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https://issues.apache.org/jira/browse/KAFKA-15046?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17728266#comment-17728266
]
Haruki Okada edited comment on KAFKA-15046 at 6/1/23 8:39 AM:
--------------------------------------------------------------
Hm, when I dug into further this, I noticed there's another path that causes
essentially same phenomenon.
{code:java}
"data-plane-kafka-request-handler-17" #169 daemon prio=5 os_prio=0
cpu=50994542.49ms elapsed=595635.65s tid=0x00007efdaebabe30 nid=0x1e707
runnable [0x00007ef9a0fdf000]
java.lang.Thread.State: RUNNABLE
at sun.nio.ch.FileDispatcherImpl.force0(java.base@11.0.17/Native Method)
at
sun.nio.ch.FileDispatcherImpl.force(java.base@11.0.17/FileDispatcherImpl.java:82)
at
sun.nio.ch.FileChannelImpl.force(java.base@11.0.17/FileChannelImpl.java:461)
at org.apache.kafka.common.utils.Utils.flushDir(Utils.java:966)
at
org.apache.kafka.common.utils.Utils.atomicMoveWithFallback(Utils.java:951)
at
org.apache.kafka.common.utils.Utils.atomicMoveWithFallback(Utils.java:925)
at
org.apache.kafka.server.common.CheckpointFile.write(CheckpointFile.java:98)
- locked <0x0000000680fc4930> (a java.lang.Object)
at
kafka.server.checkpoints.CheckpointFileWithFailureHandler.write(CheckpointFileWithFailureHandler.scala:37)
at
kafka.server.checkpoints.LeaderEpochCheckpointFile.write(LeaderEpochCheckpointFile.scala:71)
at
kafka.server.epoch.LeaderEpochFileCache.flush(LeaderEpochFileCache.scala:291)
at
kafka.server.epoch.LeaderEpochFileCache.$anonfun$truncateFromStart$3(LeaderEpochFileCache.scala:263)
at
kafka.server.epoch.LeaderEpochFileCache.$anonfun$truncateFromStart$3$adapted(LeaderEpochFileCache.scala:259)
at
kafka.server.epoch.LeaderEpochFileCache$$Lambda$571/0x000000080045f040.apply(Unknown
Source)
at scala.Option.foreach(Option.scala:437)
at
kafka.server.epoch.LeaderEpochFileCache.$anonfun$truncateFromStart$1(LeaderEpochFileCache.scala:259)
at
kafka.server.epoch.LeaderEpochFileCache.truncateFromStart(LeaderEpochFileCache.scala:254)
at
kafka.log.UnifiedLog.$anonfun$maybeIncrementLogStartOffset$4(UnifiedLog.scala:1043)
at
kafka.log.UnifiedLog.$anonfun$maybeIncrementLogStartOffset$4$adapted(UnifiedLog.scala:1043)
at kafka.log.UnifiedLog$$Lambda$2324/0x0000000800b59040.apply(Unknown
Source)
at scala.Option.foreach(Option.scala:437)
at
kafka.log.UnifiedLog.maybeIncrementLogStartOffset(UnifiedLog.scala:1043)
- locked <0x0000000680fc5080> (a java.lang.Object)
at
kafka.cluster.Partition.$anonfun$deleteRecordsOnLeader$1(Partition.scala:1476)
at kafka.cluster.Partition.deleteRecordsOnLeader(Partition.scala:1463)
at
kafka.server.ReplicaManager.$anonfun$deleteRecordsOnLocalLog$2(ReplicaManager.scala:687)
at
kafka.server.ReplicaManager$$Lambda$3156/0x0000000800d7c840.apply(Unknown
Source)
at
scala.collection.StrictOptimizedMapOps.map(StrictOptimizedMapOps.scala:28)
at
scala.collection.StrictOptimizedMapOps.map$(StrictOptimizedMapOps.scala:27)
at scala.collection.mutable.HashMap.map(HashMap.scala:35)
at
kafka.server.ReplicaManager.deleteRecordsOnLocalLog(ReplicaManager.scala:680)
at kafka.server.ReplicaManager.deleteRecords(ReplicaManager.scala:875)
at
kafka.server.KafkaApis.handleDeleteRecordsRequest(KafkaApis.scala:2216)
at kafka.server.KafkaApis.handle(KafkaApis.scala:196)
at kafka.server.KafkaRequestHandler.run(KafkaRequestHandler.scala:75)
at java.lang.Thread.run(java.base@11.0.17/Thread.java:829) {code}
LeaderEpoch checkpointing also calls fsync with holding Log#lock and blocking
request-handler threads to append in the meantime.
