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上一节咱们剖析了 Producer 的外围组件，咱们失去了一张要害的组件图。你还记得么？
简略概括下下面的图就是：
创立了 Metadata 组件，外部通过 Cluster 保护元数据
初始化了发送音讯的内存缓冲器 RecordAccumulator
创立了 NetworkClient，外部最重要的是创立了 NIO 的 Selector 组件
启动了一个 Sender 线程，Sender 援用了下面的所有组件，开始执行 run 办法。
图的最下方能够看到，上一节截止到了 run 办法的执行，这一节咱们首先会看看 run 办法外围脉络做了什么。接着剖析下 Producer 第一个外围流程：元数据拉取的源码原理。
让咱们开始吧！
Sender 的 run 办法在做什么？
这一节咱们就持续剖析下，sender 的 run 办法开始执行会做什么。
public void run() {log.debug("Starting Kafka producer I/O thread.");
// main loop, runs until close is called
while (running) {
try {run(time.milliseconds());
} catch (Exception e) {log.error("Uncaught error in kafka producer I/O thread:", e);
}
}
log.debug("Beginning shutdown of Kafka producer I/O thread, sending remaining records.");
// okay we stopped accepting requests but there may still be
// requests in the accumulator or waiting for acknowledgment,
// wait until these are completed.
while (!forceClose && (this.accumulator.hasUnsent() || this.client.inFlightRequestCount() > 0)) {
try {run(time.milliseconds());
} catch (Exception e) {log.error("Uncaught error in kafka producer I/O thread:", e);
}
}
if (forceClose) {
// We need to fail all the incomplete batches and wake up the threads waiting on
// the futures.
this.accumulator.abortIncompleteBatches();}
try {this.client.close();
} catch (Exception e) {log.error("Failed to close network client", e);
}
log.debug("Shutdown of Kafka producer I/O thread has completed.");
}这个 run 办法的外围脉络很简略。次要就是 2 个 while 循环 + 线程的 close，而 2 个 while 循环，他们都调用了 run(long time)的这个办法。
通过正文你能够看到，第二个 while 是解决非凡状况的，当第一个 while 退出后，还有未发送的申请，须要第二个 while 循环解决实现，才会敞开线程。
整体脉络如下图所示:
接着其实就该看下 run 办法次要在干什么了？
/**
* Run a single iteration of sending
*
* @param now
* The current POSIX time in milliseconds
*/
void run(long now) {Cluster cluster = metadata.fetch();
// get the list of partitions with data ready to send
RecordAccumulator.ReadyCheckResult result = this.accumulator.ready(cluster, now);
// if there are any partitions whose leaders are not known yet, force metadata update
if (result.unknownLeadersExist)
this.metadata.requestUpdate();
// remove any nodes we aren't ready to send to
Iterator<Node> iter = result.readyNodes.iterator();
long notReadyTimeout = Long.MAX_VALUE;
while (iter.hasNext()) {Node node = iter.next();
if (!this.client.ready(node, now)) {iter.remove();
notReadyTimeout = Math.min(notReadyTimeout, this.client.connectionDelay(node, now));
}
}
// create produce requests
Map<Integer, List<RecordBatch>> batches = this.accumulator.drain(cluster,
result.readyNodes,
this.maxRequestSize,
now);
if (guaranteeMessageOrder) {
// Mute all the partitions drained
for (List<RecordBatch> batchList : batches.values()) {for (RecordBatch batch : batchList)
this.accumulator.mutePartition(batch.topicPartition);
}
}
List<RecordBatch> expiredBatches = this.accumulator.abortExpiredBatches(this.requestTimeout, now);
// update sensors
for (RecordBatch expiredBatch : expiredBatches)
this.sensors.recordErrors(expiredBatch.topicPartition.topic(), expiredBatch.recordCount);
sensors.updateProduceRequestMetrics(batches);
List<ClientRequest> requests = createProduceRequests(batches, now);
// If we have any nodes that are ready to send + have sendable data, poll with 0 timeout so this can immediately
// loop and try sending more data. Otherwise, the timeout is determined by nodes that have partitions with data
// that isn't yet sendable (e.g. lingering, backing off). Note that this specifically does not include nodes
// with sendable data that aren't ready to send since they would cause busy looping.
