An overview of a light-weight, high-performance scalable deferred task (RPC) system.

0-slide-content.textile

MongoDB as RPC

• Needed for timed game events.
• Needed because Facebook’s API is slow.
• Background task execution.
• Immediate or scheduled.
• Captures responses and exceptions.
• Acts as a state machine with atomic locking.
  • Uses the “update if current” mechanism.
• Stores job data in a MongoDB collection.
  • Easy to scale. (Sharding / replication.)
• Push notifications via capped collection.
  • The same method MongoDB uses internally for replication.
• Uses Futures for parallel execution of tasks.
  • Custom thread-pool, though.
• Uses APScheduler for timed execution of tasks.

MongoDB as a low-Latency Queue

• Limited size, optionally limited document count using capped collections.
• Ring buffer design. Insert order. Updates allowed if resulting data is same or smaller size.
• Used by MongoDB internally for multi-server replication.
• Tailable cursors. Long-poll push, like IMAP IDLE.
  • Workers are immediately handed the newly inserted record.

1-example-job-record.js

{
    "_id" : ObjectId("4ea3717f9bfbb601d2000002"),
    "state" : "new", // pending, dead, cancelled, running, finished
    
    "callable" : "c__builtin__\nprint\np1\n.",
    "args" : [ "Task", 0 ],
    "kwargs" : { },
    
    "created" : ISODate("2011-10-23T01:44:31.446Z"),
    "creator" : [ "Lucifer", 298, 466 ],
    "owner" : null, // [ "Lucifer", 324, 456 ]
    
    // If scheduled, not immediate:
    "when": ISODate("...")
    
    // If in progress or completed...
    "acquired" : ISODate("..."),
    
    // If completed...
    "result" : null,
    "exception" : null,
    "completed" : ISODate("..."),
}

2-capped-collection-records.js

// Workaround for MongoDB quirk.
{ "_id" : ObjectId("4ea371629bfbb601c8000000"), "nop" : true }

{ // New job.
    "_id" : ObjectId("4ea371769bfbb601d2000001"),
    "job_id" : ObjectId("4ea371769bfbb601d2000000"),
    "creator" : [ "Lucifer", 298, 466 ]
}

{ // Finished job.
    "_id" : ObjectId("4ea371769bfbb601c8000001"),
    "job_id" : ObjectId("4ea371769bfbb601d2000000"),
    "creator" : [ "Lucifer", 324, 456 ],
    "result" : true
}

3-queue-runner.py

def queue(collection, query=None):
    if not collection.find():
        # This is to prevent a terrible infinite busy loop while empty.
        collection.insert(dict(nop=True))
    
    last = None
    query = query or {}
    
    cursor = collection.find(query, slave_ok=True, tailable=True, await_data=True)
    
    while True:                             # Primary retry loop.
        try:
            while cursor.alive:             # Inner record loop; may time out.
                for record in cursor:
                    last = record['_id']
                    yield record
        
        except OperationFailure:
            pass
        
        retry_query = {"_id": {"$gte": last}}
        retry_query.update(query)
        cursor = collection.find(retry_query, slave_ok=True, tailable=True, await_data=True)

4-job-handler.py

def handler(self, job_id):
    # Build the dictionary update.
    update = dict(acquired=datetime.utcnow(), state="running",
            owner=self.identity)
    
    try:
        result = self.jobs.update(dict(_id=job_id, state="pending", owner=None),
                {"$set": update}, safe=True)
    except:
        raise AcquireFailed()
    
    if not result['updatedExisting']: raise AcquireFailed()
    
    try:
        job = self.jobs.find(dict(_id=job_id), limit=1,
                fields=['callable', 'args', 'kwargs'])[0]
    except:  # This should, in theory, never happen unless MongoDB goes away.
        raise AcquireFailed()
    
    obj = pickle.loads(job['callable'].encode('ascii'))
    args = job.get('args', [])
    kwargs = job.get('kwargs', {})
    
    return obj(*args, **kwargs)

The rest of the slides: http://www.slideshare.net/montrealpython/mp24-the-bachelor-a-facebook-game

Have an actually working implementation: https://gist.github.com/amcgregor/52a684854fb77b6e7395

Have the implementation modernized and packaged up: https://github.com/marrow/task