Architecture

The services pgbr runs, what owns which state, and how a job flows through them.

pgbr separates the thing that decides work happens (the dashboard) from the thing that does it (the worker). Neither holds durable state, which is what makes both safe to scale and restart.

A Next.js app serving the UI and API. It never runs a dump or a restore; it validates a request, writes metadata, and puts a job on a queue. The one place it touches PostgreSQL directly is Ping, which shells out to pg_isready.

Worker

A Node process consuming three BullMQ queues. It spawns pg_dump, pg_restore, and psql as child processes, streams the result to object storage, and records the outcome in Postgres.

Who owns what state

This is the design rule that explains most of pgbr's behaviour:

State	Owner	Notes
Users, connections, job history, schedules	Postgres	The source of truth.
Queued jobs, cron schedulers	Redis	Derived state — rebuildable.
Backup artifacts	Object store	The only irreplaceable data.
Scratch files during a job	Worker's `tmpdir`	Throwaway, deleted in a `finally`.

Redis being derived is deliberate. Schedules live in Postgres; the BullMQ job schedulers in Redis are a projection of them. Every worker reconciles that projection on boot, so a flushed Redis heals itself rather than silently dropping your schedules.

Only the object store and Postgres need durable volumes. The dashboard and worker containers can be destroyed and recreated at will.

How a backup flows

The dashboard validates and enqueues

The server action checks your session, confirms the database belongs to you, and parses the flags against the shared Zod schema. It generates a job UUID, adds the job to the backup queue, and returns immediately. Nothing has run yet.

The worker claims the job

A worker picks it up and re-validates the flags — it does not trust the payload — then re-checks that the database belongs to the requesting user.

The row is written before the work starts

The worker upserts a backup_jobs row with status running before spawning anything, then calls updateProgress. That ordering is what makes the UI live: by the time the event reaches the browser, there is already a row to render.

The upsert is idempotent (onConflictDoUpdate) so a re-delivered job converges onto the same row instead of duplicating it.

`pg_dump` runs into scratch

The worker creates a temp directory, builds the argument list from your flags, and spawns pg_dump — with the connection's password in PGPASSWORD rather than the argument list, so it stays out of the container's process table. A non-zero exit becomes the job's error message, taken from pg_dump's own stderr.

The artifact is uploaded

The dump is streamed to the object store under backups/<job-id>.<ext>, counting bytes as they go, so the recorded size comes from the upload itself. Directory dumps are collapsed into a tarball first — see Storage.

The row is finalised, retention runs

Status becomes completed, size is recorded, and the database's lifetime backup counter increments. If the job came from a schedule with a retention limit, old backups are pruned. A retention failure is logged but never fails the backup.

Scratch is removed in a finally, so it goes away whether the job succeeded or not.

Restores follow the same shape in reverse: download to scratch, decompress or expand if needed, spawn pg_restore (or psql for plain SQL), record the outcome.

Why the UI updates without reloading

The dashboard exposes a Server-Sent Events stream at /api/events. It listens to all three queues and emits a lightweight signal whenever any job changes state. The browser debounces those signals and calls router.refresh(), which re-renders the server components with fresh data.

The payload is deliberately just { queue, event } — a nudge, not the data. Pages re-fetch through their normal server-side path, so there's only one place where data is loaded and authorized.

This is also how scheduled backups appear live even though no browser triggered them.

Migrations use a separate stream at /api/migrate, which holds the request open for one job and streams that job's progress. It's the one operation whose progress is tied to the request that started it.

Failure behaviour

A worker dies mid-job — BullMQ's stalled-job detection expires the lock and another worker reprocesses it. Because processors upsert idempotently, the re-delivery converges rather than orphaning a running row.
A dump fails — the row is marked failed with pg_dump's stderr, and the job throws so BullMQ records it.
Redis is flushed — schedules are re-registered from Postgres on the next worker boot.
The object store is unreachable — backups fail at upload and are recorded as failed. The dump itself is discarded with the scratch directory.

Architecture

The services

Dashboard