You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Marcin Kuthan (JIRA)" <ji...@apache.org> on 2016/10/10 12:30:20 UTC
[jira] [Created] (SPARK-17853) Kafka OffsetOutOfRangeException on
DStreams union from separate Kafka clusters with identical topic names.
Marcin Kuthan created SPARK-17853:
-------------------------------------
Summary: 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.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org