标签：rom scala gic ber 因此 常用 proc can nat
Kafka伪分布式安装的思路跟Zookeeper的伪分布式安装思路完全一样，不过比Zookeeper稍微简单些(不需要创建myid文件),
主要是针对每个Kafka服务器配置一个单独的server.properties，三个服务器分别使用server.properties，server.1.properties, server.2.properties
cp server.properties server.1.properties cp server.properties server.2.properties
修改server.1.properties和server.2.properties,主要有三个属性需要修改
broker.id=1 port=9093 log.dirs=/tmp/kafka-logs-1
port指的是Kakfa服务器监听的端口
启动三个Kafka:
bin/kafka-server-start.sh server.properties bin/kafka-server-start.sh server.1.properties bin/kafka-server-start.sh server.2.properties
--from-beginning If the consumer does not already have an established offset to consume from, start with the earliest message present in the log rather than the latest message.
--topic <topic> The topic id to consume on
--zookeeper <urls> REQUIRED: The connection string for the zookeeper connection in the form host:port. Multiple URLS can be given to allow fail-over.
--group <gid> The group id to consume on. (default: console-consumer-37803)
在consumer端，不需要指定broke-list，而是通过zookeeper和topic找到所有的持有topic消息的broker
--topic <topic> REQUIRED: The topic id to produce messages to.
--broker-list <broker-list> REQUIRED: The broker list string in the form HOST1:PORT1,HOST2:PORT2.
--create Create a new topic.
--describe List details for the given topics.
--list List all available topics.
--partitions <Integer: # of partitions> The number of partitions for the topic being created or altered (WARNING: If partitions are increased for a topic that has a key, the partition logic or ordering of the messages will be affected)
--replication-factor <Integer: replication factor> The replication factor for each partition in the topic being created
--zookeeper <urls> REQUIRED: The connection string for the zookeeper connection in the form host:port. Multiple URLS can be given to allow fail-over.
--topic <topic> The topic to be create, alter or describe. Can also accept a regular expression except for --create option
测试前，先总结有哪些测试点
目前想到的是，Partition有个leader的概念，leader partition是什么意思？干什么用的？
创建一个Topic，10个Partition，副本数为3，也就是说，每个broker上的每个分区，在其它节点都有副本，因为每个节点都有10个节点的数据
当创建完Topic后，每个Topic都会在Kakfa的配置目录下（比如/tmp/kafka-logs，建立相应的目录和文件）
topic_p10_r3-0
topic_p10_r3-1
----
topic_p10_r3-9
其中每个目录下面都有两个文件： 00000000000000000000.index 00000000000000000000.log
./kafka-topics.sh --describe --topic topic_p10_r3 --zookeeper localhost:2181
得到的结果如下：
