搭建kafka要注意版本问题，本教程使用的kafka版本是kafka_2.11-0.11.0.2.tgz；首先看下spring-boot链接kafka的使用。


      1. 添加pom依赖

<dependencies>
<dependency>
	<groupId>org.springframework.boot</groupId>
	<artifactId>spring-boot-starter-web</artifactId>
	<!--<version>1.5.8.RELEASE</version>-->
</dependency>
<dependency>
	<groupId>org.apache.kafka</groupId>
	<artifactId>kafka-clients</artifactId>
	<version>0.10.2.0</version>
</dependency>
<dependency>
	<groupId>org.springframework.boot</groupId>
	<artifactId>spring-boot-starter</artifactId>
</dependency>
<dependency>
	<groupId>org.springframework.kafka</groupId>
	<artifactId>spring-kafka</artifactId>
</dependency>
</dependencies>

2. application.properties配置 

kafka.consumer.zookeeper.connect=zookeeper-ip:2181
kafka.consumer.servers=kafka-ip:9092
kafka.consumer.enable.auto.commit=true
kafka.consumer.session.timeout=6000
kafka.consumer.auto.commit.interval=100
kafka.consumer.auto.offset.reset=latest
kafka.consumer.topic=test
kafka.consumer.group.id=test
kafka.consumer.concurrency=10

kafka.producer.servers=kafka-ip:9092
kafka.producer.retries=0
kafka.producer.batch.size=4096
kafka.producer.linger=1
kafka.producer.buffer.memory=40960


     3. 添加kafkaConsumer配置类：

    

package com.databps.bigdaf.admin.config;

import com.databps.bigdaf.admin.manager.HomePageManager;
import com.databps.bigdaf.admin.vo.HomePageVo;
import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.common.serialization.StringDeserializer;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.kafka.annotation.EnableKafka;
import org.springframework.kafka.config.ConcurrentKafkaListenerContainerFactory;
import org.springframework.kafka.config.KafkaListenerContainerFactory;
import org.springframework.kafka.core.ConsumerFactory;
import org.springframework.kafka.core.DefaultKafkaConsumerFactory;
import org.springframework.kafka.listener.ConcurrentMessageListenerContainer;

import java.util.HashMap;
import java.util.Map;
/**
 * @author haipeng
 * @create 17-11-2 上午11:39
 */
@Configuration
@EnableKafka
public class KafkaConsumerConfig {
    @Value("${kafka.consumer.servers}")
    private String servers;
    @Value("${kafka.consumer.enable.auto.commit}")
    private boolean enableAutoCommit;
    @Value("${kafka.consumer.session.timeout}")
    private String sessionTimeout;
    @Value("${kafka.consumer.auto.commit.interval}")
    private String autoCommitInterval;
    @Value("${kafka.consumer.group.id}")
    private String groupId;
    @Value("${kafka.consumer.auto.offset.reset}")
    private String autoOffsetReset;
    @Value("${kafka.consumer.concurrency}")
    private int concurrency;
    @Autowired
    private HomePageManager homePageManager;
    @Bean
    public KafkaListenerContainerFactory<ConcurrentMessageListenerContainer<String, String>> kafkaListenerContainerFactory() {
        ConcurrentKafkaListenerContainerFactory<String, String> factory = new ConcurrentKafkaListenerContainerFactory<>();
        factory.setConsumerFactory(consumerFactory());
        factory.setConcurrency(concurrency);
        factory.getContainerProperties().setPollTimeout(1500);
        return factory;
    }

    public ConsumerFactory<String, String> consumerFactory() {
        return new DefaultKafkaConsumerFactory<>(consumerConfigs());
    }


    public Map<String, Object> consumerConfigs() {
        Map<String, Object> propsMap = new HashMap<>();
//        propsMap.put("zookeeper.connect", "master1.hdp.com:2181,master2.hdp.com:2181,slave1.hdp.com:2181");
        propsMap.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, servers);
        propsMap.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, enableAutoCommit);
        propsMap.put(ConsumerConfig.AUTO_COMMIT_INTERVAL_MS_CONFIG, autoCommitInterval);
        propsMap.put(ConsumerConfig.SESSION_TIMEOUT_MS_CONFIG, sessionTimeout);
        propsMap.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
        propsMap.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
        propsMap.put(ConsumerConfig.GROUP_ID_CONFIG, groupId);
        propsMap.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, autoOffsetReset);
        return propsMap;
    }
}

