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https://issues.apache.org/jira/browse/SPARK-18475?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15706758#comment-15706758
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Cody Koeninger commented on SPARK-18475:
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Burak hasn't empirically shown that it is of benefit for a properly
partitioned, non-skewed kafka topic, especially if SSL is enabled (because of
the effect on consumer caching).
Any output operation can tell the difference in ordering.
People are welcome to convince you that this is a worthwhile option, but there
is no way it should be on by default.
> Be able to provide higher parallelization for StructuredStreaming Kafka Source
> ------------------------------------------------------------------------------
>
> Key: SPARK-18475
> URL: https://issues.apache.org/jira/browse/SPARK-18475
> Project: Spark
> Issue Type: Improvement
> Components: Structured Streaming
> Affects Versions: 2.0.2, 2.1.0
> Reporter: Burak Yavuz
>
> Right now the StructuredStreaming Kafka Source creates as many Spark tasks as
> there are TopicPartitions that we're going to read from Kafka.
> This doesn't work well when we have data skew, and there is no reason why we
> shouldn't be able to increase parallelism further, i.e. have multiple Spark
> tasks reading from the same Kafka TopicPartition.
> What this will mean is that we won't be able to use the "CachedKafkaConsumer"
> for what it is defined for (being cached) in this use case, but the extra
> overhead is worth handling data skew and increasing parallelism especially in
> ETL use cases.
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