mrq-custom


Namemrq-custom JSON
Version 0.10.1.0 PyPI version JSON
download
home_pagehttp://github.com/pricingassistant/mrq
SummaryA simple yet powerful distributed worker task queue in Python
upload_time2023-06-13 19:01:19
maintainer
docs_urlNone
authorPricing Assistant
requires_python
licenseMIT
keywords worker task distributed queue asynchronous redis mongodb job processing gevent
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI
coveralls test coverage No coveralls.
            # 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)](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.


## How to compile a new version of MRQ
- Go to mrq/version.py file and update from 0.9.28.1 to 0.9.29 (for example)
- Commit and pushes the last changes
- Go to in the master branch (after merge pull request)
- Execute the command `python setup.py sdist`
- Make sure you have installed the python twine package (`pip install twine`).
- Execute the twine command to upload the new version of mrq to pypi.org (`twine upload dist/mrq-custom-0.9.29.tar.gz`)
- In the previous step you will be asked for the pypi.org credentials are in 1password, you must request them in #password_requests
- Update all applications that use MRQ with this version in their requirements.txt


# More

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

            

Raw data

            {
    "_id": null,
    "home_page": "http://github.com/pricingassistant/mrq",
    "name": "mrq-custom",
    "maintainer": "",
    "docs_url": null,
    "requires_python": "",
    "maintainer_email": "",
    "keywords": "worker,task,distributed,queue,asynchronous,redis,mongodb,job,processing,gevent",
    "author": "Pricing Assistant",
    "author_email": "contact@pricingassistant.com",
    "download_url": "https://files.pythonhosted.org/packages/e5/9e/5e24598c94d42ff3f6f976c50e38a2bd1b55097eba37b365434a077f9489/mrq-custom-0.10.1.0.tar.gz",
    "platform": "any",
    "description": "# MRQ\n\n[![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)](LICENSE)\n\n[MRQ](http://pricingassistant.github.io/mrq) is a distributed task queue for python built on top of mongo, redis and gevent.\n\nFull documentation is available on [readthedocs](http://mrq.readthedocs.org/en/latest/)\n\n# Why?\n\nMRQ 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)\n\nMRQ 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).\n\n# Main Features\n\n * **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.\n * **Great [dashboard](http://mrq.readthedocs.org/en/latest/dashboard/):** Have visibility and control on everything: queued jobs, current jobs, worker status, ...\n * **Per-job logs:** Get the log output of each task separately in the dashboard\n * **Gevent worker:** IO-bound tasks can be done in parallel in the same UNIX process for maximum throughput\n * **Supervisord integration:** CPU-bound tasks can be split across several UNIX processes with a single command-line flag\n * **Job management:** You can retry, requeue, cancel jobs from the code or the dashboard.\n * **Performance:** Bulk job queueing, easy job profiling\n * **Easy [configuration](http://mrq.readthedocs.org/en/latest/configuration):** Every aspect of MRQ is configurable through command-line flags or a configuration file\n * **Job routing:** Like Celery, jobs can have default queues, timeout and ttl values.\n * **Builtin scheduler:** Schedule tasks by interval or by time of the day\n * **Strategies:** Sequential or parallel dequeue order, also a burst mode for one-time or periodic batch jobs.\n * **Subqueues:** Simple command-line pattern for dequeuing multiple sub queues, using auto discovery from worker side.