Topic:topic_p10_r3 PartitionCount:10 ReplicationFactor:3 Configs: Topic: topic_p10_r3 Partition: 0 Leader: 2 Replicas: 2,0,1 Isr: 2,0,1 Topic: topic_p10_r3 Partition: 1 Leader: 0 Replicas: 0,1,2 Isr: 0,1,2 Topic: topic_p10_r3 Partition: 2 Leader: 1 Replicas: 1,2,0 Isr: 1,2,0 Topic: topic_p10_r3 Partition: 3 Leader: 2 Replicas: 2,1,0 Isr: 2,1,0 Topic: topic_p10_r3 Partition: 4 Leader: 0 Replicas: 0,2,1 Isr: 0,2,1 Topic: topic_p10_r3 Partition: 5 Leader: 1 Replicas: 1,0,2 Isr: 1,0,2 Topic: topic_p10_r3 Partition: 6 Leader: 2 Replicas: 2,0,1 Isr: 2,0,1 Topic: topic_p10_r3 Partition: 7 Leader: 0 Replicas: 0,1,2 Isr: 0,1,2 Topic: topic_p10_r3 Partition: 8 Leader: 1 Replicas: 1,2,0 Isr: 1,2,0 Topic: topic_p10_r3 Partition: 9 Leader: 2 Replicas: 2,1,0 Isr: 2,1,0
具体的含义是：
Here is an explanation of output. The first line gives a summary of all the partitions, each additional line gives information about one partition
3.4 问题： 如果副本数为1，是否表示每个partition在集群中只有1份(也就是说每个partition只会存在于一个broker上)，那么leader自然就表示这个partition就在leader所指的broker上了？
建立包含10个分区，同时只有一个副本的topic
./kafka-topics.sh --create --topic topic_p10_r1 --partitions 10 --replication-factor 1 --zookeeper localhost:2181
[hadoop@hadoop bin]$ ./kafka-topics.sh --describe --topic topic_p10_r1 --zookeeper localhost:2181 Topic:topic_p10_r1 PartitionCount:10 ReplicationFactor:1 Configs: Topic: topic_p10_r1 Partition: 0 Leader: 1 Replicas: 1 Isr: 1 Topic: topic_p10_r1 Partition: 1 Leader: 2 Replicas: 2 Isr: 2 Topic: topic_p10_r1 Partition: 2 Leader: 0 Replicas: 0 Isr: 0 Topic: topic_p10_r1 Partition: 3 Leader: 1 Replicas: 1 Isr: 1 Topic: topic_p10_r1 Partition: 4 Leader: 2 Replicas: 2 Isr: 2 Topic: topic_p10_r1 Partition: 5 Leader: 0 Replicas: 0 Isr: 0 Topic: topic_p10_r1 Partition: 6 Leader: 1 Replicas: 1 Isr: 1 Topic: topic_p10_r1 Partition: 7 Leader: 2 Replicas: 2 Isr: 2 Topic: topic_p10_r1 Partition: 8 Leader: 0 Replicas: 0 Isr: 0 Topic: topic_p10_r1 Partition: 9 Leader: 1 Replicas: 1 Isr: 1
可见理解不错，每个partition有不同的leader，Leader所在的broker同时也是Replicas所在的broker(ID号一样)
因此可以理解，
1. 每个partition副本集都有一个leader
2. leader指的是partition副本集中的leader，它负责读写，然后负责将数据复制到其它的broker上。
3.一个Topic的所有partition会比较均匀的分布到多个broker上
在上面已经建立了两个Topic，一个是10个分区3个副本， 一个是10个分区1个副本。此时，假如有一个broker挂了，看看这两个Topic的容错如何？
通过jps命令可以看到有三个Kafka进程。
通过ps -ef|grep server.2.properties可以找到brokerId为2的Kakfa进程，使用kill -9将其干掉。干掉的时候，console开始刷屏，异常信息一样，都是：
[2015-02-23 02:14:00,037] WARN Reconnect due to socket error: null (kafka.consumer.SimpleConsumer)
[2015-02-23 02:14:00,039] ERROR [ReplicaFetcherThread-0-2], Error in fetch Name: FetchRequest; Version: 0; CorrelationId: 4325; ClientId: ReplicaFetcherThread-0-2; ReplicaId: 1; MaxWait: 500 ms; MinBytes: 1 bytes; RequestInfo: [topic_p10_r3,3] -> PartitionFetchInfo(0,1048576),[topic_p10_r3,9] -> PartitionFetchInfo(0,1048576),[topic_p10_r3,6] -> PartitionFetchInfo(0,1048576),[topic_p10_r3,0] -> PartitionFetchInfo(0,1048576) (kafka.server.ReplicaFetcherThread)
java.net.ConnectException: Connection refused
