I have a push task queue and each of my jobs consists of multiple similar TaskQueue tasks. Each of these tasks takes less than a second to finish and can add new tasks to the queue (they should be also completed to consider the job finished). Task results are written to a DataStore.
The goal is to understand when a job has finished, i.e. all of its tasks are completed.
Writes are really frequent and I can't store the results inside one entity group. Is there a good workaround for this?
In a similar context I used a scheme based on memcache, which doesn't have a significant write rate limitation as datastore entity groups:
each job gets a unique memcache key associated with it, which it passes to each of subsequent execution tasks it may enqueue
every execution task updates the memcache value corresponding to the job key with the current timestamp and also enqueues a completion check task, delayed with an idle timeout value, large enough to declare the job completed if elapsed.
every completion check task compares the memcache value corresponding to the job key against the current timestamp:
if the delta is less than the idle timeout it means the job is not complete (some other task was executed since this completion check task was enqueued, hence some other completion check task is in the queue)
otherwise the job is completed
Note: the idle timeout should be larger than the maximum time a task might spend in the queue.
Related
I am using an F4 instance (because of memory needs) with automatic scheduling to do some background processing. It is run from a task queue. It takes 40s to 60s to complete each invocation. Because of the high memory needs, each instance should only handle one request at a time.
The action that needs to be done is not urgent. If it doesn't get scheduled for 30 minutes that isn't a problem. Even 60 minutes is acceptable and I'd rather make use of that time rather than spin up more instances. However, if the service gets popular and the is getting more than 60 requests an hour I want to spin up more instances to make sure there isn't more than a 60 minute wait.
I am having trouble figuring out how to configure the instance and queue parameters to keep my costs down but be able to scale in that way. My initial thought was something like this:
<queue>
<name>non-urgent-queue</name>
<target>slow-service</target>
<rate>1/m</rate>
<bucket-size>1</bucket-size>
<max-concurrent-requests>1</max-concurrent-requests>
</queue>
<automatic-scaling>
<min-idle-instances>0</min-idle-instances>
<max-idle-instances>0</max-idle-instances>
<min-pending-latency>20m</min-pending-latency>
<max-pending-latency>1h</max-pending-latency>
<max-concurrent-requests>1</max-concurrent-requests>
</automatic-scaling>
First of all those latency settings are invalid, but I can't find documentation on the valid range or units. Can anyone direct me to that info?
Secondly, if I understand the queue settings correctly, this configuration would limit it to 60 invocations an hour getting to the service, even if the task queue had 60+ jobs waiting.
Thanks for your help!
Indeed, throttling at the queue level basically defeats the ability to scale when needed. So you can't use the <rate> in the queue configuration at the values you have right now, you need to use the value matching the maximum rate you're willing to accept (with you max number of instances running simultaneously):
the max rate of requests that can go through the queue being limited at 1/min means you can't scale above 60/h
the <bucket-size> set at 1 means no peaks above the rate can be handled (as soon as one task starts the token bucket empties).
the <max-concurrent-requests> set at 1 will basically prevent multiple instances dealing simultaneouly with the queued workload. They may be started by the autoscaler because of the request latencies, but they won't be able to help since only one queue task can be handled at a time.
In the <automatic-scaling> section the <max-concurrent-requests> set to 1 is good - this ensures no instance handles more than 1 request at a time - which is what you want.
The bad news is that the max values for the latencies appear to be 15s. At least when using the app.yaml config for python (but I think it's unlikely for that to differ across language sandboxes):
Error 400: --- begin server output ---
automatic_scaling.min_pending_latency (30s), must be in the range [0.010000s,15.000000s].
--- end server output ---
and
Error 400: --- begin server output ---
automatic_scaling.max_pending_latency (60s), must be in the range [0.010000s,15.000000s].
--- end server output ---
Which probably also explains why your 5m and 1h values aren't accepted - I used 30s and 60s and got the above errors.
This means you won't be able to use the autoscaling parameters to tune such a slow-moving processing like you desire.
