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kafka生产者发送消息流程深入分析讲解

开发者 https://www.devze.com 2023-03-30 10:38 出处:网络 作者: william_cr7
目录消息发送过程拦截器消息分区消息累加器发送流程源码分析生产消息的可靠性消息发送过程
目录
  • 消息发送过程
    • 拦截器
    • 消息分区
    • 消息累加器
    • 发送流程源码分析
    • 生产消息的可靠性

消息发送过程

消息的发送可能会经过拦截器、序列化、分区器等过程。消息发送的主要涉及两个线程,分别为main线程和sender线程。

kafka生产者发送消息流程深入分析讲解

如图所示,主线程由 afkaProducZIGdUcHYdver 创建消息,然后通过可能的拦截器、序列化器和分区器的作用之后缓存到消息累加器RecordAccumulator (也称为消息收集器)中。 Sender 线程负责从RecordAccumulator 获取消息并将其发送到 Kafka中。

拦截器

在消息序列化之前会经过消息拦截器,自定义拦截器需要实现ProducerInterceptor接口,接口主要有两个方案#onSend和#onAcknowledgement,在消息发送之前会调用前者方法,可以在发送之前假如处理逻辑,比如计费。在收到服务端ack响应后会触发后者方法。需要注意的是拦截器中不要加入过多的复开发者_JS教程杂业务逻辑,以免影响发送效率。

消息分区

消息ProducerRecord会将消息路由到那个分区中,分两种情况:

1.指定了partition字段

如果消息ProducerRecord中指定了 partition字段,那么就不需要走分区器,直接发往指定得partition分区中。

2.没有指定partition,但自定义了分区器

3.没指定parittion,也没有自定义分区器,但key不为空

4.没指定parittion,也没有自定义分区器,key也为空

看源码

// KafkaProducer#partition
private int partition(ProducerRecord<K, V> record,python byte[] serializedKey, byte[] serializedValue, Cluster cluster) {
//指定分区partition则直接返回,否则走分区器
        Integer partition = record.partition();
        return partition != null ?
                partition :
                partitioner.partition(
                        record.topic(), record.key(), serializedKey, record.value(),                 serializedValue, cluster);
}
//DefaultPartitioner#partition
public int partition(String topic, Object key, byte[] keyBytes, Object value, byte[] valueBytes, Cluster cluster) {
        if (keyBytes == null) {
            return stickyPartitionCache.partition(topic, cluster);
        } 
        List<PartitionInfo> partitions = cluster.partitionsForTopic(topic);
        int numPartitions = partitions.size();
        // hash the keyBytes to choose a partition
        return Utils.toPositive(Utils.murmur2(keyBytes)) % numPartitions;
    }

partition 方法中定义了分区分配逻辑 如果 ke 不为 null , 那 么默认的分区器会对 key 进行哈 希(采 MurmurHash2 算法 ,具备高运算性能及 低碰 撞率),最终根据得到 哈希值来 算分区号, 有相同 key 的消息会被写入同一个分区 如果 key null ,那么消息将会以轮询的方式发往主题内的各个可用分区。

消息累加器

分区确定好了之后,消息并不是直接发送给broker,因为一个个发送网络消耗太大,而是先缓存到消息累加器RecordAccumulator,RecordAccumulator主要用来缓存消息 Sender 线程可以批量发送,进 减少网络传输 的资源消耗以提升性能 RecordAccumulator 缓存的大 小可以通过生产者客户端参数 buffer memory 配置,默认值为 33554432B ,即 32MB如果生产者发送消息的速度超过发 送到服务器的速度 ,则会导致生产者空间不足,这个时候 KafkaProducer的send()方法调用要么 被阻塞,要么抛出异常,这个取决于参数 max block ms 的配置,此参数的默认值为 60秒。

消息累加器本质上是个ConcurrentMap,

ConcurrentMap<TopicPartition, Deque<ProducerBATch>> batches;

