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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Assigned] (SPARK-17147) Spark Streaming Kafka 0.10 Consumer Can't Handle Non-consecutive Offsets (i.e. Log Compaction)
Date Sun, 11 Feb 2018 05:29:02 GMT

     [ https://issues.apache.org/jira/browse/SPARK-17147?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Apache Spark reassigned SPARK-17147:
------------------------------------

    Assignee:     (was: Apache Spark)

> Spark Streaming Kafka 0.10 Consumer Can't Handle Non-consecutive Offsets (i.e. Log Compaction)
> ----------------------------------------------------------------------------------------------
>
>                 Key: SPARK-17147
>                 URL: https://issues.apache.org/jira/browse/SPARK-17147
>             Project: Spark
>          Issue Type: Bug
>          Components: DStreams
>    Affects Versions: 2.0.0
>            Reporter: Robert Conrad
>            Priority: Major
>
> When Kafka does log compaction offsets often end up with gaps, meaning the next requested
offset will be frequently not be offset+1. The logic in KafkaRDD & CachedKafkaConsumer
has a baked in assumption that the next offset will always be just an increment of 1 above
the previous offset. 
> I have worked around this problem by changing CachedKafkaConsumer to use the returned
record's offset, from:
> {{nextOffset = offset + 1}}
> to:
> {{nextOffset = record.offset + 1}}
> and changed KafkaRDD from:
> {{requestOffset += 1}}
> to:
> {{requestOffset = r.offset() + 1}}
> (I also had to change some assert logic in CachedKafkaConsumer).
> There's a strong possibility that I have misconstrued how to use the streaming kafka
consumer, and I'm happy to close this out if that's the case. If, however, it is supposed
to support non-consecutive offsets (e.g. due to log compaction) I am also happy to contribute
a PR.



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