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https://issues.apache.org/jira/browse/SPARK-15406?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15488496#comment-15488496
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Michael Armbrust commented on SPARK-15406:
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For the types that are coming out, the SQL way would be to do conversions using
either expressions, udfs, or as (in Datasets with encoders). This way the
schema would default to binary, but you can convert to the actual format after
the DataFrame is returned.
I'm not sure I understand the reasons why a consumer would be stateful or how
that is compatible with the model proposed in structured streaming. Can you
explain this use case further?
For configuration, I think its totally reasonable to have some way to pass in
configuration that does not involve constructing json strings manually. Thats
an example of something that is purely additive.
> Structured streaming support for consuming from Kafka
> -----------------------------------------------------
>
> Key: SPARK-15406
> URL: https://issues.apache.org/jira/browse/SPARK-15406
> Project: Spark
> Issue Type: New Feature
> Reporter: Cody Koeninger
>
> This is the parent JIRA to track all the work for the building a Kafka source
> for Structured Streaming. Here is the design doc for an initial version of
> the Kafka Source.
> https://docs.google.com/document/d/19t2rWe51x7tq2e5AOfrsM9qb8_m7BRuv9fel9i0PqR8/edit?usp=sharing
> ================== Old description =========================
> Structured streaming doesn't have support for kafka yet. I personally feel
> like time based indexing would make for a much better interface, but it's
> been pushed back to kafka 0.10.1
> https://cwiki.apache.org/confluence/display/KAFKA/KIP-33+-+Add+a+time+based+log+index
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