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 程式師世界 >> 編程語言 >> 網頁編程 >> PHP編程 >> 關於PHP編程 >> python的分布式任務huey如何實現異步化任務講解

python的分布式任務huey如何實現異步化任務講解

編輯:關於PHP編程

     本文我們來分享一個python的輕型的任務隊列程序,他可以讓python的分布式任務huey實現異步化任務,感興趣的朋友可以看看。

       

    一個輕型的任務隊列,功能和相關的broker沒有celery強大,重在輕型,而且代碼讀起來也比較的簡單。 


    關於huey的介紹:  (比celery輕型,比mrq、rq要好用 !)

    a lightweight alternative.

        written in python

        no deps outside stdlib, except redis (or roll your own backend)

        support for django

    supports:

        multi-threaded task execution

        scheduled execution at a given time

        periodic execution, like a crontab

        retrying tasks that fail

        task result storage


    安裝:

     代碼如下   Installing
    huey can be installed very easily using pip.
     
    pip install huey
    huey has no dependencies outside the standard library, but currently the only fully-implemented queue backend it ships with requires redis. To use the redis backend, you will need to install the python client.
     
    pip install redis
    Using git
    If you want to run the very latest, feel free to pull down the repo from github and install by hand.
     
    git clone https://github.com/coleifer/huey.git
    cd huey
    python setup.py install
    You can run the tests using the test-runner:
     
    python setup.py test




    關於huey的api,下面有詳細的介紹及參數介紹的。

     代碼如下   from huey import RedisHuey, crontab
     
    huey = RedisHuey('my-app', host='redis.myapp.com')
     
    @huey.task()
    def add_numbers(a, b):
        return a + b
     
    @huey.periodic_task(crontab(minute='0', hour='3'))
    def nightly_backup():
        sync_all_data()




    juey作為woker的時候,一些cli參數。 


    常用的是:  

    -l                  關於日志文件的執行 。

    -w                 workers的數目,-w的數值大了,肯定是增加任務的處理能力

    -p --periodic     啟動huey worker的時候,他會從tasks.py裡面找到 需要crontab的任務,會派出幾個線程專門處理這些事情。 

    -n                  不啟動關於crontab裡面的預周期執行,只有你觸發的時候,才會執行周期星期的任務。 

    --threads   意思你懂的。
    1

     代碼如下   # 原文:     
    The following table lists the options available for the consumer as well as their default values.
     
    -l, --logfile
    Path to file used for logging. When a file is specified, by default Huey will use a rotating file handler (1MB / chunk) with a maximum of 3 backups. You can attach your own handler (huey.logger) as well. The default loglevel is INFO.
    -v, --verbose
    Verbose logging (equates to DEBUG level). If no logfile is specified and verbose is set, then the consumer will log to the console. This is very useful for testing/debugging.
    -q, --quiet
    Only log errors. The default loglevel for the consumer is INFO.
    -w, --workers
    Number of worker threads, the default is 1 thread but for applications that have many I/O bound tasks, increasing this number may lead to greater throughput.
    -p, --periodic
    Indicate that this consumer process should start a thread dedicated to enqueueing “periodic” tasks (crontab-like functionality). This defaults to True, so should not need to be specified in practice.
    -n, --no-periodic
    Indicate that this consumer process should not enqueue periodic tasks.
    -d, --delay
    When using a “polling”-type queue backend, the amount of time to wait between polling the backend. Default is 0.1 seconds.
    -m, --max-delay
    The maximum amount of time to wait between polling, if using weighted backoff. Default is 10 seconds.
    -b, --backoff
    The amount to back-off when polling for results. Must be greater than one. Default is 1.15.
    -u, --utc
    Indicates that the consumer should use UTC time for all tasks, crontabs and scheduling. Default is True, so in practice you should not need to specify this option.
    --localtime
    Indicates that the consumer should use localtime for all tasks, crontabs and scheduling. Default is False.
    Examples
     