at sun.nio.ch.Net.connect0(Native Method)
at sun.nio.ch.Net.connect(Net.java:465)
at sun.nio.ch.Net.connect(Net.java:457)
at sun.nio.ch.SocketChannelImpl.connect(SocketChannelImpl.java:670)
at kafka.network.BlockingChannel.connect(BlockingChannel.scala:57)
at kafka.consumer.SimpleConsumer.connect(SimpleConsumer.scala:44)
at kafka.consumer.SimpleConsumer.reconnect(SimpleConsumer.scala:57)
at kafka.consumer.SimpleConsumer.liftedTree1$1(SimpleConsumer.scala:79)
at kafka.consumer.SimpleConsumer.kafka$consumer$SimpleConsumer$$sendRequest(SimpleConsumer.scala:71)
at kafka.consumer.SimpleConsumer$$anonfun$fetch$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(SimpleConsumer.scala:109)
at kafka.consumer.SimpleConsumer$$anonfun$fetch$1$$anonfun$apply$mcV$sp$1.apply(SimpleConsumer.scala:109)
at kafka.consumer.SimpleConsumer$$anonfun$fetch$1$$anonfun$apply$mcV$sp$1.apply(SimpleConsumer.scala:109)
at kafka.metrics.KafkaTimer.time(KafkaTimer.scala:33)
at kafka.consumer.SimpleConsumer$$anonfun$fetch$1.apply$mcV$sp(SimpleConsumer.scala:108)
at kafka.consumer.SimpleConsumer$$anonfun$fetch$1.apply(SimpleConsumer.scala:108)
at kafka.consumer.SimpleConsumer$$anonfun$fetch$1.apply(SimpleConsumer.scala:108)
at kafka.metrics.KafkaTimer.time(KafkaTimer.scala:33)
at kafka.consumer.SimpleConsumer.fetch(SimpleConsumer.scala:107)
at kafka.server.AbstractFetcherThread.processFetchRequest(AbstractFetcherThread.scala:96)
at kafka.server.AbstractFetcherThread.doWork(AbstractFetcherThread.scala:88)
at kafka.utils.ShutdownableThread.run(ShutdownableThread.scala:51)
[2015-02-23 02:14:00,040] WARN Reconnect due to socket error: null (kafka.consumer.SimpleConsumer)
3,9,6,0 这个四个分区 正是topic_p10_r3上broker2作为leader的partition，可见Kafka要做Leader移交，看看此时的topic_p10_r3和topic_p10_r1的情况，我们已经把broker2 kill掉了
topic_p10_r3（Partition切换到其它Leader上了。。。Rplicas还有3,。。。）
[hadoop@hadoop bin]$ ./kafka-topics.sh --describe --topic topic_p10_r3 --zookeeper localhost:2181
Topic:topic_p10_r3 PartitionCount:10 ReplicationFactor:3 Configs:
Topic: topic_p10_r3 Partition: 0 Leader: 0 Replicas: 2,0,1 Isr: 0,1
Topic: topic_p10_r3 Partition: 1 Leader: 0 Replicas: 0,1,2 Isr: 0,1
Topic: topic_p10_r3 Partition: 2 Leader: 1 Replicas: 1,2,0 Isr: 1,0
Topic: topic_p10_r3 Partition: 3 Leader: 1 Replicas: 2,1,0 Isr: 1,0
Topic: topic_p10_r3 Partition: 4 Leader: 0 Replicas: 0,2,1 Isr: 0,1
Topic: topic_p10_r3 Partition: 5 Leader: 1 Replicas: 1,0,2 Isr: 1,0
Topic: topic_p10_r3 Partition: 6 Leader: 0 Replicas: 2,0,1 Isr: 0,1
Topic: topic_p10_r3 Partition: 7 Leader: 0 Replicas: 0,1,2 Isr: 0,1
Topic: topic_p10_r3 Partition: 8 Leader: 1 Replicas: 1,2,0 Isr: 1,0
Topic: topic_p10_r3 Partition: 9 Leader: 1 Replicas: 2,1,0 Isr: 1,0