The only alternative I can think of is to have 2 queues:
a fast one feeding just trigger tasks for the slow-service jobs, but which your service intercepts and saves in the datastore. Maybe performed by some faster service (you don't want these stuck behind a slow-service job execution as it can cause unnecessary instance launching. Maybe, depending on the rest of your implementation, you can replace this queue completely with just storing the job info in the datastore instead of enqueing tasks in the fast queue.
a slow one for the actual slow-service job execution tasks
You'd also have a cron job executing once a minute, checking how many triggers are pending in the datastore, decide how much to scale and enqueue the corresponding number of slow-service job tasks in the slow queue. The autoscaler would simply bring up the corresponding number of instances (if needed). Low latency autoscaling configs would be desirable in this case - you already decided how you want your app to scale.
This is how I ended up doing it. I use a slow queue and a fast queue configured like this:
<queue>
<name>slow-queue</name>
<target>pdf-service</target>
<rate>2/m</rate>
<bucket-size>1</bucket-size>
<max-concurrent-requests>1</max-concurrent-requests>
</queue>
<queue>
<name>fast-queue</name>
<target>pdf-service</target>
<rate>10/m</rate>
<bucket-size>1</bucket-size>
<max-concurrent-requests>5</max-concurrent-requests>
</queue>
The max-concurrent-requests in the slow queue ensures only one task will run at a time, so there will only be one instance active.
Before I post to the slow queue I check to see how many items are already on the queue. The result may not be totally reliable, but for my purposes it is sufficient. In java:
QueueStatistics queueStats = queue.fetchStatistics();
if(queueStats.getNumTasks()<30) {
//post to slow queue
} else {
//post to fast queue
}
So when my slow queue gets too full, I post to the fast queue which allows concurrent requests.
The instance is configured like this:
<automatic-scaling>
<min-idle-instances>0</min-idle-instances>
<max-idle-instances>automatic</max-idle-instances>
<min-pending-latency>15s</min-pending-latency>
<max-pending-latency>15s</max-pending-latency>
<max-concurrent-requests>1</max-concurrent-requests>
</automatic-scaling>
So it will create new instances as slowly as possible (15s is the max latency) and make sure only one process runs on an instance at a time.
With this configuration I'll have a max of 6 instances at a time but that should do about 500/hr. I could increase the rate and concurrent requests to do more.
The negative of this solution is an element of unfairness. Under heavy load, some tasks will be stuck in the slow queue while others will get processed more quickly in the fast queue.
Because of that, I have decreased the max items on the slow queue to 13 so the unfairness won't be so extreme, maybe a 10 minute wait for jobs that go to the slow queue when it is full.
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.
EDIT: 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.
The small/medium/large original task queues won't help much by themselves - once the original tasks are enqueued they'll keep spawning worker tasks, potentially even breaking the worker task queue size limit. So you need to pace/control enqueing of the original tasks.
I'd keep track of the "todo" original tasks in the datastore/GCS and enqueue these original tasks only when the respective queue size is sufficiently low (1 or maybe 2 pending jobs), from either a recurring task, a cron job or a deferred task (depending on the rate at which you need to perform the original task enqueueing) which would implement the desired pacing and priority logic just like a push queue dispatcher, but without the extra latency you mentioned.
I have not used pull queues, but from my understanding they could suit your use-case very well. Your could define 3 pull queues, and have X workers all pulling tasks from them, first trying the "small" queue then moving on to "medium" if it is empty (where X is your maximum concurrency). You should not need a central dispatcher.
However, then you would be left to pay for X workers even when there are no tasks (or X / threadsPerMachine?), or scale them down & up yourself.
So, here is another thought: make a single push queue with the correct maximum concurrency. When you receive a new task, push its info to the datastore and queue up a generic job. That generic job will then consult the datastore looking for tasks in priority order, executing the first one it finds. This way a short task will still be executed by the next job, even if that job was already enqueued from a large task.
