Kafka：ZK+Kafka+Spark Streaming集群环境搭建（九）安装kafka_2.11-1.1.0
如何搭建配置centos虚拟机请参考《Kafka：ZK+Kafka+Spark Streaming集群环境搭建（一）VMW安装四台CentOS，并实现本机与它们能交互，虚拟机内部实现可以上网。》
如何安装hadoop2.9.0请参考《Kafka：ZK+Kafka+Spark Streaming集群环境搭建（二）安装hadoop2.9.0》
如何配置hadoop2.9.0 HA 请参考《Kafka：ZK+Kafka+Spark Streaming集群环境搭建（十）安装hadoop2.9.0搭建HA》
如何安装spark2.2.1请参考《Kafka：ZK+Kafka+Spark Streaming集群环境搭建（三）安装spark2.2.1》
如何安装zookeeper-3.4.12请参考《Kafka：ZK+Kafka+Spark Streaming集群环境搭建（八）安装zookeeper-3.4.12》
安装kafka的服务器：
192.168.0.120 master
192.168.0.121 slave1
192.168.0.122 slave2
192.168.0.123 slave3
备注：只在slave1,slave2,slave3三个节店上安装zookeeper，master节店不安装（其实前边hadoop中master不作为datanode节店，spark中master不作为worker节店）。
下载解压
从官网上下载kafka，并上传到slave1(192.168.0.121)的/opt目录下。这里kafka下载的是：kafka_2.11-1.1.0.tgz
在slave1上解压kafka_2.11-1.1.0.tgz
[root@slave1 opt]# tar -zxvf kafka_2.11-1.1.0.tgz
配置kafka
1）配置文件位置
路径：/opt/kafka_2.11-1.1.0/config/server.properties
[root@slave1 config]# ls connect-console-sink.properties connect-distributed.properties connect-file-source.properties connect-standalone.properties log4j.properties server.properties zookeeper.properties connect-console-source.properties connect-file-sink.properties connect-log4j.properties consumer.properties producer.properties tools-log4j.properties [root@slave1 config]#
2）server.properties默认配置
View Code
3）在slave1上，修改server.properties后配置内容
# 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 port=9092 host.name=192.168.0.121 advertised.host.name=192.168.0.121 advertised.port=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 log.dirs=/opt/kafka_2.11-1.1.0/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 for 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=192.168.0.120:2181,192.168.0.121:2181,192.168.0.122:2181 # 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
配置的详细说明请参考官方文档：http://kafka.apache.org/documentation.html#brokerconfigs
注意：按照官方文档的说法，advertised.host.name 和 advertised.port 这两个参数用于定义集群向 Producer 和 Consumer 广播的节点 host 和 port，如果不定义，会默认使用 host.name 和 port 的定义。但在实际应用中，发现如果不定义 advertised.host.name 参数，使用 Java 客户端从远端连接集群时，会发生连接超时，抛出异常：org.apache.kafka.common.errors.TimeoutException: Batch Expired
经过过 debug 发现，连接到集群是成功的，但连接到集群后更新回来的集群 meta 信息却是错误的。metadata 中的 Cluster 信息中节点的 hostname 是一串字符，而不是实际的ip地址。这串其实是远端主机的 hostname，这说明在没有配置 advertised.host.name 的情况下，Kafka 并没有像官方文档宣称的那样改为广播我们配置的 host.name，而是广播了主机配置的 hostname 。远端的客户端并没有配置 hosts，所以自然是连接不上这个 hostname 的。要解决这一问题，把 host.name 和 advertised.host.name 都配置成绝对的 ip 地址就可以了。
将配置后的kafka文件拷贝到slave2,slave3服务器上，并修改server.properties配置文件
1）将配置后的kafka文件拷贝到slave2,slave3服务器上
在slave1上执行以下命令，将拷贝kafka文件到slave2,slave3节点
在执行拷贝之前，需要在slave2,slave3上新建文件/opt/kafka_2.11-1.1.0目录，以slave3执行为例：
[spark@slave3 ~]$ su root Password: [root@slave3 spark]# mkdir /opt/kafka_2.11-1.1.0 [root@slave3 spark]# ls [root@slave3 spark]# cd /opt/ hadoop-2.9.0 jdk1.8.0_171 jdk-8u171-linux-x64.tar.gz kafka_2.11-1.1.0 scala-2.11.0 scala-2.11.0.tgz spark-2.2.1-bin-hadoop2.7 [root@slave3 opt]# chmod 777 /opt/kafka_2.11-1.1.0 [root@slave3 opt]#
