Are there any existing out of the box job queue framework? basic idea is
someone to enqueue a job with job status New
(multiple) workers get a job and work on it, mark the job as Taken. One job can only be running on at most one worker
something will monitor the worker status, if the running jobs exceed predefined timeout, will be re-queued with status New, could be worker health issue
Once a worker completes a task, it marks the task as Completed in the queue.
something keeps cleaning up completed tasks. Or at step #4 when worker completes a task, the worker simply dequeues the task.
From my investigation, things like Kafka (pub/sub) or MQ (push/pull & pub/sub) or cache (Redis, Memcached) are mostly sufficient for this work. However, they all require some sort of development around its core functionality to become a fully functional job queue.
Also looked into relational DB, the ones supports "SELECT * FOR UPDATE SKIP LOCKED" syntax is also a good candidate, this again requires a daemon between the DB and worker, which means extra effort.
Also looked into the cloud solutions, Azure Queue storage, etc. similar assessment.
So my question is, is there any out of the box solution for job queue, that are tailored and dedicated for one thing, job queuing, without much effort to set up?
Thanks
Take a look at Python Celery. https://docs.celeryproject.org/en/stable/getting-started/introduction.html
The default mode uses RabbitMQ as the message broker, but other options are available. Results can be stored in a DB if needed.
Related
Mongock looks very promising. We want to use it inside a kubernetes service that has multiple replicas that run in parallel.
We are hoping that when our service is deployed, the first replica will acquire the mongockLock and all of its ChangeLogs/ChangeSets will be completed before the other replicas attempt to run them.
We have a single instance of mongodb running in our kubernetes environment, and we want the mongock ChangeLogs/ChangeSets to execute only once.
Will the mongockLock guarantee that only one replica will run the ChangeLogs/ChangeSets to completion?
Or do I need to enable transactions (or some other configuration)?
I am going to provide the short answer first and then the long one. I suggest you to read the long one too in order to understand it properly.
Short answer
By default, Mongock guarantees that the ChangeLogs/changeSets will be run only by one pod at a time. The one owning the lock.
Long answer
What really happens behind the scenes(if it's not configured otherwise) is that when a pod takes the lock, the others will try to acquire it too, but they can't, so they are forced to wait for a while(configurable, but 4 mins by default)as many times as the lock is configured(3 times by default). After this, if i's not able to acquire it and there is still pending changes to apply, Mongock will throw an MongockException, which should mean the JVM startup fail(what happens by default in Spring).
This is fine in Kubernetes, because it ensures it will restart the pods.
So now, assuming the pods start again and changeLogs/changeSets are already applied, the pods start successfully because they don't even need to acquire the lock as there aren't pending changes to apply.
Potential problem with MongoDB without transaction support and Frameworks like Spring
Now, assuming the lock and the mutual exclusion is clear, I'd like to point out a potential issue that needs to be mitigated by the the changeLog/changeSet design.
This issue applies if you are in an environment such as Kubernetes, which has a pod initialisation time, your migration take longer than that initialisation time an the Mongock process is executed before the pod becomes ready/health(and it's a condition for it). This last condition is highly desired as it ensures the application runs with the right version of the data.
In this situation imagine the Pod starts the Mongock process. After the Kubernetes initialisation time, the process is still not finished, but Kubernetes stops the JVM abruptly. This means that some changeSets were successfully executed, some other not even started(no problem, they will be processed in the next attempt), but one changeSet was partially executed and marked as not done. This is the potential issue. The next time Mongock runs, it will see the changeSet as pending and it will execute it from the beginning. If you haven't designed your changeLogs/changeSets accordingly, you may experience some unexpected results because some part of the data process covered by that changeSet has already taken place and it will happen again.
This, somehow needs to be mitigated. Either with the help of mechanisms like transactions, with a changeLog/changeSet design that takes this into account or both.
Mongock currently provides transactions with “all or nothing”, but it doesn’t really help much as it will retry every time from scratch and will probably end up in an infinite loop. The next version 5 will provide transactions per ChangeLogs and changeSets, which together with good organisation, is the right solution for this.
Meanwhile this issue can be addressed by following this design suggestions.
Just to follow up... Mongock's locking mechanism works fine with replicas. To solve the "long-running script" problem, we will run our Mongock scripts from Kubernetes initContainer. K8s will wait for the initContainers to finish before it starts the pod's main service containers.
