系列文章目录
文章目录
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
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
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,例如我们所有processor需要继承
MmcKafkaKafkaAbastrctProcessor<T extends MmcKafkaMsg>
,其中的T为反序列化的实体类类型。
@Slf4j@ServicepublicclassOneProcessorextendsMmcKafkaKafkaAbastrctProcessor<DemoMsg>{@ResourceprivateDemoService demoService;@OverrideprotectedClass<DemoMsg>getEntityClass(){returnDemoMsg.class;}@OverrideprotectedvoiddealMessage(List<DemoMsg> datas){
demoService.dealMessage("one", datas.stream().map(x ->(MmcKafkaMsg) x).collect(Collectors.toList()));}}@Slf4j@ServicepublicclassTwoProcessorextendsMmcKafkaKafkaAbastrctProcessor<DemoMsg>{@ResourceprivateDemoService demoService;publicTwoProcessor(){}@OverrideprotectedClass<DemoMsg>getEntityClass(){returnDemoMsg.class;}@OverrideprotectedvoiddealMessage(List<DemoMsg> datas){
demoService.dealMessage("two", datas.stream().map(x ->(MmcKafkaMsg) x).collect(Collectors.toList()));}}
2、可以看到这里有两个体验不太好的地方。
- 自定义实体类DemoMsg 必须要继承 MmcKafkaMsg,很多同学会忘记这个步骤;
- 需要覆盖
getEntityClass()
父类方法,用于反序列化指定实体类的类型,这里太冗余;
因此、所以我们要升级和优化。
四、修改项目
1、取消限定符,消息实体类不再强制要求实现
MmcKafkaMsg
接口,改为可选项,作为候选插件化的能力增强(后文介绍);
@DataclassDemoMsg{privateString routekey;privateString name;privateLong timestamp;}
2、修改
MmcKafkaKafkaAbastrctProcessor
类,取消限定符并增加类型推断方法。
a、如果实现了
MmcKafkaMsg
接口,就拥有了单次消费内的batch数据去重能力;
publicvoidonMessage(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)){thrownewRuntimeException("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);}}
b、新增类型推断方法,目的是去掉子类必须实现
getEntityClass()
的约束;
protectedbooleanisSubtypeOfInterface(Class<?> interfaceClass){if(null== type){Type superClass =getClass().getGenericSuperclass();if(superClass instanceofParameterizedType){ParameterizedType parameterizedType =(ParameterizedType) superClass;Type[] typeArguments = parameterizedType.getActualTypeArguments();if(typeArguments.length >0&& typeArguments[0]instanceofClass){//noinspection unchecked
type =(Class<T>) typeArguments[0];}}}return(null!= type)&& interfaceClass.isAssignableFrom(type);}protectedClass<T>getEntityClass(){if(null== type){synchronized(this){Type superClass =getClass().getGenericSuperclass();if(superClass instanceofParameterizedType){ParameterizedType parameterizedType =(ParameterizedType) superClass;Type[] typeArguments = parameterizedType.getActualTypeArguments();if(typeArguments.length >0&& typeArguments[0]instanceofClass){//noinspection unchecked
type =(Class<T>) typeArguments[0];}}}}return type;}
c、修改去重方法,也就是取批次内最新一条消息,不再使用限定符;
protectedTfindLasted(Map.Entry<String,List<T>> entry){try{Optional<T> d = entry.getValue().stream().max(Comparator.comparing(x ->((PandoKafkaMsg) x).getRoutekey()));if(d.isPresent()){return d.get();}}catch(Exception e){String content =JsonUtil.toJsonStr(entry.getValue());
log.error("处理消息出错:{}", e.getMessage()+": "+ content, e);}returnnull;}protectedStringbuildRoutekey(T t){return((MmcKafkaMsg) t).getRoutekey();}
3、修改MmcKafkaBeanPostProcessor,取消限定符。
publicclassMmcKafkaBeanPostProcessorimplementsBeanPostProcessor{@GetterprivatefinalMap<String,MmcKafkaKafkaAbastrctProcessor<?>> suitableClass =newConcurrentHashMap<>();@OverridepublicObjectpostProcessAfterInitialization(Object bean,String beanName)throwsBeansException{if(bean instanceofMmcKafkaKafkaAbastrctProcessor){MmcKafkaKafkaAbastrctProcessor<?> target =(MmcKafkaKafkaAbastrctProcessor<?>) bean;
suitableClass.putIfAbsent(beanName, target);
suitableClass.putIfAbsent(bean.getClass().getName(), target);}return bean;}}
4、修改MmcKafkaProcessorFactory,取消限定符。
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>
2、定义一个消息实体和业务处理类。
@DataclassDemoMsg{private String routekey;private String name;private Long timestamp;}@Slf4j@ServicepublicclassOneProcessorextendsMmcKafkaKafkaAbastrctProcessor<DemoMsg>{@Resourceprivate DemoService demoService;@OverrideprotectedvoiddealMessage(List<DemoMsg> datas){
datas.forEach(x ->{
log.info("dealMessage one: {}", x);});}}
3、配置kafka地址和指定业务处理类。
spring.kafka.one.enabled=true
spring.kafka.one.consumer.bootstrapServers=${spring.embedded.kafka.brokers}
spring.kafka.one.topic=mmc-topic-one
spring.kafka.one.group-id=group-consumer-one
spring.kafka.one.processor=com.mmc.multi.kafka.starter.OneProcessor // 业务处理类名称
spring.kafka.one.consumer.auto-offset-reset=latest
spring.kafka.one.consumer.max-poll-records=10
spring.kafka.one.consumer.value-deserializer=org.apache.kafka.common.serialization.StringDeserializer
spring.kafka.one.consumer.key-deserializer=org.apache.kafka.common.serialization.StringDeserializer
4、编写测试类。
@Slf4j@ActiveProfiles("dev")@ExtendWith(SpringExtension.class)@SpringBootTest(classes ={MmcMultiConsumerAutoConfiguration.class, DemoService.class, OneProcessor.class})@TestPropertySource(value ="classpath:application.properties")@DirtiesContext@EmbeddedKafka(topics ={"${spring.kafka.one.topic}"})classAppTest{@Resourceprivate EmbeddedKafkaBroker embeddedKafkaBroker;@Value("${spring.kafka.one.topic}")private String topicOne;@Value("${spring.kafka.two.topic}")private String topicTwo;@TestvoidtestDealMessage()throws Exception {// 模拟生产数据produceMessage();
Thread.sleep(10*1000);}voidproduceMessage(){
Map<String, Object> configs =newHashMap<>(KafkaTestUtils.producerProps(embeddedKafkaBroker));
Producer<String, String> producer =newDefaultKafkaProducerFactory<>(configs,newStringSerializer(),newStringSerializer()).createProducer();for(int i =0; i <10; i++){
DemoMsg msg =newDemoMsg();
msg.setRoutekey("routekey"+ i);
msg.setName("name"+ i);
msg.setTimestamp(System.currentTimeMillis());
String json = JsonUtil.toJsonStr(msg);
producer.send(newProducerRecord<>(topicOne,"my-aggregate-id", json));
producer.send(newProducerRecord<>(topicTwo,"my-aggregate-id", json));
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。
加我加群一起交流学习!更多干货下载、项目源码和大厂内推等着你
版权归原作者 hanyi_ 所有, 如有侵权,请联系我们删除。