系列文章目录

---

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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.
     */
    @Override
    public 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 unchecked
            return 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 {

    @Getter
    private final Map<String, KafkaAbstractProcessor<?>> suitableClass = new ConcurrentHashMap<>();

    @Override
    public 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 {

    @Resource
    private 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KafkaAbstractProcessor
        Class<?> 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。

 @Bean
    public 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consumer
        for (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> {

    @Override
    public 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> {

    @Override
    protected 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();
        }

    }

    @Override
    protected 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 {


    @Resource
    private EmbeddedKafkaBroker embeddedKafkaBroker;

    @Value("${spring.kafka.pb.topic}")
    private String topicPb;


    @Test
    void 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。

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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