This is called by scheduler thread on log-segment-breaching so might be less
frequent than log roll though.
Does it make sense to also making LeaderEpochCheckpointFile-flush to be outside
of the lock?
was (Author: ocadaruma):
Hm, when I dug into further this, I noticed there's another path that causes
essentially same phenomenon.
{code:java}
"data-plane-kafka-request-handler-17" #169 daemon prio=5 os_prio=0
cpu=50994542.49ms elapsed=595635.65s tid=0x00007efdaebabe30 nid=0x1e707
runnable [0x00007ef9a0fdf000]
java.lang.Thread.State: RUNNABLE
at sun.nio.ch.FileDispatcherImpl.force0(java.base@11.0.17/Native Method)
at
sun.nio.ch.FileDispatcherImpl.force(java.base@11.0.17/FileDispatcherImpl.java:82)
at
sun.nio.ch.FileChannelImpl.force(java.base@11.0.17/FileChannelImpl.java:461)
at org.apache.kafka.common.utils.Utils.flushDir(Utils.java:966)
at
org.apache.kafka.common.utils.Utils.atomicMoveWithFallback(Utils.java:951)
at
org.apache.kafka.common.utils.Utils.atomicMoveWithFallback(Utils.java:925)
at
org.apache.kafka.server.common.CheckpointFile.write(CheckpointFile.java:98)
- locked <0x0000000680fc4930> (a java.lang.Object)
at
kafka.server.checkpoints.CheckpointFileWithFailureHandler.write(CheckpointFileWithFailureHandler.scala:37)
at
kafka.server.checkpoints.LeaderEpochCheckpointFile.write(LeaderEpochCheckpointFile.scala:71)
at
kafka.server.epoch.LeaderEpochFileCache.flush(LeaderEpochFileCache.scala:291)
at
kafka.server.epoch.LeaderEpochFileCache.$anonfun$truncateFromStart$3(LeaderEpochFileCache.scala:263)
at
kafka.server.epoch.LeaderEpochFileCache.$anonfun$truncateFromStart$3$adapted(LeaderEpochFileCache.scala:259)
at
kafka.server.epoch.LeaderEpochFileCache$$Lambda$571/0x000000080045f040.apply(Unknown
Source)
at scala.Option.foreach(Option.scala:437)
at
kafka.server.epoch.LeaderEpochFileCache.$anonfun$truncateFromStart$1(LeaderEpochFileCache.scala:259)
at
kafka.server.epoch.LeaderEpochFileCache.truncateFromStart(LeaderEpochFileCache.scala:254)
at
kafka.log.UnifiedLog.$anonfun$maybeIncrementLogStartOffset$4(UnifiedLog.scala:1043)
at
kafka.log.UnifiedLog.$anonfun$maybeIncrementLogStartOffset$4$adapted(UnifiedLog.scala:1043)
at kafka.log.UnifiedLog$$Lambda$2324/0x0000000800b59040.apply(Unknown
Source)
at scala.Option.foreach(Option.scala:437)
at
kafka.log.UnifiedLog.maybeIncrementLogStartOffset(UnifiedLog.scala:1043)
- locked <0x0000000680fc5080> (a java.lang.Object)
at
kafka.cluster.Partition.$anonfun$deleteRecordsOnLeader$1(Partition.scala:1476)
at kafka.cluster.Partition.deleteRecordsOnLeader(Partition.scala:1463)
at
