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https://issues.apache.org/jira/browse/SPARK-10320?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14724113#comment-14724113
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Cody Koeninger commented on SPARK-10320:
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It's possible this might be solvable with a user-supplied callback of the form
(Map[TopicAndPartition, LeaderOffset]) => Map[TopicAndPartition, LeaderOffset]
or maybe
(Time, Map[TopicAndPartition, LeaderOffset]) => Map[TopicAndPartition,
LeaderOffset]
that got called in the compute method of DelayedKafkaInputDStream. That would
avoid threading issues, and also allow for more-or-less arbitrary modification
of the topics and offsets, including some use cases that people have to
subclass for currently.
Actually, that just handles the ending offsets of the batch, so it'd need to be
a pair of maps, one for the beginning and the other for the ending. But the
basic idea remains.
> Kafka Support new topic subscriptions without requiring restart of the
> streaming context
> ----------------------------------------------------------------------------------------
>
> Key: SPARK-10320
> URL: https://issues.apache.org/jira/browse/SPARK-10320
> Project: Spark
> Issue Type: New Feature
> Components: Streaming
> Reporter: Sudarshan Kadambi
>
> Spark Streaming lacks the ability to subscribe to newer topics or unsubscribe
> to current ones once the streaming context has been started. Restarting the
> streaming context increases the latency of update handling.
> Consider a streaming application subscribed to n topics. Let's say 1 of the
> topics is no longer needed in streaming analytics and hence should be
> dropped. We could do this by stopping the streaming context, removing that
> topic from the topic list and restarting the streaming context. Since with
> some DStreams such as DirectKafkaStream, the per-partition offsets are
> maintained by Spark, we should be able to resume uninterrupted (I think?)
> from where we left off with a minor delay. However, in instances where
> expensive state initialization (from an external datastore) may be needed for
> datasets published to all topics, before streaming updates can be applied to
> it, it is more convenient to only subscribe or unsubcribe to the incremental
> changes to the topic list. Without such a feature, updates go unprocessed for
> longer than they need to be, thus affecting QoS.
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