topic_p10_r1:没有切换，其中分区为1,47的Leader是-1了。。 这就出错了
[hadoop@hadoop bin]$ ./kafka-topics.sh --describe --topic topic_p10_r1 --zookeeper localhost:2181 Topic:topic_p10_r1 PartitionCount:10 ReplicationFactor:1 Configs: Topic: topic_p10_r1 Partition: 0 Leader: 1 Replicas: 1 Isr: 1 Topic: topic_p10_r1 Partition: 1 Leader: -1 Replicas: 2 Isr: Topic: topic_p10_r1 Partition: 2 Leader: 0 Replicas: 0 Isr: 0 Topic: topic_p10_r1 Partition: 3 Leader: 1 Replicas: 1 Isr: 1 Topic: topic_p10_r1 Partition: 4 Leader: -1 Replicas: 2 Isr: Topic: topic_p10_r1 Partition: 5 Leader: 0 Replicas: 0 Isr: 0 Topic: topic_p10_r1 Partition: 6 Leader: 1 Replicas: 1 Isr: 1 Topic: topic_p10_r1 Partition: 7 Leader: -1 Replicas: 2 Isr: Topic: topic_p10_r1 Partition: 8 Leader: 0 Replicas: 0 Isr: 0 Topic: topic_p10_r1 Partition: 9 Leader: 1 Replicas: 1 Isr: 1
重启broker 2得到结果如下：（对于topic_p10_r3，leader没有变化，即每个Partition都有自己的Leader，新加入的broker只能follower；而topic_p10_r1,则会选出Leader）
[hadoop@hadoop bin]$ ./kafka-topics.sh --describe --topic topic_p10_r3 --zookeeper localhost:2181 Topic:topic_p10_r3 PartitionCount:10 ReplicationFactor:3 Configs: Topic: topic_p10_r3 Partition: 0 Leader: 0 Replicas: 2,0,1 Isr: 0,1,2 Topic: topic_p10_r3 Partition: 1 Leader: 0 Replicas: 0,1,2 Isr: 0,1,2 Topic: topic_p10_r3 Partition: 2 Leader: 1 Replicas: 1,2,0 Isr: 1,0,2 Topic: topic_p10_r3 Partition: 3 Leader: 1 Replicas: 2,1,0 Isr: 1,0,2 Topic: topic_p10_r3 Partition: 4 Leader: 0 Replicas: 0,2,1 Isr: 0,1,2 Topic: topic_p10_r3 Partition: 5 Leader: 1 Replicas: 1,0,2 Isr: 1,0,2 Topic: topic_p10_r3 Partition: 6 Leader: 0 Replicas: 2,0,1 Isr: 0,1,2 Topic: topic_p10_r3 Partition: 7 Leader: 0 Replicas: 0,1,2 Isr: 0,1,2 Topic: topic_p10_r3 Partition: 8 Leader: 1 Replicas: 1,2,0 Isr: 1,0,2 Topic: topic_p10_r3 Partition: 9 Leader: 1 Replicas: 2,1,0 Isr: 1,0,2 [hadoop@hadoop bin]$ ./kafka-topics.sh --describe --topic topic_p10_r1 --zookeeper localhost:2181 Topic:topic_p10_r1 PartitionCount:10 ReplicationFactor:1 Configs: Topic: topic_p10_r1 Partition: 0 Leader: 1 Replicas: 1 Isr: 1 Topic: topic_p10_r1 Partition: 1 Leader: 2 Replicas: 2 Isr: 2 Topic: topic_p10_r1 Partition: 2 Leader: 0 Replicas: 0 Isr: 0 Topic: topic_p10_r1 Partition: 3 Leader: 1 Replicas: 1 Isr: 1 Topic: topic_p10_r1 Partition: 4 Leader: 2 Replicas: 2 Isr: 2 Topic: topic_p10_r1 Partition: 5 Leader: 0 Replicas: 0 Isr: 0 Topic: topic_p10_r1 Partition: 6 Leader: 1 Replicas: 1 Isr: 1 Topic: topic_p10_r1 Partition: 7 Leader: 2 Replicas: 2 Isr: 2 Topic: topic_p10_r1 Partition: 8 Leader: 0 Replicas: 0 Isr: 0 Topic: topic_p10_r1 Partition: 9 Leader: 1 Replicas: 1 Isr: 1
标签：rom scala gic ber 因此 常用 proc can nat
原文地址：https://www.cnblogs.com/jack-Star/p/9927557.html
