1. ZooKeeper安装部署
本文在一台机器上模拟3个zk server的集群安装。
1.1. 创建目录、解压
| cd /usr/ #创建项目目录 mkdir zookeeper
cd zookeeper mkdir tmp mkdir zookeeper-1 mkdir zookeeper-2 mkdir zookeeper-3
cd tmp mkdir zk1 mkdir zk2 mkdir zk3
cd zk1 mkdir data mkdir log
cd zk2 mkdir data mkdir log
cd zk3 mkdir data mkdir log
#将压缩包分别解压一份到 zookeeper-1, zookeeper-2, zookeeper-3目录下 tar -zxvf zookeeper-3.4.10.tgz |
1.2. 创建每个目录下conf/zoo.cfg配置文件
/usr/zookeeper/zookeeper-1/zookeeper-3.4.10/conf/zoo.cfg 内容如下：
| tickTime=2000 initLimit=10 syncLimit=5 dataDir=/home/hadoop/tmp/zk1/data dataLogDir=/home/hadoop/tmp/zk1/log clientPort=2181 server.1=192.168.68.128:2287:3387 server.2=192.168.68.128:2288:3388 server.3=192.168.68.128:2289:3389 |
/usr/zookeeper/zookeeper-1/zookeeper-3.4.10/conf/zoo.cfg 内容如下：
| tickTime=2000 initLimit=10 syncLimit=5 dataDir=/home/hadoop/tmp/zk2/data dataLogDir=/home/hadoop/tmp/zk2/log clientPort=2182 server.1=192.168.68.128:2287:3387 server.2=192.168.68.128:2288:3388 server.3=192.168.68.128:2289:3389 |
/usr/zookeeper/zookeeper-1/zookeeper-3.4.10/conf/zoo.cfg 内容如下：
| tickTime=2000 initLimit=10 syncLimit=5 dataDir=/home/hadoop/tmp/zk3/data dataLogDir=/home/hadoop/tmp/zk3/log clientPort=2183 server.1=192.168.68.128:2287:3387 server.2=192.168.68.128:2288:3388 server.3=192.168.68.128:2289:3389 |
注：红色部分192.168.68.128为服务器的ip。
因为是在一台机器上模拟集群，所以端口不能重复，这里用2181~2183，2287~2289，以及3387~3389相互错开。
另外每个zk的instance，都需要设置独立的数据存储目录、日志存储目录，所以dataDir、dataLogDir这二个节点对应的目录，需要手动先创建好。即1.1所述的：
/usr/zookeeper/tmp/zk1/data
/usr/zookeeper/tmp/zk1/log
/usr/zookeeper/tmp/zk2/data
/usr/zookeeper/tmp/zk2/log
/usr/zookeeper/tmp/zk3/data
/usr/zookeeper/tmp/zk3/log
1.3. 创建每个目录下data/myid文件
另外还有一个非常关键的设置，在每个zk server配置文件的dataDir所对应的目录下，必须创建一个名为myid的文件，其中的内容必须与zoo.cfg中server.x中的x相同，即：
/usr/zookeeper/tmp/zk1/data/myid 中的内容为1，对应server.1中的1
/usr/zookeeper/tmp/zk1/data/myid 中的内容为2，对应server.2中的2
/usr/zookeeper/tmp/zk1/data/myid 中的内容为3，对应server.3中的3
生产环境中，分布式集群部署的步骤与上面基本相同，只不过因为各zk server分布在不同的机器，上述配置文件中的localhost换成各服务器的真实Ip即可。分布在不同的机器后，不存在端口冲突问题，可以让每个服务器的zk均采用相同的端口，这样管理起来比较方便。
1.4. 启动验证
| /usr/zookeeper/zookeeper-1/bin/zkServer.sh start & |
| /usr/zookeeper/zookeeper-3/bin/zkServer.sh start & |
| /usr/zookeeper/zookeeper-3/bin/zkServer.sh start & |
注：&符号表示后台启动，启动后可以退出命令行窗口。
启用成功后，输入 jps 看下进程：
2644 QuorumPeerMain
2677 QuorumPeerMain
2724 QuorumPeerMain
应该至少能看到以上几个进程。
查看zk状态命令：
| bin/zkServer.sh status |
分别查看zk状态，可以看到：
ZooKeeper JMX enabled by default
Using config: /usr/zookeeper/zookeeper-1/zookeeper-3.4.10/bin/../conf/zoo.cfg
Mode: follower
ZooKeeper JMX enabled by default
Using config: /usr/zookeeper/zookeeper-2/zookeeper-3.4.10/bin/../conf/zoo.cfg
Mode: leader
ZooKeeper JMX enabled by default
Using config: /usr/zookeeper/zookeeper-3/zookeeper-3.4.10/bin/../conf/zoo.cfg
Mode: follower
至此，zookeeper集群已经部署完成了。
2. Kafka安装部署
2.1. 创建目录、解压
| cd /usr/ #创建项目目录 mkdir kafka cd kafka mkdir tmp cd tmp #创建kafka消息目录，主要存放kafka消息 mkdir kafka-logs-1 mkdir kafka-logs-2 mkdir kafka-logs-3 #将压缩包放到usr/kafka内，解压 tar -zxvf kafka_2.10-0.10.1.0.tgz |
