Kafka集群搭建

柔光的暖阳◎ 2022-09-28 14:24 186阅读 0赞

## Kafka使用背景    ##

在我们大量使用分布式数据库、分布式计算集群的时候，是否会遇到这样一些问题：

*  我想分析一下用户行为（pageviews），以便我能设计出更好的广告位;
 *  我想对用户的搜索关键词进行统计，分析出前的流行趋势；
 *  有些数据，存数据库浪费，直接存硬盘操作效率又低;

这个时候，就可以用消息系统了，尤其是分布式消息系统；

另外：

在很多常见的大数据处理场景中，我们需要对数据进行离线分析和实时分析，离线分析借助于hadoop相关框架（mapreduce、hive等），对于实时需求可以使用storm，为了统一离线和实时计算，我们可以将离线和实时计算的数据源统一作为输入，然后将数据的流向分别经由离线分析系统和实时系统，分别进行分析处理，这是我们可以考虑将数据源（flume收集）直接连接一个消息中间件，如kafka，整合flume + kafka，flume作为消息的生产者，产生的消息数据（日志数据、业务数据等）发布到kafka中，然后使用Storm的Topology作为消息的Consumer，在Storm集群中分别进行如下两个需求场景的处理：

*  直接使用Storm的Topology对数据进行实时分析处理
 *  整合Storm+HDFS，将消息处理后写入HDFS进行离线分析处理

## kafka的定义    ##

##     是一个分布式消息系统，由LinkedIn使用Scala编写，用作LinkedIn的活动流（Activity Stream）和运营数据处理管道（Pipeline）的基础，具有高水平扩展和高吞吐量。    ##

应用领域： 已被多家不同类型的公司作为多种类型的数据管道和消息系统使用。如:

淘宝，支付宝，百度，twitter等

目前越来越多的开源分布式处理系统如Apache flume、Apache Storm、Spark,elasticsearch都支持与Kafka集成

![Center][]

### AMQP协议（advanced message queue protocol）    ###

![Center 1][]

基本消费者（Consumer）：从消息队列中请求消息的客户端应用程序；  
生产者（Producer）：向broker发布消息的客户端应用程序；  
AMQP服务器端（broker）:用来接收生产者发送的消息并将这些消息路由给服务器中的队列；

### kafka基本架构    ###

![Center 2][]

主题（Topic）：即一种类型；如一个主题类似新闻中的体育、娱乐、教育等分类概念，在实际工程中通常一个业务一个主题；  
分区（Partition）：一个topic中的消息数据按照多个分区组织，分区是kafka消息队列组织的最小单位，一个分区可以看做是一个FIFO的队列;

#### kafka集群搭建    ####

### zookeeper集群搭建（见前面章节）    ###

### kafka集群    ###

#### 环境准备    ####

*  服务器3台（192.168.0.102，192.168.0.103，192.168.0.104）
 *  kafka版本kafka\_2.9.2-0.8.1.1

#### 配置环境    ####

1.  进入自己的目录cd /usr/local/program  创建文件夹mkdir kafkaLogs
2.  配置环境变量：
3.      #set enviroment
        export JAVA_HOME=/usr/local/program/jdk1.7.0_79
        export ZK_HOME=/usr/local/program/zk/zookeeper-3.4.6
        export KAFKA_HOME=/usr/local/program/kafka/kafka_2.9.2-0.8.1.1
        export PATH=$JAVA_HOME/bin:$ZK_HOME/bin:$KAFKA_HOME/bin:$PATH
4.  配置kafka，cd /usr/local/program/kafka/kafka\_2.9.2-0.8.1.1/config/ 编辑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.
        #机器在集群中的唯一标识
        broker.id=0
        
        ############################# Socket Server Settings #############################
        
        # The port the socket server listens on
        #对外提供服务的tcp端口 默认9092
        port=19092
        
        # Hostname the broker will bind to. If not set, the server will bind to all interfaces
        #主机ip，默认localhost 
        host.name=192.168.0.102
        
        # Hostname the broker will advertise to producers and consumers. If not set, it uses the
        # value for "host.name" if configured.  Otherwise, it will use the value returned from
        # java.net.InetAddress.getCanonicalHostName().
        #advertised.host.name=<hostname routable by clients>
        
