Record a Kafka inexplicably closed problem troubleshooting

Time:2021-12-25

Phenomenon:


FT walks, it’s gone; I checked and found that Kafka was gone

Troubleshooting:

1. Reproduce it once and get the server log

[2021-09-14 16:53:07,545] ERROR [KafkaServer id=0] Fatal error during KafkaServer startup. Prepare to shutdown (kafka.server.KafkaServer) java.lang.InternalError: a fault occurred in a recent unsafe memory access operation in compiled Java code at scala.runtime.BoxesRunTime.equals2(BoxesRunTime.java:130) at scala.runtime.BoxesRunTime.equals(BoxesRunTime.java:123) at scala.collection.mutable.HashTable.elemEquals(HashTable.scala:365) at scala.collection.mutable.HashTable.elemEquals$(HashTable.scala:365) at scala.collection.mutable.HashMap.elemEquals(HashMap.scala:44) at scala.collection.mutable.HashTable.findEntry0(HashTable.scala:140) at scala.collection.mutable.HashTable.findEntry(HashTable.scala:136) at scala.collection.mutable.HashTable.findEntry$(HashTable.scala:135) at scala.collection.mutable.HashMap.findEntry(HashMap.scala:44) at scala.collection.mutable.HashMap.get(HashMap.scala:74) at kafka.log.ProducerStateManager.lastEntry(ProducerStateManager.scala:648) at kafka.log.ProducerStateManager.prepareUpdate(ProducerStateManager.scala:614) at kafka.log.LogSegment.updateProducerState(LogSegment.scala:248) at kafka.log.LogSegment.$anonfun$recover$1(LogSegment.scala:367) at kafka.log.LogSegment.$anonfun$recover$1$adapted(LogSegment.scala:344) at scala.collection.Iterator.foreach(Iterator.scala:943) at scala.collection.Iterator.foreach$(Iterator.scala:943) at scala.collection.AbstractIterator.foreach(Iterator.scala:1431) at scala.collection.IterableLike.foreach(IterableLike.scala:74) at scala.collection.IterableLike.foreach$(IterableLike.scala:73) at scala.collection.AbstractIterable.foreach(Iterable.scala:56) at kafka.log.LogSegment.recover(LogSegment.scala:344) at kafka.log.Log.recoverSegment(Log.scala:648) at kafka.log.Log.recoverLog(Log.scala:787) at kafka.log.Log.$anonfun$loadSegments$3(Log.scala:723) at scala.runtime.java8.JFunction0$mcJ$sp.apply(JFunction0$mcJ$sp.java:23) at kafka.log.Log.retryOnOffsetOverflow(Log.scala:2351) at kafka.log.Log.loadSegments(Log.scala:723) at kafka.log.Log.(Log.scala:287) at kafka.log.Log$.apply(Log.scala:2485) at kafka.log.LogManager.loadLog(LogManager.scala:274) at kafka.log.LogManager.$anonfun$loadLogs$12(LogManager.scala:353) at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511) at java.util.concurrent.FutureTask.run(FutureTask.java:266) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748)

2. First confirm the version of Kafka, installation package, java version and Java source. Because it is deployed according to standard documents, there should be no problem. Then I stopped by

df -lh
Disk space error found:

Look at Kafka’s configuration file server properties

# 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=0

############################# 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://:9092

# 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=/tmp/kafka-logs

# 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=1

# 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 to ensure availability such as 3.
offsets.topic.replication.factor=1
transaction.state.log.replication.factor=1
transaction.state.log.min.isr=1

############################# 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=localhost:2181

# Timeout in ms for connecting to zookeeper
zookeeper.connection.timeout.ms=18000


############################# 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

log. Dirs stores all the data received by Kafka. Now under / tmp, check Linux (the teacher doesn’t leave)
lsblk
It seems that the space allocation should be changed. When installing ft and other services, you should pay attention to it and check the specific occupation of Kafka data files
du -ach --max-depth=1 /tmp

3. Send it! Change the configuration file,The previous ones were all test data. Just delete them。 Then find

log.dirs=/tmp/kafka-logs
Change to a well-off home
log.dirs=/home/kafka-logs
By the way, solve the other 100%. Just uninstall it directly. It has been 18 years since the modification, which is left over from the installation. Direct / run / media / Xxh / CentOS 7 x86_ 64 deleted.
rm -rf xxh/
Send! If you can’t delete it for a long time, you will be prompted to read-only file system. After checking, it seems that it will take some time to solve it. Forget it. It’s not the key 2333 anyway.
Restart Kafka and the FT pipeline task runs successfully!

Postscript:

Someone is already ahead
https://my.oschina.net/u/4405061/blog/3326953

reference resources:

https://www.cnblogs.com/superlsj/p/11610517.html— Linux disk mount logic
https://blog.csdn.net/qq_43427482/article/details/103552588–Ultra detailed Linux usage Foundation
https://blog.csdn.net/gjalj10/article/details/95961456–How to delete a read-only file
https://blog.csdn.net/whatday/article/details/100136236/–Solve the problem of 100% full

Eggs:

**Did you find out**
In the first picture, there is a sentence “you have new mail in / var / boot / mail / root”. When I checked this email later, the content was:
image
You didn’t find out, because you only care about yourself!