delayed 0.11.0b1

a simple but robust task queue

delayed

Delayed is a simple but robust task queue inspired by rq.

Features

• Robust: all the enqueued tasks will run exactly once, even if the worker got killed at any time.
• Clean: finished tasks (including failed) won't take the space of your Redis.
• Distributed: workers as more as needed can run in the same time without further config.

Requirements

1. Python 2.7 or later, tested on Python 2.7, 3.3 - 3.9, PyPy and PyPy3.
2. UNIX-like systems (with os.fork() implemented), tested on Ubuntu and macOS.
3. Redis 2.6.0 or later.
4. Keeps syncing time among all the machines of each task queue.

Getting started

1. Run a redis server:

   $ redis-server
   

2. Install delayed:

   $ pip install delayed
   

3. Create a task queue:

   import redis
   from delayed.queue import Queue
   
   conn = redis.Redis()
   queue = Queue(name='default', conn=conn)
   

4. Four ways to enqueue a task:

   • Define a task function and enqueue it:

     from delayed.delay import delayed
     
     delayed = delayed(queue)
     
     @delayed()
     def delayed_add(a, b):
         return a + b
     
     delayed_add.delay(1, 2)  # enqueue delayed_add
     delayed_add.delay(1, b=2)  # same as above
     delayed_add(1, 2)  # call it immediately
     

   • Directly enqueue a function:

     from delayed.delay import delay, delayed
     
     delay = delay(queue)
     delayed = delayed(queue)
     
     def add(a, b):
         return a + b
     
     delay(add)(1, 2)
     delay(add)(1, b=2)  # same as above
     
     delayed()(add).delay(1, 2)
     delayed()(add).delay(1, b=2)  # same as above
     

   • Create a task and enqueue it:

     from delayed.task import Task
     
     def add(a, b):
         return a + b
     
     task = Task.create(func=add, args=(1,), kwargs={'b': 2})
     queue.enqueue(task)
     

   • Enqueue a predefined task function without importing it:

     from delayed.task import Task
     
     task = Task(id=None, func_path='test:add', args=(1,), kwargs={'b': 2})
     queue.enqueue(task)
     

5. Run a task worker (or more) in a separated process:

   import redis
   from delayed.queue import Queue
   from delayed.worker import ForkedWorker
   
   conn = redis.Redis()
   queue = Queue(name='default', conn=conn)
   worker = ForkedWorker(queue=queue)
   worker.run()
   

6. Run a task sweeper in a separated process to recovery lost tasks (mainly due to the worker got killed):

   import redis
   from delayed.queue import Queue
   from delayed.sweeper import Sweeper
   
   conn = redis.Redis()
   queue = Queue(name='default', conn=conn)
   sweeper = Sweeper(queues=[queue])
   sweeper.run()
   

Examples

See examples.

```bash
$ redis-server &
$ pip install delayed
$ python -m examples.sweeper &
$ python -m examples.forked_worker &  # or python -m examples.preforked_worker &
$ python -m examples.caller
```

QA

1. Q: What's the limitation on a task function?
   A: A task function should be defined in module level (except the __main__ module). Its args and kwargs should be picklable.

2. Q: What's the name param of a queue?
   A: It's the key used to store the tasks of the queue. A queue with name "default" will use those keys:

   • default: list, enqueued tasks.
   • default_id: str, the next task id.
   • default_noti: list, the same length as enqueued tasks.
   • default_enqueued: sorted set, enqueued tasks with their timeouts.
   • default_dequeued: sorted set, dequeued tasks with their dequeued timestamps.

3. Q: Why the worker is slow?
   A: The ForkedWorker forks a new process for each new task. So all the tasks are isolated and you won't leak memory.
   To reduce the overhead of forking processes and importing modules, if your task function code won't be changed in the worker's lifetime, you can switch to PreforkedWorker:

   import redis
   from delayed.queue import Queue
   from delayed.worker import PreforkedWorker
   
   conn = redis.Redis()
   queue = Queue(name='default', conn=conn)
   worker = PreforkedWorker(queue=queue)
   worker.run()
   

4. Q: How does a ForkedWorker run?
   A: It runs such a loop:

   1. It dequeues a task from the queue periodically.
   2. It forks a child process to run the task.
   3. It kills the child process if the child runs out of time.
   4. When the child process exits, it releases the task.

5. Q: How does a PreforkedWorker run?
   A: It runs such a loop:

   1. It dequeues a task from the queue periodically.
   2. If it has no child process, it forks a new one.
   3. It sends the task through a pipe to the child.
   4. It kills the child process if the child runs out of time.
   5. When the child process exits or it received result from the pipe, it releases the task.

