Say I have a container image that contains a large command-line program that is executed from the shell. I have another container that contains a scheduler whose job it is to invoke the first container when it receives a certain signal. For various reasons I don't want to put them in the same container (mainly because the scheduler can invoke many different tools, and different versions of those tools, and I don't want to have to put all the tools and their versions in the same container image.)
I know how to put two containers in the same pod. However, the default behavior is to run both containers at startup. What I want to be able to do is to have the scheduler be able to decide when to invoke the other container, and to be able to specify the command-line arguments (and ideally, environment variables) for it. Also, I need to know the exit status. Extra credit for getting stdout/stderr, but I can hack around with volumes if I need to.
I also know how to do this if the second container was a server, but in this case it's a shell program.
A quick way to do this is:
Add a kubectl proxy in your container startup
Then call a kubernetes job from the first pod.
This would create a lightweight solution in which the desired job can be queried for success state, seemingly fulfilling your requirements
Related
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.
Let's say I want to execute a cleanup script whenever container termination is triggered. How do I go about this using docker-compose?
This could be handy to automatically back up the files, databases, etc for the dev container.
docker containers are meant to be ephemeral:
By "ephemeral", we mean that the container can be stopped and destroyed, then rebuilt and replaced with an absolute minimum set up and configuration.
Building upon this concept docker itself does not offer anything to hook into the shutdown process. docker-compose is built on top of docker and also does not add such functionality.
Maybe you can rethink your problem the docker way to better fit the intended use of docker. Without further context it is hard to say what could be a good solution but maybe one of the following approaches helps you out:
docker stop sends a SIGTERM signal to the main process in the container. You could use a custom entrypoint or supervisor process that would trigger the appropriate actions on a SIGTERM. This approach requires custom containers. With the stop_signal attribute you can also configure a custom signa to be sent in your docker-compose.yml
if you just want to persist data files from the containers just configuring the right volumes might be enough
you could use docker events to listen and act upon any types of events emitted by the docker daemon
I'm currently running two Kubernetes clusters one on Google cloud and one on IBM cloud. To manage them I use kubectl. I've made a script that executes some commands on one of the clusters then switches to the other and does some other work there.
This works fine as long as the script only runs in one process, however when run in parallel the credentials are sometimes overwritten by one process when in use by another and this obviously causes issues.
I therefore want to know if I can supply kubectl with a credentials file for every call, instead of storing it in a environmental variable with kubectl config set-credentials.
Any help/solution is much appreciated.
If I need to work with multiple clusters using kubectl I am splitting my terminal and setting KUBECONFIG for each split:
For my first split:
export KUBECONFIG=~/.kube/cluster1
For the second split
export KUBECONFIG=~/.kube/cluster2
It is working pretty well, but this approach has one issue:
If you are using some kind of prompt with the current Kubernetes context it will give you different output and it might be missing leading.
For scripts, I am just changing value of KUBECONFIG in for loop, to loop over each cluster.
You need to use Kubefed in order to manage multiple clusters.
It will take one cluster as the main one, and execute all the same requests to the second cluster.
I have a python app that builds a dataset for a machine learning task on GCP.
Currently I have to start an instance of a VM that we have, and then SSH in, and run the app, which will complete in 2-24 hours depending on the size of the dataset requested.
Once the dataset is complete the VM needs to be shutdown so we don't incur additional charges.
I am looking to streamline this process as much as possible, so that we have a "1 click" or "1 command" solution, but I'm not sure the best way to go about it.
From what I've read about so far it seems like containers might be a good way to go, but I'm inexperienced with docker.
Can I setup a container that will pip install the latest app from our private GitHub and execute the dataset build before shutting down? How would I pass information to the container such as where to get the config file etc? It's conceivable that we will have multiple datasets being generated at the same time based on different config files.
Is there a better gcloud feature that suits our purpose more effectively than containers?
I'm struggling to get information regarding these basic questions, it seems like container tutorials are dominated by web apps.
It would be useful to have a batch-like container service that runs a container until its process completes. I'm unsure whether such a service exists. I'm most familiar with Google Cloud Platform and this provides a wealth of compute and container services. However -- to your point -- these predominantly scale by (HTTP) requests.
