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SDK Harness Deployment


Hi,

The current plan for running the SDK harness is to execute docker to launch SDK containers with service endpoints provided by the runner in the docker command line.

In the case of Flink runner (prototype), the service endpoints are dynamically allocated per executable stage. There is typically one Flink task manager running per machine. Each TM has multiple task slots. A subset of these task slots will run the Beam executable stages. Flink allows multiple jobs in one TM, so we could have executable stages of different pipelines running in a single TM, depending on how users deploy. The prototype also has no cleanup for the SDK containers, they remain running and orphaned once the runner is gone.

I'm trying to find out how this approach can be augmented for deployment on Kubernetes. Our deployments won't allow multiple jobs per task manager, so all task slots will belong to the same pipeline context. The intent is to deploy SDK harness containers along with TMs in the same pod. No assumption can be made about the order in which the containers are started, and the SDK container wouldn't know the connect address at startup (it can only be discovered after the pipeline gets deployed into the TMs).

I talked about that a while ago with Henning and one idea was to set a fixed endpoint address so that the boot code in the SDK container knows upfront where to connect to, even when that endpoint isn't available yet. This approach may work with minimal changes to runner and little or no change to SDK container (as long as the SDK is prepared to retry). The downside is that all (parallel) task slots of the TM will use the same SDK worker, which will likely lead to performance issues, at least with the Python SDK that we are planning to use.

An alternative may be to define an SDK worker pool per pod, with a discovery mechanism for workers to find the runner endpoints and a coordination mechanism that distributes the dynamically allocated endpoints that are provided by the executable stage task slots over the available workers.

Any thoughts on this? Is anyone else looking at a docker free deployment?

Thanks,
Thomas