在slave1执行拷贝：
scp -r /opt/kafka_2.11-1.1.0 spark@slave2:/opt/ scp -r /opt/kafka_2.11-1.1.0 spark@slave3:/opt/
2）并修改server.properties配置文件
修改1：slave2,slave3上的/opt/kafka_2.11-1.1.0/config/server.properties
主要修改：
host.name=192.168.0.121 advertised.host.name=192.168.0.121
确保ip修改为自己的ip。
修改2：slave2,slave3上/opt/kafka_2.11-1.1.0/config/server.properties的broker.id配置项，使得slave2的broker.id=1，slave3的broker.id=2。否则会出现下边的错误broker.id重复抛出异常，导致启动kafka失败。
在slave1,slave2,slave3主机上分别启动 Kafka 服务
cd /opt/kafka_2.11-1.1.0/ bin/kafka-server-start.sh -daemon config/server.properties
官方给出的启动方法是：
bin/kafka-server-start.sh config/server.properties &
1）启动失败：此时启动slave2,slave3启动一会后，自动杀掉kafka进程，从/opt/kafka_2.11-1.1.0/logs/server.log日志中查找到抛出了异常：
[2018-07-01 07:10:45,198] INFO Creating /brokers/ids/0 (is it secure? false) (kafka.zk.KafkaZkClient) [2018-07-01 07:10:45,204] ERROR Error while creating ephemeral at /brokers/ids/0, node already exists and owner '144115199316656129' does not match current session '72057669184061443' (kafka.zk.KafkaZkCli ent$CheckedEphemeral) [2018-07-01 07:10:45,204] INFO Result of znode creation at /brokers/ids/0 is: NODEEXISTS (kafka.zk.KafkaZkClient) [2018-07-01 07:10:45,208] ERROR [KafkaServer id=0] Fatal error during KafkaServer startup. Prepare to shutdown (kafka.server.KafkaServer) org.apache.zookeeper.KeeperException$NodeExistsException: KeeperErrorCode = NodeExists at org.apache.zookeeper.KeeperException.create(KeeperException.java:119) at kafka.zk.KafkaZkClient.checkedEphemeralCreate(KafkaZkClient.scala:1476) at kafka.zk.KafkaZkClient.registerBrokerInZk(KafkaZkClient.scala:84) at kafka.server.KafkaServer.startup(KafkaServer.scala:254) at kafka.server.KafkaServerStartable.startup(KafkaServerStartable.scala:38) at kafka.Kafka$.main(Kafka.scala:92) at kafka.Kafka.main(Kafka.scala) [2018-07-01 07:10:45,209] INFO [KafkaServer id=0] shutting down (kafka.server.KafkaServer)
错误原因：server.properties文件中的broker.id的值，在集群环境下重复了，即，一个kafka的集群环境下，broker.id的值是不能重复的，必须唯一。就算kafka服务在不同机器上。
解决方案：修改slave2,slave3上/opt/kafka_2.11-1.1.0/config/server.properties的broker.id配置项，使得slave2的broker.id=1，slave3的broker.id=2。
以slave1启动为例：
[root@slave1 kafka_2.11-1.1.0]# cd /opt/kafka_2.11-1.1.0/ [root@slave1 kafka_2.11-1.1.0]# bin/kafka-server-start.sh -daemon config/server.properties [root@slave1 kafka_2.11-1.1.0]# jps 1347 QuorumPeerMain 2493 Jps 2431 Kafka
创建分区和 topic
1）在slave1(192.168.0.121)上创建一个名为 my-topic，拥有两个分区，两个副本的Topic
cd /opt/kafka_2.11-1.1.0/ bin/kafka-topics.sh --create --zookeeper 192.168.0.120:2181,192.168.0.121:2181,192.168.0.122:2181 --replication-factor 2 --partitions 2 --topic my-topic
返回信息：
[root@slave1 kafka_2.11-1.1.0]# cd /opt/kafka_2.11-1.1.0/ [root@slave1 kafka_2.11-1.1.0]# bin/kafka-topics.sh --create --zookeeper 192.168.0.120:2181,192.168.0.121:2181,192.168.0.122:2181 --replication-factor 2 --partitions 2 --topic my-topic Created topic "my-topic". [root@slave1 kafka_2.11-1.1.0]#
2）验证：同一个名称的topic，在一个kafka的集群环境下，不能重复创建。