long pollTimeout = Math.min(result.nextReadyCheckDelayMs, notReadyTimeout);
if (result.readyNodes.size() > 0) {log.trace("Nodes with data ready to send: {}", result.readyNodes);
log.trace("Created {} produce requests: {}", requests.size(), requests);
pollTimeout = 0;
}
for (ClientRequest request : requests)
client.send(request, now);
// if some partitions are already ready to be sent, the select time would be 0;
// otherwise if some partition already has some data accumulated but not ready yet,
// the select time will be the time difference between now and its linger expiry time;
// otherwise the select time will be the time difference between now and the metadata expiry time;
this.client.poll(pollTimeout, now);
}下面的代码，你如果第一次看，你必定会感觉，这个脉络十分不清晰，不晓得重点在哪里。不过还好有些正文，你能大体猜到他在干嘛。
accumulator 的 ready，networkclient 的 ready、networkclient 的 send、networkclient 的 poll
这些如同是在筹备内存区域、筹备网络连接的 node 节点、发送数据、拉取响应后果的意思。
可是如果你猜不到，该怎么办呢？
这时候就能够 祭出 debug 这个杀器了。因为是 producer，咱们能够在 Hellowolrd 的这个客户端打断点，一步一步看下。
当你对 run 办法一步一步打了断点之后你会发现：
accumulator 的 ready，networkclient 的 ready、networkclient 的 send 这些的逻辑简直都没有执行，全部都是初始化空对象，或者办法外部间接 return。
间接一路执行到了 client.poll 办法。如下图所示：
那么，你能够得出一个论断，while 第一次循环这个 run 办法的外围逻辑，其实只有一句话：
client.poll(pollTimeout, now)
整体脉络如下所示：
看来接下来，这个 NetworkClient 的 poll 办法，就是要害中的要害了：
/**
* Do actual reads and writes to sockets.
* 对套接字进行理论读取和写入
*
* @param timeout The maximum amount of time to wait (in ms) for responses if there are none immediately,
* must be non-negative. The actual timeout will be the minimum of timeout, request timeout and
* metadata timeout
* @param now The current time in milliseconds
* @return The list of responses received
*/
@Override
public List<ClientResponse> poll(long timeout, long now) {long metadataTimeout = metadataUpdater.maybeUpdate(now);
try {this.selector.poll(Utils.min(timeout, metadataTimeout, requestTimeoutMs));
} catch (IOException e) {log.error("Unexpected error during I/O", e);
}
// process completed actions
long updatedNow = this.time.milliseconds();
List<ClientResponse> responses = new ArrayList<>();
handleCompletedSends(responses, updatedNow);
handleCompletedReceives(responses, updatedNow);
handleDisconnections(responses, updatedNow);
handleConnections();
handleTimedOutRequests(responses, updatedNow);
// invoke callbacks
for (ClientResponse response : responses) {if (response.request().hasCallback()) {
try {response.request().callback().onComplete(response);
} catch (Exception e) {log.error("Uncaught error in request completion:", e);
}
}
}
return responses;
}这个办法的脉络就清晰多了, 通过办法名和正文，咱们简直能够猜出他的一些作用次要有：
1）正文说：对套接字进行理论读取和写入
2）metadataUpdater.maybeUpdate()，你还记得 NetworkClient 的组件 DefaultMetadataUpdater 么，办法名意思是可能进行元数据更新。这个如同很要害的样子
3）接着执行了 Selector 的 poll 办法，这个是 NetworkClient 的另一个组件 Selector，还记得么？它底层封装了原生的 NIO Selector。这个办法应该也比拟要害。
4）后续对 response 执行了一系列的办法，从名字上看，handleCompletedSends 解决实现发送的申请、handleCompletedReceives 解决实现承受的申请、handleDisconnections 解决断开连接的申请、handleConnections 解决连贯胜利的申请、解决超时的申请 handleTimedOutRequests。依据不同状况有不同的解决。
5）最初还有一个 response 的相干的回调解决，如果注册了回调函数，会执行下。这个应该不是很要害的逻辑
也就是简略的说就是NetworkClient 执行 poll 办法，次要通过 selector 解决申请的读取和写入，对响应后果做不同的解决而已。
如下图所示：
到这里其实咱们根本摸清出了 run 办法次要在做的一件事件了，因为是第一次循环，之前的 accumulator 的 ready，networkclient 的 ready、networkclient 的 send 什么都没做，第一次 while 循环 run 办法外围执行的是 networkclient.poll 办法。而 poll 办法的次要逻辑就是下面图中所示的了。
maybeUpdate 可能在在拉取元数据？
方才咱们剖析到，poll 办法首先执行的是 DefaultMetadataUpdater 的 maybeUpdate 办法，它是可能更新的意思。咱们来一起看下他的逻辑吧。
public long maybeUpdate(long now) {
// should we update our metadata?
