kafka生产者发送消息流程深入分析讲解

消息发送过程

消息的发送可能会经过 * 、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。

如图所示，主线程由 afkaProducer 创建消息，然后通过可能的 * 、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。

*

在消息序列化之前会经过消息 * ，自定义 * 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 * 中不要加入过多的复杂业务逻辑，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, 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 * 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 {
clusterAndWaitTime = waitOnMetadata(record.topic(), record.partition(), nowMs, maxBlockTimeMs);
} catch (KafkaException 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
interceptCallback = 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。

来源：https://blog.csdn.net/CHINACR07/article/details/129699173