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Using Prow at Scale

If you are maintaining a Prow instance that will need to scale to handle a large load, consider using the following best practices, features, and additional tools. You may also be interested in "Getting more out of Prow".

Features and Tools

Separate Build Cluster(s)

It is frequently not secure to run all ProwJobs in the same cluster that runs Prow's service components (hook, plank, etc.). In particular, ProwJobs that execute presubmit tests should typically be isolated from Prow's microservices. This isolation prevents a malicious PR author from modifying the presubmit test to do something evil like breaking out of the container and stealing secrets that live in the cluster.

More than one build cluster can be used in order to isolate specific jobs from each other, improve scalability, and offer different node shapes. Instructions for configuring jobs to run in different clusters can be found here.

Pull Request Merge Automation

Pull Requests can be automatically merged when they satisfy configured merge requirements using tide. Automating merge is critical for large projects where allowing human to click the merge button is either a bottle neck, a security concern, or both. Tide ensures that PRs have been tested against the most recent base branch commit before merging (retesting if necessary), and automatically groups multiple PRs to be tested and merged as a batch whenever possible.

Config File Split

If your Prow config starts to grow too large, consider splitting the job config files into more specific and easily reviewed files. To use this pattern simply aggregate all job configs in a directory of files with unique base names and supply the directory path to components via --job-config-path.

The updateconfig plugin supports this pattern by allowing multiple files to loaded into a single configmap under different keys (different files once mounted to a container).

GitHub API Cache

ghproxy is a reverse proxy HTTP cache optimized for the GitHub API. It takes advantage of how GitHub responds to E-tags in order to fulfill repeated requests without spending additional API tokens. Check out this tool if you find that your GitHub bot is consuming or approaching it's token limit. Similarly, re-deploying Prow components may trigger a large amount of API requests to GitHub which may trip the abuse detection mechanisms. At scale, the tide deployment itself may create enough API throughput to trigger this on its own. Deploying the GitHub proxy cache is critical to ensuring that Prow does not trip this mechanism when operating at scale.

Config Driven GitHub Org Management

Managing org and repo scoped settings across multiple orgs and repos is not easy with the mechanisms that GitHub provides. Only a few people have access to the settings, they must be manually synced between repos, and they can easily become inconsistent. These problems grow with number of orgs/repos and with the number of contributors. We have a few tools that automate this kind of administration and integrate well with Prow:

  • label_sync is a tool that synchronizes labels and their metadata across multiple orgs and repos in order to provide a consistent user experience in a multi-repo project.
  • branch_protector is a Prow component that synchronizes GitHub branch requirements and restrictions based on config.
  • peribolos is a tool that synchronizes org settings, teams, and memberships based on config.

Metrics

Prow exposes some Prometheus metrics that can be used to generate graphs and alerts. If you are maintaining a Prow instance that handles important workloads you should consider using these metrics for monitoring.

Best Practices

Don’t share Prow’s GitHub bot token with other automation.

Some parts of Prow do not behave well if the GitHub bot token's rate limit is exhausted. It is imperative to avoid this so it is a good practice to avoid using the bot token that Prow uses for any other purposes.

Working around GitHub's limited ACLs.

GitHub provides an extremely limited access control system that makes it impossible to control granular permissions like authority to add and remove specific labels from PRs and issues. Instead, write access to the entire repo must be granted. This problem grows as projects scale and granular permissions become more important.

Much of the GitHub automation that Prow provides is designed to fill in the gaps in GitHub's permission system. The core idea is to limit repo write access to the Prow bot (and a minimal number of repo admins) and then let Prow determine if users have the appropriate permissions before taking action on their behalf. The following is an overview of some of the automation Prow implements to work around GitHub's limited permission system:

  • Permission to trigger presubmit tests is determined based on org membership as configured in the triggers plugin config section.
  • File ownership is described with OWNERS files and change approval is enforced with the approve plugin. See the docs for details.
  • Org member review of the most recent version of the PR is enforced with the lgtm plugin.
  • Various other plugins manage labels, milestone, and issue state based on /foo style commands from authorized users. Authorization may be based on org membership, GitHub team membership, or OWNERS file membership.
  • Tide provides PR merge automation so that humans do not need to (and are not allowed to) merge PRs. Without Tide, a user either has no permission to merge or they have repo write access which grants permission to merge any PR in the entire repo. Additionally, Tide enforces merge requirements like required and forbidden labels that humans may not respect if they are allowed to manually click the merge button.