Launch spark shell on multiple executors

Example:

spark-shell --deploy-mode cluster --master yarn --executor-cores 4 --num-executors 6 --executor-memory 12g

Like how we tune spark-submit parameters, same tuning parameters are applicable for spark-shell as-well. Except that deploy-mode can not be 'cluster', of-course right.

Also make sure spark.dynamicAllocation.enabled is set to  true.

With these settings, you can see that Yarn Executors are allocated on demand and removed when no more required.









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