kafka中__consumer_offsets中某分区下的log日志占比超过磁盘一半,如何解决?

想喝好几罐八宝粥的男孩 发表于: 2023-01-07   最后更新时间: 2023-01-09 13:36:14   190 游览

最近kakfa集群磁盘系统监控发现某块盘占比超过50%,随即发送异常短信报警,经过排查发现是主题__consumer_offsets下面的分区6,下面的log日志每个日志大小为100M,但是却存有半个月的log一直未删除。

用命令查看该主题的默认删除时间为1天,删除策略已经改为:cleanup.policy=delete。

通过专门工具排查发现是某个消费者组:metereventservice在消费某个主题(test),挤压数据长达58亿,该消费者组metereventservice在消费后,明明按照默认的1天后删除位移日志,但是却一直没有删除,而且该消费者组的挤压数据还在增加,请问有什么好的方法来删除该分区下面的log日志吗?除了暴力删除(直接rm -rf 删除后缀为log)外。

以下是我的配置文件的参数:

# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# see kafka.server.KafkaConfig for additional details and defaults

############################# Server Basics #############################

# The id of the broker. This must be set to a unique integer for each broker.
broker.id=2

############################# Socket Server Settings #############################

# The address the socket server listens on. It will get the value returned from 
# java.net.InetAddress.getCanonicalHostName() if not configured.
#   FORMAT:
#     listeners = listener_name://host_name:port
#   EXAMPLE:
#     listeners = PLAINTEXT://your.host.name:9092
listeners=PLAINTEXT://:22000
port=22000
host.name=39.62.80.27
advertised.host.name=39.62.80.27
advertised.port=22000
# Hostname and port the broker will advertise to producers and consumers. If not set, 

# it uses the value for "listeners" if configured.  Otherwise, it will use the value
# returned from java.net.InetAddress.getCanonicalHostName().
#advertised.listeners=PLAINTEXT://your.host.name:9092

# Maps listener names to security protocols, the default is for them to be the same. See the config documentation for more details
#listener.security.protocol.map=PLAINTEXT:PLAINTEXT,SSL:SSL,SASL_PLAINTEXT:SASL_PLAINTEXT,SASL_SSL:SASL_SSL

# The number of threads that the server uses for receiving requests from the network and sending responses to the network
num.network.threads=3

# The number of threads that the server uses for processing requests, which may include disk I/O
num.io.threads=8

# The send buffer (SO_SNDBUF) used by the socket server
socket.send.buffer.bytes=102400

# The receive buffer (SO_RCVBUF) used by the socket server
socket.receive.buffer.bytes=102400

# The maximum size of a request that the socket server will accept (protection against OOM)
socket.request.max.bytes=104857600


############################# Log Basics #############################

# A comma separated list of directories under which to store log files
log.dirs=/data/disk1,/data/disk2,/data/disk3,/data/disk4,/data/disk5,/data/disk6,/data/disk7,/data/disk8

# The default number of log partitions per topic. More partitions allow greater
# parallelism for consumption, but this will also result in more files across
# the brokers.
num.partitions=9

# The number of threads per data directory to be used for log recovery at startup and flushing at shutdown.
# This value is recommended to be increased for installations with data dirs located in RAID array.
num.recovery.threads.per.data.dir=1

############################# Internal Topic Settings  #############################
# The replication factor for the group metadata internal topics "__consumer_offsets" and "__transaction_state"
# For anything other than development testing, a value greater than 1 is recommended for to ensure availability such as 3.
offsets.topic.replication.factor=3
transaction.state.log.replication.factor=3
transaction.state.log.min.isr=3

############################# Log Flush Policy #############################

# Messages are immediately written to the filesystem but by default we only fsync() to sync
# the OS cache lazily. The following configurations control the flush of data to disk.
# There are a few important trade-offs here:
#    1. Durability: Unflushed data may be lost if you are not using replication.
#    2. Latency: Very large flush intervals may lead to latency spikes when the flush does occur as there will be a lot of data to flush.
#    3. Throughput: The flush is generally the most expensive operation, and a small flush interval may lead to excessive seeks.
# The settings below allow one to configure the flush policy to flush data after a period of time or
# every N messages (or both). This can be done globally and overridden on a per-topic basis.

# The number of messages to accept before forcing a flush of data to disk
#log.flush.interval.messages=10000

# The maximum amount of time a message can sit in a log before we force a flush
#log.flush.interval.ms=1000

############################# Log Retention Policy #############################

# The following configurations control the disposal of log segments. The policy can
# be set to delete segments after a period of time, or after a given size has accumulated.
# A segment will be deleted whenever *either* of these criteria are met. Deletion always happens
# from the end of the log.