One possibility may be Cloud Run and to trigger jobs using Cloud Pub/Sub. I see there's async capabilities too and this may be interesting (I've not explored).
Another runtime for you to consider is Kubernetes itself. While Kubernetes requires some overhead in having Google, AWS or Azure manage a cluster for you (I strongly recommend you don't run Kubernetes yourself) and some inertia in the capacity of the cluster's nodes vs. the needs of your jobs, as you scale the number of jobs, you will smooth these needs. A big advantage with Kubernetes is that it will scale (nodes|pods) as you need them. You tell Kubernetes to run X container jobs, it does it (and cleans-up) without much additional management on your part.
I'm biased and approach the container vs image question mostly from a perspective of defaulting to container-first. In this case, you'd receive several benefits from containerizing your solution:
reproducible: the same image is more probable to produce the same results
deployability: container run vs. manage OS, app stack, test for consistency etc.
maintainable: smaller image representing your app, less work to maintain it
One (beneficial!?) workflow change if you choose to use containers is that you will need to build your images before using them. Something like Knative combines these steps but, I'd stick with doing-this-yourself initially. A common solution is to trigger builds (Docker, GitHub Actions, Cloud Build) from your source code repo. Commonly you would run tests against the images that are built but you may also run your machine-learning tasks this way too.
Your containers would container only your code. When you build your container images, you would pip install, perhaps pip install --requirement requirements.txt to pull the appropriate packages. Your data (models?) are better kept separate from your code when this makes sense. When your runtime platform runs containers for you, you provide configuration information (environment variables and|or flags) to the container.
The use of a startup script seems to better fit the bill compared to containers. The instance always executes startup scripts as root, thus you can do anything you like, as the command will be executed as root.
A startup script will perform automated tasks every time your instance boots up. Startup scripts can perform many actions, such as installing software, performing updates, turning on services, and any other tasks defined in the script.
Keep in mind that a startup script cannot stop an instance but you can stop an instance through the guest operating system.
This would be the ideal solution for the question you posed. This would require you to make a small change in your Python app where the Operating system shuts off when the dataset is complete.
Q1) Can I setup a container that will pip install the latest app from our private GitHub and execute the dataset build before shutting down?
A1) Medium has a great article on installing a package from a private git repo inside a container. You can execute the dataset build before shutting down.
Q2) How would I pass information to the container such as where to get the config file etc?
A2) You can use ENV to set an environment variable. These will be available within the container.
You may consider looking into Docker for more information about container.
I currently do have a problem with the statefulset under the following condition:
I have a percona SQL cluster running with persistent storage and 2 nodes
now i do force both pods to fail.
first i will force pod-0 to fail
Afterwards i will force pod-1 to fail
Now the cluster is not able to recover without manual interference and possible dataloss
Why:
The statefulset is trying to bring pod-0 up first, however this one will not be brought online because of the following message:
[ERROR] WSREP: It may not be safe to bootstrap the cluster from this node. It was not the last one to leave the cluster and may not contain all the updates. To force cluster bootstrap with this node, edit the grastate.dat file manually and set safe_to_bootstrap to 1
What i could do alternatively, but what i dont really like:
I could change ".spec.podManagementPolicy" to "Parallel" but this could lead to race conditions when forming the cluster. Thus i would like to avoid that, i basically like the idea of starting the nodes one after another
What i would like to have:
the possibility to have ".spec.podManagementPolicy":"OrderedReady" activated but with the possibility to adjust the order somehow
to be able to put specific pods into "inactive" mode so they are being ignored until i enable them again
Is something like that available? Does someone have any other ideas?
Unfortunately, nothing like that is available in standard functions of Kubernetes.
I see only 2 options here:
Use InitContainers to somehow check the current state on relaunch.
That will allow you to run any code before the primary container is started so you can try to use a custom script in order to resolve the problem etc.
Modify the database startup script to allow it to wait for some Environment Variable or any flag file and use PostStart hook to check the state before running a database.
But in both options, you have to write your own logic of startup order.