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搭建大型分布式服务（三十八）SpringBoot 整合多个kafka数据源-支持protobuf

news 2026/2/7 15:16:59

系列文章目录

文章目录

系列文章目录
前言
- - 一、本文要点
  - 二、开发环境
  - 三、原项目
  - 四、修改项目
  - 五、测试一下
  - 五、小结

前言

本插件稳定运行上百个kafka项目，每天处理上亿级的数据的精简小插件，快速上手。

<dependency><groupId>io.github.vipjoey</groupId><artifactId>multi-kafka-consumer-starter</artifactId><version>最新版本号</version>
</dependency>

例如下面这样简单的配置就完成SpringBoot和kafka的整合，我们只需要关心com.mmc.multi.kafka.starter.OneProcessor和com.mmc.multi.kafka.starter.TwoProcessor 这两个Service的代码开发。

## topic1的kafka配置
spring.kafka.one.enabled=true
spring.kafka.one.consumer.bootstrapServers=${spring.embedded.kafka.brokers}
spring.kafka.one.topic=mmc-topic-one
spring.kafka.one.group-id=group-consumer-one
spring.kafka.one.processor=com.mmc.multi.kafka.starter.OneProcessor // 业务处理类名称
spring.kafka.one.consumer.auto-offset-reset=latest
spring.kafka.one.consumer.max-poll-records=10
spring.kafka.one.consumer.value-deserializer=org.apache.kafka.common.serialization.StringDeserializer
spring.kafka.one.consumer.key-deserializer=org.apache.kafka.common.serialization.StringDeserializer## topic2的kafka配置
spring.kafka.two.enabled=true
spring.kafka.two.consumer.bootstrapServers=${spring.embedded.kafka.brokers}
spring.kafka.two.topic=mmc-topic-two
spring.kafka.two.group-id=group-consumer-two
spring.kafka.two.processor=com.mmc.multi.kafka.starter.TwoProcessor // 业务处理类名称
spring.kafka.two.consumer.auto-offset-reset=latest
spring.kafka.two.consumer.max-poll-records=10
spring.kafka.two.consumer.value-deserializer=org.apache.kafka.common.serialization.StringDeserializer
spring.kafka.two.consumer.key-deserializer=org.apache.kafka.common.serialization.StringDeserializer## pb 消息消费者
spring.kafka.pb.enabled=true
spring.kafka.pb.consumer.bootstrapServers=${spring.embedded.kafka.brokers}
spring.kafka.pb.topic=mmc-topic-pb
spring.kafka.pb.group-id=group-consumer-pb
spring.kafka.pb.processor=pbProcessor
spring.kafka.pb.consumer.auto-offset-reset=latest
spring.kafka.pb.consumer.max-poll-records=10
spring.kafka.pb.consumer.key-deserializer=org.apache.kafka.common.serialization.StringDeserializer
spring.kafka.pb.consumer.value-deserializer=org.apache.kafka.common.serialization.ByteArrayDeserializer

国籍惯例，先上源码：Github源码

一、本文要点

本文将介绍通过封装一个starter，来实现多kafka数据源的配置，通过通过源码，可以学习以下特性。系列文章完整目录

SpringBoot 整合多个kafka数据源
SpringBoot 批量消费kafka消息
SpringBoot 优雅地启动或停止消费kafka
SpringBoot kafka本地单元测试（免集群）
SpringBoot 利用map注入多份配置
SpringBoot BeanPostProcessor 后置处理器使用方式
SpringBoot 将自定义类注册到IOC容器
SpringBoot 注入bean到自定义类成员变量
Springboot 取消限定符
Springboot 支持消费protobuf类型的kafka消息

二、开发环境

jdk 1.8
maven 3.6.2
springboot 2.4.3
kafka-client 2.6.6
idea 2020

三、原项目

1、接前文，我们开发了一个kafka插件，但在使用过程中发现有些不方便的地方，在公共接口MmcKafkaStringInputer 显示地继承了BatchMessageListener<String, String>，导致我们没办法去指定消费protobuf类型的message。

