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From "Lalwani, Jayesh" <>
Subject Re: How to Scale Streaming Application to Multiple Workers
Date Thu, 15 Oct 2020 13:14:07 GMT
Parallelism of streaming depends on the input source. If you are getting one small file per
microbatch, then Spark will read it in one worker. You can always repartition your data frame
after reading it to increase the parallelism.

´╗┐On 10/14/20, 11:26 PM, "Artemis User" <> wrote:

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    We have a streaming application that read microbatch csv files and
    involves the foreachBatch call.  Each microbatch can be processed
    independently.  I noticed that only one worker node is being utilized.
    Is there anyway or any explicit method to distribute the batch work load
    to multiple workers?  I would think Spark would execute foreachBatch
    method on different workers since each batch can be treated as atomic?



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