long timeToNextMetadataUpdate = metadata.timeToNextUpdate(now);
long timeToNextReconnectAttempt = Math.max(this.lastNoNodeAvailableMs + metadata.refreshBackoff() - now, 0);
long waitForMetadataFetch = this.metadataFetchInProgress ? Integer.MAX_VALUE : 0;
// if there is no node available to connect, back off refreshing metadata
long metadataTimeout = Math.max(Math.max(timeToNextMetadataUpdate, timeToNextReconnectAttempt),
waitForMetadataFetch);
if (metadataTimeout == 0) {// Beware that the behavior of this method and the computation of timeouts for poll() are
// highly dependent on the behavior of leastLoadedNode.
Node node = leastLoadedNode(now);
maybeUpdate(now, node);
}
return metadataTimeout;
}
/**
* The next time to update the cluster info is the maximum of the time the current info will expire and the time the
* current info can be updated (i.e. backoff time has elapsed); If an update has been request then the expiry time
* is now
*/
public synchronized long timeToNextUpdate(long nowMs) {long timeToExpire = needUpdate ? 0 : Math.max(this.lastSuccessfulRefreshMs + this.metadataExpireMs - nowMs, 0);
long timeToAllowUpdate = this.lastRefreshMs + this.refreshBackoffMs - nowMs;
return Math.max(timeToExpire, timeToAllowUpdate);
}原来这里有一个工夫的判断，当判断满足才会执行 maybeUpdate。
这个工夫计算如同比较复杂，然而大体能够看进去，metadataTimeout 是依据三个工夫综合判断进去的，如果是 0 才会执行真正的 maybeUpdate()。
像这种时候，咱们能够间接在 metadataTimeout 这里打一个断点，看下它的值是如何计算的，比方下图：
你会发现，当第一次执行 while 循环，执行到 poll 办法，执行到这个 maybeUpdate 的时候，决定 metadataTimeout 的 3 个值，有两个是 0，其中一个是非 0，是一个 299720 的值。最终导致 metadataTimeout 也是非 0，是 299720。
也就是说，第一次 while 循环不会执行 maybeUpdate 的任何逻辑。
那么接着向下执行 Selector 的 poll()办法。
/**
* Do whatever I/O can be done on each connection without blocking. This includes completing connections, completing
* disconnections, initiating new sends, or making progress on in-progress sends or receives.
* 在不阻塞的状况下，在每个连贯上做任何能够做的 I/O。这包含实现连贯实现、断开连接，启动新的发送，或在进行中的发送或接管申请
*/
@Override
public void poll(long timeout) throws IOException {if (timeout < 0)
throw new IllegalArgumentException("timeout should be >= 0");
clear();
if (hasStagedReceives() || !immediatelyConnectedKeys.isEmpty())
timeout = 0;
/* check ready keys */
long startSelect = time.nanoseconds();
// 这个办法是 NIO 底层 Selector.select()，会阻塞监听
int readyKeys = select(timeout);
long endSelect = time.nanoseconds();
currentTimeNanos = endSelect;
this.sensors.selectTime.record(endSelect - startSelect, time.milliseconds());
// 如果监听到有操作的 SelectionKeys, 也就是 readyKeys>0< 会执行一些操作
if (readyKeys > 0 || !immediatelyConnectedKeys.isEmpty()) {pollSelectionKeys(this.nioSelector.selectedKeys(), false);
pollSelectionKeys(immediatelyConnectedKeys, true);
}
addToCompletedReceives();
long endIo = time.nanoseconds();
this.sensors.ioTime.record(endIo - endSelect, time.milliseconds());
maybeCloseOldestConnection();}
private int select(long ms) throws IOException {if (ms < 0L)
throw new IllegalArgumentException("timeout should be >= 0");
if (ms == 0L)
return this.nioSelector.selectNow();
else
return this.nioSelector.select(ms);
}下面的脉络次要是 2 步：
1）select(timeout): NIO 底层 selector.select()，会阻塞监听
2）pollSelectionKeys(): 监听到有操作的 SelectionKeys，做了一些操作
也就是说，最终，Sender 线程的 run 办法，第一次 while 循环执行 poll 办法，最初什么都没干，会被 selector.select()阻塞住。
如下图所示：
new KafkaProducer 之后
剖析完了 run 办法的执行，咱们剖析的 KafkaProducerHelloWorld 第一步 new KafkaProducer()根本就实现了。
大家经验了一节半的工夫，终于剖析分明了 KafkaProducer 创立的原理。不不晓得你对 Kafka 的 Producer 是不是有了更深的了解了。
