最近发现一个Kafka producer异步发送在某些情况会阻塞主线程，后来在排查解决问题过程中发现这可以算是Kafka的一个说明不恰当的地方。

问题说明

在很多场景下我们会使用异步方式来发送Kafka的消息，会使用KafkaProducer中的以下方法：

public Future send(ProducerRecord record, Callback callback) {}

根据文档的说明它是一个异步的发送方法，按道理不管如何它都不应该阻塞主线程，但实际中某些情况下会出现阻塞线程，比如broker未正确运行，topic未创建等情况，有些时候我们不需要对发送的结果做保证，但是如果出现阻塞的话，会影响其他业务逻辑。

问题出现点

从KafkaProducer send这个方法声明上看并没有什么问题，那么我们来看一下她的具体实现：

public Future send(ProducerRecord record, Callback callback) {
// intercept the record, which can be potentially modified; this method does not throw exceptions
ProducerRecord interceptedRecord = this.interceptors.onSend(record);
return doSend(interceptedRecord, callback);
}
/**
* Implementation of asynchronously send a record to a topic.
*/
private Future doSend(ProducerRecord record, Callback callback) {
TopicPartition tp = null;
try {
throwIfProducerClosed();
// first make sure the metadata for the topic is available
ClusterAndWaitTime clusterAndWaitTime;
try {
clusterAndWaitTime = waitOnMetadata(record.topic(), record.partition(), maxBlockTimeMs); //出现问题的地方
} catch (KafkaException e) {
if (metadata.isClosed())
throw new KafkaException("Producer closed while send in progress", e);
throw e;
}
...
} catch (ApiException e) {
...
}
}
private ClusterAndWaitTime waitOnMetadata(String topic, Integer partition, long maxWaitMs) throws InterruptedException {
// add topic to metadata topic list if it is not there already and reset expiry
Cluster cluster = metadata.fetch();
if (cluster.invalidTopics().contains(topic))
throw new InvalidTopicException(topic);
metadata.add(topic);
Integer partitionsCount = cluster.partitionCountForTopic(topic);
// Return cached metadata if we have it, and if the record's partition is either undefined
// or within the known partition range
if (partitionsCount != null && (partition == null || partition < partitionsCount))
return new ClusterAndWaitTime(cluster, 0);
long begin = time.milliseconds();
long remainingWaitMs = maxWaitMs;
long elapsed;
//一直获取topic的元数据信息，直到获取成功，若获取时间超过maxWaitMs，则抛出异常
do {
if (partition != null) {
log.trace("Requesting metadata update for partition {} of topic {}.", partition, topic);
} else {
log.trace("Requesting metadata update for topic {}.", topic);
}
metadata.add(topic);
int version = metadata.requestUpdate();
sender.wakeup();
try {
metadata.awaitUpdate(version, remainingWaitMs);
} catch (TimeoutException ex) {
// Rethrow with original maxWaitMs to prevent logging exception with remainingWaitMs
throw new TimeoutException(
String.format("Topic %s not present in metadata after %d ms.",
topic, maxWaitMs));
}
cluster = metadata.fetch();
elapsed = time.milliseconds() - begin;
if (elapsed >= maxWaitMs) { //判断执行时间是否大于maxWaitMs
throw new TimeoutException(partitionsCount == null ?
String.format("Topic %s not present in metadata after %d ms.",
topic, maxWaitMs) :
String.format("Partition %d of topic %s with partition count %d is not present in metadata after %d ms.",
partition, topic, partitionsCount, maxWaitMs));
}
metadata.maybeThrowException();
remainingWaitMs = maxWaitMs - elapsed;
partitionsCount = cluster.partitionCountForTopic(topic);
} while (partitionsCount == null || (partition != null && partition >= partitionsCount));
return new ClusterAndWaitTime(cluster, elapsed);
}

从它的实现我们可以看出，会导致线程阻塞的原因在于以下这个逻辑：

private ClusterAndWaitTime waitOnMetadata(String topic, Integer partition, long maxWaitMs) throws InterruptedException

通过KafkaProducer 执行send的过程中需要先获取Metadata，而这是一个不断循环的操作，直到获取成功，或者抛出异常。

其实Kafka本意这么实现并没有问题，因为你要发送消息的前提就是能获取到border和topic的信息，问题在于这个send对外暴露的是Future的方法，但是内部实现却是有阻塞的，那么在有些时候没有考虑到这种情况，一旦出现border或者topic异常，将会阻塞系统线程，导致系统响应变慢，直到奔溃。

问题解决

其实解决这个问题很简单，就是单独创建几个线程用于消息发送，这样即使遇到意外情况，也只会阻塞几个线程，不会引起系统线程大面积阻塞，不可用，具体实现：

import java.util.concurrent.Callable
import java.util.concurrent.ExecutorService
import java.util.concurrent.Executors
import org.apache.kafka.clients.producer.{Callback, KafkaProducer, ProducerRecord, RecordMetadata}
class ProducerF[K,V](kafkaProducer: KafkaProducer[K,V]) {
val executor: ExecutorService = Executors.newScheduledThreadPool(1)
def sendAsync(producerRecord: ProducerRecord[K,V], callback: Callback) = {
executor.submit(new Callable[RecordMetadata]() {
def call = kafkaProducer.send(producerRecord, callback).get()
})
}
}

这是一种实现方式，当然你也可以自己维护一个Kafka版本，但这样或许有点麻烦，具体用什么方式根据自己场景来做选择。

使用例子:

import java.util.Properties
import org.apache.kafka.clients.producer.{KafkaProducer, ProducerRecord, RecordMetadata}
object FixExample extends App {
val props = new Properties()
props.put("max.block.ms", "3000")
props.put("bootstrap.servers", "localhost:9092")
props.put("client.id", "ProducerSendFixExample")
props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer")
props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer")
val producer = new KafkaProducer[String, String](props)
val topic = "topic-trace-one"
val userId = "godpan"
val msg = "login wechat"
val data = new ProducerRecord[String, String](topic, userId, msg)
val startTime = System.currentTimeMillis()
val producerF = new ProducerF(producer)
producerF.sendAsync(data,(metadata: RecordMetadata, exception: Exception) => {
println(s"[producerF-sendAsync] data producerRecord: ${data}, exception: ${exception}")
})
// 如果想要得到发送结果，可以线程等待4s
// Thread.sleep(4000)
System.exit(0)
}