EDIT: I now migrated to a simpler solution, similar to what #eric-simonton described:
I have multiple PULL queues, one for each priority
Many workers pull on an endpoint (handler)
The handler generates a random number and does a simple "if less than 0.6, try first the small queue and then the large queue, else vice-versa (large then small)"
If the workers get no tasks or an error, they do semi-random exponential backoff up to maximum timeout (ie. they start pulling every 1 second and approximately double the timeout after each empty pull up to 30 seconds)
This final point is needed - amongst other reasons - because the number of pulls / second from a PULL queue is limited to 10k/s: https://cloud.google.com/appengine/docs/python/taskqueue/overview-pull#Python_Leasing_tasks
I implemented the solution described in the UPDATE:
two PULL queues (medium-tasks and large-tasks)
a dispatcher (PUSH) queue with a concurrency of 1
a worker (PUSH) queue with a concurrency equal to the number of workers
See the question for more details. Some notes:
there is some delay in task visibility due to eventual consistency (ie. the dispatchers tasks sometimes don't see the tasks from the pull queue even if they are inserted together) - I worked around by adding a countdown of 5 seconds to the dispatcher tasks and also adding a cron job that adds a dispatcher task every minute (so if the original dispatcher task doesn't "see" the task from the pull queue, an other will come along later)
made sure to name every task to eliminate the possibility of double-dispatching them
you can't lease 0 items from the PULL queues :-)
batch operations have an upper limit, so you have to do your own batching over the batch taskqueue calls
there doesn't seem to be a way to programatically get the "maximum parallelism" value for a queue, so I had to hard-code that in the dispatcher (to calculate how many more tasks it can schedule)
don't add dispatcher tasks if they are already some (at least 10) in the queue
We have an application that takes some input from a user and makes ~50 RPC calls. Each call takes around 4-5 minutes.
In the backend we are using a push queue and enqueuing each of these 50 calls as tasks. This is our queue spec:
queue:
- name: some-name
rate: 500/s
bucket_size: 100
max_concurrent_requests: 500
My understanding is that all 50 requests should be run in parallel, and thus all of them should be complete in 4-5 minutes. But what's actually happening is that only around ~15 of these requests are returning results, while the rest cross the 10 min limit and time out. Another thing to note is that this seems to work fine if we bring down the number of requests to < 10.
There's always the possibility that the requests that timed out did so because the RPC response actually took that long. But what I wanted to confirm is :
My understanding of the tasks running in parallel is correct.
Our queue config and the number of tasks we're enqueuing has nothing to do with these requests timing out.
Are these correct ?
(1) Parallel execution
Yes, tasks can be executed in parallel (up to 500 in your case), but in push queues, your app has no control in which particular order the tasks in a push queue are executed and no direct control how many tasks are executed at once. (Your app can control in which sequence tasks are added to a queue though, see the pattern in (2) below)
App Engine uses certain factors to decide how fast and which tasks are executed, especially the queue configuration and also the scaling configuration (e.g. in app.yaml). Since you pay for every first 15 minutes of an instance, it could get very expensive to really have 50 instances launched, then idling for 15 minutes before shutting them down (until the next request). In this regard, the mechanism that spawns new instances is a little smarter, whether it is HTTP requests by users or task queues.
(2) Request time outs
Yes, it is very unlikely that the enqueuing has anything to do with these request time outs. Unless the time-outs are an unintentional side-effect of the wrong assumption that a particular task was executed before.
In order to avoid request time outs in general, it makes sense to split a task into multiple tasks. For example, if you have a task do_foo and those executions exceed the time outs frequently (or memory limits), you could instead have do_foo load off work to other tasks that will do the actual jobs.
For some migration tasks I use this pattern in a linear / sequential way. E.g. classmethod do_foo just queries entities of a certain kind (ordered by creation timestamp for example), maybe filtered, by page (e.g. 50 in transactions with ancestor). It does some writes to the entities first, and only at the very end after successful commit it creates a new transactional do_foo task with cursor parameter to the next page, eventually with a countdown of 1 sec to avoid transaction errors. The next execution of do_foo will continue with the next page (of course only after the task with the previous page completed).
Depending on the nature of the tasks, you could alternatively have each task fan out into multiple tasks per execution, e.g. do_foo triggers do_bar, do_something and do_more. Also note that up to five tasks can be created transactionally inside a transaction.
Today while browsing the source I noticed this comment in Pipeline.start method:
Returns:
A taskqueue.Task instance if return_task was True. This task will *not*
have a name, thus to ensure reliable execution of your pipeline you
should add() this task as part of a separate Datastore transaction.
Interesting, I do want reliable execution of my pipeline after all.