    Running the consumer with 8 threads, a logfile for errors only, and a very short polling interval:
     
    huey_consumer.py my.app.huey -l /var/log/app.huey.log -w 8 -b 1.1 -m 1.0





    任務隊列huey 是靠著redis來實現queue的任務存儲,所以需要咱們提前先把redis-server和redis-py都裝好。 安裝的方法就不說了,自己搜搜吧。 


    我們首先創建下huey的鏈接實例 :

     代碼如下   # config.py
    from huey import Huey
    from huey.backends.redis_backend import RedisBlockingQueue
     
    queue = RedisBlockingQueue('test-queue', host='localhost', port=6379)
    huey = Huey(queue)


    然後就是關於任務的,也就是你想讓誰到任務隊列這個圈子裡面,和celey、rq,mrq一樣,都是用tasks.py表示的。

     代碼如下   from config import huey # import the huey we instantiated in config.py
     
     
    @huey.task()
    def count_beans(num):
        print '-- counted %s beans --' % num




    再來一個真正去執行的 。  main.py 相當於生產者,tasks.py相當於消費者的關系。  main.py負責喂數據。

     代碼如下   main.py
    from config import huey  # import our "huey" object
    from tasks import count_beans  # import our task
     
     
    if __name__ == '__main__':
        beans = raw_input('How many beans? ')
        count_beans(int(beans))
        print 'Enqueued job to count %s beans' % beans


    Ensure you have Redis running locally

    Ensure you have installed huey

    Start the consumer: huey_consumer.py main.huey (notice this is “main.huey” and not “config.huey”).

    Run the main program: python main.py




    和celery、rq一樣,他的結果獲取是需要在你的config.py或者主代碼裡面指明他的存儲的方式,現在huey還僅僅是支持redis,但相對他的特點和體積,這已經很足夠了 !


    只是那幾句話而已,導入RedisDataStore庫,申明下存儲的地址。

     代碼如下   from huey import Huey
    from huey.backends.redis_backend import RedisBlockingQueue
    from huey.backends.redis_backend import RedisDataStore  # ADD THIS LINE
     
     
    queue = RedisBlockingQueue('test-queue', host='localhost', port=6379)
    result_store = RedisDataStore('results', host='localhost', port=6379)  # ADDED
     
    huey = Huey(queue, result_store=result_store) # ADDED result store




    這個時候,我們在ipython再次去嘗試的時候,會發現可以獲取到tasks.py裡面的return值了 其實你在main.py裡面獲取的時候,他還是通過uuid從redis裡面取出來的。

     代碼如下   >>> from main import count_beans
    >>> res = count_beans(100)
    >>> res  # what is "res" ?
    <huey.api.AsyncData object at 0xb7471a4c>
    >>> res.get()  # get the result of this task
    'Counted 100 beans'




    huey也是支持celey的延遲執行和crontab的功能 。  這些功能很是重要,可以自定義的優先級或者不用再借助linux本身的crontab。


    用法很簡單,多加一個delay的時間就行了,看了下huey的源碼,他默認是立馬執行的。當然還是要看你的線程是否都是待執行的狀態了。

     代碼如下   >>> import datetime
    >>> res = count_beans.schedule(args=(100,), delay=60)
    >>> res
    <huey.api.AsyncData object at 0xb72915ec>
    >>> res.get()  # this returns None, no data is ready
    >>> res.get()  # still no data...
    >>> res.get(blocking=True)  # ok, let's just block until its ready
    'Counted 100 beans'


    python的分布式任務huey如何實現異步化任務講解   三聯


    再來一個重試retry的介紹,huey也是有retry,這個很是實用的東西。 如果大家有看到我的上面文章關於celery重試機制的介紹,應該也能明白huey是個怎麼個回事了。  是的,他其實也是在tasks裡具體函數的前面做了裝飾器,裝飾器裡面有個func try 異常重試的邏輯 。 大家懂的。

