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Posted to dev@kafka.apache.org by "Daniyar (Jira)" <ji...@apache.org> on 2019/11/19 23:35:00 UTC
[jira] [Created] (KAFKA-9213) BufferOverflowException on rolling
new segment after upgrading Kafka from 1.1.0 to 2.3.1
Daniyar created KAFKA-9213:
------------------------------
Summary: BufferOverflowException on rolling new segment after upgrading Kafka from 1.1.0 to 2.3.1
Key: KAFKA-9213
URL: https://issues.apache.org/jira/browse/KAFKA-9213
Project: Kafka
Issue Type: Bug
Components: log
Affects Versions: 2.3.1
Environment: Ubuntu 16.04, AWS instance d2.8xlarge.
JAVA Options:
-Xms16G
-Xmx16G
-XX:G1HeapRegionSize=16M
-XX:MetaspaceSize=96m
-XX:MinMetaspaceFreeRatio=50
Reporter: Daniyar
We updated our Kafka cluster from 1.1.0 version to 2.3.1. We followed up to step 2 of the [update instruction|[https://kafka.apache.org/documentation/#upgrade]].
Message format and inter-broker protocol versions were left the same:
inter.broker.protocol.version=1.1
log.message.format.version=1.1
After upgrading, we started to get some occasional exceptions:
{code:java}
2019/11/19 05:30:53 INFO [ProducerStateManager
partition=matchmaker_retry_clicks_15m-2] Writing producer snapshot at
offset 788532 (kafka.log.ProducerStateManager)
2019/11/19 05:30:53 INFO [Log partition=matchmaker_retry_clicks_15m-2,
dir=/mnt/kafka] Rolled new log segment at offset 788532 in 1 ms.
(kafka.log.Log)
2019/11/19 05:31:01 ERROR [ReplicaManager broker=0] Error processing append
operation on partition matchmaker_retry_clicks_15m-2
(kafka.server.ReplicaManager)
2019/11/19 05:31:01 java.nio.BufferOverflowException
2019/11/19 05:31:01 at java.nio.Buffer.nextPutIndex(Buffer.java:527)
2019/11/19 05:31:01 at
java.nio.DirectByteBuffer.putLong(DirectByteBuffer.java:797)
2019/11/19 05:31:01 at
kafka.log.TimeIndex.$anonfun$maybeAppend$1(TimeIndex.scala:134)
2019/11/19 05:31:01 at
scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
2019/11/19 05:31:01 at
kafka.utils.CoreUtils$.inLock(CoreUtils.scala:253)
2019/11/19 05:31:01 at
kafka.log.TimeIndex.maybeAppend(TimeIndex.scala:114)
2019/11/19 05:31:01 at
kafka.log.LogSegment.onBecomeInactiveSegment(LogSegment.scala:520)
2019/11/19 05:31:01 at kafka.log.Log.$anonfun$roll$8(Log.scala:1690)
2019/11/19 05:31:01 at
kafka.log.Log.$anonfun$roll$8$adapted(Log.scala:1690)
2019/11/19 05:31:01 at scala.Option.foreach(Option.scala:407)
2019/11/19 05:31:01 at kafka.log.Log.$anonfun$roll$2(Log.scala:1690)
2019/11/19 05:31:01 at
kafka.log.Log.maybeHandleIOException(Log.scala:2085)
2019/11/19 05:31:01 at kafka.log.Log.roll(Log.scala:1654)
2019/11/19 05:31:01 at kafka.log.Log.maybeRoll(Log.scala:1639)
2019/11/19 05:31:01 at kafka.log.Log.$anonfun$append$2(Log.scala:966)
2019/11/19 05:31:01 at
kafka.log.Log.maybeHandleIOException(Log.scala:2085)
2019/11/19 05:31:01 at kafka.log.Log.append(Log.scala:850)
2019/11/19 05:31:01 at kafka.log.Log.appendAsLeader(Log.scala:819)
2019/11/19 05:31:01 at
kafka.cluster.Partition.$anonfun$appendRecordsToLeader$1(Partition.scala:772)
2019/11/19 05:31:01 at
kafka.utils.CoreUtils$.inLock(CoreUtils.scala:253)
2019/11/19 05:31:01 at
kafka.utils.CoreUtils$.inReadLock(CoreUtils.scala:259)
2019/11/19 05:31:01 at
kafka.cluster.Partition.appendRecordsToLeader(Partition.scala:759)
2019/11/19 05:31:01 at
kafka.server.ReplicaManager.$anonfun$appendToLocalLog$2(ReplicaManager.scala:763)
2019/11/19 05:31:01 at
scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:238)
2019/11/19 05:31:01 at
scala.collection.mutable.HashMap.$anonfun$foreach$1(HashMap.scala:149)
2019/11/19 05:31:01 at
scala.collection.mutable.HashTable.foreachEntry(HashTable.scala:237)
2019/11/19 05:31:01 at
scala.collection.mutable.HashTable.foreachEntry$(HashTable.scala:230)
2019/11/19 05:31:01 at
scala.collection.mutable.HashMap.foreachEntry(HashMap.scala:44)
2019/11/19 05:31:01 at
scala.collection.mutable.HashMap.foreach(HashMap.scala:149)
2019/11/19 05:31:01 at
scala.collection.TraversableLike.map(TraversableLike.scala:238)
2019/11/19 05:31:01 at
scala.collection.TraversableLike.map$(TraversableLike.scala:231)
2019/11/19 05:31:01 at
scala.collection.AbstractTraversable.map(Traversable.scala:108)
2019/11/19 05:31:01 at
kafka.server.ReplicaManager.appendToLocalLog(ReplicaManager.scala:751)
2019/11/19 05:31:01 at
kafka.server.ReplicaManager.appendRecords(ReplicaManager.scala:492)
2019/11/19 05:31:01 at
kafka.server.KafkaApis.handleProduceRequest(KafkaApis.scala:544)
2019/11/19 05:31:01 at
kafka.server.KafkaApis.handle(KafkaApis.scala:113)
2019/11/19 05:31:01 at
kafka.server.KafkaRequestHandler.run(KafkaRequestHandler.scala:69)
2019/11/19 05:31:01 at java.lang.Thread.run(Thread.java:748)
{code}
The error persists until broker gets restarted (or leadership gets moved to another broker).
Brokers config:
{code:java}
advertised.host.name={{ hostname }}
port=9092
# Default number of partitions if a value isn't set when the topic is created.
num.partitions=3
auto.create.topics.enable=false
delete.topic.enable=false
# Prevent not in-sync replica to become a leader.
unclean.leader.election.enable=false
# The number of threads per data directory to be used for log recovery at
# startup and flushing at shutdown.
num.recovery.threads.per.data.dir=36
log.flush.interval.messages=10000
log.flush.interval.ms=2000
# 1 week
log.retention.hours=168
log.retention.check.interval.ms=300000
log.cleaner.enable=false
# Use broker time for message timestamps.
log.message.timestamp.type=LogAppendTime
zookeeper.connect={{zookeeper_host }}:2181
zookeeper.connection.timeout.ms=6000
controller.socket.timeout.ms=30000
controller.message.queue.size=10
# Replication configuration
num.replica.fetchers=10
# Socket server configuration
num.io.threads=32
num.network.threads=16
socket.request.max.bytes=104857600
socket.receive.buffer.bytes=1048576
socket.send.buffer.bytes=1048576
queued.max.requests=32
fetch.purgatory.purge.interval.requests=100
producer.purgatory.purge.interval.requests=100
inter.broker.protocol.version=1.1
log.message.format.version=1.1
{code}
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