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
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；
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;
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
进入自己的目录cd /usr/local/program 创建文件夹mkdir kafkaLogs配置环境变量： #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 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
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 & 同样的操作对于其它机器创建主题topic,执行命令：kafka-topics.sh –create –zookeeper localhost:12181 –replication-factor 3 –partitions 1 –topic my-replicated-topic查看主题topic,执行命令：kafka-topics.sh –describe –zookeeper localhost:12181 –topic my-replicated-topic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。
ok 至此搭建完成kafka集群。
参考：jikexueyuan