I suspect the comment is a bit inaccurate, since if you use the default return_task=False option the task is added inside a transaction anyway (by _PipelineContext.start)... it seems like the reason you'd want to add the task yourself is only if you want the starting of the pipeline to depend on success of something in your own transaction.
Can anyone confirm my suspicion or suggest how else following the comment's advice may effect 'reliable execution of your pipeline' ?
If you don't include the parameter when you call Pipeline.start(), the task is enqueued in the queue given by the Pipeline's inner variable context (type _PipelineContext). The default name for this queue is "default".
If you do include the parameter when you call Pipeline.start(), the task is not enqueued within these methods. Pipeline.start() will return _PipelineContext.start(), we see that it relies on an inner method txn(). This method is annotated transactional since it first does a bit of book-keeping for the Datastore records used to run this pipeline. Then, after this book-keeping is done, it creates a task without a name property (see the Task class definition here).
If return_task was not provided, it will go ahead and add that (un-named) task to the default queue for this pipeline's context. It also sets transactional on that task, so that it will be a "transactional task" which will only be added if the enclosing datastore transaction is committed successfully (ie. with all the bookkeeping in the txn() method successful so that this task, when run, will interact properly with the other parts of the pipeline etc.)
If, on the other hand, return_task was not defined, the un-named task, un-added to a queue, is returned. The txn() book-keeping work will nonetheless have taken place to prepare it to run. _PipelineContext.start() returns to Pipeline.start(), and user code will get the un-named, un-added task.
You're absolutely correct to say that the reason you would want this pattern is if you want pipeline execution to be part of a transaction in your code. Maybe you want to receive and store some data, kick off a pipeline, and store the pipeline id on a user's profile somewhere in Datastore. Of course, this means you want not only the datastore events but also the pipeline execution event to be grouped together into this atomic transaction. This pattern would allow you to do such a thing. If the transaction failed, the transactional task would not execute, and handle_run_exception() will be able to catch the TransactionFailedError and run pipeline.abort() to make sure the Datastore book-keeping data had been destroyed for the task that never ran.
The fact that the pipeline is un-named will not cause any disruption, since un-named tasks are auto-generated a unique name when added to a queue if the name is undefined, and in fact not having a name is actually a requirement for tasks which are added with transactional=True.
All in all, I think the comment just means to say that due to the fact that the task returned is transactional, in order for it to be reliably executed, you should make sure that the task.add (queue_name...) takes place in a transaction. It's not saying that the returned task is somehow "unreliable" just because you set return_task, it's basically using the word "reliable" superfluously, due to the fact that the task is run in a transaction.
In my application, I have a long task, so I split it into n smaller tasks. After these n tasks complete, another one task is to be performed and it depends on the results of those n tasks. How do I achieve this dependency with Task API? i.e. perform one task after other n tasks.
I think there are 2 methods that can solve this problem.
Suppose the task TD depends on n other tasks TA, and there is a queue Q.
Push n TA tasks in to queue Q. When each task TA finishes, it checks if itself is the last one in the queue Q. If a TA is the last task in queue Q, it pushes TD to queue Q.
Push n TA tasks and TD to queue Q. When TD run, it checks if all TA task finish. If there is any TA unfinished, TD cancels its execution by returning any HTTP status code outside of the range 200-299.
The key of these methods is to get number of tasks in the queue Q. Although I haven't tried, I know there is a Python API provides an experimental method to get TaskQueue resource of a specific queue. The stats.totalTasks property is the total number of queues in the queue.
Please see http://code.google.com/intl/en/appengine/docs/python/taskqueue/rest.html
Take a look on the GAE Pipeline API, it is used to build complex task workflow like the one you described.
Yet another approach could be to start by adding all tasks to the queue. Have the N initial tasks log info to the datastore upon completion, in some manner that allows you to query the datastore to see if they have all run.
When the dependent task runs, it performs this datastore query to see if its conditions are met (checks that all initial tasks have logged that they are finished). If not, it needs to run later.
To accomplish this, the dependent task could add a copy of itself to the queue, scheduled to run after some given time interval. Alternately (as in the answer above), the dependent task could terminate with an error status code, in which case it will be automatically retried at some later point, as long as the retry_limit for the queue or the task is not exceeded.