Kafka集群配置

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前言

最近在利用 Spark streaming 和Kafka构建一个实时的数据分析系统，对图书阅读数据进行分析，做实时推荐。Spark Streaming 模块是对于 Spark Core 的一个扩展，目的是为了以高吞吐量，并且容错的方式处理持续性的数据流。目前 Spark Streaming 支持的外部数据源有 Flume、Kafka、Twitter、ZeroMQ、TCP Socket 等。Apache Kafka是一个分布式的消息发布 - 订阅系统，Kafka 可以作为流计算系统的数据源，本例中 Spark Streaming 将从 Kafka 中消费数据。

系统环境

软件版本

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Spark: 1.4.1
Kafka: 0.8.1.1
zookeeper: 3.4.6

集群节点

一共有四台主机，主机名分别为 nn0001, dn0001, dn0002, dn0003。

192.168.186.12   nn0001
192.168.186.13   dn0001
192.168.186.14   dn0002
192.168.186.15   dn0003

zookeeper 安装

kafka 使用 zookeeper 来管理，存储一些 meta 信息，并使用了 zookeeper watch 机制来发现 meta 信息的变更并作出相应的动作(比如 consumer 失效, 触发负载均衡等)。
Zookeeper 的配置在机器 1 上完成后分发到其他三台机器即可。

[bigdata@nn0001 ~]$ wget http://archive.apache.org/dist/zookeeper/stable/zookeeper-3.4.6.tar.gz
[bigdata@nn0001 ~]$ tar -zxvf zookeeper-3.4.6.tar.gz
[bigdata@nn0001 ~]$cd zookeeper-3.4.6/conf
[bigdata@nn0001 conf]$ pwd
/home/bigdata/bigprosoft/zookeeper-3.4.6/conf
[bigdata@nn0001 conf]$ cp zoo_sample.cfg zoo.cfg

修改配置文件

[bigdata@nn0001 conf]$ vi zoo.cfg 
tickTime=2000
dataDir=/home/bigdata/bigprosoft/zookeeper/data
clientPort=2181
initLimit=10
syncLimit=5
server.1=nn0001:2888:3888
server.2=dn0001:2888:3888
server.3=dn0002:2888:3888
server.4=dn0003:2888:3888

在 dataDir 目录下创建 myid 文件，nn0001 机器的内容为 1，dn0001 机器的内容为 2，更多依此类推。

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[bigdata@nn0001 data]$ echo 1 > myid
[bigdata@nn0001 data]$ cat myid
1

启动测试

[bigdata@nn0001 bin]$ ./zkServer.sh start
[bigdata@nn0001 bin]$ jps
10805 QuorumPeerMain   # 已经启动成功了
15494 Master
11816 NameNode
20958 Jps
17539 Worker
12084 ResourceManager
12945 RunJar
12944 RunJar

停止

`1`	`[bigdata@nn0001 bin]$ ./zkServer.sh stop`

其它机器相同操作,scp 过去即可。

kafka 安装

Kafka 的 broker、producer、consumer、topic 等概念以及原理可以查阅官方文档
本次实验采用的多节点多 broker 集群模式，为每一台机器分配一个broker id。

[bigdata@nn0001 ~]$ wget http://mirror.bit.edu.cn/apache/kafka/0.8.1.1/kafka_2.10-0.8.1.1.tgz
[bigdata@nn0001 ~]$ tar zxf kafka_2.10-0.8.1.1.tgz
[bigdata@nn0001 ~]$ cd kafka_2.10-0.8.1.1
[bigdata@nn0001 kafka_2.10-0.8.1.1]$ cd conf
[bigdata@nn0001 conf]$ vi server.properties
broker.id=1  # 其它机器的 id 依次递增即可
port=9092
host.name=192.168.186.12
advertised.host.name=192.168.186.12
zookeeper.connect=192.168.186.12:2181,192.168.186.13:2181,192.168.186.14:2181,192.168.186.15:2181

修改完成后分发到另外三台机器上。

启动测试

[bigdata@nn0001 bin]$ nohup ./kafka-server-start.sh ../config/server.properties &
[bigdata@nn0001 conf]$ jps
10805 QuorumPeerMain
21282 Jps
15494 Master
21209 Kafka
11816 NameNode
17539 Worker
12084 ResourceManager
12945 RunJar
12944 RunJar

依次启动机器

kafka 使用测试

创建 topic

`1`	`[bigdata@nn0001 bin]$ ./kafka-topics.sh --create --zookeeper nn0001:2181 --replication-factor 3 --partitions 1 --topic test`