2.2. 修改配置文件
进入到config目录
| cd /usr/kafka/kafka_2.10-0.10.1.0/config |
主要关注：server.properties 这个文件即可。将其拷贝三份到同级目录：
config/server-1.properties
config/server-3.properties
config/server-2.properties
以下为默认配置：
| # 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
# Switch to enable topic deletion or not, default value is false #delete.topic.enable=true
############################# 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 = security_protocol://host_name:port # EXAMPLE: # listeners = PLAINTEXT://your.host.name:9092 #listeners=PLAINTEXT://: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
# The number of threads handling network requests num.network.threads=3
# The number of threads doing 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 seperated list of directories under which to store log files log.dirs=/tmp/kafka-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
############################# 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 log.retention.hours=168
# 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. 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=localhost:2181
# Timeout in ms for connecting to zookeeper zookeeper.connection.timeout.ms=6000 |
需要修改的只有四处：
broker.id=0
#listeners=PLAINTEXT://:9092
log.dirs=/tmp/kafka-logs
zookeeper.connect=localhost:2181
分别修改三个配置文件，修改上面四处为：
config/server-1.properties
| broker.id=1 listeners=PLAINTEXT://192.168.68.128:9092 log.dirs=/usr/kafka/tmp/kafka-logs-1 zookeeper.connect=192.168.68.128:2181,192.168.68.128:2182,192.168.68.128:2183 |
config/server-2.properties
| broker.id=2 listeners=PLAINTEXT://192.168.68.128:9092 log.dirs=/usr/kafka/tmp/kafka-logs-2 zookeeper.connect=192.168.68.128:2181,192.168.68.128:2182,192.168.68.128:2183 |
config/server-3.properties
| broker.id=3 listeners=PLAINTEXT://192.168.68.128:9092 log.dirs=/usr/kafka/tmp/kafka-logs-3 zookeeper.connect=192.168.68.128:2181,192.168.68.128:2182,192.168.68.128:2183 |
注：红色部分为服务器的ip。
2.3. 启动验证
进入kafka目录，后台启动kafka集群：
| bin/kafka-server-start.sh ./config/server-1.properties & |
| bin/kafka-server-start.sh ./config/server-2.properties & |
| bin/kafka-server-start.sh ./config/server-3.properties & |
执行命令jps验证是否启动：
2820 QuorumPeerMain
9366 Kafka
9655 Kafka
9924 Kafka
2877 QuorumPeerMain
2923 QuorumPeerMain
10189 Jps
至此，kafka集群已经部署完成了。
3. Kafka的java开发环境搭建
3.1. 导入jar包
解压kafka压缩包，进入kafka_2.10-0.10.1.0\libs，拷贝一下jar包到java工程的lib目录下：
3.2. Producer
| package com.pers.producer;
import java.util.Properties; import java.util.concurrent.TimeUnit;
import kafka.javaapi.producer.Producer; import kafka.producer.KeyedMessage; import kafka.producer.ProducerConfig; import kafka.serializer.StringEncoder;
/** * @author liangyadong * @date 2017年5月26日 下午3:04:07 * @version 1.0 */ public class KafkaProducer {
private String topic; public KafkaProducer(String topic){ super(); this.topic = topic; }
public void run(){
Producer producer = createProducer();
int i = 0; while(true){ producer.send(new KeyedMessage
