java自己手动控制kafka的offset操作
作者：lijie_cq
这篇文章主要介绍了java自己手动控制kafka的offset操作，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧
之前使用kafka的KafkaStream，让每个消费者和对应的patition建立对应的流来读取kafka上面的数据，如果comsumer得到数据，那么kafka就会自动去维护该comsumer的offset，例如在获取到kafka的消息后正准备入库（未入库），但是消费者挂了，那么如果让kafka自动去维护offset，它就会认为这条数据已经被消费了，那么会造成数据丢失。
但是kafka可以让你自己去手动提交，如果在上面的场景中，那么需要我们手动commit，如果comsumer挂了 那么程序就不会执行commit这样的话 其他同group的消费者又可以消费这条数据，保证数据不丢，先要做如下设置：
//设置不自动提交，自己手动更新offset
properties.put("enable.auto.commit", "false");
使用如下api提交：
consumer.commitSync();
注意：
刚做了个测试，如果我从kafka中取出5条数据，分别为1,2,3,4,5，如果消费者在执行一些逻辑在执行1,2,3，4的时候都失败了未提交commit，然后消费5做逻辑成功了提交了commit，那么offset也会被移动到5那一条数据那里，1,2,3,4 相当于也会丢失
如果是做消费者取出数据执行一些操作，全部都失败的话，然后重启消费者，这些数据会从失败的时候重新开始读取
所以消费者还是应该自己做容错机制
测试项目结构如下:
其中ConsumerThreadNew类：
package com.lijie.kafka;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
/**
*
*
* @Filename ConsumerThreadNew.java
*
* @Description
*
* @Version 1.0
*
* @Author Lijie
*
* @Email lijiewj39069@touna.cn
*
* @History
*<li>Author: Lijie</li>
*<li>Date: 2017年3月21日</li>
*<li>Version: 1.0</li>
*<li>Content: create</li>
*
*/
public class ConsumerThreadNew implements Runnable {
private static Logger LOG = LoggerFactory.getLogger(ConsumerThreadNew.class);
//KafkaConsumer kafka生产者
private KafkaConsumer<String, String> consumer;
//消费者名字
private String name;
//消费的topic组
private List<String> topics;
//构造函数
public ConsumerThreadNew(KafkaConsumer<String, String> consumer, String topic, String name) {
super();
this.consumer = consumer;
this.name = name;
this.topics = Arrays.asList(topic);
}
@Override
public void run() {
consumer.subscribe(topics);
List<ConsumerRecord<String, String>> buffer = new ArrayList<>();
// 批量提交数量
final int minBatchSize = 1;
while (true) {
ConsumerRecords<String, String> records = consumer.poll(100);
for (ConsumerRecord<String, String> record : records) {
LOG.info("消费者的名字为:" + name + ",消费的消息为：" + record.value());
buffer.add(record);
}
if (buffer.size() >= minBatchSize) {
//这里就是处理成功了然后自己手动提交
consumer.commitSync();
LOG.info("提交完毕");
buffer.clear();
}
}
}
}
MyConsume类如下：
package com.lijie.kafka;
import java.util.Properties;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
/**
*
*
* @Filename MyConsume.java
*
* @Description
*
* @Version 1.0
*
* @Author Lijie
*
* @Email lijiewj39069@touna.cn
*
* @History
*<li>Author: Lijie</li>
*<li>Date: 2017年3月21日</li>
*<li>Version: 1.0</li>
*<li>Content: create</li>
*
*/
public class MyConsume {
private static Logger LOG = LoggerFactory.getLogger(MyConsume.class);
public MyConsume() {
// TODO Auto-generated constructor stub
}
public static void main(String[] args) {
Properties properties = new Properties();
properties.put("bootstrap.servers", "10.0.4.141:19093,10.0.4.142:19093,10.0.4.143:19093");
//设置不自动提交，自己手动更新offset
properties.put("enable.auto.commit", "false");
properties.put("auto.offset.reset", "latest");
properties.put("zookeeper.connect", "10.0.4.141:2181,10.0.4.142:2181,10.0.4.143:2181");
properties.put("session.timeout.ms", "30000");
properties.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
properties.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
properties.put("group.id", "lijieGroup");
properties.put("zookeeper.connect", "192.168.80.123:2181");
properties.put("auto.commit.interval.ms", "1000");
ExecutorService executor = Executors.newFixedThreadPool(5);
//执行消费
for (int i = 0; i < 7; i++) {
executor.execute(new ConsumerThreadNew(new KafkaConsumer<String, String>(properties),
"lijietest", "消费者" + (i + 1)));
}
}
}
MyProducer类如下：
package com.lijie.kafka;
import java.util.Properties;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerRecord;
/**
*
*
* @Filename MyProducer.java
*
* @Description
*
* @Version 1.0
*
* @Author Lijie
*
* @Email lijiewj39069@touna.cn
*
* @History
*<li>Author: Lijie</li>
*<li>Date: 2017年3月21日</li>
*<li>Version: 1.0</li>
*<li>Content: create</li>
*
*/
public class MyProducer {
private static Properties properties;
private static KafkaProducer<String, String> pro;
static {
//配置
properties = new Properties();
properties.put("bootstrap.servers", "10.0.4.141:19093,10.0.4.142:19093,10.0.4.143:19093");
//序列化类型
properties
.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
properties.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
//创建生产者
pro = new KafkaProducer<>(properties);
}
public static void main(String[] args) throws Exception {
produce("lijietest");