剖析了 new KafkaProducer()之后呢？
咱们持续接着 KafkaProducerHelloWorld 往下剖析，你还记得 KafkaProducerHelloWorld 的代码么？
public class KafkaProducerHelloWorld {public static void main(String[] args) throws Exception {
// 配置 Kafka 的一些参数
Properties props = new Properties();
props.put("bootstrap.servers", "mengfanmao.org:9092");
props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
// 创立一个 Producer 实例
KafkaProducer<String, String> producer = new KafkaProducer<>(props);
// 封装一条音讯
ProducerRecord<String, String> record = new ProducerRecord<>("test-topic", "test-key", "test-value");
// 同步形式发送音讯，会阻塞在这里，直到发送实现
// producer.send(record).get();
// 异步形式发送音讯，不阻塞，设置一个监听回调函数即可
producer.send(record, new Callback() {
@Override
public void onCompletion(RecordMetadata metadata, Exception exception) {if(exception == null) {System.out.println("音讯发送胜利");
} else {exception.printStackTrace();
}
}
});
Thread.sleep(5 * 1000);
// 退出 producer
producer.close();}KafkaProducerHelloWorld 次要就 3 步：
1）new KafkaProducer 这个咱们曾经剖析完了，次要剖析了配置文件的解析、各个组件是什么、有什么，还有就是方才剖析的 run 线程第一次循环到底执行了什么。
2）new ProducerRecord 创立待发送的音讯
3）producer.send() 发送音讯
首先创立待发送的音讯：
ProducerRecord<String, String> record = new ProducerRecord<>("test-topic", "test-key", "test-value");
public ProducerRecord(String topic, K key, V value) {this(topic, null, null, key, value);
}
/**
* Creates a record with a specified timestamp to be sent to a specified topic and partition
* 创立具备指定工夫戳的记录以发送到指定主题和分区
* @param topic The topic the record will be appended to
* @param partition The partition to which the record should be sent
* @param timestamp The timestamp of the record
* @param key The key that will be included in the record
* @param value The record contents
*/
public ProducerRecord(String topic, Integer partition, Long timestamp, K key, V value) {if (topic == null)
throw new IllegalArgumentException("Topic cannot be null");
if (timestamp != null && timestamp < 0)
throw new IllegalArgumentException("Invalid timestamp" + timestamp);
this.topic = topic;
this.partition = partition;
this.key = key;
this.value = value;
this.timestamp = timestamp;
}咱们之前提过，Record 示意了一条音讯的形象封装。这个 ProducerRecord 其实就示意了一条音讯。
从构造函数的正文能够看进去，ProducerRecord 能够指定往哪个 topic，哪一个分区 partition，并且音讯能够设置一个工夫戳。分区和工夫戳默认能够不指定
其实看这块源码，咱们次要失去的信息就是这些了，这些都比较简单。就不画图了。
发送音讯时的元数据拉取触发
当 Producer 和 Record 都创立好了之后，能够用同步或者异步的形式发送音讯。
// 同步形式发送音讯，会阻塞在这里，直到发送实现
// producer.send(record).get();
// 异步形式发送音讯，不阻塞，设置一个监听回调函数即可
producer.send(record, new Callback() {
@Override
public void onCompletion(RecordMetadata metadata, Exception exception) {if(exception == null) {System.out.println("音讯发送胜利");
} else {exception.printStackTrace();
}
}
});
// 同步发送
@Override
public Future<RecordMetadata> send(ProducerRecord<K, V> record) {return send(record, null);
}
// 异步发送
public Future<RecordMetadata> send(ProducerRecord<K, V> record, Callback callback) {
// intercept the record, which can be potentially modified; this method does not throw exceptions
ProducerRecord<K, V> interceptedRecord = this.interceptors == null ? record : this.interceptors.onSend(record);
return doSend(interceptedRecord, callback);
}
同步和异步的整个发送逻辑如下图所示：
从上图你会发现，然而无论同步发送还是异步底层都会调用同一个办法 doSend()。区别就是有没有 callBack 回调函数而已，他们还都在调用前注册一些拦截器，这里咱们抓大放小下，咱们重点还是关注 doSend 办法。
doSend 办法如下：
/**
* Implementation of asynchronously send a record to a topic. Equivalent to <code>send(record, null)</code>.
