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How to run a test class from a script with spark-operator instead of spark-submit? #719
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Here is a gist with the output of a run of that script: |
What about doing some enhancement to |
I would explore extending
Yes, the
There's some issue with keeping track of executor states, mainly because executors get deleted after they finished before the operator get a chance to be able to list and check their status. We put some fixes for this issue. |
That could work, but it would be nice if the underlying api had this as a primitive so that there is no need to install another tool but the spark application could be waited on from
Great. That is what we will use. It would also be great to find a home in the doc for this information.
Excellent. Can you point to the patch set so I can roll a version to try? |
The latest master has the fix. |
I built using the gitlab-ci.yml and tested that the spark-pi example now works with a master image that contains #727 - thanks! |
Closing this with reference to #732 as a more focused discussion. |
TL;DR:
How can I run a test class with spark-operator from a script, wait until the application has either succeeded or failed, and recover that success or failure?
Detail:
I have a number of test classes that I can run with spark-submit with something like:
What I would like is a recipe to employ the spark-operator from a shell script.
I can create the yaml equivalent of the spark-submit, but it doesn't appear straightforward to run/wait/test-status for spark applications reliably and without race conditions.
I'm hoping that someone out there has already figured out how to do this.
My background is primarily Scala/Java/Python and almost no exposure to Go and just powering up on k8s.
Ideally, I would like to just be a consumer of
spark-operator
with my learning focused to setting up the right YAML and/or config maps to run applications.All help appreciated!
It looks like this issue indicates that
kubectl wait
is in need of generalizing: kubernetes/kubernetes#83094I have created a branch located here that contains a script to run in a development cluster:
It asks this question as well these questions:
indicate completion, just COMPLETED or FAILED?
should be avoided. Why doesn't spark.stop() cause executors to exit cleanly?
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