For transactions, we will follow the advice above of making our scripts idempotent.
I am currently running a Flink session cluster (Kubernetes, 1 JobManager, 1 TaskManager, Zookeeper, S3) in which multiple jobs run.
As we are working on adding more jobs, we are looking to improve our deployment and cluster management strategies. We are considering migrating to using job clusters, however there is reservation about the number of containers which will be spawned. One container per job is not an issue, but two containers (1 JM and 1 TM) per job raises concerns about memory consumption. Several of the jobs need high-availability and the ability to use checkpoints and restore from/take savepoints as they aggregate events over a window.
From my reading of the documentation and spending time on Google, I haven't found anything that seems to state whether or not what is being considered is really possible.
Is it possible to do any of these three things:
run both the JobManager and TaskManager as separate processes in the same container and have that serve as the Flink cluster, or
run the JobManager and TaskManager as literally the same process, or
run the job as a standalone JAR with the ability to recover from/take checkpoints and the ability to take a savepoint and restore from that savepoint?
(If anyone has any better ideas, I'm all ears.)
One of the responsibilities of the job manager is to monitor the task manager(s), and initiate restarts when failures have occurred. That works nicely in containerized environments when the JM and TMs are in separate containers; otherwise it seems like you're asking for trouble. Keeping the TMs separate also makes sense if you are ever going to scale up, though that may moot in your case.
What might be workable, though, would be to run the job using a LocalExecutionEnvironment (so that everything is in one process -- this is sometimes called a Flink minicluster). This path strikes me as feasible, if you're willing to work at it, but I can't recommend it. You'll have to somehow keep track of the checkpoints, and arrange for the container to be restarted from a checkpoint when things fail. And there are other things that may not work very well -- see this question for details. The LocalExecutionEnvironment wasn't designed with production deployments in mind.
What I'd suggest you explore instead is to see how far you can go toward making the standard, separate container solution affordable. For starters, you should be able to run the JM with minimal resources, since it doesn't have much to do.
Check this operator which automates the lifecycle of deploying and managing Flink in Kubernetes. The project is in beta but you can still get some idea about how to do it or directly use this operator if it fits your requirement. Here Job Manager and Task manager is separate kubernetes deployment.
I have few questions regarding celery. Please help me with that.
Do we need to put the project code in every celery worker? If yes, if I am increasing the number of workers and also I am updating my code, what is the best way to update the code in all the worker instances (without manually pushing code to every instance everytime)?
Using -Ofair in celery worker as argument disable prefetching in workers even if have set PREFETCH_LIMIT=8 or so?
IMPORTANT: Does rabbitmq broker assign the task to the workers or do workers pull the task from the broker?
Does it make sense to have more than one celery worker (with as many subprocesses as number of cores) in a system? I see few people run multiple celery workers in a single system.
To add to the previous question, whats the performance difference between the two scenarios: single worker (8 cores) in a system or two workers (with concurrency 4)
Please answer my questions. Thanks in advance.
Do we need to put the project code in every celery worker? If yes, if I am increasing the number of workers and also I am updating my code, what is the best way to update the code in all the worker instances (without manually pushing code to every instance everytime)?
Yes. A celery worker runs your code, and so naturally it needs access to that code. How you make the code accessible though is entirely up to you. Some approaches include:
Code updates and restarting of workers as part of deployment
If you run your celery workers in kubernetes pods this comes down to building a new docker image and upgrading your workers to the new image. Using rolling updates this can be done with zero downtime.
Scheduled synchronization from a repository and worker restarts by broadcast
If you run your celery workers in a more traditional environment or for some reason you don't want to rebuild whole images, you can use some central file system available to all workers, where you update the files e.g. syncing a git repository on a schedule or by some trigger. It is important you restart all celery workers so they reload the code. This can be done by remote control.
Dynamic loading of code for every task
For example in omega|ml we provide lambda-style serverless execution of
arbitrary python scripts which are dynamically loaded into the worker process.
To avoid module loading and dependency issues it is important to keep max-tasks-per-child=1 and use the prefork pool. While this adds some overhead it is a tradeoff that we find is easy to manage (in particular we run machine learning tasks and so the little overhead of loading scripts and restarting workers after every task is not an issue)
Using -Ofair in celery worker as argument disable prefetching in workers even if have set PREFETCH_LIMIT=8 or so?