* See {@link #send(ProducerRecord, Callback)} for details.
*/
private Future<RecordMetadata> doSend(ProducerRecord<K, V> record, Callback callback) {
TopicPartition tp = null;
try {
// first make sure the metadata for the topic is available
long waitedOnMetadataMs = waitOnMetadata(record.topic(), this.maxBlockTimeMs);
long remainingWaitMs = Math.max(0, this.maxBlockTimeMs - waitedOnMetadataMs);
byte[] serializedKey;
try {serializedKey = keySerializer.serialize(record.topic(), 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");
}
byte[] serializedValue;
try {serializedValue = valueSerializer.serialize(record.topic(), 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");
}
int partition = partition(record, serializedKey, serializedValue, metadata.fetch());
int serializedSize = Records.LOG_OVERHEAD + Record.recordSize(serializedKey, serializedValue);
ensureValidRecordSize(serializedSize);
tp = new TopicPartition(record.topic(), partition);
long timestamp = record.timestamp() == null ? time.milliseconds() : record.timestamp();
log.trace("Sending 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 = this.interceptors == null ? callback : new InterceptorCallback<>(callback, this.interceptors, tp);
RecordAccumulator.RecordAppendResult result = accumulator.append(tp, timestamp, serializedKey, serializedValue, interceptCallback, remainingWaitMs);
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 (ApiException e) {log.debug("Exception occurred during message send:", e);
if (callback != null)
callback.onCompletion(null, e);
this.errors.record();
if (this.interceptors != null)
this.interceptors.onSendError(record, tp, e);
return new FutureFailure(e);
} catch (InterruptedException e) {this.errors.record();
if (this.interceptors != null)
this.interceptors.onSendError(record, tp, e);
throw new InterruptException(e);
} catch (BufferExhaustedException e) {this.errors.record();
this.metrics.sensor("buffer-exhausted-records").record();
if (this.interceptors != null)
this.interceptors.onSendError(record, tp, e);
throw e;
} catch (KafkaException e) {this.errors.record();
if (this.interceptors != null)
this.interceptors.onSendError(record, tp, e);
throw e;
} catch (Exception e) {
// we notify interceptor about all exceptions, since onSend is called before anything else in this method
if (this.interceptors != null)
this.interceptors.onSendError(record, tp, e);
throw e;
}
}这个办法的脉络尽管比拟长，然而脉络还是比拟清晰，次要先执行了：
1）waitOnMetadata 应该是期待元数据拉取
2）keySerializer.serialize 和 valueSerializer.serialize，很显著就是将 Record 序列化成 byte 字节数组
3）通过 partition 进行路由分区，依照肯定路由策略抉择 Topic 下的某个分区
4）accumulator.append 将音讯放入缓冲器中
5）唤醒 Sender 线程的 selector.select()的阻塞，开始解决内存缓冲器中的数据。
用图来示意如下所示：
这两节咱们重点剖析元数据拉取的这个场景的源码原理。
所以这里咱们着重先看下步骤 1，之后的 4 步咱们之后会剖析到的。
waitOnMetadata 如何期待元数据拉取的?
既然 send 的第一步是执行 waitOnMetadata 办法，首先看下它的代码：
/**
* Wait for cluster metadata including partitions for the given topic to be available.