发送流程源码分析

//KafkaProducer
@Override
public Future<RecordMetadata> send(ProducerRecord<K, V> record, Callback callback) {
	// intercept the record, which can be potentially modified; this method does not throw exceptions
    //首先执行拦截器链
	ProducerRecord<K, V> interceptedRecord = this.interceptors.onSend(record);
	return DOSend(interceptedRecord, callback);
}
private Future<RecordMetadata> doSend(ProducerRecord<K, V> record, Callback callback) {
        TopicPartition tp = null;
	try {
		throwIfProducerClosed();
		// first make sure the metadata for the topic is available
		long nowMs = time.milliseconds();
		ClusterAndwaitTime clusterAndWaitTime;
		try {
			clusterAndWaitTimehttp://www.devze.com = waitOnMetadata(record.topic(), record.partition(), nowMs, maxBlockTimeMs);
		} catch (KafkaExceptioZIGdUcHYdvn e) {
			if (metadata.isClosed())
				throw new KafkaException("Producer closed while send in progress", e);
			throw e;
		}
		nowMs += clusterAndWaitTime.waitedOnMetadataMs;
		long remainingWaitMs = Math.max(0, maxBlockTimeMs - clusterAndWaitTime.waitedOnMetadataMs);
		Cluster cluster = clusterAndWaitTime.cluster;
		byte[] serializedKey;
		try {
			//key序列化
			serializedKey = keySerializer.serialize(record.topic(), record.headers(), record.key());
		} catch (ClassCastException cce) {
			throw new SerializationException("Can't convert key of class " + record.key().getClass().getName() +
					" to class " + producerConfig.getClass(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG).getName() +
					" specified in key.serializer", cce);
		}
		byte[] serializedValue;
		try {
			//value序列化
			serializedValue = valueSerializer.serialize(record.topic(), record.headers(), record.value());
		} catch (ClassCastException cce) {
			throw new SerializationException("Can't convert value of class " + record.value().getClass().getName() +
					" to class " + producerConfig.getClass(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG).getName() +
					" specified in value.serializer", cce);
		}
		//获取分区partition
		int partition = partition(record, serializedKey, serializedValue, cluster);
		tp = new TopicPartition(record.topic(), partition);
		setReadOnly(record.headers());
		Header[] headers = record.headers().toArray();
		//消息压缩
		int serializedSize = AbstractRecords.estimateSizeInBytesUpperBound(apiVersions.maxUsableProduceMagic(),
				compressionType, serializedKey, serializedValue, headers);
		//判断消息是否超过最大允许大小,消息缓存空间是否已满
		ensureValidRecordSize(serializedSize);
		long timestamp = record.timestamp() == null ? nowMs : record.timestamp();
		if (log.isTraceEnabled()) {
			log.trace("Attempting to append record {} with callback {} to topic {} partition {}", record, callback, record.topic(), partition);
		}
		// producer callback will make sure to call both 'callback' and interceptor callback
		Callback interceptCallback = new InterceptorCallback<>(callback, this.interceptors, tp);
 
		if (transactionManager != null && transactionManager.isTransactional()) {
			transactionManager.failIfNotReadyForSend();
		}
		//将消息缓存在消息累加器RecordAccumulator中
		RecordAccumulator.RecordAppendResult result = accumulator.append(tp, timestamp, serializedKey,
				serializedValue, headers, interceptCallback, remainingWaitMs, true, nowMs);
        //开辟新的ProducerBatch
		if (result.abortForNewBatch) {
			int prevPartition = partition;
			partitioner.onNewBatch(record.topic(), cluster, prevPartition);
			partition = partition(record, serializedKey, serializedValue, cluster);
			tp = new TopicPartition(record.topic(), partition);
			if (log.isTraceEnabled()) {
				log.trace("Retrying append due to new batch creation for topic {} partition {}. The old partition was {}", record.topic(), partition, prevPartition);
			}
			// producer callback will make sure to call both 'callback' and interceptor callback
			intercep编程客栈tCallback = new InterceptorCallback<>(callback, this.interceptors, tp);
 
			result = accumulator.append(tp, timestamp, serializedKey,
				serializedValue, headers, interceptCallback, remainingWaitMs, false, nowMs);
		}
		if (transactionManager != null && transactionManager.isTransactional())
			transactionManager.maybeAddPartitionToTransaction(tp);
		//判断消息是否已满,唤醒sender线程进行发送消息
		if (result.batchIsFull || result.newBatchCreated) {
			log.trace("Waking up the sender since topic {} partition {} is either full or getting a new batch", record.topic(), partition);
			this.sender.wakeup();
		}
		return result.future;
		// handling exceptions and record the errors;
		// for API exceptions return them in the future,
		// for other exceptions throw directly
	} catch (Exception e) {
		// we notify interceptor about all exceptions, since onSend is called before anything else in this method
		this.interceptors.onSendError(record, tp, e);
		throw e;
	}
}

生产消息的可靠性

消息发送到broker,什么情况下生产者才确定消息写入成功了呢?ack是生产者一个重要的参数,它有三个值,ack=1表示leader副本写入成功服务端即可返回给生产者,是吞吐量和消息可靠性的平衡方案;ack=0表示生产者发送消息之后不需要等服务端响应,这种消息丢失风险最大;ack=-1表示生产者需要等等ISR中所有副本写入成功后才能收到响应,这种消息可靠性最高但吞吐量也是最小的。

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