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.

On Monday, July 25, 2016 at 8:01:08 AM UTC-4, Attila-Mihaly Balazs wrote:
>
> 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:
>
>    1. I have a series of "tasks" which I would like to execute
>    2. 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)
>    3. I would like for these "tasks" to be executed "as soon as possible" 
>    (ie. minimum latency)
>    4. 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.
>
>

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