[jira] [Assigned] (SPARK-17853) Kafka OffsetOutOfRangeException on DStreams union from separate Kafka clusters with identical topic names.
[
https://issues.apache.org/jira/browse/SPARK-17853?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-17853:
Assignee: (was: Apache Spark)
> Kafka OffsetOutOfRangeException on DStreams union from separate Kafka
> clusters with identical topic names.
> --
>
> Key: SPARK-17853
> URL: https://issues.apache.org/jira/browse/SPARK-17853
> Project: Spark
> Issue Type: Bug
> Components: Streaming
>Affects Versions: 2.0.0
>Reporter: Marcin Kuthan
>
> During migration from Spark 1.6 to 2.0 I observed OffsetOutOfRangeException
> reported by Kafka client. In our scenario we create single DStream as a union
> of multiple DStreams. One DStream for one Kafka cluster (multi dc solution).
> Both Kafka clusters have the same topics and number of partitions.
> After quick investigation, I found that class DirectKafkaInputDStream keeps
> offset state for topic and partitions, but it is not aware of different Kafka
> clusters.
> For every topic, single DStream is created as a union from all configured
> Kafka clusters.
> {code}
> class KafkaDStreamSource(configs: Iterable[Map[String, String]]) {
> def createSource(ssc: StreamingContext, topic: String): DStream[(String,
> Array[Byte])] = {
> val streams = configs.map { config =>
> val kafkaParams = config
> val kafkaTopics = Set(topic)
> KafkaUtils.
> createDirectStream[String, Array[Byte]](
> ssc,
> LocationStrategies.PreferConsistent,
> ConsumerStrategies.Subscribe[String, Array[Byte]](kafkaTopics,
> kafkaParams)
> ).map { record =>
> (record.key, record.value)
> }
> }
> ssc.union(streams.toSeq)
> }
> }
> {code}
> At the end, offsets from one Kafka cluster overwrite offsets from second one.
> Fortunately OffsetOutOfRangeException was thrown because offsets in both
> Kafka clusters are significantly different.
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[jira] [Assigned] (SPARK-17853) Kafka OffsetOutOfRangeException on DStreams union from separate Kafka clusters with identical topic names.
[
https://issues.apache.org/jira/browse/SPARK-17853?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-17853:
Assignee: Apache Spark
> Kafka OffsetOutOfRangeException on DStreams union from separate Kafka
> clusters with identical topic names.
> --
>
> Key: SPARK-17853
> URL: https://issues.apache.org/jira/browse/SPARK-17853
> Project: Spark
> Issue Type: Bug
> Components: Streaming
>Affects Versions: 2.0.0
>Reporter: Marcin Kuthan
>Assignee: Apache Spark
>
> During migration from Spark 1.6 to 2.0 I observed OffsetOutOfRangeException
> reported by Kafka client. In our scenario we create single DStream as a union
> of multiple DStreams. One DStream for one Kafka cluster (multi dc solution).
> Both Kafka clusters have the same topics and number of partitions.
> After quick investigation, I found that class DirectKafkaInputDStream keeps
> offset state for topic and partitions, but it is not aware of different Kafka
> clusters.
> For every topic, single DStream is created as a union from all configured
> Kafka clusters.
> {code}
> class KafkaDStreamSource(configs: Iterable[Map[String, String]]) {
> def createSource(ssc: StreamingContext, topic: String): DStream[(String,
> Array[Byte])] = {
> val streams = configs.map { config =>
> val kafkaParams = config
> val kafkaTopics = Set(topic)
> KafkaUtils.
> createDirectStream[String, Array[Byte]](
> ssc,
> LocationStrategies.PreferConsistent,
> ConsumerStrategies.Subscribe[String, Array[Byte]](kafkaTopics,
> kafkaParams)
> ).map { record =>
> (record.key, record.value)
> }
> }
> ssc.union(streams.toSeq)
> }
> }
> {code}
> At the end, offsets from one Kafka cluster overwrite offsets from second one.
> Fortunately OffsetOutOfRangeException was thrown because offsets in both
> Kafka clusters are significantly different.
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