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Posted to jira@kafka.apache.org by "Riley Zimmerman (JIRA)" <ji...@apache.org> on 2018/04/17 12:24:00 UTC
[jira] [Created] (KAFKA-6798) Kafka leader rebalance failures
Riley Zimmerman created KAFKA-6798:
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Summary: Kafka leader rebalance failures
Key: KAFKA-6798
URL: https://issues.apache.org/jira/browse/KAFKA-6798
Project: Kafka
Issue Type: Bug
Affects Versions: 1.0.1, 0.10.2.1
Reporter: Riley Zimmerman
I am running 3 Kafka (version 0.10.2.1 and more recently moved to 1.0.1) with 3 Zookeeper (v3.4.9) as statefulsets in a kubernetes v1.9.1 deployment. My partitions are replication factor 3. My main workload involves a kafka streams consumer/producer (storing offsets in kafka) and a second kafka consumer storing offsets in zookeeper (only commits every 30 seconds). There are ~200,000 kafka messages going through each per minute. The log.retention settings are all 4 hours. I have auto.leader.rebalance.enabled.
I am randomly having failures during the rebalances. The result is that partitions for both topics and consumer_offsets go out of sync and the partition leader becomes -1. After 4 hours there is another (auto?) rebalance and sometimes it sorts itself out. Sometimes it runs for weeks without problems, other times it it happens multiple times in a few days. It appears to happen earlier in test runs if it is going to happen.
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Topic:__consumer_offsets PartitionCount:50 ReplicationFactor:3 Configs:segment.bytes=104857600,cleanup.policy=compact,compression.type=producer
Topic: __consumer_offsets Partition: 0 Leader: -1 Replicas: 2,0,1 Isr:
Topic: __consumer_offsets Partition: 1 Leader: 0 Replicas: 0,1,2 Isr: 1,2,0
Topic: __consumer_offsets Partition: 2 Leader: 1 Replicas: 1,2,0 Isr: 2,1,0
Topic: __consumer_offsets Partition: 3 Leader: -1 Replicas: 2,1,0 Isr:
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[2018-03-20 12:42:32,180] WARN [Controller 2]: Partition [agent.metadata,5] failed to complete preferred replica leader election. Leader is -1 (kafka.controller.KafkaController)
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[2018-03-20 11:02:32,099] TRACE Controller 2 epoch 27 started leader election for partition [__consumer_offsets,30] (state.change.logger)
[2018-03-20 11:02:32,101] ERROR Controller 2 epoch 27 encountered error while electing leader for partition [__consumer_offsets,30] due to: Preferred replica 2 for partition [__consumer_offsets,30] is either not alive or not in the isr. Current leader and ISR: [{"leader":-1,"leader_epoch":59,"isr":[]}]. (state.change.logger)
[2018-03-20 11:02:32,101] ERROR Controller 2 epoch 27 initiated state change for partition [__consumer_offsets,30] from OnlinePartition to OnlinePartition failed (state.change.logger)
kafka.common.StateChangeFailedException: encountered error while electing leader for partition [__consumer_offsets,30] due to: Preferred replica 2 for partition [__consumer_offsets,30] is either not alive or not in the isr. Current leader and ISR: [{"leader":-1,"leader_epoch":59,"isr":[]}].
