How can tasks be prioritized when using the task queue on google app engine?

[Attila-Mihaly Balazs]

Hello,

I also posted this to stackoverflow, but I'm cross-posting it here in the hope of getting a larger number of eyes on it: http://stackoverflow.com/questions/38567153/how-can-tasks-be-prioritized-when-using-the-task-queue-on-google-app-engine
 

I'm trying to solve the following problem: I have a series of "tasks" which I would like to execute I have a fixed number of workers to execute these workers (since they call an external API using urlfetch and the number of parallel calls to this API is limited) I would like for these "tasks" to be executed "as soon as possible" (ie. minimum latency) These tasks are parts of larger tasks and can be categorized based on the size of the original task (ie. a small original task might generate 1 to 100 tasks, a medium one 100 to 1000 and a large one over 1000). The tricky part: I would like to do all this efficiently (ie. minimum latency and use as many parallel API calls as possible - without getting over the limit), but at the same time try to prevent a large number of tasks generated from "large" original tasks to delay the tasks generated from "small" original tasks. To put it an other way: I would like to have a "priority" assigned to each task with "small" tasks having a higher priority and thus prevent starvation from "large" tasks. Some searching around doesn't seem to indicate that anything pre-made is available, so I came up with the following: create three push queues: tasks-small, tasks-medium, tasks-large set a maximum number of concurrent request for each such that the total is the maximum number of concurrent API calls (for example if the max. no. concurrent API calls is 200, I could set up tasks-small to have a max_concurrent_requests of 30, tasks-medium 60 and tasks-large 100) when enqueueing a task, check the no. pending task in each queue (using something like the QueueStatistics class), and, if an other queue is not 100% utilized, enqueue the task there, otherwise just enqueue the task on the queue with the corresponding size. For example, if we have task T1 which is part of a small task, first check if tasks-small has free "slots" and enqueue it there. Otherwise check tasks-medium and tasks-large. If none of them have free slots, enqueue it on tasks-small anyway and it will be processed after the tasks added before it are processed (note: this is not optimal because if "slots" free up on the other queues, they still won't process pending tasks from the tasks-small queue) An other option would be to use PULL queue and have a central "coordinator" pull from that queue based on priorities and dispatch them, however that seems to add a little more latency. However this seems a little bit hackish and I'm wondering if there are better alternatives out there.

[Adam (Cloud Platform Support)]

Off the top of my head, I would add a constraint to your solution to prevent resource starvation for smaller tasks:

- Create three queues, #1, #2, and #3

- "Large" tasks can use queue #3

- "Medium" tasks can use queue #2, then queue #3 if it has free slots

- "Small" tasks can use queue #1, then #2 (if free), then #3 (if free)

This would ensure small and medium tasks would never get fully stepped on by their larger counterparts and could also take advantage of idle queue resources when available. You can tune it further by tweaking the balance of concurrent requests between the different queues.

[Attila-Mihaly Balazs]

After some thoughts and feedback I'm thinking of using PULL queue after all in the following way:

• have two PULL queues (medium-tasks and large-tasks)
• have a dispatcher (PUSH) queue with a concurrency of 1 (so that only one dispatch task runs at any time). Dispatch tasks are created in multiple ways:
  • by a once-a-minute cron job
  • after adding a medium/large task to the push queues
  • after a worker task finishes
• have a worker (PUSH) queue with a concurrency equal to the number of workers

And the workflow:

• small tasks are added directly to the worker queue
• the dispatcher task, whenever it is triggered, does the following:
  • estimates the number of free workers (by looking at the number of running tasks in the worker queue)
  • for any "free" slots it takes a task from the medium/large tasks PULL queue and enqueues it on a worker (or more precisely: adds it to the worker PUSH queue which will result in it being executed - eventually - on a worker).

I'll report back once this is implemented and at least moderately tested.