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Posted to issues@spark.apache.org by "Sean Owen (JIRA)" <ji...@apache.org> on 2015/06/26 11:01:05 UTC

[jira] [Resolved] (SPARK-8474) [STREAMING] Kafka DirectStream API stops receiving messages if collective size of the messages specified in spark.streaming.kafka.maxRatePerPartition exceeds the default fetch size ( fetch.message.max.bytes) of SimpleConsumer

     [ https://issues.apache.org/jira/browse/SPARK-8474?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sean Owen resolved SPARK-8474.
------------------------------
    Resolution: Cannot Reproduce

> [STREAMING] Kafka DirectStream API stops receiving messages if collective size of the messages specified in spark.streaming.kafka.maxRatePerPartition exceeds the default fetch size ( fetch.message.max.bytes) of SimpleConsumer
> ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
>
>                 Key: SPARK-8474
>                 URL: https://issues.apache.org/jira/browse/SPARK-8474
>             Project: Spark
>          Issue Type: Bug
>          Components: Streaming
>    Affects Versions: 1.4.0
>            Reporter: Dibyendu Bhattacharya
>            Priority: Critical
>
> The issue is , if in Kafka there are variable size messages ranging from few KB to few hundred KBs , setting the rate limiting by number of messages can leads to potential issue.
> Let say size of messages in Kafka are such that for default fetch.message.max.bytes (which is 1 MB ) limit ONLY 1000 messages can be pulled, whereas I specified the spark.streaming.kafka.maxRatePerPartition number as say 2000. Now with this settings when Kafka RDD pulls messages for its offset range , it will only pull 1000 messages (limited by size of the pull in SimpleConsumer API) and can never be able to pull messages till the desired untilOffset and in KafkaRDD it failed in this assert call..
> assert(requestOffset == part.untilOffset, errRanOutBeforeEnd(part))



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