MRQ is a distributed task queue for python built on top of mongo, redis and gevent.
Full documentation is available on readthedocs
MRQ was first developed at Pricing Assistant and its initial feature set matches the needs of worker queues with heterogenous jobs (IO-bound & CPU-bound, lots of small tasks & a few large ones).
- Simple code: We originally switched from Celery to RQ because Celery's code was incredibly complex and obscure (Slides). MRQ should be as easy to understand as RQ and even easier to extend.
- Great dashboard: Have visibility and control on everything: queued jobs, current jobs, worker status, ...
- Per-job logs: Get the log output of each task separately in the dashboard
- Gevent worker: IO-bound tasks can be done in parallel in the same UNIX process for maximum throughput
- Supervisord integration: CPU-bound tasks can be split across several UNIX processes with a single command-line flag
- Job management: You can retry, requeue, cancel jobs from the code or the dashboard.
- Performance: Bulk job queueing, easy job profiling
- Easy configuration: Every aspect of MRQ is configurable through command-line flags or a configuration file
- Job routing: Like Celery, jobs can have default queues, timeout and ttl values.
- Builtin scheduler: Schedule tasks by interval or by time of the day
- Strategies: Sequential or parallel dequeue order, also a burst mode for one-time or periodic batch jobs.
- Subqueues: Simple command-line pattern for dequeuing multiple sub queues, using auto discovery from worker side.
- Thorough testing: Edge-cases like worker interrupts, Redis failures, ... are tested inside a Docker container.
- Greenlet tracing: See how much time was spent in each greenlet to debug CPU-intensive jobs.
- Integrated memory leak debugger: Track down jobs leaking memory and find the leaks with objgraph.