We've got Celery/SQS set up for asynchronous task management. We're running Django for our framework. We have a celery task that has a self.retry() in it. Max_retries is set to 15. The retry is happening with an exponential backoff and takes 182 hours to complete all 15 retries.
Last week, this task went haywire, I think due to a bug in our code not properly handling a service outage. It resulted in exponential creation (retrying?) of the same celery task. It eventually used up all available memory and the worker crashed. Restarting the worker results in another crash a couple hours later, since all those tasks (and their retries) keep retrying and spawning new retries until we run out of memory again. Ultimately we ended up with nearly 600k tasks created!
We need our workers to ignore all the tasks with a specific celery GUID. Ideally we could just get rid of them for good. I was going to use revoke() but, per documentation (http://docs.celeryproject.org/en/3.1/userguide/workers.html#commands), this is only implemented for Redis and RabbitMQ, not SQS. Furthermore, when I go to the SQS service in the AWS console, it's showing zero messages in flight so it's not like I can just flush it.
Is there a way to delete or revoke a specific message from SQS using the Celery task ID? Or is there another way to fix this problem? Obviously we need to fix our code so we don't get into this situation again, but first we need to get our worker up and running because without it our website has reduced functionality. Thanks!
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After running for 17 hours, my Dataflow job failed with the following message:
The job failed because a work item has failed 4 times. Look in previous log entries for the cause of each one of the 4 failures.
The 4 failures consist of 3 workers losing contact with the service, and one worker reported dead:
****-q15f Root cause: The worker lost contact with the service.
****-pq33 Root cause: The worker lost contact with the service.
****-fzdp Root cause: The worker ****-fzdp has been reported dead. Aborting lease 4624388267005979538.
****-nd4r Root cause: The worker lost contact with the service.
I don't see any errors in the worker logs for the job in Stackdriver. Is this just bad luck? I don't know how frequently work items need to be retried, so I don't know what the probability is that a single work item will fail 4 times over the course of a 24 hour job. But this same type of job failure happens frequently for this long-running job, so it seems like we need some way to either decrease the failure rate of work items, or increase the allowed number of retries. Is either possible? This doesn't seem related to my pipeline code, but in case it's relevant, I'm using the Python SDK with apache-beam==2.15.0. I'd appreciate any advice on how to debug this.
Update: The "STACK TRACES" section in the console is totally empty.
I was having the same problem and it was solved by scaling up my workers resources. Specifically, I set --machine_type=n1-highcpu-96 in my pipeline configs. See this for a more extensive list on machine type options.
Edit: Set it to highcpu or highmem depending on the requirements of your pipeline process
As I explained in this post, I'm trying to scrape tweets from Twitter.
I implemented the suggested solution with services, so that the actual heavy lifting happens in the backend.
The problem is that after about one day, I get this error
"Process terminated because the request deadline was exceeded. (Error code 123)"
I guess this is because the manual scaling has the requests timing out after 24 hours.
Is it possible to make it run for more than 24 hours?
You can't make a single request / task run for more than 24 hours but you can split up your request into different parts and each lasting a day. It's unwise to have a request run indefinitely that's why app engine closes them after a certain time to prevent idling / loopy request that lasts indefinitely.
I would recommend having your task fire a call at the end to trigger the queuing of the next task, that way it's automatic and you don't have to queue a task daily. Make sure there's some cursor or someway for your task to communicate progress so it won't duplicate work.
So I'm struggling to figure out the optimal way to schedule some events to happen at some point in the future using celery. An example of this is when a new user has registered, we want to send them an email the next day.
We have celery setup and some of you may allude to the eta parameter when calling apply_async. However that won't work for us, as we use SQS which has a visibility timeout that would conflict and generally the eta param shouldn't be used for lengthy periods.
One solution we've implemented at this point is to create events and store them in the database with a 'to-process' timestamp (refers to when to process the event). We use the celery beat scheduler with a task that runs literally every second to see if there are any new events that are ready to process. If there are, we carry out the subsequent tasks.
This solution works, although it doesn't feel great since we're queueing a task every second on SQS. Any thoughts or ideas on this would be great?
I am trying to use Amazon Elastic Beanstalk to run a very long numerical simulation - up to 20 hours. The code works beautifully when I tell it to do a short, 20 second simulation. However, when running a longer one, I get the error "The following instances have not responded in the allowed command timeout time (they might still finish eventually on their own)".
After browsing the web, it seems to me that the issue is that Elastic Beanstalk allows worker processes to run for 30 minutes at most, and then they time out because the instance has not responded (i.e. finished the simulation). The solution some have proposed is to send a message every 30 seconds or so that "pings" Elastic Beanstalk, letting it know that the simulation is going well so it doesn't time out, which would let me run a long worker process. So I have a few questions:
Is this the correct approach?
If so, what code or configuration would I add to the project to make it stop terminating early?
If not, how can I smoothly run a 12+ hour simulation on AWS or more generally, the cloud?
Add on information
Thank you for the feedback, Rohit. To give some more information, I'm using Python with Flask.
