respond to changes in metric values written to Cloud Monitoring. The target Sourcegraph instance is expected to continuously write these values.

To write the scaling metric to Cloud Monitoring, the worker service must have defined the following environment variables.

Environment VariableDescription
EXECUTOR_METRIC_ENVIRONMENT_LABELSame value as executor_metrics_environment_label
EXECUTOR_METRIC_GCP_PROJECT_IDThe GCP Project ID

Then either one of the following environment variables must be set.

Environment VariableDescription
EXECUTOR_METRIC_GOOGLE_APPLICATION_CREDENTIALS_FILE_CONTENTThe base64-decoded output of metric_writer_credentials_file
EXECUTOR_METRIC_GOOGLE_APPLICATION_CREDENTIALS_FILEThe path to the file containing the base64-decoded output of metric_writer_credentials_file

Testing auto scaling

Once the environment variables have been set and the worker service has been restarted, you should be able to find the scaling metrics in Cloud Monitoring.

To test if the metric is correctly reported: go to the Metrics explorer. Select Resource type: Global and then Metric: custom/executors/queue/size. You should see values reported here. 0 is also an indicator that it works correctly.

Next, you can test whether the number of executors rises and shrinks as load spikes occur. Keep in mind that auto-scaling is not a real-time operation and usually takes a short moment and can have some delays between the metric going down and the desired machine count adjusting.

Upgrading executors

Upgrading executors is relatively uninvolved. Simply follow the instructions below. Also, check the changelog for any Executors related breaking changes or new features or flags that you might want to configure. See Executors maintenance for version compatibility.

Step 1: Update the source version of the terraform modules

NOTE: Keep in mind that only one minor version bumps are guaranteed to be disruption-free.

DIFF
module "executors" {
  source = "sourcegraph/executors/google"
 
  # Find the latest version matching your Sourcegraph version here:
  # https://github.com/sourcegraph/terraform-google-executors/tags
-  version = "7.3.0"
+  version = "7.4.0"
 
  region = "<region>"
  zone   = "<zone>"
 
  executor_sourcegraph_external_url            = "<external url>"
  executor_sourcegraph_executor_proxy_password = "<shared secret>"
 
  # Either:
  executor_queue_name                          = "<codeintel | batches>"
  # Or:
  executor_queue_names                         = ["codeintel", "batches"]
 
  executor_instance_tag                        = "<tag to filter in stackdriver monitoring>"
  executor_metrics_environment_label           = "<label to filter custom metrics>"
  executor_use_firecracker                     = true
}

Step 2: Reapply the terraform configuration

Simply reapply the terraform configuration and executors will be ready to go again.

BASH
terraform apply