PullApprove 5
CODEREVIEW.toml
Concepts
Self-Hosted
Log in

Operations

Day-to-day operations for running and maintaining your self-hosted PullApprove5 instance.

Container Commands

The Docker image supports the following commands:

  • server — starts the server
  • worker — starts a background job worker
  • migrate — runs database migrations
  • enable-admin-user <email> — grants admin access to a user (must have logged in first)
  • python-shell — opens an interactive Python shell for debugging and data inspection

The server and worker commands automatically run preflight checks on startup (skip with --skip-preflight if needed).

The admin UI is available at /admin/. To grant access, run enable-admin-user after the user has logged in:

docker run --env-file .env pullapprove/pullapprove5:5.x.x enable-admin-user user@example.com

# Or on Kubernetes:
kubectl exec -it deploy/pullapprove-server -- enable-admin-user user@example.com

Health Check and Scaling

PullApprove5 exposes a GET /up/ endpoint that returns a 200 OK response when the server is running. Use this for load balancer health checks and Kubernetes readiness/liveness probes.

PullApprove5 is stateless and horizontally scalable. All shared state is stored in PostgreSQL.

  • Server — run multiple server containers behind a load balancer. Set PULLAPPROVE_SERVER_WORKERS to control worker processes per container.
  • Worker — run multiple worker containers to increase background job throughput. Set PULLAPPROVE_JOBS_WORKER_MAX_PROCESSES to control job processes per container. The job queue uses database-level locking to prevent duplicate processing, and workers can be stopped or restarted at any time — interrupted jobs are detected and handled automatically.

Set a CPU limit. Both counts default to the CPU count the container detects, so in Kubernetes a CPU request without a limit lets the container see the whole node and start more processes than its memory allows (OOMKills). Set resources.limits.cpu, or pin PULLAPPROVE_SERVER_WORKERS / PULLAPPROVE_JOBS_WORKER_MAX_PROCESSES.

Proxy Configuration

The server container serves HTTP on port 8000. Terminate TLS at your reverse proxy and forward traffic to the container.

Your reverse proxy must send the X-Forwarded-Proto: https header. PullApprove redirects HTTP to HTTPS and uses this header to detect whether the original request was already over HTTPS. Without it, requests will enter a redirect loop.

# Change the proxy header
# (default: "X-Forwarded-Proto: https")
PULLAPPROVE_HTTPS_PROXY_HEADER="X-Forwarded-Proto: https"

# Disable HTTPS redirect (default: true)
PULLAPPROVE_HTTPS_REDIRECT_ENABLED=false

If your network requires an HTTP proxy for outbound traffic, contact us for configuration details.

Logging and Monitoring

Both the server and worker containers log to stdout in structured key-value format. Each line starts with the log level in brackets, followed by the message and key-value context (values containing special characters are quoted):

[INFO] Completed job job_class="app.pullrequests.jobs.PullRequestProcessUpdates" job_queue=default job_duration="0.014"
[INFO] Job worker stats worker_processes=2 worker_queues=default jobs_requested=4 jobs_processing=1
[INFO] API response url="https://api.github.com/repos/..." status=200 from_cache=True

Log lines do not include timestamps — the container runtime records one for every line, so use its timestamps when viewing logs directly:

kubectl logs --timestamps deploy/pullapprove-server

docker logs --timestamps <container>

Log aggregators capture these timestamps automatically. If you need timestamps in the log lines themselves, set PULLAPPROVE_LOG_FORMAT=json, which includes a timestamp field.

Key fields to be aware of:

  • request_id — included in request logs for end-to-end tracing
  • job_class and job_duration — track background job performance
  • jobs_requested — pending job backlog (see What to Monitor)
  • jobs_processing and worker_processes — monitor worker utilization
  • rate_limit_remaining — track Git provider API rate limit consumption

Log Configuration

# Log level for application logs
# Set to DEBUG for troubleshooting (default: INFO)
PULLAPPROVE_LOG_LEVEL=INFO

# "keyvalue" (default) or "json"
# Use "json" for log aggregation (Datadog, Splunk, ELK)
PULLAPPROVE_LOG_FORMAT=keyvalue

# "split" (default), "stdout", or "stderr"
# "split" sends INFO to stdout, WARNING+ to stderr
PULLAPPROVE_LOG_STREAM=split

Sentry

PullApprove5 comes with Sentry support built-in — just provide a DSN to enable error tracking and performance monitoring.