• I am indeed using an Elastic Beanstalk worker tier with SQS queues
• In my code, I'm running a simulation of variable length - from as short as 20 seconds to as long as 20 hours. 99% of the work that Elastic Beanstalk does is running the simulation. The other 1% involves saving results, sending emails, etc.
• The simulation itself involves using generating many random numbers and working with objects that I defined. I use numpy heavily here.
Let me know if I can provide any more information. I really appreciate the help :)
After talking to a friend who's more in the know about this stuff than me, I solved the problem. It's a little sketchy, but got the job done. For future reference, here is an outline of what I did:
1) Wrote a main script that used Amazon's boto library to connect to my SQS queue. Wrote an infinite while loop to poll the queue every 60 seconds. When there's a message on the queue, run a simulation and then continue through with the loop
2) Borrowed a beautiful /etc/init.d/ template to run my script as a daemon (http://blog.scphillips.com/2013/07/getting-a-python-script-to-run-in-the-background-as-a-service-on-boot/)
3) Made my main script and the script in (2) executable
4) Set up a cron job to make sure the script would start back up if it failed.
Once again, thank you Rohit for taking the time to help me out. I'm glad I still got to use Amazon even though Elastic Beanstalk wasn't the right tool for the job
From your question it seems you are running into launches timing out because some commands during launch that run on your instance take more than 30 minutes.
As explained here, you can adjust the Timeout option in the aws:elasticbeanstalk:command namespace. This can have values between 1 and 1800. This means if your commands finish within 30 minutes you won't see this error. The commands might eventually finish as the error message says but since Elastic Beanstalk has not received a response within the specified period it does not know what is going on your instance.
It would be helpful if you could add more details about your usecase. What commands you are running during startup? Apparently you are using ebextensions to launch commands which take a long time. Is it possible to run those commands in the background or do you need these commands to run during server startup?
If you are running a Tomcat web app you could also use something like servlet init method to run app bootstrapping code. This code can take however long it needs without giving you this error message.
Unfortunately, there is no way to 'process a message' from an SQS queue for more than 12 hours (see the description of ChangeVisibilityTimeout).
With that being the case, this approach doesn't fit your application well. I have ran into the same problem.
The correct way to do this: I don't know. However, I would suggest an alternate approach where you grab a message off of your queue, spin off a thread or process to run your long running simulation, and then delete the message (signaling successful processing). In this approach, be careful of spinning off too many threads on one machine and also be wary of machines shutting down before the simulation has ended, because the queue message has already been deleted.
Final note: your question is excellently worded and sufficiently detailed :)
For those looking to run jobs shorter than 10 hours, it needs to be mentioned that the current inactivity timeout limit is 36000 seconds, so exactly 10 hours and not anymore 30 minutes, like mentioned in posts all over the web (which led me to think a workaround like described above is needed).
Check out the docs: https://docs.aws.amazon.com/elasticbeanstalk/latest/dg/using-features-managing-env-tiers.html
A very nice write-up can be found here: https://dev.to/rizasaputra/understanding-aws-elastic-beanstalk-worker-timeout-42hi
1) I am currently working on a web application that exposes a REST api and uses Django and Celery to handle request and solve them. For a request in order to get solved, there have to be submitted a set of celery tasks to an amqp queue, so that they get executed on workers (situated on other machines). Each task is very CPU intensive and takes very long (hours) to finish.
I have configured Celery to use also amqp as results-backend, and I am using RabbitMQ as Celery's broker.
Each task returns a result that needs to be stored afterwards in a DB, but not by the workers directly. Only the "central node" - the machine running django-celery and publishing tasks in the RabbitMQ queue - has access to this storage DB, so the results from the workers have to return somehow on this machine.
The question is how can I process the results of the tasks execution afterwards? So after a worker finishes, the result from it gets stored in the configured results-backend (amqp), but now I don't know what would be the best way to get the results from there and process them.
All I could find in the documentation is that you can either check on the results's status from time to time with:
result.state
which means that basically I need a dedicated piece of code that runs periodically this command, and therefore keeps busy a whole thread/process only with this, or to block everything with:
result.get()
until a task finishes, which is not what I wish.
The only solution I can think of is to have on the "central node" an extra thread that runs periodically a function that basically checks on the async_results returned by each task at its submission, and to take action if the task has a finished status.
Does anyone have any other suggestion?
Also, since the backend-results' processing takes place on the "central node", what I aim is to minimize the impact of this operation on this machine.
What would be the best way to do that?
2) How do people usually solve the problem of dealing with the results returned from the workers and put in the backend-results? (assuming that a backend-results has been configured)
I'm not sure if I fully understand your question, but take into account each task has a task id. If tasks are being sent by users you can store the ids and then check for the results using json as follows:
#urls.py
from djcelery.views import is_task_successful
urlpatterns += patterns('',
url(r'(?P<task_id>[\w\d\-\.]+)/done/?$', is_task_successful,
name='celery-is_task_successful'),
)
Other related concept is that of signals each finished task emits a signal. A finnished task will emit a task_success signal. More can be found on real time proc.