-O fair stops workers from prefetching tasks unless there is an idle process. However there is a quirk with rate limits which I recently stumbled upon. In practice I have not experienced a problem with neither prefetching nor rate limiting, however as with any distributed system it pays of to think about the effects of the asynchronous nature of execution (this is not particular to Celery but applies to all such such systems).
IMPORTANT: Does rabbitmq broker assign the task to the workers or do workers pull the task from the broker?
Rabbitmq does not know of the workers (nor do any of the other broker supported by celery) - they just maintain a queue of messages. That is, it is the workers that pull tasks from the broker.
A concern that may come up with this is what if my worker crashes while executing tasks. There are several aspects to this: There is a distinction between a worker and the worker processes. The worker is the single task started to consume tasks from the broker, it does not execute any of the task code. The task code is executed by one of the worker processes. When using the prefork pool (which is the default) a failed worker process is simply restarted without affecting the worker as a whole or other worker processes.
Does it make sense to have more than one celery worker (with as many subprocesses as number of cores) in a system? I see few people run multiple celery workers in a single system.
That depends on the scale and type of workload you need to run. In general CPU bound tasks should be run on workers with a concurrency setting that doesn't exceed the number of cores. If you need to process more of these tasks than you have cores, run multiple workers to scale out. Note if your CPU bound task uses more than one core at a time (e.g. as is often the case in machine learning workloads/numerical processing) it is the total number of cores used per task, not the total number of tasks run concurrently that should inform your decision.
To add to the previous question, whats the performance difference between the two scenarios: single worker (8 cores) in a system or two workers (with concurrency 4)
Hard to say in general, best to run some tests. For example if 4 concurrently run tasks use all the memory on a single node, adding another worker will not help. If however you have two queues e.g. with different rates of arrival (say one for low frequency but high-priority execution, another for high frequency but low-priority) both of which can be run concurrently on the same node without concern for CPU or memory, a single node will do.
When we run multiple concurrent jobs with different parameters, how can we control (stop, restart) the appropriate jobs? Our internal code provides the jobExecution object, but under the covers The jobOperator uses the job name to get the job instance.
In our case all of the jobs are from "do-stuff.xml" (okay, it's sanitized and not very original). After looking at the spring-batch source code, our concern is that if there is more then one job running and we stop a job it will take the most recently submitted job and stop it.
The JobOperator will allow you to fetch all running executions of the job using getRunningExecutions(String jobName). You should be able to iterate over that list to find the one you want. Then, just call stop(long executionId) on the one you want.
Alternatively, we've also implemented listeners (both at step and chunk level) to check an outage status table. When we want to implement a system-wide outage, we add the outage there and have our listener throw an exception to bring our jobs down. once the outage is lifted, all "failed" executions may be restarted.
I have a few jobs in Jenkins that use Selenium to modify a database through a website's front end. If some of these jobs run at the same time, errors due to dirty reads can result. Is there a way to force certain jobs in Jenkins to be unable to run at the same time? I would prefer not to have to place or pick up a lock on the database, which could be read or modified by any number of users who are also testing.
You want the Throttle Concurrent Builds plugin which lets you define global and per-node semaphores.
Locks and latches is being deprecated in favor of Throttle Concurrent builds.
I've tried both the locks & latches plugin and the port allocator plugin as ways to achieve what you're trying to do. Neither worked reliably for me. Locks & latches worked some of the time, but I'd occasionally get hung jobs. Using port allocator as a hack will work unless you have multiple jenkins nodes, but the config overhead is kind of high. What I've ultimately settled upon is another hack, but it works reliably and uses core Jenkins stuff (no plugins):
create a slave node running on the same box as the master (or not, if you have lots of boxes)
give this slave a single executor (key)
tie the 2 (or n) jobs that must not run together to this new slave node
optionally set the slave's usage to 'tied jobs only' if it'll screw up your other jobs if they happen to run on the new slave
Since the slave has only one executor, the jobs tied to it can never run together.
If you regard the database as a shared resource that can only be used exclusively then this fits the usecase of the Lockable resources plugin.
It is being actively developed and improved and is very versatile.