try{ TimeUnit.SECONDS.sleep(1); } catch(InterruptedException e) { e.printStackTrace(); } } }
private Producer createProducer(){
Properties properties = new Properties(); properties.put("zookeeper.connect", "192.168.68.128:2181,192.168.68.128:2182,192.168.68.128:2183");// 声明zookeeper properties.put("serializer.class", StringEncoder.class.getName()); properties.put("metadata.broker.list", "192.168.68.128:9092,192.168.68.128:9093,192.168.68.128:9094");// 声明kafka
return new Producer }
public static void main(String[] args) { new KafkaProducer("test111").run();// 创建主题,发送消息 }
} |
3.3. Consumer
| package com.pers.consumer;
import java.util.HashMap; import java.util.List; import java.util.Map; import java.util.Properties;
import kafka.consumer.Consumer; import kafka.consumer.ConsumerConfig; import kafka.consumer.ConsumerIterator; import kafka.consumer.KafkaStream; import kafka.javaapi.consumer.ConsumerConnector;
/** * @author liangyadong * @date 2017年5月26日 下午4:01:37 * @version 1.0 */ public class KafkaConsumer extends Thread{
private String topic;
public KafkaConsumer(String topic){ super(); this.topic = topic; }
public void run() { ConsumerConnector consumer = createConsumer(); Map topicCountMap.put(topic, 1); // 一次从主题中获取一个数据 Map KafkaStream<byte[], byte[]> stream = messageStreams.get(topic).get(0);// 获取每次接收到的这个数据 ConsumerIterator<byte[], byte[]> iterator = stream.iterator(); while(iterator.hasNext()){ String message = new String(iterator.next().message()); System.out.println("接收到: " + message); } }
private ConsumerConnector createConsumer(){
Properties properties = new Properties(); properties.put("zookeeper.connect", "192.168.68.128:2181,192.168.68.128:2182,192.168.68.128:2183");// 声明zookeeper properties.put("group.id", "group5");// 必须要使用别的组名称， 如果生产者和消费者都在同一组，则不能访问同一组内的topic数据 return Consumer.createJavaConsumerConnector(new ConsumerConfig(properties));
}
public static void main(String[] args) { new KafkaConsumer("test111").run();// 使用kafka集群中创建好的主题 test
}
} |
3.4. 启动验证
1、启动生产者
运行KafkaProducer.java中的main方法。
2、启动消费者
运行KafkaConsumer.java中的main方法。
控制台输出内容如下：
| 接收到: message:1 接收到: message:2 接收到: message:3 接收到: message:4 接收到: message:5 接收到: message:6 ... |
至此，搭建完成。
4. 常用命令
4.1. Zookeeper
4.1.1. 启动服务
| bin/kafka-server-start.sh ./config/server-1.properties & |
4.1.2. 关闭服务
| zkServer.sh stop |
4.2. Kafka
4.2.1. 启动服务（先启动zookeeper）
| bin/kafka-server-start.sh ./config/server-1.properties & |
4.2.2. 关闭服务（先关闭zookeeper，再关闭kafka）
| kafka-server-stop.sh |
4.2.3. 查看当前主题列表
| ./kafka-topics.sh --zookeeper 192.168.68.128:2181 --list |
4.2.4. 创建主题（注意partitions分区数目）
| kafka-topics.sh --zookeeper 192.168.68.128:2181 --create --topic XXX --partitions 2 --replication-factor 1 |
4.2.5. 删除主题
| kafka-topics.sh --zookeeper 192.168.68.128:2181 --delete --topic XXX |
4.2.6. 创建生产者
| kakfa-console-producer.sh --broker-list 192.168.68.128:9092 --topic XXX |
4.2.7. 创建消费者
| kafka-console-consumer.sh --zookeeper 192.168.68.128:2181 --topic XXX [--from-beginning 添加改选项则重置offset从头开始接收，若不配置，从启动时开始接收] |