\n * **Thorough [testing](http://mrq.readthedocs.org/en/latest/tests):** Edge-cases like worker interrupts, Redis failures, ... are tested inside a Docker container.\n * **Greenlet tracing:** See how much time was spent in each greenlet to debug CPU-intensive jobs.\n * **Integrated memory leak debugger:** Track down jobs leaking memory and find the leaks with objgraph.\n\n# Dashboard Screenshots\n\n![Job view](http://i.imgur.com/xaXmrvX.png)\n\n![Worker view](http://i.imgur.com/yYUMCbm.png)\n\n# Get Started\n\nThis 5-minute tutorial will show you how to run your first jobs with MRQ.\n\n## Installation\n\n - Make sure you have installed the [dependencies](dependencies.md) : Redis and MongoDB\n - Install MRQ with `pip install mrq`\n - Start a mongo server with `mongod &`\n - Start a redis server with `redis-server &`\n\n\n## Write your first task\n\nCreate a new directory and write a simple task in a file called `tasks.py` :\n\n```makefile\n$ mkdir test-mrq && cd test-mrq\n$ touch __init__.py\n$ vim tasks.py\n```\n\n```python\nfrom mrq.task import Task\nimport urllib2\n\n\nclass Fetch(Task):\n\n    def run(self, params):\n\n        with urllib2.urlopen(params[\"url\"]) as f:\n          t = f.read()\n          return len(t)\n```\n\n## Run it synchronously\n\nYou can now run it from the command line using `mrq-run`:\n\n```makefile\n$ mrq-run tasks.Fetch url http://www.google.com\n\n2014-12-18 15:44:37.869029 [DEBUG] mongodb_jobs: Connecting to MongoDB at 127.0.0.1:27017/mrq...\n2014-12-18 15:44:37.880115 [DEBUG] mongodb_jobs: ... connected.\n2014-12-18 15:44:37.880305 [DEBUG] Starting tasks.Fetch({'url': 'http://www.google.com'})\n2014-12-18 15:44:38.158572 [DEBUG] Job None success: 0.278229s total\n17655\n```\n\n## Run it asynchronously\n\nLet's schedule the same task 3 times with different parameters:\n\n```makefile\n$ mrq-run --queue fetches tasks.Fetch url http://www.google.com &&\n  mrq-run --queue fetches tasks.Fetch url http://www.yahoo.com &&\n  mrq-run --queue fetches tasks.Fetch url http://www.wordpress.com\n\n2014-12-18 15:49:05.688627 [DEBUG] mongodb_jobs: Connecting to MongoDB at 127.0.0.1:27017/mrq...\n2014-12-18 15:49:05.705400 [DEBUG] mongodb_jobs: ... connected.\n2014-12-18 15:49:05.729364 [INFO] redis: Connecting to Redis at 127.0.0.1...\n5492f771520d1887bfdf4b0f\n2014-12-18 15:49:05.957912 [DEBUG] mongodb_jobs: Connecting to MongoDB at 127.0.0.1:27017/mrq...\n2014-12-18 15:49:05.967419 [DEBUG] mongodb_jobs: ... connected.\n2014-12-18 15:49:05.983925 [INFO] redis: Connecting to Redis at 127.0.0.1...\n5492f771520d1887c2d7d2db\n2014-12-18 15:49:06.182351 [DEBUG] mongodb_jobs: Connecting to MongoDB at 127.0.0.1:27017/mrq...\n2014-12-18 15:49:06.193314 [DEBUG] mongodb_jobs: ... connected.\n2014-12-18 15:49:06.209336 [INFO] redis: Connecting to Redis at 127.0.0.1...\n5492f772520d1887c5b32881\n```\n\nYou 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.\n\nNow start MRQ's dasbhoard with `mrq-dashboard &` and go check your newly created queue and jobs on [localhost:5555](http://localhost:5555/#jobs)\n\nThey 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.\n\n```makefile\n$ mrq-worker fetches\n\n2014-12-18 15:52:57.362209 [INFO] Starting Gevent pool with 10 worker greenlets (+ report, logs, adminhttp)\n2014-12-18 15:52:57.388033 [INFO] redis: Connecting to Redis at 127.0.0.1...\n2014-12-18 15:52:57.389488 [DEBUG] mongodb_jobs: Connecting to MongoDB at 127.0.0.1:27017/mrq...\n2014-12-18 15:52:57.390996 [DEBUG] mongodb_jobs: ... connected.\n2014-12-18 15:52:57.391336 [DEBUG] mongodb_logs: Connecting to MongoDB at 127.0.0.1:27017/mrq...\n2014-12-18 15:52:57.392430 [DEBUG] mongodb_logs: ... connected.