# Sentry DSN for error tracking
SENTRY_DSN=https://your-sentry-dsn@sentry.io/project

# Environment name shown in Sentry
# (e.g., production, staging)
SENTRY_ENVIRONMENT=production

What to Monitor

  • Webhook endpoint — response codes and latency on your webhook path (e.g., POST /webhooks/github/)
  • Worker liveness — each worker logs Job worker stats every 60 seconds. Alert if the line stops appearing for ~5 minutes: the worker is dead or hung, which container health probes won't always catch.
  • Job backlog — jobs_requested in the Job worker stats line is the count of jobs waiting to run. This is the primary "workers aren't keeping up" signal — more meaningful than CPU or memory. It idles at 1–2 (upcoming scheduled jobs count toward it), so alert on sustained elevation (e.g. above 20 for 10+ minutes) rather than above zero. The counts are database-wide, so any single worker's line is authoritative. If backlog stays elevated, add worker containers — they are safe to add or remove at any time.
  • Job throughput — alert if Completed job lines drop to zero over a ~2 hour window. Internal scheduled jobs run at least hourly, so a healthy instance always completes something; silence means job processing is stuck even if the containers look healthy.
  • Job failures — failed jobs log at ERROR level with a full traceback (sent to stderr with the default split log stream). Alert on error-level worker logs, or configure Sentry to capture them.
  • Rate limits — logs containing rate_limit_remaining track Git provider API quota consumption
  • Database — connection count, cache hit rate, and disk usage

Rate Limits

GitHub Apps have an API rate limit per installation (per org the app is installed on). PullApprove monitors rate limit consumption and automatically pauses background sync jobs (history, insights) when remaining requests drop below a safe threshold. This preserves API capacity for real-time pull request processing, which is never paused. Paused jobs retry automatically when the rate limit window resets.

API calls are cached where possible and retried with exponential backoff on transient errors. Webhook deliveries are handled idempotently — if GitHub retries a webhook (due to a timeout or error), PullApprove will process it safely without duplicating work.

Upgrading

To upgrade to a new version:

  1. Take a database snapshot before upgrading
  2. Obtain the new image (pull it or load the tarball) and make it available to your cluster
  3. Run migrations: docker run --env-file .env pullapprove/pullapprove5:5.x.x migrate
  4. Restart the server and worker containers with the new image

Migrations run separately from the application, so rolling deploys generally work without downtime. Release notes will call out any exceptions that require a maintenance window.

We recommend upgrading sequentially through each release rather than skipping versions, as skipping versions is untested. Release notes are provided with each new version.

Rollback: Database migrations do not have reverse operations. To roll back a failed upgrade, restore your database from the snapshot taken in step 1 and revert to the previous Docker image.

Your license agreement allows non-production instances, so you can run a staging environment to test upgrades before applying them to production.

Backups and Data Retention

PostgreSQL is the only stateful component. Back up your database using your preferred method (e.g., pg_dump, managed database snapshots). There is no additional file storage to back up.

If PullApprove5 is temporarily unavailable, webhooks from GitHub and GitLab will be retried automatically by the provider. When the instance comes back up, incoming webhooks will re-trigger processing for open PRs. Historical insights and metrics data cannot be recovered from the Git provider, so regular database backups are recommended.

PullApprove automatically cleans up some data:

  • Background job history — deleted after 7 days
  • Insights summaries — deleted after the organization's configured retention period (default 60 days)

Pull request records, processing results, and event history are not automatically cleaned up and will grow over time. For most organizations this growth is modest.