SpringBoot如何处理@KafkaListener消息
作者：小豹子的技术笔记
Spring通过KafkaMessageListenerContainer、ConcurrentMessageListenerContainer等组件实现Kafka消息的监听和处理,并通过@KafkaListener注解将业务逻辑与Kafka消费者连接起来,Spring Boot自动配置Kafka相关组件,简化了Kafka的使用
消息监听容器
1、KafkaMessageListenerContainer
由spring提供用于监听以及拉取消息，并将这些消息按指定格式转换后交给由@KafkaListener注解的方法处理，相当于一个消费者；
看看其整体代码结构：
可以发现其入口方法为doStart(), 往上追溯到实现了SmartLifecycle接口，很明显，由spring管理其start和stop操作；
ListenerConsumer, 内部真正拉取消息消费的是这个结构，其 实现了Runable接口，简言之，它就是一个后台线程轮训拉取并处理消息（while true死循环拉取消息）。
在doStart方法中会创建ListenerConsumer并交给线程池处理
以上步骤就开启了消息监听过程。
KafkaMessageListenerContainer#doStart
protected void doStart() {
if (isRunning()) {
return;
}
ContainerProperties containerProperties = getContainerProperties();
if (!this.consumerFactory.isAutoCommit()) {
AckMode ackMode = containerProperties.getAckMode();
if (ackMode.equals(AckMode.COUNT) || ackMode.equals(AckMode.COUNT_TIME)) {
Assert.state(containerProperties.getAckCount() > 0, "'ackCount' must be > 0");
}
if ((ackMode.equals(AckMode.TIME) || ackMode.equals(AckMode.COUNT_TIME))
&& containerProperties.getAckTime() == 0) {
containerProperties.setAckTime(5000);
}
}
Object messageListener = containerProperties.getMessageListener();
Assert.state(messageListener != null, "A MessageListener is required");
if (containerProperties.getConsumerTaskExecutor() == null) {
SimpleAsyncTaskExecutor consumerExecutor = new SimpleAsyncTaskExecutor(
(getBeanName() == null ? "" : getBeanName()) + "-C-");
containerProperties.setConsumerTaskExecutor(consumerExecutor);
}
Assert.state(messageListener instanceof GenericMessageListener, "Listener must be a GenericListener");
this.listener = (GenericMessageListener<?>) messageListener;
ListenerType listenerType = ListenerUtils.determineListenerType(this.listener);
if (this.listener instanceof DelegatingMessageListener) {
Object delegating = this.listener;
while (delegating instanceof DelegatingMessageListener) {
delegating = ((DelegatingMessageListener<?>) delegating).getDelegate();
}
listenerType = ListenerUtils.determineListenerType(delegating);
}
// 这里创建了监听消费者对象
this.listenerConsumer = new ListenerConsumer(this.listener, listenerType);
setRunning(true);
// 将消费者对象放入到线程池中执行
this.listenerConsumerFuture = containerProperties
.getConsumerTaskExecutor()
.submitListenable(this.listenerConsumer);
}KafkaMessageListenerContainer.ListenerConsumer#run
public void run() {
this.consumerThread = Thread.currentThread();
if (this.genericListener instanceof ConsumerSeekAware) {
((ConsumerSeekAware) this.genericListener).registerSeekCallback(this);
}
if (this.transactionManager != null) {
ProducerFactoryUtils.setConsumerGroupId(this.consumerGroupId);
}
this.count = 0;
this.last = System.currentTimeMillis();
if (isRunning() && this.definedPartitions != null) {
try {
initPartitionsIfNeeded();
}
catch (Exception e) {
this.logger.error("Failed to set initial offsets", e);
}
}
long lastReceive = System.currentTimeMillis();
long lastAlertAt = lastReceive;
while (isRunning()) {
try {
if (!this.autoCommit && !this.isRecordAck) {
processCommits();
}
processSeeks();
if (!this.consumerPaused && isPaused()) {
this.consumer.pause(this.consumer.assignment());
this.consumerPaused = true;
if (this.logger.isDebugEnabled()) {
this.logger.debug("Paused consumption from: " + this.consumer.paused());
}
publishConsumerPausedEvent(this.consumer.assignment());
}
// 拉取信息
ConsumerRecords<K, V> records = this.consumer.poll(this.containerProperties.getPollTimeout());
this.lastPoll = System.currentTimeMillis();
if (this.consumerPaused && !isPaused()) {
if (this.logger.isDebugEnabled()) {
this.logger.debug("Resuming consumption from: " + this.consumer.paused());
}
