I am trying to implement codedeploy poc setup. I have a ELB behind that attached more than 10 servers.We have servers in stopped state kept for add incase load increased. All these servers are tagged with same value to identify easily.
Problem is Codedeploy identifies these tags and while deploying these servers are marked as Fail causing deployment gets failed.
My concern is :
1. Is there any way to exclude stopped instances from Codedeploy.
2. Is there any better approach for this problem.
Any help would be highly appreciated.
The most elegant solution would be to use Autoscalng instead of stopped instances. However if you must use stopped instances, I can think of 2 quick solutions:
Use a second set of tags to distinguish stopped vs running instances.
Adjust the MINIMUM_HEALTHY_HOSTS constraint in away to make the deployment pass, even if it considers the stopped instances as failed.
Thanks,
Amartya Datta Gupta
Related
Our usecase is pretty simple, however, I haven't found a solution for it yet.
In the organization I'm working at, we decided to move to Kubernetes as our container manager in order to spin-up slaves.
Until we moved to this kind of environment, we used to have dedicated slaves per each team. Each got the resources it needs and based on that, it was working.
However, when we moved to use Kubernetes, it started to cause issues as each team shares the same pile of resources, which, can lead to congestion or job failures.
The suggested solution was to create Kubernetes cluster per each team, however, this will lead to burnout of the teams involved with maintanance of multiple clusters.
Searching online, I didn't found any solution avilable, hence, I'm asking here - what is the best way to approach the solution? I understand that we might need to implament a dispacher, but currently it's not possible in the way the Kubernetes plugin is developed.
Thanks,
I am trying to find a solution to run a cron job in a Kubernetes-deployed app without unwanted duplicates. Let me describe my scenario, to give you a little bit of context.
I want to schedule jobs that execute once at a specified date. More precisely: creating such a job can happen anytime and its execution date will be known only at that time. The job that needs to be done is always the same, but it needs parametrization.
My application is running inside a Kubernetes cluster, and I cannot assume that there always will be only one instance of it running at the any moment in time. Therefore, creating the said job will lead to multiple executions of it due to the fact that all of my application instances will spawn it. However, I want to guarantee that a job runs exactly once in the whole cluster.
I tried to find solutions for this problem and came up with the following ideas.
Create a local file and check if it is already there when starting a new job. If it is there, cancel the job.
Not possible in my case, since the duplicate jobs might run on other machines!
Utilize the Kubernetes CronJob API.
I cannot use this feature because I have to create cron jobs dynamically from inside my application. I cannot change the cluster configuration from a pod running inside that cluster. Maybe there is a way, but it seems to me there have to be a better solution than giving the application access to the cluster it is running in.
Would you please be as kind as to give me any directions at which I might find a solution?
I am using a managed Kubernetes Cluster on Digital Ocean (Client Version: v1.22.4, Server Version: v1.21.5).
After thinking about a solution for a rather long time I found it.
The solution is to take the scheduling of the jobs to a central place. It is as easy as building a job web service that exposes endpoints to create jobs. An instance of a backend creating a job at this service will also provide a callback endpoint in the request which the job web service will call at the execution date and time.
The endpoint in my case links back to the calling backend server which carries the logic to be executed. It would be rather tedious to make the job service execute the logic directly since there are a lot of dependencies involved in the job. I keep a separate database in my job service just to store information about whom to call and how. Addressing the startup after crash problem becomes trivial since there is only one instance of the job web service and it can just re-create the jobs normally after retrieving them from the database in case the service crashed.
Do not forget to take care of failing jobs. If your backends are not reachable for some reason to take the callback, there must be some reconciliation mechanism in place that will prevent this failure from staying unnoticed.
A little note I want to add: In case you also want to scale the job service horizontally you run into very similar problems again. However, if you think about what is the actual work to be done in that service, you realize that it is very lightweight. I am not sure if horizontal scaling is ever a requirement, since it is only doing requests at specified times and is not executing heavy work.
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 am building a CI/CD pipeline to release SF Stateless Application packages into clusters using parameters for everything. This is to ensure environments (DEV/UAT/PROD) can be scoped with different settings.
For example in a DEV cluster an application package may have an instance count of 3 (in a 10 node cluster)
I have noticed that if an application is in the cluster and running with an instance count (for example) of 3, and I change the deployment parameter to anything else (e.g. 5), the application package will upload and register the type, but will fail on attempting to do a rolling upgrade of the running application.
This also works the other way e.g. if the running app is -1 and you want to reduce the count on next rolling deployment.
Have I missed a setting or config somewhere, is this how it is supposed to be? At present its not lending itself to being something that is easily scaled without downtime.
At its simplest form we just want to be able to change instance counts on application updates, as we have an infrastructure-as-code approach to changes, builds and deployments for full tracking ability.
Thanks in advance
This is a common error when using Default services.
This has been already answered multiple times in these places:
Default service descriptions can not be modified as part of upgrade set EnableDefaultServicesUpgrade to true
https://blogs.msdn.microsoft.com/maheshk/2017/05/24/azure-service-fabric-error-to-allow-it-set-enabledefaultservicesupgrade-to-true/
https://github.com/Microsoft/service-fabric/issues/253#issuecomment-442074878
I'm interested in using Celery for an app I'm working on. It all seems pretty straight forward, but I'm a little confused about what I need to do if I have multiple load balanced application servers. All of the documentation assumes that the broker will be on the same server as the application. Currently, all of my application servers sit behind an Amazon ELB and tasks need to be able to come from any one of them.
This is what I assume I need to do:
Run a broker server on a separate instance
Configure each application instance to connect to that broker server
Each application instance will also be be a celery working (running
celeryd)?
My only beef with that is: What happens if my broker instance dies? Can I run 2 broker instances some how so I'm safe if one goes under?
Any tips or information on what to do in a setup like mine would be greatly appreciated. I'm sure I'm missing something or not understanding something.
For future reference, for those who do prefer to stick with RabbitMQ...
You can create a RabbitMQ cluster from 2 or more instances. Add those instances to your ELB and point your celeryd workers at the ELB. Just make sure you connect the right ports and you should be all set. Don't forget to allow your RabbitMQ machines to talk among themselves to run the cluster. This works very well for me in production.
One exception here: if you need to schedule tasks, you need a celerybeat process. For some reason, I wasn't able to connect the celerybeat to the ELB and had to connect it to one of the instances directly. I opened an issue about it and it is supposed to be resolved (didn't test it yet). Keep in mind that celerybeat by itself can only exist once, so that's already a single point of failure.
You are correct in all points.
How to make reliable broker: make clustered rabbitmq installation, as described here:
http://www.rabbitmq.com/clustering.html
Celery beat also doesn't have to be a single point of failure if you run it on every worker node with:
https://github.com/ybrs/single-beat