z4j-scheduler 1.6.7

Engine-agnostic dynamic scheduler for the z4j stack (Apache 2.0)

z4j-scheduler

The engine-agnostic dynamic scheduler for z4j.

One service drives Celery, RQ, Dramatiq, Huey, arq, and TaskIQ from a single dashboard. Schedules live in z4j's database, you edit them live without restarting anything, every change is recorded in an HMAC-chained audit log, and importers + exporters keep the door open in either direction. This is the canonical scheduler when you want one place to manage cron / interval / one-shot / solar schedules across mixed engines.

Compatibility

Python 3.11+. PostgreSQL 17+ for shared-database HA (SQLite supported for single-node deployments). Fans out to whichever engines you install on the same host (Celery, RQ, Dramatiq, Huey, arq, TaskIQ); each engine extra carries its own upstream version floor.

Full per-adapter matrix at https://z4j.dev/reference/compatibility/.

What makes z4j-scheduler different

z4j-scheduler is a deliberate alternative to in-language schedulers like celery-beat, rq-scheduler, and APScheduler. The differences that matter day to day:

• Engine-agnostic. One scheduler service for every supported Python task engine. A project running Celery for legacy services and arq for a FastAPI rewrite uses the same scheduler for both, with one dashboard and one audit trail.
• Live editing. Schedules live in z4j's Postgres database. Create, edit, pause, resume, rename, and delete from the dashboard or REST API without a daemon restart. celery-beat needs a beat-process restart for static-config edits, rq-scheduler stores schedules in Redis but has no editing UI, APScheduler edits are in-process.
• HMAC-chained audit log. Every schedule mutation (who, what, when, from which IP) is recorded in a tamper-evident audit chain alongside the brain's other audit rows. celery-beat keeps no record. django-celery-beat keeps a partial record only if you wired up django-auditlog.
• HA-ready. Multiple scheduler instances can run against the same Postgres database. Postgres advisory locks elect one leader to tick; followers stay warm. Rolling restarts and leader failovers are seconds, not minutes, with no missed-fire window beyond the per-schedule catch-up policy.
• Reversible by design. Importers cover celery-beat (static and django-celery-beat), rq-scheduler, APScheduler jobstores, Huey @periodic_task, arq cron, taskiq schedule sources, and system crontab. Exporters write any schedule back to those same formats. Round-trip integrity is pinned by tests so you can leave whenever you want; the schedule definitions are yours, not ours.

What it ships

Capability	Notes
Schedule kinds	cron, interval, one-shot, solar (sunrise / sunset / dawn / dusk / noon / midnight at a given lat / lon)
Live editing	dashboard, REST API, declarative config; no restart required
Engine fan-out	Celery, RQ, Dramatiq, Huey, arq, TaskIQ
Importers	celery / django-celery-beat / rq-scheduler / apscheduler / huey-periodic / arq-cron / taskiq-scheduler / cron
Exporters	every native format above; round-trip integrity tested
HA leader election	Postgres advisory lock, followers stay warm
Audit log	every mutation HMAC-chained, tamper-evident
Catch-up policy	per-schedule: skip, fire one missed, fire all missed
Timezones	IANA zones validated at the boundary; DST fall-back fold fixed (no double-fires); spring-forward gap handled
Trigger surface	brain dispatches trigger_now over a private gRPC channel so the scheduler's last-fire cache stays consistent

Install

Standalone (recommended for production):

pip install z4j-scheduler
z4j-scheduler serve

Embedded inside z4j (recommended for homelab and small teams):

pip install 'z4j[scheduler-embedded]'
# enable in brain settings: Z4J_EMBEDDED_SCHEDULER=true

Migrate existing schedules in:

pip install 'z4j-scheduler[celery-import]'
z4j-scheduler import \
  --from celery \
  --celery-app myapp.celery:app \
  --project myproject \
  --brain-url https://brain.example.com \
  --api-token "$Z4J_SCHEDULER_BRAIN_API_TOKEN"

The other importer subcommands follow the same shape (--from rq, --from apscheduler, --from django-celery-beat, etc.). All run in --dry-run mode by default; you review the diff before any write.

When to choose z4j-scheduler

You probably want it if:

• You run more than one Python task engine and want one schedule surface across all of them.
• An auditor or a security review asks who paused the nightly billing job last Tuesday and you don't have a clean answer.
• You're tired of restarting celery-beat to change a cron expression.
• You want HA scheduling without standing up a second control plane.
• You're considering a one-time migration from celery-beat / rq-scheduler / APScheduler and want a reversible path.

You probably don't need it if:

• You run a single engine (typically Celery), have no compliance pressure, and the in-language scheduler already meets your needs. Stay where you are; we ship z4j-celerybeat / z4j-rqscheduler / z4j-apscheduler as observation-only adapters that surface those schedules in the z4j dashboard without taking ownership.

Documentation

Full docs at z4j.dev/scheduler/. The migration guide at z4j.dev/scheduler/migrating-from-celery-beat/ walks the importer + dashboard verification path step by step.

License

Apache-2.0, see LICENSE. Importing z4j-scheduler does not affect your application's licensing. z4j (server + dashboard + API) is AGPL v3, isolated in its own process; the scheduler is separate.

Links

• Homepage: https://z4j.com
• Documentation: https://z4j.dev
• PyPI: https://pypi.org/project/z4j-scheduler/
• Issues: https://github.com/z4jdev/z4j-scheduler/issues
• Changelog: CHANGELOG.md
• Security: [e-mail] (see SECURITY.md)