public interface MmcKafkaStringInputer extends MmcInputer, BatchMessageListener<String, String> {}/*** 消费kafka消息.*/@Overridepublic void onMessage(List<ConsumerRecord<String, String>> records) {if (null == records || CollectionUtils.isEmpty(records)) {log.warn("{} records is null or records.value is empty.", name);return;}Assert.hasText(name, "You must pass the field `name` to the Constructor or invoke the setName() after the class was created.");Assert.notNull(properties, "You must pass the field `properties` to the Constructor or invoke the setProperties() after the class was created.");try {Stream<T> dataStream = records.stream().map(ConsumerRecord::value).flatMap(this::doParse).filter(Objects::nonNull).filter(this::isRightRecord);// 支持配置强制去重或实现了接口能力去重if (properties.isDuplicate() || isSubtypeOfInterface(MmcKafkaMsg.class)) {// 检查是否实现了去重接口if (!isSubtypeOfInterface(MmcKafkaMsg.class)) {throw new RuntimeException("The interface "+ MmcKafkaMsg.class.getName() + " is not implemented if you set the config `spring.kafka.xxx.duplicate=true` .");}dataStream = dataStream.collect(Collectors.groupingBy(this::buildRoutekey)).entrySet().stream().map(this::findLasted).filter(Objects::nonNull);}List<T> datas = dataStream.collect(Collectors.toList());if (CommonUtil.isNotEmpty(datas)) {this.dealMessage(datas);}} catch (Exception e) {log.error(name + "-dealMessage error ", e);}}

2、由于实现了BatchMessageListener<String, String>接口，抽象父类必须实现onMessage(List<ConsumerRecord<String, String>> records)方法，这样会导致子类局限性很大，没办法去实现其它kafka的xxxListener接口，例如手工提交offset，单条消息消费等。

因此、所以我们要升级和优化。

四、修改项目

1、新增KafkaAbastrctProcessor抽象父类，直接实现MmcInputer接口，要求所有子类都需要继承本类，子类通过调用{@link #receiveMessage(List)} 模板方法来实现通用功能；

@Slf4j
@Setter
abstract class KafkaAbstractProcessor<T> implements MmcInputer {// 类的内容基本和MmcKafkaKafkaAbastrctProcessor保持一致// 主要修改了doParse方法，目的是让子类可以自定义解析protobuf/*** 将kafka消息解析为实体，支持json对象或者json数组.** @param msg kafka消息* @return 实体类*/protected Stream<T> doParse(ConsumerRecord<String, Object> msg) {// 消息对象Object record = msg.value();// 如果是pb格式if (record instanceof byte[]) {return doParseProtobuf((byte[]) record);} else if (record instanceof String) {// 普通kafka消息String json = record.toString();if (json.startsWith("[")) {// 数组List<T> datas = doParseJsonArray(json);if (CommonUtil.isEmpty(datas)) {log.warn("{} doParse error, json={} is error.", name, json);return Stream.empty();}// 反序列对象后，做一些初始化操作datas = datas.stream().peek(this::doAfterParse).collect(Collectors.toList());return datas.stream();} else {// 对象T data = doParseJsonObject(json);if (null == data) {log.warn("{} doParse error, json={} is error.", name, json);return Stream.empty();}// 反序列对象后，做一些初始化操作doAfterParse(data);return Stream.of(data);}} else if (record instanceof MmcKafkaMsg) {// 如果本身就是PandoKafkaMsg对象，直接返回//noinspection uncheckedreturn Stream.of((T) record);} else {throw new UnsupportedForMessageFormatException("not support message type");}}/*** 将json消息解析为实体.** @param json kafka消息* @return 实体类*/protected T doParseJsonObject(String json) {if (properties.isSnakeCase()) {return JsonUtil.parseSnackJson(json, getEntityClass());} else {return JsonUtil.parseJsonObject(json, getEntityClass());}}/*** 将json消息解析为数组.** @param json kafka消息* @return 数组*/protected List<T> doParseJsonArray(String json) {if (properties.isSnakeCase()) {try {return JsonUtil.parseSnackJsonArray(json, getEntityClass());} catch (Exception e) {throw new RuntimeException(e);}} else {return JsonUtil.parseJsonArray(json, getEntityClass());}}/*** 序列化为pb格式，假设你消费的是pb消息，需要自行实现这个类.** @param record pb字节数组* @return pb实体类流*/protected Stream<T> doParseProtobuf(byte[] record) {throw new NotImplementedException();}
}

2、修改MmcKafkaBeanPostProcessor类，暂存KafkaAbastrctProcessor的子类。

public class MmcKafkaBeanPostProcessor implements BeanPostProcessor {@Getterprivate final Map<String, KafkaAbstractProcessor<?>> suitableClass = new ConcurrentHashMap<>();@Overridepublic Object postProcessAfterInitialization(Object bean, String beanName) throws BeansException {if (bean instanceof KafkaAbstractProcessor) {KafkaAbstractProcessor<?> target = (KafkaAbstractProcessor<?>) bean;suitableClass.putIfAbsent(beanName, target);suitableClass.putIfAbsent(bean.getClass().getName(), target);}return bean;}
}

