[jira] [Updated] (KAFKA-9693) Kafka latency spikes caused by log segment flush on roll

["João Oliveirinha (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/KAFKA-9693?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

João Oliveirinha updated KAFKA-9693:
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    Attachment: image-2020-06-23-12-24-46-548.png

> Kafka latency spikes caused by log segment flush on roll
> --------------------------------------------------------
>
>                 Key: KAFKA-9693
>                 URL: https://issues.apache.org/jira/browse/KAFKA-9693
>             Project: Kafka
>          Issue Type: Improvement
>          Components: core
>         Environment: OS: Amazon Linux 2
> Kafka version: 2.2.1
>            Reporter: Paolo Moriello
>            Assignee: Paolo Moriello
>            Priority: Major
>              Labels: Performance, latency, performance
>         Attachments: image-2020-03-10-13-17-34-618.png, image-2020-03-10-14-36-21-807.png, image-2020-03-10-15-00-23-020.png, image-2020-03-10-15-00-54-204.png, image-2020-06-23-12-24-46-548.png, image-2020-06-23-12-24-58-788.png, latency_plot2.png
>
>
> h1. Summary
> When a log segment fills up, Kafka rolls over onto a new active segment and force the flush of the old segment to disk. When this happens, log segment _append_ duration increase causing important latency spikes on producer(s) and replica(s). This ticket aims to highlight the problem and propose a simple mitigation: add a new configuration to enable/disable rolled segment flush.
> h1. 1. Phenomenon
> Response time of produce request (99th ~ 99.9th %ile) repeatedly spikes to ~50x-200x more than usual. For instance, normally 99th %ile is lower than 5ms, but when this issue occurs, it marks 100ms to 200ms. 99.9th and 99.99th %iles even jump to 500-700ms.
> Latency spikes happen at constant frequency (depending on the input throughput), for small amounts of time. All the producers experience a latency increase at the same time.
> h1. !image-2020-03-10-13-17-34-618.png|width=942,height=314!
> {{Example of response time plot observed during on a single producer.}}
> URPs rarely appear in correspondence of the latency spikes too. This is harder to reproduce, but from time to time it is possible to see a few partitions going out of sync in correspondence of a spike.
> h1. 2. Experiment
> h2. 2.1 Setup
> Kafka cluster hosted on AWS EC2 instances.
> h4. Cluster
>  * 15 Kafka brokers: (EC2 m5.4xlarge)
>  ** Disk: 1100Gb EBS volumes (4750Mbps)
>  ** Network: 10 Gbps
>  ** CPU: 16 Intel Xeon Platinum 8000
>  ** Memory: 64Gb
>  * 3 Zookeeper nodes: m5.large
>  * 6 producers on 6 EC2 instances in the same region
>  * 1 topic, 90 partitions - replication factor=3
> h4. Broker config
> Relevant configurations:
> {quote}num.io.threads=8
>  num.replica.fetchers=2
>  offsets.topic.replication.factor=3
>  num.network.threads=5
>  num.recovery.threads.per.data.dir=2
>  min.insync.replicas=2
>  num.partitions=1
> {quote}
> h4. Perf Test
>  * Throughput ~6000-8000 (~40-70Mb/s input + replication = ~120-210Mb/s per broker)
>  * record size = 20000
>  * Acks = 1, linger.ms = 1, compression.type = none
>  * Test duration: ~20/30min
> h2. 2.2 Analysis
> Our analysis showed an high +correlation between log segment flush count/rate and the latency spikes+. This indicates that the spikes in max latency are related to Kafka behavior on rolling over new segments.
> The other metrics did not show any relevant impact on any hardware component of the cluster, eg. cpu, memory, network traffic, disk throughput...
>  
>  !latency_plot2.png|width=924,height=308!
>  {{Correlation between latency spikes and log segment flush count. p50, p95, p99, p999 and p9999 latencies (left axis, ns) and the flush #count (right axis, stepping blue line in plot).}}
> Kafka schedules logs flushing (this includes flushing the file record containing log entries, the offset index, the timestamp index and the transaction index) during _roll_ operations. A log is rolled over onto a new empty log when:
>  * the log segment is full
>  * the maxtime has elapsed since the timestamp of first message in the segment (or, in absence of it, since the create time)
>  * the index is full
> In this case, the increase in latency happens on _append_ of a new message set to the active segment of the log. This is a synchronous operation which therefore blocks producers requests, causing the latency increase.
> To confirm this, I instrumented Kafka to measure the duration of FileRecords.append(MemoryRecords) method, which is responsible of writing memory records to file. As a result, I observed the same spiky pattern as in the producer latency, with a one-to-one correspondence with the append duration.
>  !image-2020-03-10-14-36-21-807.png|width=780,height=415!
>  {{FileRecords.append(MemoryRecords) duration during test run.}}
> Therefore, every time a new log segment (log.segment.bytes is set to default value of 1Gb) is rolled, Kafka forces a flush of the completed segment, which appears to slowdown the subsequent append requests on the active segment.
> h2. 2.3 Solution
> I managed to completely mitigate the problem by disabling the flush happening on log segment roll. Latency spikes and append duration flattened down.
>  !image-2020-03-10-15-00-23-020.png|width=906,height=302!
>  !image-2020-03-10-15-00-54-204.png|width=903,height=301!
> {{Producer response time before and after disabling log flush.}}
>   
>  Generally, it is possible to control Kafka's flush behavior by setting a bunch of log.flush.xxx configurations. This flush policy can be controlled to force data to disk after a period of time or after a certain number of messages has been written.
>   
>  However, these configuration don't have any impact on the flush of "rolled segments", which is scheduled and executed anyway.
>   
>  Therefore, the suggested solution is to add a new configuration to potentially control (enable/disable) this flush invocation.
> Note: what are the implications of disabling the log segment flush? Forcing the flush of old segments provides higher durability guarantees. In case of system crash, in fact, we would potentially lose only messages in the active segment log. By disabling this operation, instead, we'd increase the risk of losing more data.
>   



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