Haruki Okada created KAFKA-15046: ------------------------------------ Summary: Produce performance issue under high disk load Key: KAFKA-15046 URL: https://issues.apache.org/jira/browse/KAFKA-15046 Project: Kafka Issue Type: Improvement Affects Versions: 3.3.2 Reporter: Haruki Okada 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 happens almost all the time. This caused producers to delay sending messages at the incidental time. ** At the incidental 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 hundreds of partition leaders with high load, 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 -- This message was sent by Atlassian Jira (v8.20.10#820010)