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https://issues.apache.org/jira/browse/SPARK-8474?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14593275#comment-14593275
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Sean Owen commented on SPARK-8474:
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Yes, that's more accurate, it controls the maximum amount of data you can fetch
in one go, yes. But you don't need to request all records in one go right? the
problem arises when one message is bigger than this limit.
Are you saying that the implementation won't make multiple requests? it keeps
requesting in fetchBatch
> [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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