I currently use Airflow (via Cloud Composer) with the Celery Executor and the KubernetesPodOperator.
One challenge I have is to use resources efficiently when some Airflow tasks use relatively little memory and others use many GB of memory.
It would be great if Airflow tasks could be used in conjunction with the Kubernetes Vertical Pod Autoscaler. The idea being that over time Kubernetes will see how much resources a task tends to use and scale it accordingly. In my case it would be the Google Vertical Pod Autoscaler but the differences don't seem too important here. Of course, I would start with the VPA turned off and just look at the recommendations.
Since my Airflow tasks each generally process the same amount of data from day to day it seems doable. In the case that one day a task has a lot more data (and hence needs more resources) my hope would be that either:
The pod will eke by and then get larger the next time it runs.
The pod will be evicted and when airflow retries the task the next pod will use more resources.
I've read that when using Celery in general one challenge of scaling is that the "worker" is a pod and it is not easy to know how many resources to give to a worker because any task can be assigned to that worker. However, when using the KubernetesPodOperator this doesn't seem like a problem.
There is also the factor of possibly using the Kubernetes Executor but since that's not supported in Cloud Composer yet so we can leave that factor out.
I did search for tutorials or others sharing their experience but didn't have luck finding anyone using VPA with Airflow. If you know of any resources please do share them.
In summary: Does this sound reasonable/feasible? Or are there any other factors that could create issues here?
Edit:
I have documented the problem more clearly in this repo: https://github.com/RayBB/airflow-vpa
Related
Current Setup
We have kubernetes cluster setup with 3 kubernetes pods which run spring boot application. We run a job every 12 hrs using spring boot scheduler to get some data and cache it.(there is queue setup but I will not go on those details as my query is for the setup before we get to queue)
Problem
Because we have 3 pods and scheduler is at application level , we make 3 calls for data set and each pod gets the response and pod which processes at caches it first becomes the master and other 2 pods replicate the data from that instance.
I see this as a problem because we will increase number of jobs for get more datasets , so this will multiply the number of calls made.
I am not from Devops side and have limited azure knowledge hence I need some help from community
Need
What are the options available to improve this? I want to separate out Cron schedule to run only once and not for each pod
1 - Can I keep cronjob at cluster level , i have read about it here https://kubernetes.io/docs/concepts/workloads/controllers/cron-jobs/
Will this solve a problem?
2 - I googled and found other option is to run a Cronjob which will schedule a job to completion, will that help and not sure what it really means.
Thanks in Advance to taking out time to read it.
Based on my understanding of your problem, it looks like you have following two choices (at least) -
If you continue to have scheduling logic within your springboot main app, then you may want to explore something like shedlock that helps make sure your scheduled job through app code executes only once via an external lock provider like MySQL, Redis, etc. when the app code is running on multiple nodes (or kubernetes pods in your case).
If you can separate out the scheduler specific app code into its own executable process (i.e. that code can run in separate set of pods than your main application code pods), then you can levarage kubernetes cronjob to schedule kubernetes job that internally creates pods and runs your application logic. Benefit of this approach is that you can use native kubernetes cronjob parameters like concurrency and few others to ensure the job runs only once during scheduled time through single pod.
With approach (1), you get to couple your scheduler code with your main app and run them together in same pods.
With approach (2), you'd have to separate your code (that runs in scheduler) from overall application code, containerize it into its own image, and then configure kubernetes cronjob schedule with this new image referring official guide example and kubernetes cronjob best practices (authored by me but can find other examples).
Both approaches have their own merits and de-merits, so you can evaluate them to suit your needs best.
I'm having trouble with finding a solution that allows to terminate only certain pods in a deployment.
The application running inside the pods does some processing which can a take lot of time to be finished.
Let's say I have 10 tasks that are stored in a database and I issue a command to scale the deployment to 10 pods.
Let's say that after some time 3 of the pods have finished their tasks and are no longer required.
How can i scale down the deployment from 10 to 7 while terminate only the pods that have finished the tasks and not the pods that are still processing those tasks?
I don't know if more details are needed but i will happily edit the question if there are more details needed to give an answer for this kind of problem.
In this case Kubernetes Job might be better suited for this kind of task.
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'm running some I/O intensive Python code on Dask and want to increase the number of threads per worker. I've deployed a Kubernetes cluster that runs Dask distributed via helm. I see from the worker deployment template that the number of threads for a worker is set to the number of CPUs, but I'd like to set the number of threads higher unless that's an anti-pattern. How do I do that?
It looks like from this similar question that I can ssh to the dask scheduler and spin up workers with dask-worker? But ideally I'd be able to configure the worker resources via helm so that I don't have to interact with the scheduler other than submitting jobs to it via the Client.
Kubernetes resource limits and requests should match the --memory-limit and --nthreads parameters given to the dask-worker command. For more information please follow the link 1 (Best practices described on Dask`s official documentation) and 2
Threading in Python is a careful art and is really dependent on your code. To do the easy one, -nprocs should almost certainly be 1, if you want more processes, launch more replicas instead. For the thread count, first remember the GIL means only one thread can be running Python code at a time. So you only get concurrency gains under two main sitations: 1) some threads are blocked on I/O like waiting to hear back from a database or web API or 2) some threads are running non-GIL-bound C code inside NumPy or friends. For the second situation, you still can't get more concurrency than the number of CPUs since that's just how many slots there are to run at once, but the first can benefit from more threads than CPUs in some situations.
There's a limitation of Dask's helm chart that doesn't allow for the setting of --nthreads in the chart. I confirmed this with the Dask team and filed an issue: https://github.com/helm/charts/issues/18708.
In the meantime, use Dask Kubernetes for a higher degree of customization.
I’m finally dipping my toes in the kubernetes pool and wanted to get some advice on the best way to approach a problem I have:
Tech we are using:
GCP
GKE
GCP Pub/Sub
We need to do bursts of batch processing spread out across a fleet and have decided on the following approach:
New raw data flows in
A node analyses this and breaks the data up into manageable portions which are pushed onto a queue
We have a cluster with Autoscaling On and Min Size ‘0’
A Kubernetes job spins up a pod for each new message on this cluster
When pods can’t pull anymore messages they terminate successfully
The question is:
What is the standard approach for triggering jobs such as this?
Do you create a new job each time or are jobs meant to be long lived and re-run?
I have only seen examples of using a yaml file however we would probably want the node which did the portioning of work to create the job as it knows how many parallel pods should be run. Would it be recommended to use the python sdk to create the job spec programatically? Or if jobs are long lived would you simply hit the k8 api and modify the parallel pods required then re-run job?
Jobs in Kubernetes are meant to be short-lived and are not designed to be reused. Jobs are designed for run-once, run-to-completion workloads. Typically they are be assigned a specific task, i.e. to process a single queue item.
However, if you want to process multiple items in a work queue with a single instance then it is generally advisable to instead use a Deployment to scale a pool of workers that continue to process items in the queue, scaling the number of pool workers dependent on the number of items in the queue. If there are no work items remaining then you can scale the deployment to 0 replicas, scaling back up when there is work to be done.
To create and control your workloads in Kubernetes the best-practice would be to use the Kubernetes SDK. While you can generate YAML files and shell out to another tool like kubectl using the SDK simplifies configuration and error handling, as well as allowing for simplified introspection of resources in the cluster as well.