kafka.server.ReplicaManager.$anonfun$deleteRecordsOnLocalLog$2(ReplicaManager.scala:687)
at
kafka.server.ReplicaManager$$Lambda$3156/0x0000000800d7c840.apply(Unknown
Source)
at
scala.collection.StrictOptimizedMapOps.map(StrictOptimizedMapOps.scala:28)
at
scala.collection.StrictOptimizedMapOps.map$(StrictOptimizedMapOps.scala:27)
at scala.collection.mutable.HashMap.map(HashMap.scala:35)
at
kafka.server.ReplicaManager.deleteRecordsOnLocalLog(ReplicaManager.scala:680)
at kafka.server.ReplicaManager.deleteRecords(ReplicaManager.scala:875)
at
kafka.server.KafkaApis.handleDeleteRecordsRequest(KafkaApis.scala:2216)
at kafka.server.KafkaApis.handle(KafkaApis.scala:196)
at kafka.server.KafkaRequestHandler.run(KafkaRequestHandler.scala:75)
at java.lang.Thread.run(java.base@11.0.17/Thread.java:829) {code}
LeaderEpoch checkpointing also calls fsync with holding Log#lock and blocking
request-handler threads to append in the meantime.
This is called by scheduler thread on log-segment-breaching so might be less
frequent than log roll though.
Does it make sense to also making LeaderEpochCheckpointFile-flush to be
asynchronous?
> Produce performance issue under high disk load
> ----------------------------------------------
>
> Key: KAFKA-15046
> URL: https://issues.apache.org/jira/browse/KAFKA-15046
> Project: Kafka
> Issue Type: Improvement
> Components: core
> Affects Versions: 3.3.2
> Reporter: Haruki Okada
> Priority: Major
> Labels: performance
> Attachments: image-2023-06-01-12-46-30-058.png,
> image-2023-06-01-12-52-40-959.png, image-2023-06-01-12-54-04-211.png,
> image-2023-06-01-12-56-19-108.png
>
>
> * Phenomenon:
> ** !image-2023-06-01-12-46-30-058.png|width=259,height=236!
> ** Producer response time 99%ile got quite bad when we performed replica
> reassignment on the cluster
> *** RequestQueue scope was significant
> ** Also request-time throttling happened at the incidental time. This caused
> producers to delay sending messages in the mean time.
> ** The disk I/O latency was higher than usual due to the high load for
> replica reassignment.
> *** !image-2023-06-01-12-56-19-108.png|width=255,height=128!
> * Analysis:
> ** The request-handler utilization was much higher than usual.
> *** !image-2023-06-01-12-52-40-959.png|width=278,height=113!
> ** Also, thread time utilization was much higher than usual on almost all
> users
> *** !image-2023-06-01-12-54-04-211.png|width=276,height=110!
> ** From taking jstack several times, for most of them, we found that a
> request-handler was doing fsync for flusing ProducerState and meanwhile other
> request-handlers were waiting Log#lock for appending messages.
