[GitHub] [spark] anandchangediya commented on issue #21038: [SPARK-22968][DStream] Throw an exception on partition revoking issue

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anandchangediya commented on issue #21038: [SPARK-22968][DStream] Throw an exception on partition revoking issue
URL: https://github.com/apache/spark/pull/21038#issuecomment-541604823
 
 
   @koeninger According to Kafka documentation
   
   `If all the consumer instances have the same consumer group, then the records will effectively be load-balanced over the consumer instances`
   This means I can have multiple consumers with same groupId which can help me to load balance my application and scale accordingly.
   I don't know why it is said "fundamentally wrong" to have multiple consumers with the same groupId in spark.
   So how can I achieve scalability to listen to a single partition and increase consumption rate with multiple spark consumers?
   Is this the spark design fault or any other way to achieve that which I am unaware of?
   
   @SehanRathnayake Any thoughts?

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