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https://issues.apache.org/jira/browse/SPARK-27048?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sean Owen updated SPARK-27048:
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Target Version/s: (was: 2.4.0)
> A way to execute functions on Executor Startup and Executor Exit in Standalone
> ------------------------------------------------------------------------------
>
> Key: SPARK-27048
> URL: https://issues.apache.org/jira/browse/SPARK-27048
> Project: Spark
> Issue Type: Wish
> Components: Deploy, Spark Submit
> Affects Versions: 2.3.1, 2.3.3
> Reporter: Ross Brigoli
> Priority: Major
> Labels: usability
>
> *Background*
> We have a Spark Standalone ETL workload that is heavily dependent on Apache
> Ignite KV store for lookup/reference data. There are hundreds (400+) of
> lookup data some are up to 300K records. We formerly used broadcast variables
> but later found out that it was not fast enough.
> So we decided implement a caching mechanism by retrieving reference data from
> JDBC source and put them in-memory through Apache ignite as replicated cache.
> Each Spark worker node is also running an Ignite node (JVM). Then we let the
> spark executors retrieve the data from Ignite through "shared memory port".
> This is very fast but is causing instability in the Ignite cluster. The
> reason is that when the Spark executor JVM terminates, the Ignite Data Grid
> is terminated abnormally. This makes the Ignite cluster wait for the client
> node (which is the spark executor) to reconnect making the Ignite cluster
> non-responsive for a while.
> *Wish*
> We have this need for an ability to close the ignite client node gracefully
> just before the Executor process ends. So a feature that makes it possible to
> pass an EventHandler for "executor.onStart" and "executor.exitExecutor()"
> would be really really useful.
> It could be a spark-submit argument or an entry in the spark-defaults.conf
> that looks something like:
> {{spark.executor.startUpClass=com.company.ExecutorInitializer}}
> {{spark.executor.shutdownClass=com.company.ExecutorCleaner}}
> The class will have to implement an interface provided by Spark. This class
> can then be loaded dynamically in the CoarseGrainedExecutorBackend and called
> on the onStart() and exitExecutor() methods respectively
> This is also useful for opening and closing JDBC connections per executor
> instead of per partition.
>
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