at kafka.controller.PartitionStateMachine.electLeaderForPartition(PartitionStateMachine.scala:362)
at kafka.controller.PartitionStateMachine.kafka$controller$PartitionStateMachine$$handleStateChange(PartitionStateMachine.scala:202)
at kafka.controller.PartitionStateMachine$$anonfun$handleStateChanges$2.apply(PartitionStateMachine.scala:141)
at kafka.controller.PartitionStateMachine$$anonfun$handleStateChanges$2.apply(PartitionStateMachine.scala:140)
at scala.collection.immutable.Set$Set1.foreach(Set.scala:94)
at kafka.controller.PartitionStateMachine.handleStateChanges(PartitionStateMachine.scala:140)
at kafka.controller.KafkaController.onPreferredReplicaElection(KafkaController.scala:662)
at kafka.controller.KafkaController$$anonfun$kafka$controller$KafkaController$$checkAndTriggerPartitionRebalance$4$$anonfun$apply$16$$anonfun$apply$5.apply$mcV$sp(KafkaController.scala:1230)
at kafka.controller.KafkaController$$anonfun$kafka$controller$KafkaController$$checkAndTriggerPartitionRebalance$4$$anonfun$apply$16$$anonfun$apply$5.apply(KafkaController.scala:1225)
at kafka.controller.KafkaController$$anonfun$kafka$controller$KafkaController$$checkAndTriggerPartitionRebalance$4$$anonfun$apply$16$$anonfun$apply$5.apply(KafkaController.scala:1225)
at kafka.utils.CoreUtils$.inLock(CoreUtils.scala:213)
at kafka.controller.KafkaController$$anonfun$kafka$controller$KafkaController$$checkAndTriggerPartitionRebalance$4$$anonfun$apply$16.apply(KafkaController.scala:1222)
at kafka.controller.KafkaController$$anonfun$kafka$controller$KafkaController$$checkAndTriggerPartitionRebalance$4$$anonfun$apply$16.apply(KafkaController.scala:1221)
at scala.collection.mutable.HashMap$$anon$1$$anonfun$foreach$2.apply(HashMap.scala:103)
at scala.collection.mutable.HashMap$$anon$1$$anonfun$foreach$2.apply(HashMap.scala:103)
at scala.collection.mutable.HashTable$class.foreachEntry(HashTable.scala:230)
at scala.collection.mutable.HashMap.foreachEntry(HashMap.scala:40)
at scala.collection.mutable.HashMap$$anon$1.foreach(HashMap.scala:103)
at kafka.controller.KafkaController$$anonfun$kafka$controller$KafkaController$$checkAndTriggerPartitionRebalance$4.apply(KafkaController.scala:1221)
at kafka.controller.KafkaController$$anonfun$kafka$controller$KafkaController$$checkAndTriggerPartitionRebalance$4.apply(KafkaController.scala:1203)
at scala.collection.immutable.Map$Map3.foreach(Map.scala:161)
at kafka.controller.KafkaController.kafka$controller$KafkaController$$checkAndTriggerPartitionRebalance(KafkaController.scala:1203)
at kafka.controller.KafkaController$$anonfun$onControllerFailover$1.apply$mcV$sp(KafkaController.scala:352)
at kafka.utils.KafkaScheduler$$anonfun$1.apply$mcV$sp(KafkaScheduler.scala:110)
at kafka.utils.CoreUtils$$anon$1.run(CoreUtils.scala:57)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:522)
at java.util.concurrent.FutureTask.runAndReset(FutureTask.java:319)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$301(ScheduledThreadPoolExecutor.java:191)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:305)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1160)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
at java.lang.Thread.run(Thread.java:811)
Caused by: kafka.common.StateChangeFailedException: Preferred replica 2 for partition [__consumer_offsets,30] is either not alive or not in the isr. Current leader and ISR: [{"leader":-1,"leader_epoch":59,"isr":[]}]
at kafka.controller.PreferredReplicaPartitionLeaderSelector.selectLeader(PartitionLeaderSelector.scala:157)
at kafka.controller.PartitionStateMachine.electLeaderForPartition(PartitionStateMachine.scala:339)
... 31 more
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There are these messages in the zookeeper logs, but they are happening all of the time, not only when the failures happen:
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2018-03-29 04:46:43,495 [myid:0] - WARN [NIOServerCxn.Factory:0.0.0.0/0.0.0.0:2181:NIOServerCnxn@368] - caught end of stream exception
EndOfStreamException: Unable to read additional data from client sessionid 0x0, likely client has closed socket
at org.apache.zookeeper.server.NIOServerCnxn.doIO(NIOServerCnxn.java:239)
at org.apache.zookeeper.server.NIOServerCnxnFactory.run(NIOServerCnxnFactory.java:203)
at java.lang.Thread.run(Thread.java:811)
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2018-03-29 08:56:46,195 [myid:1] - INFO [ProcessThread(sid:1 cport:-1)::PrepRequestProcessor@648] - Got user-level KeeperException when processing sessionid:0x62633bc4724c26 type:setData cxid:0x654465 zxid:0x100361191 txntype:-1 reqpath:n/a Error Path:/brokers/topics/metric.json/partitions/1/state Error:KeeperErrorCode = BadVersion for /brokers/topics/metric.json/partitions/1/state
2018-03-29 08:56:46,201 [myid:1] - INFO [ProcessThread(sid:1 cport:-1)::PrepRequestProcessor@648] - Got user-level KeeperException when processing sessionid:0x62633bc4724c26 type:setData cxid:0x654467 zxid:0x100361192 txntype:-1 reqpath:n/a Error Path:/brokers/topics/metric.json/partitions/10/state Error:KeeperErrorCode = BadVersion for /brokers/topics/metric.json/partitions/10/state
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I saw https://issues.apache.org/jira/browse/KAFKA-4084 which involves major changes to the rebalances. I'm in the process of moving to kafka 1.1.0 to see if it helps.
Any advice on what else to look into would be appreciated.
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