# The minimum age of a log file to be eligible for deletion due to age
log.retention.hours=168

# A size-based retention policy for logs. Segments are pruned from the log unless the remaining
# segments drop below log.retention.bytes. Functions independently of log.retention.hours.
#log.retention.bytes=1073741824

# The maximum size of a log segment file. When this size is reached a new log segment will be created.
log.segment.bytes=1073741824

# The interval at which log segments are checked to see if they can be deleted according
# to the retention policies
log.retention.check.interval.ms=300000

############################# Zookeeper #############################

# Zookeeper connection string (see zookeeper docs for details).
# This is a comma separated host:port pairs, each corresponding to a zk
# server. e.g. "127.0.0.1:3000,127.0.0.1:3001,127.0.0.1:3002".
# You can also append an optional chroot string to the urls to specify the
# root directory for all kafka znodes.
zookeeper.connect=39.62.80.26:30181,39.62.80.27:30181,39.62.80.75:30181

# Timeout in ms for connecting to zookeeper
zookeeper.connection.timeout.ms=6000


############################# Group Coordinator Settings #############################

# The following configuration specifies the time, in milliseconds, that the GroupCoordinator will delay the initial consumer rebalance.
# The rebalance will be further delayed by the value of group.initial.rebalance.delay.ms as new members join the group, up to a maximum of max.poll.interval.ms.
# The default value for this is 3 seconds.
# We override this to 0 here as it makes for a better out-of-the-box experience for development and testing.
# However, in production environments the default value of 3 seconds is more suitable as this will help to avoid unnecessary, and potentially expensive, rebalances during application startup.
group.initial.rebalance.delay.ms=0
delete.topic.enable=true
auto.create.topics.enable=false
发表于 2023-01-07
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我也没有更好的办法,只能看看kafka日志是否有异常导致无法清理。
如果在kafka正常的情况下,我只是怀疑,58亿的积压是否是1天的,而不是一直积压的?

部署上一天的,是那个主题数据量太大,在一台机器上根本消费不完。但是就是很奇怪为啥标注了默认一天删除,为啥没删除呢

你获取一下,是几天的消息?

我用kafka自带的工具是2022.12.30 的数据,类似下面的数据:

[metereventservice,CODE_TABLE_NEW,3]::[OffsetMetadata[25060321211,NO_METADATA],CommitTime 1672391927662,ExpirationTime 1672478327662]
[metereventservice,CODE_TABLE_NEW,1]::[OffsetMetadata[25227291781,NO_METADATA],CommitTime 1672391927662,ExpirationTime 1672478327662]
[metereventservice,CODE_TABLE_NEW,26]::[OffsetMetadata[25510019401,NO_METADATA],CommitTime 1672391927662,ExpirationTime 1672478327662]
[metereventservice,CODE_TABLE_NEW,24]::[OffsetMetadata[25045032932,NO_METADATA],CommitTime 1672391927662,ExpirationTime 1672478327662]
[metereventservice,CODE_TABLE_NEW,20]::[OffsetMetadata[25506232855,NO_METADATA],CommitTime 1672391927662,ExpirationTime 1672478327662]
[metereventservice,CODE_TABLE_NEW,35]::[OffsetMetadata[25414027329,NO_METADATA],CommitTime 1672391927662,ExpirationTime 1672478327662]
[metereventservice,CODE_TABLE_NEW,12]::[OffsetMetadata[25028753334,NO_METADATA],CommitTime 1672391927662,ExpirationTime 1672478327662]
[metereventservice,CODE_TABLE_NEW,25]::[OffsetMetadata[25322214711,NO_METADATA],CommitTime 1672391927663,ExpirationTime 1672478327663]
[metereventservice,CODE_TABLE_NEW,17]::[OffsetMetadata[25318665259,NO_METADATA],CommitTime 1672391927663,ExpirationTime 1672478327663]
[metereventservice,CODE_TABLE_NEW,14]::[OffsetMetadata[25200680372,NO_METADATA],CommitTime 1672391927663,ExpirationTime 1672478327663]
[metereventservice,CODE_TABLE_NEW,18]::[OffsetMetadata[25015840009,NO_METADATA],CommitTime 1672391927663,ExpirationTime 1672478327663]
[metereventservice,CODE_TABLE_NEW,33]::[OffsetMetadata[25368076610,NO_METADATA],CommitTime 1672391927663,ExpirationTime 1672478327663]
[metereventservice,CODE_TABLE_NEW,19]::[OffsetMetadata[24886406032,NO_METADATA],CommitTime 1672391927663,ExpirationTime 1672478327663]
[metereventservice,CODE_TABLE_NEW,6]::[OffsetMetadata[25488781305,NO_METADATA],CommitTime 1672391927663,ExpirationTime 1672478327663]

我获取了一下,就只有2022.12.30这天的数据。但是日志有800多G

你这个过期时间显示的是2023-1-9 9:47:36

commit time:显示的是2022.12.30,

时间戳:1672391927662

毫秒(ms)
翻译对应时间
2022-12-30 17:18:47
北京时间

我说的不是过期时间吗...

过期时间是2022.12.31啊。
时间戳
1672478327662

毫秒(ms)

2022-12-31 17:18:47
北京时间

奇怪,我刚才算错了?
这个显示数据保留7天呀

log.retention.hours=168

我今天中午排查了以下确实是__consumer_offsets下面某个分区的确实是按照7天默认进行删除的,那照这么说,我其他配置的参数对__consumer_offsets这个主题无用啊。

你怎么设置的,贴下配置。

kafka-configs.sh --zookeeper hdp3:2181 --alter --entity-name __consumer_offsets --entity-type topics --add-config cleanup.policy=delete

这样设置的,这样__consumer_offsets下面的log日志默认是100M,但是现场观察到了100M并不是接着删除,而是到了7天后才进行删除的。

我现在的策略是将消费很慢的程序直接停掉了,然后磁盘使用率就降低下来了,但至于是不是__consumer——offsets是删除策略还是压缩策略导致的,这个目前无法得知。

你的答案

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