Set<TopicPartition> paused = this.consumer.paused();
this.consumer.resume(paused);
this.consumerPaused = false;
publishConsumerResumedEvent(paused);
}
if (records != null && this.logger.isDebugEnabled()) {
this.logger.debug("Received: " + records.count() + " records");
if (records.count() > 0 && this.logger.isTraceEnabled()) {
this.logger.trace(records.partitions().stream()
.flatMap(p -> records.records(p).stream())
// map to same format as send metadata toString()
.map(r -> r.topic() + "-" + r.partition() + "@" + r.offset())
.collect(Collectors.toList()));
}
}
if (records != null && records.count() > 0) {
if (this.containerProperties.getIdleEventInterval() != null) {
lastReceive = System.currentTimeMillis();
}
invokeListener(records);
}
else {
if (this.containerProperties.getIdleEventInterval() != null) {
long now = System.currentTimeMillis();
if (now > lastReceive + this.containerProperties.getIdleEventInterval()
&& now > lastAlertAt + this.containerProperties.getIdleEventInterval()) {
publishIdleContainerEvent(now - lastReceive, this.isConsumerAwareListener
? this.consumer : null, this.consumerPaused);
lastAlertAt = now;
if (this.genericListener instanceof ConsumerSeekAware) {
seekPartitions(getAssignedPartitions(), true);
}
}
}
}
}
catch (WakeupException e) {
// Ignore, we're stopping
}
catch (NoOffsetForPartitionException nofpe) {
this.fatalError = true;
ListenerConsumer.this.logger.error("No offset and no reset policy", nofpe);
break;
}
catch (Exception e) {
handleConsumerException(e);
}
}
ProducerFactoryUtils.clearConsumerGroupId();
if (!this.fatalError) {
if (this.kafkaTxManager == null) {
commitPendingAcks();
try {
this.consumer.unsubscribe();
}
catch (WakeupException e) {
// No-op. Continue process
}
}
}
else {
ListenerConsumer.this.logger.error("No offset and no reset policy; stopping container");
KafkaMessageListenerContainer.this.stop();
}
this.monitorTask.cancel(true);
if (!this.taskSchedulerExplicitlySet) {
((ThreadPoolTaskScheduler) this.taskScheduler).destroy();
}
this.consumer.close();
this.logger.info("Consumer stopped");
}2、ConcurrentMessageListenerContainer
并发消息监听，相当于创建消费者；其底层逻辑仍然是通过KafkaMessageListenerContainer实现处理；从实现上看就是在KafkaMessageListenerContainer上做了层包装，有多少的concurrency就创建多个KafkaMessageListenerContainer，也就是concurrency个消费者。
protected void doStart() {
if (!isRunning()) {
ContainerProperties containerProperties = getContainerProperties();
TopicPartitionInitialOffset[] topicPartitions = containerProperties.getTopicPartitions();
if (topicPartitions != null
&& this.concurrency > topicPartitions.length) {
this.logger.warn("When specific partitions are provided, the concurrency must be less than or "
+ "equal to the number of partitions; reduced from " + this.concurrency + " to "
+ topicPartitions.length);
this.concurrency = topicPartitions.length;
}
setRunning(true);
// 创建多个消费者
for (int i = 0; i < this.concurrency; i++) {
KafkaMessageListenerContainer<K, V> container;
if (topicPartitions == null) {
container = new KafkaMessageListenerContainer<>(this, this.consumerFactory,
containerProperties);
}
else {
container = new KafkaMessageListenerContainer<>(this, this.consumerFactory,
containerProperties, partitionSubset(containerProperties, i));
}
String beanName = getBeanName();
container.setBeanName((beanName != null ? beanName : "consumer") + "-" + i);
if (getApplicationEventPublisher() != null) {
container.setApplicationEventPublisher(getApplicationEventPublisher());
}
container.setClientIdSuffix("-" + i);
container.setAfterRollbackProcessor(getAfterRollbackProcessor());
container.start();
this.containers.add(container);
}
}
}3、@KafkaListener底层监听原理
上面已经介绍了KafkaMessageListenerContainer的作用是拉取并处理消息，但还缺少关键的一步，即 如何将我们的业务逻辑与KafkaMessageListenerContainer的处理逻辑联系起来？