        # The port to publish to ZooKeeper for clients to use. If this is not set,
        # it will publish the same port that the broker binds to.
        #advertised.port=<port accessible by clients>
        
        # The number of threads handling network requests
        #broker进行网络处理的线程数
        num.network.threads=3
         
        # The number of threads doing disk I/O
        #broker进行io处理的线程数
        num.io.threads=8
        
        # The send buffer (SO_SNDBUF) used by the socket server
        #kafka发送消息的缓冲区5m 默认102400
        socket.send.buffer.bytes=1048576
        
        # The receive buffer (SO_RCVBUF) used by the socket server
        #kafka接收消息的缓冲区5m 默认102400
        socket.receive.buffer.bytes=1048576
        
        # The maximum size of a request that the socket server will accept (protection against OOM)
        #向kafka请求 或者接收消息的最大数，不能超过java堆栈大小
        socket.request.max.bytes=104857600
        
        
        ############################# Log Basics #############################
        
        # A comma seperated list of directories under which to store log files
        #kafka消息日志目录 多个以逗号分割
        log.dirs=/usr/local/program/kafka/kafkaLogs
        
        # 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.
        #每个topic的分区数
        num.partitions=2
        
        ############################# 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 exceessive 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
        #kafka消息的驻留时间，168小时【7天】
        log.retention.hours=168
        
        #往kafka发送的消息每条不超过的大小5m（默认为1m）
        message.max.byte=5048576
        #默认的复制因子，每个topic中的partion的副本（默认为1）
        default.replication.factor=2
        replica.fetch.max.bytes=5048576
        
        # A size-based retention policy for logs. Segments are pruned from the log as long as the remaining
        # segments don't drop below log.retention.bytes.
        #log.retention.bytes=1073741824
        
        # The maximum size of a log segment file. When this size is reached a new log segment will be created.
        #每个消息文件的大小（因为消息是追加写入），超过这个数就会新起一个文件
        log.segment.bytes=536870912
        
        # The interval at which log segments are checked to see if they can be deleted according 
        # to the retention policies
        #每隔这个时间查看kafkalog是否失效，即查看是否有过期消息，如果有则删除
        log.retention.check.interval.ms=60000
        
        # By default the log cleaner is disabled and the log retention policy will default to just delete segments after their retention expires.
        # If log.cleaner.enable=true is set the cleaner will be enabled and individual logs can then be marked for log compaction.
        log.cleaner.enable=false
        
        ############################# 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集群地址
        zookeeper.connect=192.168.0.102:12181,192.168.0.103:12181,192.168.0.104:12181
        
        # Timeout in ms for connecting to zookeeper
        #kafka集群连接zk的超时时间
        zookeeper.connection.timeout.ms=1000000
5.  同样的操作对于其它机器，配置文件的broker.id分别改为1和2，hostname对应相应的主机ip
6.  后台启动kafka，执行命令：kafka-server-start.sh /usr/local/program/kafka/kafka\_2.9.2-0.8.1.1/config/server.properties & 
7.  同样的操作对于其它机器
8.  创建主题topic,执行命令：kafka-topics.sh –create –zookeeper localhost:12181 –replication-factor 3 –partitions 1 –topic my-replicated-topic
9.  查看主题topic,执行命令：kafka-topics.sh –describe –zookeeper localhost:12181 –topic my-replicated-topic
10. 创建生产者producer,执行命令：kafka-console-producer.sh –broker-list 192.168.0.102:19092 –topic my-replicated-topic
11. 创建消费者consumer（另外一台机器）,执行命令：kafka-console-consumer.sh –zookeeper localhost:12181 –from-beginning –topic my-replicated-topic
    
    测试：生产者者命令行输入信息，即可在消费者命令行看到对应消息的输出。
12. ok 至此搭建完成kafka集群。

![Center 3][]

参考：jikexueyuan

[Center]: /images/20220708/7db610dee3f14bd7b115c9619e47addc.png
[Center 1]: /images/20220708/ba3c1d34bb9143078388f97b4687f03f.png
[Center 2]: /images/20220708/7c4b867529be45f6877951aa239f304e.png
[Center 3]: /images/20220708/286e0931637a4b16bd5bc2f666ce4d47.png