* @param topic The topic we want metadata for
* @param maxWaitMs The maximum time in ms for waiting on the metadata
* @return The amount of time we waited in ms
*/
private long waitOnMetadata(String topic, long maxWaitMs) throws InterruptedException {
// add topic to metadata topic list if it is not there already.
if (!this.metadata.containsTopic(topic))
this.metadata.add(topic);
if (metadata.fetch().partitionsForTopic(topic) != null)
return 0;
long begin = time.milliseconds();
long remainingWaitMs = maxWaitMs;
while (metadata.fetch().partitionsForTopic(topic) == null) {log.trace("Requesting metadata update for topic {}.", topic);
int version = metadata.requestUpdate();
sender.wakeup();
metadata.awaitUpdate(version, remainingWaitMs);
long elapsed = time.milliseconds() - begin;
if (elapsed >= maxWaitMs)
throw new TimeoutException("Failed to update metadata after" + maxWaitMs + "ms.");
if (metadata.fetch().unauthorizedTopics().contains(topic))
throw new TopicAuthorizationException(topic);
remainingWaitMs = maxWaitMs - elapsed;
}
return time.milliseconds() - begin;}
/**
* Get the current cluster info without blocking
*/
public synchronized Cluster fetch() {return this.cluster;}
public synchronized int requestUpdate() {
this.needUpdate = true;
return this.version;
}
/**
* Wait for metadata update until the current version is larger than the last version we know of
*/
public synchronized void awaitUpdate(final int lastVersion, final long maxWaitMs) throws InterruptedException {if (maxWaitMs < 0) {throw new IllegalArgumentException("Max time to wait for metadata updates should not be < 0 milli seconds");
}
long begin = System.currentTimeMillis();
long remainingWaitMs = maxWaitMs;
while (this.version <= lastVersion) {if (remainingWaitMs != 0)
wait(remainingWaitMs);
long elapsed = System.currentTimeMillis() - begin;
if (elapsed >= maxWaitMs)
throw new TimeoutException("Failed to update metadata after" + maxWaitMs + "ms.");
remainingWaitMs = maxWaitMs - elapsed;
}
}这个办法外围就是判断了是否有 Cluster 元数据信息，如果没有，进行了如下操作：
1）metadata.requestUpdate(); 更新了一个 needUpdate 标记，这个值会影响之前 maybeUpdate 的 metadataTimeout 的计算，能够让 metadataTimeout 为 0
2）sender.wakeup(); 唤醒之前 nioSelector.select()的阻塞，继续执行
3）metadata.awaitUpdate(version, remainingWaitMs); 次要进行了版本比拟，如果不是最新版本，调用了 Metadata.wait()办法（wait 办法是每个 Object 都会有的办法，个别和 notify 或者 notifyAll 组合应用）
整个过程我间接用图给大家示意一下，如下所示：
整个图就是明天咱们剖析的要害后果了，这里通过两种阻塞和唤醒机制，一个是 NIO 中 Selector 的 select()和 wakeUp()，一个是 MetaData 对象的 wait()和 notifyAll()机制。所以这里要联合之前 Sender 线程的阻塞逻辑一起来了解。
是不是很有意思一种应用，这里没有用任何线程的 join、sleep、wait、park、unpark、notify 这些办法。
小结
最初咱们简略小结下，这里一节咱们次要剖析了如下 Producer 的源码原理：
初始化 KafkaProducer 时并没有去拉取元数据，然而创立了 Selector 组件，启动了 Sender 线程，select 阻塞期待申请响应。因为还没有发送任何申请，所以初始化时并没有去真正拉取元数据。
真正拉取元数据是在第一次 send 办法调用时，会唤醒唤醒 Selector 之前阻塞的 select(), 进入第二次 while 循环，从而发送拉取元数据申请，并且通过 Obejct.wait 的机制期待 60s，等到从 Broker 拉取元数据胜利后，才会继续执行真正的生产音讯的申请，否则会报拉取元数据超时异样。
这一节咱们只是看到了进行了 wait 如何期待元数据拉取。
而唤醒 Selector 的 select 之后应该会进入第二次 while 循环
那第二次 while 循环如何发送申请拉取元数据申请，并且在胜利后 notifyAll()进行唤醒操作的呢?
让咱们下一节持续剖析，大家敬请期待！咱们下一节见！
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