You are viewing a plain text version of this content. The canonical link for it is here.
Posted to jira@kafka.apache.org by "Manikumar (JIRA)" <ji...@apache.org> on 2018/04/18 06:37:00 UTC
[jira] [Commented] (KAFKA-6798) Kafka leader rebalance failures
[ https://issues.apache.org/jira/browse/KAFKA-6798?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16441972#comment-16441972 ]
Manikumar commented on KAFKA-6798:
----------------------------------
ISR is set to empty if all replicas are out of sync and unclean leader election is disabled. We need to find out the reason for replicas going async. check for any zk connection/network errors, GC issues. monitoring under partitions may help to identify the issue.
> Kafka leader rebalance failures
> -------------------------------
>
> Key: KAFKA-6798
> URL: https://issues.apache.org/jira/browse/KAFKA-6798
> Project: Kafka
> Issue Type: Bug
> Affects Versions: 0.10.2.1, 1.0.1
> Reporter: Riley Zimmerman
> Priority: Critical
>
> 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.
> {noformat}
> 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:
> {noformat}
> {noformat}
> [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)
> {noformat}
> {noformat}
> [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
> {noformat}
> There are these messages in the zookeeper logs, but they are happening all of the time, not only when the failures happen:
> {noformat}
> 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)
> {noformat}
> {noformat}
> 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
> {noformat}
> 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.
>
--
This message was sent by Atlassian JIRA
(v7.6.3#76005)