\n2014-12-18 15:52:57.523329 [INFO] Fetching 1 jobs from ['fetches']\n2014-12-18 15:52:57.567311 [DEBUG] Starting tasks.Fetch({u'url': u'http://www.google.com'})\n2014-12-18 15:52:58.670492 [DEBUG] Job 5492f771520d1887bfdf4b0f success: 1.135268s total\n2014-12-18 15:52:57.523329 [INFO] Fetching 1 jobs from ['fetches']\n2014-12-18 15:52:57.567747 [DEBUG] Starting tasks.Fetch({u'url': u'http://www.yahoo.com'})\n2014-12-18 15:53:01.897873 [DEBUG] Job 5492f771520d1887c2d7d2db success: 4.361895s total\n2014-12-18 15:52:57.523329 [INFO] Fetching 1 jobs from ['fetches']\n2014-12-18 15:52:57.568080 [DEBUG] Starting tasks.Fetch({u'url': u'http://www.wordpress.com'})\n2014-12-18 15:53:00.685727 [DEBUG] Job 5492f772520d1887c5b32881 success: 3.149119s total\n2014-12-18 15:52:57.523329 [INFO] Fetching 1 jobs from ['fetches']\n2014-12-18 15:52:57.523329 [INFO] Fetching 1 jobs from ['fetches']\n```\n\nYou can interrupt the worker with Ctrl-C once it is finished.\n\n## Going further\n\nThis was a preview on the very basic features of MRQ. What makes it actually useful is that:\n\n* You can run multiple workers in parallel. Each worker can also run multiple greenlets in parallel.\n* Workers can dequeue from multiple queues\n* You can queue jobs from your Python code to avoid using `mrq-run` from the command-line.\n\nThese features will be demonstrated in a future example of a simple web crawler.\n\n\n## How to compile a new version of MRQ\n- Go to mrq/version.py file and update from 0.9.28.1 to 0.9.29 (for example)\n- Commit and pushes the last changes\n- Go to in the master branch (after merge pull request)\n- Execute the command `python setup.py sdist`\n- Make sure you have installed the python twine package (`pip install twine`).\n- Execute the twine command to upload the new version of mrq to pypi.org (`twine upload dist/mrq-custom-0.9.29.tar.gz`)\n- In the previous step you will be asked for the pypi.org credentials are in 1password, you must request them in #password_requests\n- Update all applications that use MRQ with this version in their requirements.txt\n\n\n# More\n\nFull documentation is available on [readthedocs](http://mrq.readthedocs.org/en/latest/)\n",
    "bugtrack_url": null,
    "license": "MIT",
    "summary": "A simple yet powerful distributed worker task queue in Python",
    "version": "0.10.1.0",
    "project_urls": {
        "Homepage": "http://github.com/pricingassistant/mrq"
    },
    "split_keywords": [
        "worker",
        "task",
        "distributed",
        "queue",
        "asynchronous",
        "redis",
        "mongodb",
        "job",
        "processing",
        "gevent"
    ],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "e59e5e24598c94d42ff3f6f976c50e38a2bd1b55097eba37b365434a077f9489",
                "md5": "aaed4824a2bc92d5eb5afa4c054a6f46",
                "sha256": "186e22fd8e3642495c4e07f29fc8cc6716798831b367db4492c8a85dc74b653a"
            },
            "downloads": -1,
            "filename": "mrq-custom-0.10.1.0.tar.gz",
            "has_sig": false,
            "md5_digest": "aaed4824a2bc92d5eb5afa4c054a6f46",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": null,
            "size": 538936,
            "upload_time": "2023-06-13T19:01:19",
            "upload_time_iso_8601": "2023-06-13T19:01:19.250006Z",
            "url": "https://files.pythonhosted.org/packages/e5/9e/5e24598c94d42ff3f6f976c50e38a2bd1b55097eba37b365434a077f9489/mrq-custom-0.10.1.0.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2023-06-13 19:01:19",
    "github": true,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "github_user": "pricingassistant",
    "github_project": "mrq",
    "travis_ci": true,
    "coveralls": false,
    "github_actions": false,
    "requirements": [],
    "lcname": "mrq-custom"
}
        
Elapsed time: 0.10125s