3、修改MmcKafkaProcessorFactory，更换构造的目标类为KafkaAbstractProcessor。

public class MmcKafkaProcessorFactory {@Resourceprivate DefaultListableBeanFactory defaultListableBeanFactory;public KafkaAbstractProcessor<? > buildInputer(String name, MmcMultiKafkaProperties.MmcKafkaProperties properties,Map<String, KafkaAbstractProcessor<? >> suitableClass) throws Exception {// 如果没有配置process，则直接从注册的Bean里查找if (!StringUtils.hasText(properties.getProcessor())) {return findProcessorByName(name, properties.getProcessor(), suitableClass);}// 如果配置了process，则从指定配置中生成实例// 判断给定的配置是类，还是bean名称if (!isClassName(properties.getProcessor())) {throw new IllegalArgumentException("It's not a class, wrong value of ${spring.kafka." + name + ".processor}.");}// 如果ioc容器已经存在该处理实例，则直接使用，避免既配置了process，又使用了@Service等注解KafkaAbstractProcessor<? > inc = findProcessorByClass(name, properties.getProcessor(), suitableClass);if (null != inc) {return inc;}// 指定的processor处理类必须继承KafkaAbstractProcessorClass<?> clazz = Class.forName(properties.getProcessor());boolean isSubclass = KafkaAbstractProcessor.class.isAssignableFrom(clazz);if (!isSubclass) {throw new IllegalStateException(clazz.getName() + " is not subClass of KafkaAbstractProcessor.");}// 创建实例Constructor<?> constructor = clazz.getConstructor();KafkaAbstractProcessor<? > ins = (KafkaAbstractProcessor<? >) constructor.newInstance();// 注入依赖的变量defaultListableBeanFactory.autowireBean(ins);return ins;}private KafkaAbstractProcessor<? > findProcessorByName(String name, String processor, Map<String,KafkaAbstractProcessor<? >> suitableClass) {return suitableClass.entrySet().stream().filter(e -> e.getKey().startsWith(name) || e.getKey().equalsIgnoreCase(processor)).map(Map.Entry::getValue).findFirst().orElseThrow(() -> new RuntimeException("Can't found any suitable processor class for the consumer which name is " + name+ ", please use the config ${spring.kafka." + name + ".processor} or set name of Bean like @Service(\"" + name + "Processor\") "));}private KafkaAbstractProcessor<? > findProcessorByClass(String name, String processor, Map<String,KafkaAbstractProcessor<? >> suitableClass) {return suitableClass.entrySet().stream().filter(e -> e.getKey().startsWith(name) || e.getKey().equalsIgnoreCase(processor)).map(Map.Entry::getValue).findFirst().orElse(null);}private boolean isClassName(String processor) {// 使用正则表达式验证类名格式String regex = "^[a-zA-Z_$][a-zA-Z\\d_$]*([.][a-zA-Z_$][a-zA-Z\\d_$]*)*$";return Pattern.matches(regex, processor);}}

4、修改MmcMultiConsumerAutoConfiguration，更换构造的目标类的父类为KafkaAbstractProcessor。

 @Beanpublic MmcKafkaInputerContainer mmcKafkaInputerContainer(MmcKafkaProcessorFactory factory,MmcKafkaBeanPostProcessor beanPostProcessor) throws Exception {Map<String, MmcInputer> inputers = new HashMap<>();Map<String, MmcMultiKafkaProperties.MmcKafkaProperties> kafkas = mmcMultiKafkaProperties.getKafka();// 逐个遍历，并生成consumerfor (Map.Entry<String, MmcMultiKafkaProperties.MmcKafkaProperties> entry : kafkas.entrySet()) {// 唯一消费者名称String name = entry.getKey();// 消费者配置MmcMultiKafkaProperties.MmcKafkaProperties properties = entry.getValue();// 是否开启if (properties.isEnabled()) {// 生成消费者KafkaAbstractProcessor inputer = factory.buildInputer(name, properties, beanPostProcessor.getSuitableClass());// 输入源容器ConcurrentMessageListenerContainer<Object, Object> container = concurrentMessageListenerContainer(properties);// 设置容器inputer.setContainer(container);inputer.setName(name);inputer.setProperties(properties);// 设置消费者container.setupMessageListener(inputer);// 关闭时候停止消费Runtime.getRuntime().addShutdownHook(new Thread(inputer::stop));// 直接启动container.start();// 加入集合inputers.put(name, inputer);}}return new MmcKafkaInputerContainer(inputers);}

5、修改MmcKafkaKafkaAbastrctProcessor，用于实现kafka的BatchMessageListener 接口，当然你也可以实现其它Listener接口，或者在这基础上扩展。

public abstract class MmcKafkaKafkaAbastrctProcessor<T> extends KafkaAbstractProcessor<T> implements BatchMessageListener<String, Object> {@Overridepublic void onMessage(List<ConsumerRecord<String, Object>> records) {if (null == records || CollectionUtils.isEmpty(records)) {log.warn("{} records is null or records.value is empty.", name);return;}receiveMessage(records);}
}

