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https://issues.apache.org/jira/browse/SPARK-47952?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17840265#comment-17840265
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TakawaAkirayo commented on SPARK-47952:
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Hi [~gurwls223]
I'd like to understand the design approach of SparkConnect and whether there
are existing community solutions that implement the same requirements as mine.
As I understand it, it seems that end users need to start the SparkConnect
server themselves before using the SparkConnect client to connect. If I'm
correct, users (individuals or company organizations) need to address where and
how to start the SparkConnect server, either by themselves or through centrally
provisioned servers within the enterprise.
If that's the case, does my approach in the PR align with the expected usage
pattern If I want to leverage the Hadoop cluster for scaling the SparkConnect
Server?
> Support retrieving the real SparkConnectService GRPC address and port
> programmatically when running on Yarn
> -----------------------------------------------------------------------------------------------------------
>
> Key: SPARK-47952
> URL: https://issues.apache.org/jira/browse/SPARK-47952
> Project: Spark
> Issue Type: Story
> Components: Connect
> Affects Versions: 4.0.0
> Reporter: TakawaAkirayo
> Priority: Minor
> Labels: pull-request-available
>
> 1.User Story:
> Our data analysts and data scientists use Jupyter notebooks provisioned on
> Kubernetes (k8s) with limited CPU/memory resources to run Spark-shell/pyspark
> in the terminal via Yarn Client mode. However, Yarn Client mode consumes
> significant local memory if the job is heavy, and the total resource pool of
> k8s for notebooks is limited. To leverage the abundant resources of our
> Hadoop cluster for scalability purposes, we aim to utilize SparkConnect. This
> allows the driver on Yarn with SparkConnectService started and uses
> SparkConnect client to connect to the remote driver.
> To provide a seamless experience with one command startup for both server and
> client, we've wrapped the following processes in one script:
> 1) Start a local coordinator server (implemented by us, not in this PR) with
> a specified port.
> 2) Start SparkConnectServer by spark-submit via Yarn Cluster mode with
> user-input Spark configurations and the local coordinator server's address
> and port. Append an additional listener class in the configuration for
> SparkConnectService callback with the actual address and port on Yarn to the
> coordinator server.
> 3) Wait for the coordinator server to receive the address callback from the
> SparkConnectService on Yarn and export the real address.
> 4) Start the client (pyspark --remote) with the remote address.
> Finally, a remote SparkConnect Server is started on Yarn with a local
> SparkConnect client connected. Users no longer need to start the server
> beforehand and connect to the remote server after they manually explore the
> address on Yarn.
> 2.Problem statement of this change:
> 1) The specified port for the SparkConnectService GRPC server might be
> occupied on the node of the Hadoop Cluster. To increase the success rate of
> startup, it needs to retry on conflicts rather than fail directly.
> 2) Because the final binding port could be uncertain based on #1 and the
> remote address is unpredictable on Yarn, we need to retrieve the address and
> port programmatically and inject it automatically on the start of `pyspark
> --remote`. The SparkConnectService needs to communicate its location back to
> the launcher side.
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