那么这个桥梁就是@KafkaListener注解
KafkaListenerAnnotationBeanPostProcessor, 从后缀BeanPostProcessor就可以知道这是Spring IOC初始化bean相关的操作，当然这里也是；
此类会扫描带@KafkaListener注解的类或者方法，通过 KafkaListenerContainerFactory工厂创建对应的KafkaMessageListenerContainer，并调用start方法启动监听，也就是这样打通了这条路…
4、Spring Boot 自动加载kafka相关配置
1、KafkaAutoConfiguration
自动生成kafka相关配置，比如当缺少这些bean的时候KafkaTemplate、ProducerListener、ConsumerFactory、ProducerFactory等，默认创建bean实例
2、KafkaAnnotationDrivenConfiguration
主要是针对于spring-kafka提供的注解背后的相关操作，比如 @KafkaListener;
在开启了@EnableKafka注解后，spring会扫描到此配置并创建缺少的bean实例，比如当配置的工厂beanName不是kafkaListenerContainerFactory的时候，就会默认创建一个beanName为kafkaListenerContainerFactory的实例，这也是为什么在springboot中不用定义consumer的相关配置也可以通过@KafkaListener正常的处理消息
5、消息处理
单条消息处理
@Configuration
public class KafkaConsumerConfiguration {
@Bean
KafkaListenerContainerFactory<ConcurrentMessageListenerContainer<Integer, String>> kafkaCustomizeContainerFactory() {
ConcurrentKafkaListenerContainerFactory<Integer, String> factory = new ConcurrentKafkaListenerContainerFactory<>();
factory.setConsumerFactory(consumerFactory());
factory.setConcurrency(2);
factory.getContainerProperties().setPollTimeout(3000);
return factory;
}
private ConsumerFactory<Integer, String> consumerFactory() {
return new DefaultKafkaConsumerFactory<>(consumerConfigs());
}
private Map<String, Object> consumerConfigs() {
Map<String, Object> props = new HashMap<>();
props.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, bizConfig.getReconciliationInstanceKafkaServers());
props.put(ConsumerConfig.GROUP_ID_CONFIG, bizConfig.getReconciliationInstanceKafkaConsumerGroupId());
props.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "latest");
props.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, true);
props.put(ConsumerConfig.AUTO_COMMIT_INTERVAL_MS_CONFIG, 100);
props.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
props.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
props.put(ConsumerConfig.MAX_POLL_RECORDS_CONFIG, 100);
props.put(ConsumerConfig.FETCH_MIN_BYTES_CONFIG, 300);
// poll 一次拉取的阻塞的最大时长，单位：毫秒。这里指的是阻塞拉取需要满足至少 fetch-min-size 大小的消息
props.put(ConsumerConfig.FETCH_MAX_BYTES_CONFIG, 10000);
return props;
}
}这种方式的@KafkaLisener中的参数是单条的。
批量处理
@Configuration
@EnableKafka
public class KafkaConfig {
@Bean
public KafkaListenerContainerFactory<?, ?> batchFactory() {
ConcurrentKafkaListenerContainerFactory<Integer, String> factory =
new ConcurrentKafkaListenerContainerFactory<>();
factory.setConsumerFactory(consumerFactory());
// 增加开启批量处理
factory.setBatchListener(true); // <<<<<<<<<<<<<<<<<<<<<<<<<
return factory;
}
@Bean
public ConsumerFactory<Integer, String> consumerFactory() {
return new DefaultKafkaConsumerFactory<>(consumerConfigs());
}
@Bean
public Map<String, Object> consumerConfigs() {
Map<String, Object> props = new HashMap<>();
props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, embeddedKafka.getBrokersAsString());
...
return props;
}
}
// 注意：这里接受的是集合类型
@KafkaListener(id = "list", topics = "myTopic", containerFactory = "batchFactory")
public void listen(List<String> list) {
...
}这种方式的@KafkaLisener中的参数是多条的。
6、线程池相关
如果没有额外给Kafka指定线程池，底层默认用的是SimpleAsyncTaskExecutor类，它不使用线程池，而是为每个任务创建新线程。相当于一个消费者用一个独立的线程来跑。
总结
spring为了将kafka融入其生态，方便在spring大环境下使用kafka，开发了spring-kafa这一模块，本质上是为了帮助开发者更好的以spring的方式使用kafka
@KafkaListener就是这么一个工具，在同一个项目中既可以有单条的消息处理，也可以配置多条的消息处理，稍微改变下配置即可实现，很是方便
当然，@KafkaListener单条或者多条消息处理仍然是spring自行封装处理，与kafka-client客户端的拉取机制无关；比如一次性拉取50条消息，对于单条处理来说就是循环50次处理，而多条消息处理则可以一次性处理50条；本质上来说这套逻辑都是spring处理的，并不是说单条消费就是通过kafka-client一次只拉取一条消息
在使用过程中需要注意spring自动的创建的一些bean实例，当然也可以覆盖其自动创建的实例以满足特定的需求场景。
以上为个人经验，希望能给大家一个参考，也希望大家多多支持脚本之家。