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https://issues.apache.org/jira/browse/KAFKA-16226?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Mayank Shekhar Narula updated KAFKA-16226:
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Attachment: (was: image-20240201-105752.png)
> Java client: Performance regression in Trogdor benchmark with high partition
> counts
> -----------------------------------------------------------------------------------
>
> Key: KAFKA-16226
> URL: https://issues.apache.org/jira/browse/KAFKA-16226
> Project: Kafka
> Issue Type: Bug
> Components: clients
> Affects Versions: 3.7.0, 3.6.1
> Reporter: Mayank Shekhar Narula
> Assignee: Mayank Shekhar Narula
> Priority: Major
> Labels: kip-951
> Fix For: 3.6.2, 3.8.0, 3.7.1
>
>
> h1. Background
> https://issues.apache.org/jira/browse/KAFKA-15415 implemented optimisation in
> java-client to skip backoff period if client knows of a newer leader, for
> produce-batch being retried.
> h1. What changed
> The implementation introduced a regression noticed on a trogdor-benchmark
> running with high partition counts(36000!).
> With regression, following metrics changed on the produce side.
> # record-queue-time-avg: increased from 20ms to 30ms.
> # request-latency-avg: increased from 50ms to 100ms.
> h1. Why it happened
> As can be seen from the original
> [PR|https://github.com/apache/kafka/pull/14384]
> RecordAccmulator.partitionReady() & drainBatchesForOneNode() started using
> synchronised method Metadata.currentLeader(). This has led to increased
> synchronization between KafkaProducer's application-thread that call send(),
> and background-thread that actively send producer-batches to leaders.
> See lock profiles that clearly show increased synchronisation in KAFKA-15415
> PR(highlighted in {color:#de350b}Red{color}) Vs baseline. Note the
> synchronisation is much worse for paritionReady() in this benchmark as its
> called for each partition, and it has 36k partitions!
> h2. Lock Profile: Kafka-15415
> !Screenshot 2024-02-01 at 11.06.36.png!
> h2. Lock Profile: Baseline
> !image-20240201-105752.png!
> h1. Fix
> Synchronization has to be reduced between 2 threads in order to address this.
> [https://github.com/apache/kafka/pull/15323] is a fix for it, as it avoids
> using Metadata.currentLeader() instead rely on Cluster.leaderFor().
>
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