     代碼如下   # tasks.py
    from datetime import datetime
     
    from config import huey
     
    @huey.task(retries=3, retry_delay=10)
    def try_thrice():
        print 'trying....%s' % datetime.now()
        raise Exception('nope')


    wKioL1QM--mT7Xm-AAPqmSwzRoA504.jpg


    huey是給你反悔的機會餓 ~  也就是說,你做了deley的計劃任務後,如果你又想取消,那好看,直接revoke就可以了。

     代碼如下   # count some beans
    res = count_beans(10000000)
     
    res.revoke()
    The same applies to tasks that are scheduled in the future:
     
    res = count_beans.schedule(args=(100000,), eta=in_the_future)
    res.revoke()
     
    @huey.task(crontab(minute='*'))
    def print_time():
        print datetime.now()


    task() - 透明的裝飾器,讓你的函數變得優美點。 

    periodic_task() - 這個是周期性的任務

    crontab() - 啟動worker的時候,附帶的crontab的周期任務。 

    BaseQueue - 任務隊列

    BaseDataStore - 任務執行後,可以把 結果塞入進去。  BAseDataStore可以自己重寫。

     


    官方的huey的git庫裡面是提供了相關的測試代碼的: 


    main.py

     代碼如下   from config import huey
    from tasks import count_beans
     
     
    if __name__ == '__main__':
        beans = raw_input('How many beans? ')
        count_beans(int(beans))
        print('Enqueued job to count %s beans' % beans)




    tasks.py

     代碼如下   import random
    import time
    from huey import crontab
     
    from config import huey
     
     
    @huey.task()
    def count_beans(num):
        print "start..."
        print('-- counted %s beans --' % num)
        time.sleep(3)
        print "end..."
        return 'Counted %s beans' % num
     
    @huey.periodic_task(crontab(minute='*/5'))
    def every_five_mins():
        print('Consumer prints this every 5 mins')
     
    @huey.task(retries=3, retry_delay=10)
    def try_thrice():
        if random.randint(1, 3) == 1:
            print('OK')
        else:
            print('About to fail, will retry in 10 seconds')
            raise Exception('Crap something went wrong')
     
    @huey.task()
    def slow(n):
        time.sleep(n)
        print('slept %s' % n)




    run.sh

     代碼如下   #!/bin/bash
    echo "HUEY CONSUMER"
    echo "-------------"
    echo "In another terminal, run 'python main.py'"
    echo "Stop the consumer using Ctrl+C"
    PYTHONPATH=.:$PYTHONPATH
    python ../../huey/bin/huey_consumer.py main.huey --threads=2

    =>



    咱們可以先clone下huey的代碼庫。 裡面有個examples例子目錄,可以看到他是支持django的,但是這不是重點 !

     代碼如下   [xiaorui@devops /tmp ]$ git clone https://github.com/coleifer/huey.git
    Cloning into 'huey'...
    remote: Counting objects: 1423, done.
    remote: Compressing objects: 100% (9/9), done.
    Receiving objects:  34% (497/1423), 388.00 KiB | 29.00 KiB/s   KiB/s
     
    Receiving objects:  34% (498/1423), 628.00 KiB | 22.00 KiB/s
     
     
    remote: Total 1423 (delta 0), reused 0 (delta 0)
    Receiving objects: 100% (1423/1423), 2.24 MiB | 29.00 KiB/s, done.
    Resolving deltas: 100% (729/729), done.
    Checking connectivity... done.
    [xiaorui@devops /tmp ]$cd huey/examples/simple
    [xiaorui@devops simple (master)]$ ll
    total 40
    -rw-r--r--  1 xiaorui  wheel    79B  9  8 08:49 README
    -rw-r--r--  1 xiaorui  wheel     0B  9  8 08:49 __init__.py
    -rw-r--r--  1 xiaorui  wheel    56B  9  8 08:49 config.py
    -rwxr-xr-x  1 xiaorui  wheel   227B  9  8 08:49 cons.sh
    -rw-r--r--  1 xiaorui  wheel   205B  9  8 08:49 main.py
    -rw-r--r--  1 xiaorui  wheel   607B  9  8 08:49 tasks.py
    [xiaorui@devops simple (master)]$



    wKiom1QM_s6S2FseAAMlgrYlP_U022.jpg

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