> *
> **
> ***
> {code:java}
> "data-plane-kafka-request-handler-14" #166 daemon prio=5 os_prio=0
> cpu=51264789.27ms elapsed=599242.76s tid=0x00007efdaeba7770 nid=0x1e704
> runnable [0x00007ef9a12e2000]
> java.lang.Thread.State: RUNNABLE
> at sun.nio.ch.FileDispatcherImpl.force0(java.base@11.0.17/Native
> Method)
> at
> sun.nio.ch.FileDispatcherImpl.force(java.base@11.0.17/FileDispatcherImpl.java:82)
> at
> sun.nio.ch.FileChannelImpl.force(java.base@11.0.17/FileChannelImpl.java:461)
> at
> kafka.log.ProducerStateManager$.kafka$log$ProducerStateManager$$writeSnapshot(ProducerStateManager.scala:451)
> at
> kafka.log.ProducerStateManager.takeSnapshot(ProducerStateManager.scala:754)
> at kafka.log.UnifiedLog.roll(UnifiedLog.scala:1544)
> - locked <0x000000060d75d820> (a java.lang.Object)
> at kafka.log.UnifiedLog.maybeRoll(UnifiedLog.scala:1523)
> - locked <0x000000060d75d820> (a java.lang.Object)
> at kafka.log.UnifiedLog.append(UnifiedLog.scala:919)
> - locked <0x000000060d75d820> (a java.lang.Object)
> at kafka.log.UnifiedLog.appendAsLeader(UnifiedLog.scala:760)
> at
> kafka.cluster.Partition.$anonfun$appendRecordsToLeader$1(Partition.scala:1170)
> at kafka.cluster.Partition.appendRecordsToLeader(Partition.scala:1158)
> at
> kafka.server.ReplicaManager.$anonfun$appendToLocalLog$6(ReplicaManager.scala:956)
> at
> kafka.server.ReplicaManager$$Lambda$2379/0x0000000800b7c040.apply(Unknown
> Source)
> at
> scala.collection.StrictOptimizedMapOps.map(StrictOptimizedMapOps.scala:28)
> at
> scala.collection.StrictOptimizedMapOps.map$(StrictOptimizedMapOps.scala:27)
> at scala.collection.mutable.HashMap.map(HashMap.scala:35)
> at
> kafka.server.ReplicaManager.appendToLocalLog(ReplicaManager.scala:944)
> at kafka.server.ReplicaManager.appendRecords(ReplicaManager.scala:602)
> at kafka.server.KafkaApis.handleProduceRequest(KafkaApis.scala:666)
> at kafka.server.KafkaApis.handle(KafkaApis.scala:175)
> at kafka.server.KafkaRequestHandler.run(KafkaRequestHandler.scala:75)
> at java.lang.Thread.run(java.base@11.0.17/Thread.java:829) {code}
> *
> ** Also there were bunch of logs that writing producer snapshots took
> hundreds of milliseconds.
> ***
> {code:java}
> ...
> [2023-05-01 11:08:36,689] INFO [ProducerStateManager partition=xxx-4] Wrote
> producer snapshot at offset 1748817854 with 8 producer ids in 809 ms.
> (kafka.log.ProducerStateManager)
> [2023-05-01 11:08:37,319] INFO [ProducerStateManager partition=yyy-34] Wrote
> producer snapshot at offset 247996937813 with 0 producer ids in 547 ms.
> (kafka.log.ProducerStateManager)
> [2023-05-01 11:08:38,887] INFO [ProducerStateManager partition=zzz-9] Wrote
> producer snapshot at offset 226222355404 with 0 producer ids in 576 ms.
> (kafka.log.ProducerStateManager)
> ... {code}
> * From the analysis, we summarized the issue as below:
> *
> ** 1. Disk write latency got worse due to the replica reassignment
> *** We already use replication quota, and lowering the quota further may not
> be acceptable for too long assignment duration
> ** 2. ProducerStateManager#takeSnapshot started to take time due to fsync
> latency
> *** This is done at every log segment roll.
> *** In our case, the broker hosts high load partitions so log roll is
> occurring very frequently.
> ** 3. During ProducerStateManager#takeSnapshot is doing fsync, all
> subsequent produce requests to the partition is blocked due to Log#lock
> ** 4. During produce requests waiting the lock, they consume request handler
> threads time so it's accounted as thread and caused throttling
> * Suggestion:
> ** We didn't see this phenomenon when we used Kafka 2.4.1.
> *** ProducerState fsync was introduced in 2.8.0 by this:
> https://issues.apache.org/jira/browse/KAFKA-9892
> ** The reason why ProducerState needs to be fsync is not well described in
> above ticket though, we think fsync is not really necessary here. Because:
> *
> **
> *** If ProducerState snapshot file was not written to the disk after power
> failure, it will be just rebuilt from logs.
> *** Also, even if ProducerState snapshot was corrupted after power failure,
> it will be rebuilt from logs thanks to CRC
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