     4. 添加kafkaProducer配置类

package com.databps.bigdaf.admin.config;


import java.util.HashMap;
import java.util.Map;

import org.apache.kafka.clients.producer.ProducerConfig;
import org.apache.kafka.common.serialization.StringSerializer;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.kafka.annotation.EnableKafka;
import org.springframework.kafka.core.DefaultKafkaProducerFactory;
import org.springframework.kafka.core.KafkaTemplate;
import org.springframework.kafka.core.ProducerFactory;
/**
 * @author haipeng
 * @create 17-11-2 上午11:37
 */
@Configuration
@EnableKafka
public class KafkaProducerConfig {
    @Value("${kafka.producer.servers}")
    private String servers;
    @Value("${kafka.producer.retries}")
    private int retries;
    @Value("${kafka.producer.batch.size}")
    private int batchSize;
    @Value("${kafka.producer.linger}")
    private int linger;
    @Value("${kafka.producer.buffer.memory}")
    private int bufferMemory;


    public Map<String, Object> producerConfigs() {
        Map<String, Object> props = new HashMap<>();
        props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, servers);
        props.put(ProducerConfig.RETRIES_CONFIG, retries);
        props.put(ProducerConfig.BATCH_SIZE_CONFIG, batchSize);
        props.put(ProducerConfig.LINGER_MS_CONFIG, linger);
        props.put(ProducerConfig.BUFFER_MEMORY_CONFIG, bufferMemory);
        props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class);
        props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class);
        return props;
    }

    public ProducerFactory<String, String> producerFactory() {
        return new DefaultKafkaProducerFactory<>(producerConfigs());
    }

    @Bean
    public KafkaTemplate<String, String> kafkaTemplate() {
        return new KafkaTemplate<String, String>(producerFactory());
    }

}

    5. 生产者调用过程：

     （1）添加kafkaTemplate注入

  @Autowired
  private KafkaTemplate kafkaTemplate;
     

    （2）将要传输数据转化为json发送，本实例通过Gson进行转换

    AuditVo auditVo=new AuditVo();
    long sortData=Long.parseLong(DateUtils.getNowDateTime());
    auditVo.setId("sdfdf");
    auditVo.setCmpyId(cmpyId);
    auditVo.setUser("whp");
    auditVo.setPluginIp("192.168.1.53");
    auditVo.setAccessTime(DateUtils.getNowDateTime());
    auditVo.setAccessType("WRITE");
    auditVo.setAction("write");
    auditVo.setAccessResult("success");
    auditVo.setServiceType("hbase");
    auditVo.setResourcePath("/whp");
    Gson gson=new Gson();
    kafkaTemplate.send("test", gson.toJson(auditVo));
      

   （3）消费者类只要在方法上添加注解就可以了：@KafkaListener(topics = {"test"})

@Component
public class KafkaConsumer {
    @KafkaListener(topics = {"test"})
    public void processMessage(String content) {
        System.out.println("消息被消费"+content);
    }
}

    使用原生kafka的java API进行kafka测试方法如下：


    1. pom文件引入依赖

 <mapr-storm-kafka.version>1.0.1</mapr-storm-kafka.version>
    <scala.version>2.11</scala.version>
    <kafka.version>0.10.0.0</kafka.version>

    <dependency>
      <groupId>org.apache.kafka</groupId>
      <artifactId>kafka_${scala.version}</artifactId>
      <version>${kafka.version}</version>
    </dependency>


    <dependency>
      <groupId>org.apache.storm</groupId>
      <artifactId>storm-kafka</artifactId>
      <version>${mapr-storm-kafka.version}</version>
      <exclusions>
        <exclusion>
          <groupId>ch.qos.logback</groupId>
          <artifactId>logback-classic</artifactId>
        </exclusion>
        <exclusion>
          <groupId>log4j</groupId>
          <artifactId>log4j</artifactId>
        </exclusion>
        <exclusion>
          <groupId>org.slf4j</groupId>
          <artifactId>log4j-over-slf4j</artifactId>
        </exclusion>
      </exclusions>
    </dependency>

