Ed Mitchell created SPARK-32151:
-----------------------------------
Summary: Kafka does not allow Partition Rebalance Handling
Key: SPARK-32151
URL: https://issues.apache.org/jira/browse/SPARK-32151
Project: Spark
Issue Type: Improvement
Components: DStreams
Affects Versions: 2.4.5
Reporter: Ed Mitchell
When a consumer group rebalance occurs when the Spark driver is using the
Subscribe or Subscribe Pattern ConsumerStrategy, driver's offsets are cleared
when partitions are revoked and then reassigned.
While this doesn't happen in the normal rebalance scenario of more consumers
joining the group (though it could), it does happen when the partition leader
is reelected because of a Kafka node being stopped or decommissioned.
This seems to only occur when users specify their own offsets and do not use
Kafka as the persistent store of offsets (they use their own database, and
possibly if using checkpointing).
This could probably affect Structured Streaming.
This presents itself as an "NoOffsetForPartitionException":
{noformat}
20/05/13 01:37:00 ERROR JobScheduler: Error generating jobs for time
1589333820000
msorg.apache.kafka.clients.consumer.NoOffsetForPartitionException: Undefined
offset with no reset policy for partitions: [production-ad-metrics-1,
production-ad-metrics-2, production-ad-metrics-0, production-ad-metrics-5,
production-ad-metrics-6, production-ad-metrics-3, production-ad-metrics-4,
production-ad-metrics-7] at
org.apache.kafka.clients.consumer.internals.SubscriptionState.resetMissingPositions(SubscriptionState.java:391)
at
org.apache.kafka.clients.consumer.KafkaConsumer.updateFetchPositions(KafkaConsumer.java:2185)
at
org.apache.kafka.clients.consumer.KafkaConsumer.updateAssignmentMetadataIfNeeded(KafkaConsumer.java:1222)
at
org.apache.kafka.clients.consumer.KafkaConsumer.poll(KafkaConsumer.java:1181)
at
org.apache.kafka.clients.consumer.KafkaConsumer.poll(KafkaConsumer.java:1115)
at
org.apache.spark.streaming.kafka010.DirectKafkaInputDStream.paranoidPoll(DirectKafkaInputDStream.scala:172)
at
org.apache.spark.streaming.kafka010.DirectKafkaInputDStream.latestOffsets(DirectKafkaInputDStream.scala:191)
at
org.apache.spark.streaming.kafka010.DirectKafkaInputDStream.compute(DirectKafkaInputDStream.scala:228)
at
org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:342)
at
org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:342)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58) at
org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:341)
at
org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:341)
at
org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:416)
at
org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:336)
at
org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:334)
at scala.Option.orElse(Option.scala:289) at
org.apache.spark.streaming.dstream.DStream.getOrCompute(DStream.scala:331) at
org.apache.spark.streaming.dstream.ForEachDStream.generateJob(ForEachDStream.scala:48)
at
org.apache.spark.streaming.DStreamGraph$$anonfun$1.apply(DStreamGraph.scala:122)
at
org.apache.spark.streaming.DStreamGraph$$anonfun$1.apply(DStreamGraph.scala:121)
at
scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at
scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48) at
scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241) at
scala.collection.AbstractTraversable.flatMap(Traversable.scala:104) at
org.apache.spark.streaming.DStreamGraph.generateJobs(DStreamGraph.scala:121)
at
org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$3.apply(JobGenerator.scala:249)
at
org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$3.apply(JobGenerator.scala:247)
at scala.util.Try$.apply(Try.scala:192) at
org.apache.spark.streaming.scheduler.JobGenerator.generateJobs(JobGenerator.scala:247)
at
org.apache.spark.streaming.scheduler.JobGenerator.org$apache$spark$streaming$scheduler$JobGenerator$$processEvent(JobGenerator.scala:183)
at
org.apache.spark.streaming.scheduler.JobGenerator$$anon$1.onReceive(JobGenerator.scala:89)
at
org.apache.spark.streaming.scheduler.JobGenerator$$anon$1.onReceive(JobGenerator.scala:88)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49){noformat}
This can be fixed by allowing the user to specify an
{code:java}
org.apache.kafka.clients.consumer.ConsumerRebalanceListener{code}
in the KafkaConsumer#subscribe method.
The documentation for ConsumerRebalanceListener states that you can use
KafkaConsumer#seek with fetched offsets
I'm suggesting adding a new ConsumerStrategy that allows users to specify a
function to fetch offsets with a Collection of TopicPartitions. The reason for
this is to keep the Spark user from having to interact with the Kafka API
directly.
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
