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From "Paolo Moriello (Jira)" <j...@apache.org>
Subject [jira] [Created] (KAFKA-9693) Kafka latency spikes caused by log segment flush on roll
Date Tue, 10 Mar 2020 15:12:00 GMT
Paolo Moriello created KAFKA-9693:
-------------------------------------

             Summary: 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
         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, latency_plot.png

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=513,height=171!

{{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...

 
!image-2020-03-10-14-14-49-131.png|width=514,height=274!
{{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=513,height=273!
{{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=513,height=171!
!image-2020-03-10-15-00-54-204.png|width=513,height=171!{{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.
 



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