Spark streaming job not able to launch more number of executors
We have a spark streaming job which reads from two kafka topics with 10 partitions each. And we are running the streaming job with 3 concurrent microbatches. (So total 20 partitions and 3 concurrency)
We have following question:
In our processing DAG, we do a rdd.persist() at one stage, after which we fork out the DAG into two. Each of the forks has an action (forEach) at the end. In this case, we are observing that the number of executors is not exceeding the number of input kafka partitions. Job is not spawning more than 60 executors (2*10*3). And we see that the tasks from the two actions and the 3 concurrent microbatches are competing with each other for resources. So even though the max processing time of a task is 'x', the overall processing time of the stage is much greater than 'x'.
Is there a way by which we can ensure that the two forks of the DAG get processed in parallel by spawning more number of executors?
(We have not put any cap of maxExecutors)
Following are the job configurations:
Please let us know if you have any ideas that can be useful here.
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