查看 topic

[bigdata@nn0001 bin]$ ./kafka-topics.sh --describe --zookeeper nn0001:2181
Topic:mytest    PartitionCount:2        ReplicationFactor:2     Configs:
        Topic: mytest   Partition: 0    Leader: 2       Replicas: 3,2   Isr: 2
        Topic: mytest   Partition: 1    Leader: -1      Replicas: 4,3   Isr: 
Topic:test      PartitionCount:1        ReplicationFactor:3     Configs:
        Topic: test     Partition: 0    Leader: 2       Replicas: 2,3,4 Isr: 2

producer 测试

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[bigdata@nn0001 bin]$ ./kafka-console-producer.sh --broker-list 192.168.186.12:9092 --topic test
gsdggfgfgfd
gdfgdfgdf

conumer 测试

[bigdata@nn0001 bin]$ ./kafka-console-consumer.sh --zookeeper  192.168.186.12:2181 --from-beginning --topic test


abfsfsdfsdfs
ffsdfs
gsdggfgfgfd
gdfgdfgdf
^C[2015-08-28 17:48:40,991] WARN Reconnect due to socket error: null (kafka.consumer.SimpleConsumer)
Consumed 7 messages
`

测试高可用

[bigdata@nn0001 bin]$ ./kafka-topics.sh --describe --zookeeper 192.168.186.12:2181,192.168.186.13:2181,192.168.186.14:2181,192.168.186.15:2181 --from-beginning --topic test
Topic:test      PartitionCount:1        ReplicationFactor:3     Configs:
        Topic: test     Partition: 0    Leader: 2       Replicas: 2,3,4 Isr: 2,4
# 可以看到 leader 是2，是 dn0001 机器，把此机器上的 kafka 进程杀掉，再查看 topic 的 leader

[bigdata@dn0002 bin]$ ./kafka-topics.sh --describe --zookeeper 192.168.186.12:2181,192.168.186.13:2181,192.168.186.14:2181,192.168.186.15:2181 --topic test
Topic:test      PartitionCount:1        ReplicationFactor:3     Configs:
        Topic: test     Partition: 0    Leader: 4       Replicas: 2,3,4 Isr: 4
# 此时 leader 变成了4，对应的机器是 dn0003.

[bigdata@nn0001 bin]$ ./kafka-console-consumer.sh --zookeeper 192.168.186.12:2181,192.168.186.13:2181,192.168.186.14:2181,192.168.186.15:2181 --from-beginning --topic test


abfsfsdfsdfs
ffsdfs
gsdggfgfgfd
gdfgdfgdf
q

^C[2015-08-31 10:14:50,964] WARN Reconnect due to socket error: null (kafka.consumer.SimpleConsumer)
Consumed 7 messages
# 消费者消费信息测试

ok，搭建过程就完成，下面用 python/java/scala 进行开发实例即可。

排错

问题 1 描述

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SLF4J: Failed to load class "org.slf4j.impl.StaticLoggerBinder".
SLF4J: Defaulting to no-operation (NOP) logger implementation
SLF4J: See http://www.slf4j.org/codes.html#StaticLoggerBinder for further details.

解决方法

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[bigdata@nn0001 ~]$ wget http://www.slf4j.org/dist/slf4j-1.7.12.tar.gz
[bigdata@nn0001 ~]$ cd slf4j-1.7.12
[bigdata@nn0001 ~]$ cp slf4j-nop-1.7.12.jar ~/bigprosoft/kafka/libs/

问题 2 描述

[bigdata@nn0001 bin]$ ./kafka-console-producer.sh --broker-list nn0001:9092  --topic test
fsfsdfsdf
……
[2015-08-28 17:24:18,417] ERROR Failed to send requests for topics test with correlation ids in [0,8] (kafka.producer.async.DefaultEventHandler)
[2015-08-28 17:24:18,419] ERROR Error in handling batch of 1 events (kafka.producer.async.ProducerSendThread)
kafka.common.FailedToSendMessageException: Failed to send messages after 3 tries.
        at kafka.producer.async.DefaultEventHandler.handle(DefaultEventHandler.scala:90)
        at kafka.producer.async.ProducerSendThread.tryToHandle(ProducerSendThread.scala:104)
        at kafka.producer.async.ProducerSendThread$$anonfun$processEvents$3.apply(ProducerSendThread.scala:87)
        at kafka.producer.async.ProducerSendThread$$anonfun$processEvents$3.apply(ProducerSendThread.scala:67)
        at scala.collection.immutable.Stream.foreach(Stream.scala:547)
        at kafka.producer.async.ProducerSendThread.processEvents(ProducerSendThread.scala:66)
        at kafka.producer.async.ProducerSendThread.run(ProducerSendThread.scala:44)
……

解决方法，把 server.properties 中主机名改为 IP 地址即可。

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host.name=10.171.59.221
advertised.host.name=10.171.59.221
zookeeper.connect=192.168.186.12:2181,192.168.186.13:2181,192.168.186.14:2181,192.168.186.15:2181