6. Q: How does the child process of a worker run?
   A: The child of a ForkedWorker just runs the task, unmarks the task as dequeued, then exits. The child of a PreforkedWorker runs such a loop:

   1. It tries to receive a task from the pipe.
   2. If the pipe has been closed, it exits.
   3. It runs the task.
   4. It sends the task result to the pipe.
   5. It releases the task.

7. Q: What's lost tasks?
   A: There are 2 situations a task might get lost:

   • a worker popped a task notification, then got killed before dequeueing the task.
   • a worker dequeued a task, then both the monitor and its child process got killed before they releasing the task.

8. Q: How to recovery lost tasks?
   A: Runs a sweeper. It dose two things:

   • it keeps the task notification length the same as the task queue.
   • it moves the timeout dequeued tasks back to the task queue.

9. Q: How to set the timeout of tasks?
   A: You can set default_timeout of a queue or timeout of a task:

   from delayed.delay import delay_with_params
   
   queue = Queue('default', conn, default_timeout=60)
   
   delayed_add.timeout(10)(1, 2)
   
   delay_with_params(queue)(timeout=10)(add)(1, 2)
   

10. Q: How to enqueue a task in front of the queue?
    A: You can set prior of the task to True:

    task = Task(id=None, func_path='test:add', args=(1, 2), prior=True)
    queue.enqueue(task)
    

11. Q: How to handle the failed tasks?
    A: Sets the error_handler of the task. The handlers would be called in a forked process, except the forked process got killed or the monitor process raised an exception.

    from delayed.delay import delay_with_params
    
    def error_handler(task, kill_signal, exc_info):
        if kill_signal:
            logging.error('task %d got killed by signal %d', task.id, kill_signal)
        else:
            logging.exception('task %d failed', task.id, exc_info=exc_info)
    
    @delayed_with_param(queue)(error_handler=error_handler)
    def error():
        raise Exception
    
    def error2():
        raise Exception
    
    task = Task.create(func_path='test:error2', error_handler=error_handler)
    

12. Q: Why does sometimes the error_handler not be called for a failed task?
    A: If both the child process and the monitor process got killed at the same time, there is no chance to call the error_handler.

13. Q: How to turn on the debug logs?
    A: Adds a logging.DEBUG level handler to delayed.logger.logger. The simplest way is to call delayed.logger.setup_logger():

    from delayed.logger import setup_logger
    
    setup_logger()
    

14. Q: Can I enqueue and dequeue tasks in different Python versions?
    A: delayed uses the pickle module to serialize and deserialize tasks. If pickle.HIGHEST_PROTOCOL is equal among all your Python runtimes, you can use it without any configurations. Otherwise you have to choose the lowest pickle.HIGHEST_PROTOCOL of all your Python runtime as the pickle protocol. eg: If you want to enqueue a task in Python 3.7 and dequeue it in Python 2.7. Their pickle.HIGHEST_PROTOCOL are 4 and 2, so you need to set the version to 2:

    from delayed.task import set_pickle_protocol_version
    
    set_pickle_protocol_version(2)
    

15. Q: Why not use JSON or MessagePack to serialize tasks?
    A: These serializations may confuse some types (eg: bytes / str, list / tuple).

16. Q: What will happen if I changed the pipe capacity?
    A: delayed assumes the pipe capacity is 65536 bytes (the default value on Linux and macOS). To reduce syscalls, it won't check whether the pipe is writable if the length of data to be written is less than 65536. If your system has a lower pipe capacity, the PreforkedWorker may not working well for some large tasks. To fix it, you can set a lower value to delayed.constants.BUF_SIZE:

    import delayed.constants
    
    delayed.constants.BUF_SIZE = 1024
    

Release notes

• 0.11:

  1. Sleeps random time when a Worker fails to pop a task before retrying.

• 0.10:

  1. The Sweeper can handle multiple queues now. Its queue param has been changed to queues. (BREAKING CHANGE)
  2. Changes the separator between module_path and func_name from . to :. (BREAKING CHANGE)

• 0.9:

  1. Adds prior and error_handler params to deleyed.delayed(), removes its timeout() method. (BREAKING CHANGE)
  2. Adds examples.

• 0.8:

  1. The Task struct has been changed, it's not compatible with older versions. (BREAKING CHANGE)
     • Removes module_name and func_name from Task, adds func_path instead.
     • Adds error_handler_path to Task.
  2. Removes success_handler and error_handler from Worker. (BREAKING CHANGE)

• 0.7:

  1. Implements prior task.

• 0.6:

  1. Adds dequeued_len() and index to Queue.

• 0.5:

  1. Adds delayed.task.set_pickle_protocol_version().

• 0.4:

  1. Refactories and fixes bugs.

• 0.3:

  1. Changes param second to timeout for delayed.delayed(). (BREAKING CHANGE)
  2. Adds debug log.

• 0.2:

  1. Adds timeout() to delayed.delayed().

• 0.1:

  1. Init version.