五、测试一下

1、引入kafka测试需要的jar。参考文章：kafka单元测试

        <dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-test</artifactId><scope>test</scope></dependency><dependency><groupId>org.springframework.kafka</groupId><artifactId>spring-kafka-test</artifactId><scope>test</scope></dependency><dependency><groupId>com.google.protobuf</groupId><artifactId>protobuf-java</artifactId><version>3.18.0</version><scope>test</scope></dependency><dependency><groupId>com.google.protobuf</groupId><artifactId>protobuf-java-util</artifactId><version>3.18.0</version><scope>test</scope></dependency>

2、定义一个pb类型消息和业务处理类。

(1) 定义pb，然后通过命令生成对应的实体类；

syntax = "proto2";package  com.mmc.multi.kafka;option java_package = "com.mmc.multi.kafka.starter.proto";
option java_outer_classname = "DemoPb";message PbMsg {optional string routekey = 1;optional string cosImgUrl = 2;optional string base64str = 3;}

（2）创建PbProcessor消息处理类，用于消费protobuf类型的消息；

@Slf4j
@Service("pbProcessor")
public class PbProcessor extends MmcKafkaKafkaAbastrctProcessor<DemoMsg> {@Overrideprotected Stream<DemoMsg> doParseProtobuf(byte[] record) {try {DemoPb.PbMsg msg = DemoPb.PbMsg.parseFrom(record);DemoMsg demo = new DemoMsg();BeanUtils.copyProperties(msg, demo);return Stream.of(demo);} catch (InvalidProtocolBufferException e) {log.error("parssPbError", e);return Stream.empty();}}@Overrideprotected void dealMessage(List<DemoMsg> datas) {System.out.println("PBdatas: " + datas);}
}

3、配置kafka地址和指定业务处理类。

## pb 消息消费者
spring.kafka.pb.enabled=true
spring.kafka.pb.consumer.bootstrapServers=${spring.embedded.kafka.brokers}
spring.kafka.pb.topic=mmc-topic-pb
spring.kafka.pb.group-id=group-consumer-pb
spring.kafka.pb.processor=pbProcessor
spring.kafka.pb.consumer.auto-offset-reset=latest
spring.kafka.pb.consumer.max-poll-records=10
spring.kafka.pb.consumer.key-deserializer=org.apache.kafka.common.serialization.StringDeserializer
spring.kafka.pb.consumer.value-deserializer=org.apache.kafka.common.serialization.ByteArrayDeserializer

4、编写测试类。

@Slf4j
@ActiveProfiles("dev")
@ExtendWith(SpringExtension.class)
@SpringBootTest(classes = {MmcMultiConsumerAutoConfiguration.class, DemoService.class, PbProcessor.class})
@TestPropertySource(value = "classpath:application-pb.properties")
@DirtiesContext
@EmbeddedKafka(partitions = 1, brokerProperties = {"listeners=PLAINTEXT://localhost:9092", "port=9092"},topics = {"${spring.kafka.pb.topic}"})
class KafkaPbMessageTest {@Resourceprivate EmbeddedKafkaBroker embeddedKafkaBroker;@Value("${spring.kafka.pb.topic}")private String topicPb;@Testvoid testDealMessage() throws Exception {Thread.sleep(2 * 1000);// 模拟生产数据produceMessage();Thread.sleep(10 * 1000);}void produceMessage() {Map<String, Object> configs = new HashMap<>(KafkaTestUtils.producerProps(embeddedKafkaBroker));Producer<String, byte[]> producer = new DefaultKafkaProducerFactory<>(configs, new StringSerializer(), new ByteArraySerializer()).createProducer();for (int i = 0; i < 10; i++) {DemoPb.PbMsg msg = DemoPb.PbMsg.newBuilder().setCosImgUrl("http://google.com").setRoutekey("routekey-" + i).build();producer.send(new ProducerRecord<>(topicPb, "my-aggregate-id", msg.toByteArray()));producer.flush();}}
}

5、运行一下，测试通过。
在这里插入图片描述

五、小结

将本项目代码构建成starter，就可以大大提升我们开发效率，我们只需要关心业务代码的开发，github项目源码：轻触这里。如果对你有用可以打个星星哦。下一篇，升级本starter，在kafka单分区下实现十万级消费处理速度。

《搭建大型分布式服务（三十六）SpringBoot 零代码方式整合多个kafka数据源》
《搭建大型分布式服务（三十七）SpringBoot 整合多个kafka数据源-取消限定符》
《搭建大型分布式服务（三十八）SpringBoot 整合多个kafka数据源-支持protobuf》

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