java自己手動控制kafka的offset操作
之前使用kafka的KafkaStream,讓每個消費者和對應的patition建立對應的流來讀取kafka上面的數(shù)據(jù),如果comsumer得到數(shù)據(jù),那么kafka就會自動去維護該comsumer的offset,例如在獲取到kafka的消息后正準備入庫(未入庫),但是消費者掛了,那么如果讓kafka自動去維護offset,它就會認為這條數(shù)據(jù)已經(jīng)被消費了,那么會造成數(shù)據(jù)丟失。
但是kafka可以讓你自己去手動提交,如果在上面的場景中,那么需要我們手動commit,如果comsumer掛了 那么程序就不會執(zhí)行commit這樣的話 其他同group的消費者又可以消費這條數(shù)據(jù),保證數(shù)據(jù)不丟,先要做如下設置:
//設置不自動提交,自己手動更新offset
properties.put("enable.auto.commit", "false");
使用如下api提交:
consumer.commitSync();
注意:
剛做了個測試,如果我從kafka中取出5條數(shù)據(jù),分別為1,2,3,4,5,如果消費者在執(zhí)行一些邏輯在執(zhí)行1,2,3,4的時候都失敗了未提交commit,然后消費5做邏輯成功了提交了commit,那么offset也會被移動到5那一條數(shù)據(jù)那里,1,2,3,4 相當于也會丟失
如果是做消費者取出數(shù)據(jù)執(zhí)行一些操作,全部都失敗的話,然后重啟消費者,這些數(shù)據(jù)會從失敗的時候重新開始讀取
所以消費者還是應該自己做容錯機制
測試項目結(jié)構(gòu)如下:

其中ConsumerThreadNew類:
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生產(chǎn)者
private KafkaConsumer<String, String> consumer;
//消費者名字
private String name;
//消費的topic組
private List<String> topics;
//構(gòu)造函數(shù)
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<>();
// 批量提交數(shù)量
final int minBatchSize = 1;
while (true) {
ConsumerRecords<String, String> records = consumer.poll(100);
for (ConsumerRecord<String, String> record : records) {
LOG.info("消費者的名字為:" + name + ",消費的消息為:" + record.value());
buffer.add(record);
}
if (buffer.size() >= minBatchSize) {
//這里就是處理成功了然后自己手動提交
consumer.commitSync();
LOG.info("提交完畢");
buffer.clear();
}
}
}
}
MyConsume類如下:
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");
//設置不自動提交,自己手動更新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);
//執(zhí)行消費
for (int i = 0; i < 7; i++) {
executor.execute(new ConsumerThreadNew(new KafkaConsumer<String, String>(properties),
"lijietest", "消費者" + (i + 1)));
}
}
}
MyProducer類如下:
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");
//序列化類型
properties
.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
properties.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
//創(chuàng)建生產(chǎn)者
pro = new KafkaProducer<>(properties);
}
public static void main(String[] args) throws Exception {
produce("lijietest");
}
public static void produce(String topic) throws Exception {
//模擬message
// String value = UUID.randomUUID().toString();
for (int i = 0; i < 10000; i++) {
//封裝message
ProducerRecord<String, String> pr = new ProducerRecord<String, String>(topic, i + "");
//發(fā)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>
補充:kafka javaAPI 手動維護偏移量
我就廢話不多說了,大家還是直接看代碼吧~
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,
//與老版的kafka的區(qū)別是,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
//所以在讀取數(shù)據(jù)時,應當設置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());
}
}
}
//手動維護偏移量
@Test
public void autoManageOffset2(){
Properties props = new Properties();
//broker的地址
props.put("bootstrap.servers",ips);
//這是消費者組
props.put("group.id","groupPP");
//設置消費的偏移量,如果以前消費過則接著消費,如果沒有就從頭開始消費
props.put("auto.offset.reset","earliest");
//設置自動提交偏移量為false
props.put("enable.auto.commit","false");
//設置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一個消費者
KafkaConsumer<String, String> consumer = new KafkaConsumer<>(props);
//指定消費的topic
consumer.subscribe(Arrays.asList("my-topic"));
while(true){
ConsumerRecords<String, String> records = consumer.poll(1000);
//通過records獲取這個集合中的數(shù)據(jù)屬于那幾個partition
Set<TopicPartition> partitions = records.partitions();
for(TopicPartition tp : partitions){
//通過具體的partition把該partition中的數(shù)據(jù)拿出來消費
List<ConsumerRecord<String, String>> partitionRecords = records.records(tp);
for(ConsumerRecord r : partitionRecords){
System.out.println(r.offset() +" "+r.key()+" "+r.value());
}
//獲取新這個partition中的最后一條記錄的offset并加1 那么這個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)));
}
}
}
}
以上為個人經(jīng)驗,希望能給大家一個參考,也希望大家多多支持腳本之家。如有錯誤或未考慮完全的地方,望不吝賜教。
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