    2. 添加测试类 

package com.example.demo.kafka;

import java.util.Arrays;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Properties;
import kafka.consumer.ConsumerConfig;
import kafka.consumer.ConsumerIterator;
import kafka.consumer.KafkaStream;
import kafka.javaapi.consumer.ConsumerConnector;
import kafka.serializer.StringDecoder;
import kafka.utils.VerifiableProperties;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.Producer;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.junit.Test;


public class kafkaConsumer {

  private String topic="test";

  @Test
  public void Producer(){
    Properties props = new Properties();
    props.put("bootstrap.servers", "master1.hdp.com:6667");
    props.put("acks", "all"); //ack方式，all，会等所有的commit最慢的方式
    props.put("retries", 0); //失败是否重试，设置会有可能产生重复数据
    props.put("batch.size", 16384); //对于每个partition的batch buffer大小
    props.put("linger.ms", 1);  //等多久，如果buffer没满，比如设为1，即消息发送会多1ms的延迟，如果buffer没满
    props.put("buffer.memory", 33554432); //整个producer可以用于buffer的内存大小
    props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
    props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
    Producer<String, String> producer = new KafkaProducer<>(props);
    producer.send(new ProducerRecord<String, String>(topic, "", Integer.toString(1)));


    producer.close();
  }

  private  ConsumerConnector consumer;

  @Test
  public  void kafkaConsumer() {

    Properties props = new Properties();

    // zookeeper 配置

    props.put("zookeeper.connect", "master1.hdp.com:2181,master2.hdp.com:2181,slave1.hdp.com:2181");

    // group 代表一个消费组

    props.put("group.id", "jd-group");

    // zk连接超时

    props.put("zookeeper.session.timeout.ms", "4000");

    props.put("zookeeper.sync.time.ms", "200");

    props.put("auto.commit.interval.ms", "1000");

    props.put("auto.offset.reset", "largest");

    // 序列化类

    props.put("serializer.class", "kafka.serializer.StringEncoder");

    ConsumerConfig config = new ConsumerConfig(props);

    consumer = (ConsumerConnector) kafka.consumer.Consumer.createJavaConsumerConnector(config);

    Map<String, Integer> topicCountMap = new HashMap<String, Integer>();

    topicCountMap.put("test", new Integer(1));

    StringDecoder keyDecoder = new StringDecoder(new VerifiableProperties());

    StringDecoder valueDecoder = new StringDecoder(
        new VerifiableProperties());
    Map<String, List<KafkaStream<String, String>>> consumerMap =
        consumer.createMessageStreams(topicCountMap, keyDecoder, valueDecoder);
    KafkaStream<String, String> stream = consumerMap.get(
        "test").get(0);
    ConsumerIterator<String, String> it = stream.iterator();
    while (it.hasNext())
      System.out.println(it.next().message());
  }
}


代码请参考个人git账号：https://github.com/whpHarper/springboot-kafka

点击打开链接

# kafka
Logo
Kafka开源项目指南

Kafka开源项目指南提供详尽教程，助开发者掌握其架构、配置和使用，实现高效数据流管理和实时处理。它高性能、可扩展，适合日志收集和实时数据处理，通过持久化保障数据安全，是企业大数据生态系统的核心。

更多推荐

Kafka入门（一） 概述、部署与API的简单使用

Kafka概述、部署与API的简单使用

avatar Kafka开源项目指南

基于canal和kafka同步，实现binlog同步ElasticSearch

文章目录前言elasticsearch 安装canal安装canal-adapter 安装及配置mysql 安装zk及kafaka安装查看效果注意事项前言中间件版本elasticsearch7.5.2canal1.1.4client-adapter1.1.5-alpha-1zookeeper3.4.13kafka2.6.0mysql5.7.31elasticsearch 安装{"settings"

avatar Kafka开源项目指南

基于 Iceberg 的湖仓一体架构在 B 站的实践

背景在B站，每天都有PB级的数据注入到大数据平台，经过离线或实时的ETL建模后，提供给下游的分析、推荐及预测等场景使用。面对如此大规模的数据，如何高效低成本地满足下游数据的分析需求，一直是我们重点的工作方向。我们之前的数据处理流程基本上是这样的：采集端将客户端埋点、服务端埋点、日志、业务数据库等数据收集到HDFS、Kafka等存储系统中，然后通过Hive、Spark、Fl...

avatar Kafka开源项目指南