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[ADAP-427] Add support for custom containers while using dataproc serverless #642
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Hey @bveber, excuse my lack of expertise on the topic, but what kind of changes are required on Is that a new parameter in the profile that points to a container image, wired to the call dbt makes to dataproc? |
Hi @Fleid, you are correct. The custom container can be defined in the runtime_config that is passed to the dataproc call. It expects an image that exists in Container Registry or Artifact Registry with a naming format like |
Sweet! It looks like you would be able to contribute a PR here? |
I'm happy to raise a PR for this. I do have another related feature request for the dataproc serverless integration, specifically the ability to pass in custom Spark properties. Currently the number of executor instances is hard-coded and it would be nice to be able to define this in the model config as well as any other relevant properties. Should I open a new issue to track that change or should I try to implement it in my upcoming PR? |
Is this your first time submitting a feature request?
Describe the feature
I would like to use a custom container for my dataproc serveless jobs.
Describe alternatives you've considered
Dataproc serverless does not offer the ability to install extra packages in their standard runtime. The only other way I could add unsupported dependencies is by using a cluster.
Who will this benefit?
Users creating python models with additional dependencies not supported by the standard dataproc serverless runtime.
Are you interested in contributing this feature?
I have a working fork but haven't run the test suite yet.
Anything else?
No response
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