}
public static void produce(String topic) throws Exception {
//模拟message
// String value = UUID.randomUUID().toString();
for (int i = 0; i < 10000; i++) {
//封装message
ProducerRecord<String, String> pr = new ProducerRecord<String, String>(topic, i + "");
//发送消息
pro.send(pr);
Thread.sleep(1000);
}
}
}
pom文件如下：
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>lijie-kafka-offset</groupId>
<artifactId>lijie-kafka-offset</artifactId>
<version>0.0.1-SNAPSHOT</version>
<dependencies>
<dependency>
<groupId>org.apache.kafka</groupId>
<artifactId>kafka_2.11</artifactId>
<version>0.10.1.1</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-common</artifactId>
<version>2.2.0</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-hdfs</artifactId>
<version>2.2.0</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>2.2.0</version>
</dependency>
<dependency>
<groupId>org.apache.hbase</groupId>
<artifactId>hbase-client</artifactId>
<version>1.0.3</version>
</dependency>
<dependency>
<groupId>org.apache.hbase</groupId>
<artifactId>hbase-server</artifactId>
<version>1.0.3</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-hdfs</artifactId>
<version>2.2.0</version>
</dependency>
<dependency>
<groupId>jdk.tools</groupId>
<artifactId>jdk.tools</artifactId>
<version>1.7</version>
<scope>system</scope>
<systemPath>${JAVA_HOME}/lib/tools.jar</systemPath>
</dependency>
<dependency>
<groupId>org.apache.httpcomponents</groupId>
<artifactId>httpclient</artifactId>
<version>4.3.6</version>
</dependency>
</dependencies>
<build>
<plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<configuration>
<source>1.7</source>
<target>1.7</target>
</configuration>
</plugin>
</plugins>
</build>
</project>
补充：kafka javaAPI 手动维护偏移量
我就废话不多说了，大家还是直接看代码吧~
package com.kafka;
import kafka.javaapi.PartitionMetadata;
import kafka.javaapi.consumer.SimpleConsumer;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import org.apache.kafka.clients.consumer.OffsetAndMetadata;
import org.apache.kafka.common.TopicPartition;
import org.junit.Test;
import java.util.*;
public class ConsumerManageOffet {
//broker的地址，
//与老版的kafka的区别是，新版本的kafka把偏移量保存到了broker，而老版本的是把偏移量保存到了zookeeper中
//所以在读取数据时，应当设置broker的地址
private static String ips = "192.168.136.150:9092,192.168.136.151:9092,192.168.136.152:9092";
public static void main(String[] args) {
Properties props = new Properties();
props.put("bootstrap.servers",ips);
props.put("group.id","test02");
props.put("auto.offset.reset","earliest");
props.put("max.poll.records","10");
props.put("key.deserializer","org.apache.kafka.common.serialization.StringDeserializer");
props.put("value.deserializer","org.apache.kafka.common.serialization.StringDeserializer");
KafkaConsumer<String,String> consumer = new KafkaConsumer<>(props);
consumer.subscribe(Arrays.asList("my-topic"));
System.out.println("---------------------");
while(true){
ConsumerRecords<String,String> records = consumer.poll(10);
System.out.println("+++++++++++++++++++++++");
for(ConsumerRecord<String,String> record: records){
System.out.println("---");
System.out.printf("offset=%d,key=%s,value=%s%n",record.offset(),
record.key(),record.value());
}
}
}
//手动维护偏移量
@Test
public void autoManageOffset2(){
Properties props = new Properties();
//broker的地址
props.put("bootstrap.servers",ips);
//这是消费者组
props.put("group.id","groupPP");
//设置消费的偏移量，如果以前消费过则接着消费，如果没有就从头开始消费
props.put("auto.offset.reset","earliest");
//设置自动提交偏移量为false
props.put("enable.auto.commit","false");
//设置Key和value的序列化
props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
//new一个消费者
KafkaConsumer<String, String> consumer = new KafkaConsumer<>(props);
//指定消费的topic
consumer.subscribe(Arrays.asList("my-topic"));
while(true){
ConsumerRecords<String, String> records = consumer.poll(1000);
//通过records获取这个集合中的数据属于那几个partition
Set<TopicPartition> partitions = records.partitions();
for(TopicPartition tp : partitions){
//通过具体的partition把该partition中的数据拿出来消费
List<ConsumerRecord<String, String>> partitionRecords = records.records(tp);
for(ConsumerRecord r : partitionRecords){
System.out.println(r.offset() +" "+r.key()+" "+r.value());
}
//获取新这个partition中的最后一条记录的offset并加1 那么这个位置就是下一次要提交的offset
long newOffset = partitionRecords.get(partitionRecords.size() - 1).offset() + 1;
consumer.commitSync(Collections.singletonMap(tp,new OffsetAndMetadata(newOffset)));
}
}
}
}
以上为个人经验，希望能给大家一个参考，也希望大家多多支持脚本之家。如有错误或未考虑完全的地方，望不吝赐教。