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mrq

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Mr. Queue - A distributed worker task queue in Python using Redis & gevent

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# MRQ

[![Build Status](https://travis-ci.org/pricingassistant/mrq.svg?branch=master)](https://travis-ci.org/pricingassistant/mrq) [![MIT License](https://img.shields.io/github/license/pricingassistant/mrq.svg)](https://raw.githubusercontent.com/pricingassistant/mrq/master/LICENSE)

[MRQ](http://pricingassistant.github.io/mrq) is a distributed task queue for python built on top of mongo, redis and gevent.

Full documentation is available on [readthedocs](http://mrq.readthedocs.org/en/latest/)

# Why?

MRQ is an opinionated task queue. It aims to be simple and beautiful like [RQ](http://python-rq.org) while having performances close to [Celery](http://celeryproject.org)

MRQ was first developed at [Pricing Assistant](http://pricingassistant.com) 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).

# Main Features

* **Simple code:** We originally switched from Celery to RQ because Celery's code was incredibly complex and obscure ([Slides](http://www.slideshare.net/sylvinus/why-and-how-pricing-assistant-migrated-from-celery-to-rq-parispy-2)). MRQ should be as easy to understand as RQ and even easier to extend.
 * **Great [dashboard](http://mrq.readthedocs.org/en/latest/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](http://mrq.readthedocs.org/en/latest/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](http://mrq.readthedocs.org/en/latest/tests):** 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.

# Dashboard Screenshots

![Job view](http://i.imgur.com/xaXmrvX.png)

![Worker view](http://i.imgur.com/yYUMCbm.png)

# Get Started

This 5-minute tutorial will show you how to run your first jobs with MRQ.

## Installation

- Make sure you have installed the [dependencies](dependencies.md) : Redis and MongoDB
 - Install MRQ with `pip install mrq`
 - Start a mongo server with `mongod &`
 - Start a redis server with `redis-server &`

## Write your first task

Create a new directory and write a simple task in a file called `tasks.py` :

```makefile
$ mkdir test-mrq && cd test-mrq
$ touch __init__.py
$ vim tasks.py
```

```python
from mrq.task import Task
import urllib2

class Fetch(Task):

def run(self, params):

with urllib2.urlopen(params["url"]) as f:
          t = f.read()
          return len(t)
```

## Run it synchronously

You can now run it from the command line using `mrq-run`:

```makefile
$ mrq-run tasks.Fetch url http://www.google.com

2014-12-18 15:44:37.869029 [DEBUG] mongodb_jobs: Connecting to MongoDB at 127.0.0.1:27017/mrq...
2014-12-18 15:44:37.880115 [DEBUG] mongodb_jobs: ... connected.
2014-12-18 15:44:37.880305 [DEBUG] Starting tasks.Fetch({'url': 'http://www.google.com'})
2014-12-18 15:44:38.158572 [DEBUG] Job None success: 0.278229s total
17655
```

## Run it asynchronously

Let's schedule the same task 3 times with different parameters:

```makefile
$ mrq-run --queue fetches tasks.Fetch url http://www.google.com &&
  mrq-run --queue fetches tasks.Fetch url http://www.yahoo.com &&
  mrq-run --queue fetches tasks.Fetch url http://www.wordpress.com

2014-12-18 15:49:05.688627 [DEBUG] mongodb_jobs: Connecting to MongoDB at 127.0.0.1:27017/mrq...
2014-12-18 15:49:05.705400 [DEBUG] mongodb_jobs: ... connected.
2014-12-18 15:49:05.729364 [INFO] redis: Connecting to Redis at 127.0.0.1...
5492f771520d1887bfdf4b0f
2014-12-18 15:49:05.957912 [DEBUG] mongodb_jobs: Connecting to MongoDB at 127.0.0.1:27017/mrq...
2014-12-18 15:49:05.967419 [DEBUG] mongodb_jobs: ... connected.
2014-12-18 15:49:05.983925 [INFO] redis: Connecting to Redis at 127.0.0.1...
5492f771520d1887c2d7d2db
2014-12-18 15:49:06.182351 [DEBUG] mongodb_jobs: Connecting to MongoDB at 127.0.0.1:27017/mrq...
2014-12-18 15:49:06.193314 [DEBUG] mongodb_jobs: ... connected.
2014-12-18 15:49:06.209336 [INFO] redis: Connecting to Redis at 127.0.0.1...
5492f772520d1887c5b32881
```

You can see that instead of executing the tasks and returning their results right away, `mrq-run` has added them to the queue named `fetches` and printed their IDs.

Now start MRQ's dasbhoard with `mrq-dashboard &` and go check your newly created queue and jobs on [localhost:5555](http://localhost:5555/#jobs)

They are ready to be dequeued by a worker. Start one with `mrq-worker` and follow it on the dashboard as it executes the queued jobs in parallel.

```makefile
$ mrq-worker fetches

2014-12-18 15:52:57.362209 [INFO] Starting Gevent pool with 10 worker greenlets (+ report, logs, adminhttp)
2014-12-18 15:52:57.388033 [INFO] redis: Connecting to Redis at 127.0.0.1...
2014-12-18 15:52:57.389488 [DEBUG] mongodb_jobs: Connecting to MongoDB at 127.0.0.1:27017/mrq...
2014-12-18 15:52:57.390996 [DEBUG] mongodb_jobs: ... connected.
2014-12-18 15:52:57.391336 [DEBUG] mongodb_logs: Connecting to MongoDB at 127.0.0.1:27017/mrq...
2014-12-18 15:52:57.392430 [DEBUG] mongodb_logs: ... connected.
2014-12-18 15:52:57.523329 [INFO] Fetching 1 jobs from ['fetches']
2014-12-18 15:52:57.567311 [DEBUG] Starting tasks.Fetch({u'url': u'http://www.google.com'})
2014-12-18 15:52:58.670492 [DEBUG] Job 5492f771520d1887bfdf4b0f success: 1.135268s total
2014-12-18 15:52:57.523329 [INFO] Fetching 1 jobs from ['fetches']
2014-12-18 15:52:57.567747 [DEBUG] Starting tasks.Fetch({u'url': u'http://www.yahoo.com'})
2014-12-18 15:53:01.897873 [DEBUG] Job 5492f771520d1887c2d7d2db success: 4.361895s total
2014-12-18 15:52:57.523329 [INFO] Fetching 1 jobs from ['fetches']
2014-12-18 15:52:57.568080 [DEBUG] Starting tasks.Fetch({u'url': u'http://www.wordpress.com'})
2014-12-18 15:53:00.685727 [DEBUG] Job 5492f772520d1887c5b32881 success: 3.149119s total
2014-12-18 15:52:57.523329 [INFO] Fetching 1 jobs from ['fetches']
2014-12-18 15:52:57.523329 [INFO] Fetching 1 jobs from ['fetches']
```

You can interrupt the worker with Ctrl-C once it is finished.

## Going further

This was a preview on the very basic features of MRQ. What makes it actually useful is that:

* You can run multiple workers in parallel. Each worker can also run multiple greenlets in parallel.
* Workers can dequeue from multiple queues
* You can queue jobs from your Python code to avoid using `mrq-run` from the command-line.

These features will be demonstrated in a future example of a simple web crawler.

# More

Full documentation is available on [readthedocs](http://mrq.readthedocs.org/en/latest/)

mrq

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