在slave1(192.168.0.121)上创建一个名为 my-topic，拥有两个分区，两个副本的Topic
[root@slave1 kafka_2.11-1.1.0]# cd /opt/kafka_2.11-1.1.0/ [root@slave1 kafka_2.11-1.1.0]# bin/kafka-topics.sh --create --zookeeper 192.168.0.120:2181,192.168.0.121:2181,192.168.0.122:2181 --replication-factor 2 --partitions 2 --topic my-topic Error while executing topic command : Topic 'my-topic' already exists. [2018-07-01 07:31:23,274] ERROR org.apache.kafka.common.errors.TopicExistsException: Topic 'my-topic' already exists. (kafka.admin.TopicCommand$) [root@slave1 kafka_2.11-1.1.0]#
在salve2(192.168.0.122)上创建一个名为 my-topic，拥有两个分区，两个副本的Topic
[root@slave2 kafka_2.11-1.1.0]# cd /opt/kafka_2.11-1.1.0/ [root@slave2 kafka_2.11-1.1.0]# bin/kafka-topics.sh --create --zookeeper 192.168.0.120:2181,192.168.0.121:2181,192.168.0.122:2181 --replication-factor 2 --partitions 2 --topic my-topic Error while executing topic command : Topic 'my-topic' already exists. [2018-07-01 07:32:08,099] ERROR org.apache.kafka.common.errors.TopicExistsException: Topic 'my-topic' already exists. (kafka.admin.TopicCommand$) [root@slave2 kafka_2.11-1.1.0]#
3）查看 Topic 状态
[root@slave2 kafka_2.11-1.1.0]# cd /opt/kafka_2.11-1.1.0/ [root@slave2 kafka_2.11-1.1.0]# bin/kafka-topics.sh --describe --zookeeper 192.168.0.120:2181,192.168.0.121:2181,192.168.0.122:2181 --topic my-topic Topic:my-topic PartitionCount:2 ReplicationFactor:2 Configs: Topic: my-topic Partition: 0 Leader: 2 Replicas: 2,0 Isr: 2,0 Topic: my-topic Partition: 1 Leader: 0 Replicas: 0,2 Isr: 0,2
4）查看当前kafka包含的topics列表
[spark@slave1 kafka_2.11-1.1.0]$ cd /opt/kafka_2.11-1.1.0/ [spark@slave1 kafka_2.11-1.1.0]$ bin/kafka-topics.sh --list --zookeeper 192.168.0.120:2181,192.168.0.121:2181,192.168.0.122:2181 my-topic t-my t-order
5）删除某个topic
[spark@slave1 kafka_2.11-1.1.0]$ cd /opt/kafka_2.11-1.1.0/ [spark@slave1 kafka_2.11-1.1.0]$ bin/kafka-topics.sh --delete --zookeeper 192.168.0.120:2181,192.168.0.121:2181,192.168.0.122:2181 --topic my-topic Topic my-topic is marked for deletion. Note: This will have no impact if delete.topic.enable is not set to true. [spark@slave1 kafka_2.11-1.1.0]$ bin/kafka-topics.sh --list --zookeeper 192.168.0.120:2181,192.168.0.121:2181,192.168.0.122:2181 t-my t-order [spark@slave1 kafka_2.11-1.1.0]$
此时，Kafka 集群的搭建已成功完成！
其它常用命令：
查看指定topic信息
bin/kafka-topics.sh --zookeeper 192.168.0.120:2181,192.168.0.121:2181,192.168.0.122:2181 --describe --topic t-my
控制台向topic生产数据
bin/kafka-console-producer.sh --broker-list 192.168.0.121:9092,192.168.0.122:9092,192.168.0.123:9092 --topic t-my
控制台消费topic的数据
bin/kafka-console-consumer.sh --zookeeper 192.168.0.120:2181,192.168.0.121:2181,192.168.0.122:2181 --topic t-my --from-beginning
查看topic某分区偏移量最大（小）值
bin/kafka-run-class.sh kafka.tools.GetOffsetShell --topic t-my --time -1 --broker-list 192.168.0.121:9092,192.168.0.122:9092,192.168.0.123:9092 --partitions 0
注： time为-1时表示最大值，time为-2时表示最小值
参考《https://www.cnblogs.com/RUReady/p/6479464.html》
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