标签：creat use buffered trace erro nec tco 时间戳 数据
Kafka是一款很棒的消息系统，今天我们就来深入了解一下它的实现细节，首先关注Producer这一方。
要使用kafka首先要实例化一个KafkaProducer，需要有brokerIP、序列化器等必要Properties以及acks（0、1、n）、compression、retries、batch.size等非必要Properties，通过这个简单的接口可以控制Producer大部分行为，实例化后就可以调用send方法发送消息了。
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.onSend(record);//①
return doSend(interceptedRecord, callback);//②
}
通过不同的模式可以实现发送即忘（忽略返回结果）、同步发送（获取返回的future对象，回调函数置为null）、异步发送（设置回调函数）三种消息模式。
我们来看看消息类ProducerRecord有哪些属性：
private final String topic;//主题
private final Integer partition;//分区
private final Headers headers;//头
private final K key;//键
private final V value;//值
private final Long timestamp;//时间戳
它有多个构造函数，可以适应不同的消息类型：比如有无分区、有无key等。
①中ProducerInterceptors（有0 ~ 无穷多个，形成一个拦截链）对ProducerRecord进行拦截处理（比如打上时间戳，进行审计与统计等操作）
public ProducerRecord<K, V> onSend(ProducerRecord<K, V> record) {
ProducerRecord<K, V> interceptRecord = record;
for (ProducerInterceptor<K, V> interceptor : this.interceptors) {
try {
interceptRecord = interceptor.onSend(interceptRecord);
} catch (Exception e) {
// 不抛出异常，继续执行下一个拦截器
if (record != null)
log.warn("Error executing interceptor onSend callback for topic: {}, partition: {}", record.topic(), record.partition(), e);
else
log.warn("Error executing interceptor onSend callback", e);
}
}
return interceptRecord;
}
如果用户有定义就进行处理并返回处理后的ProducerRecord，否则直接返回本身。
然后②中doSend真正发送消息，并且是异步的（源码太长只保留关键）：
private Future<RecordMetadata> doSend(ProducerRecord<K, V> record, Callback callback) {
TopicPartition tp = null;
try {
// 序列化 key 和 value
byte[] serializedKey;
try {
serializedKey = keySerializer.serialize(record.topic(), record.headers(), record.key());
} catch (ClassCastException cce) {
}
byte[] serializedValue;
try {
serializedValue = valueSerializer.serialize(record.topic(), record.headers(), record.value());
} catch (ClassCastException cce) {
}
// 计算分区获得主题与分区
int partition = partition(record, serializedKey, serializedValue, cluster);
tp = new TopicPartition(record.topic(), partition);
// 回调与事务处理省略。
Header[] headers = record.headers().toArray();
// 消息追加到RecordAccumulator中
RecordAccumulator.RecordAppendResult result = accumulator.append(tp, timestamp, serializedKey,
serializedValue, headers, interceptCallback, remainingWaitMs);
// 该批次满了或者创建了新的批次就要唤醒IO线程发送该批次了，也就是sender的wakeup方法
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;
} catch (Exception e) {
// 拦截异常并抛出
this.interceptors.onSendError(record, tp, e);
throw e;
}
}
下面是计算分区的方法：
private int partition(ProducerRecord<K, V> record,
byte[] serializedKey, byte[] serializedValue, Cluster cluster) {
Integer partition = record.partition();
// 消息有分区就直接使用，否则就使用分区器计算
return partition != null ?
partition :
partitioner.partition(
record.topic(), record.key(), serializedKey,
record.value(), serializedValue, cluster);
}
默认的分区器DefaultPartitioner实现方式是如果partition存在就直接使用，否则根据key计算partition，如果key也不存在就使用round robin算法分配partition。
/**
* The default partitioning strategy:
* <ul>
* <li>If a partition is specified in the record, use it
* <li>If no partition is specified but a key is present choose a partition based on a hash of the key
* <li>If no partition or key is present choose a partition in a round-robin fashion
*/
public class DefaultPartitioner implements Partitioner {
private final ConcurrentMap<String, AtomicInteger> topicCounterMap = new ConcurrentHashMap<>();
public int partition(String topic, Object key, byte[] keyBytes, Object value, byte[] valueBytes, Cluster cluster) {
List<PartitionInfo> partitions = cluster.partitionsForTopic(topic);
int numPartitions = partitions.size();
if (keyBytes == null) {//key为空
int nextValue = nextValue(topic);
List<PartitionInfo> availablePartitions = cluster.availablePartitionsForTopic(topic);//可用的分区
if (availablePartitions.size() > 0) {//有分区，取模就行
int part = Utils.toPositive(nextValue) % availablePartitions.size();
return availablePartitions.get(part).partition();
} else {// 无分区，
return Utils.toPositive(nextValue) % numPartitions;
}
} else {// key 不为空，计算key的hash并取模获得分区
return Utils.toPositive(Utils.murmur2(keyBytes)) % numPartitions;
}
}
private int nextValue(String topic) {
AtomicInteger counter = topicCounterMap.get(topic);
if (null == counter) {
counter = new AtomicInteger(ThreadLocalRandom.current().nextInt());
AtomicInteger currentCounter = topicCounterMap.putIfAbsent(topic, counter);
if (currentCounter != null) {
counter = currentCounter;
}
}
return counter.getAndIncrement();//返回并加一，在取模的配合下就是round robin
}
}
以上就是发送消息的逻辑处理，接下来我们再看看消息发送的物理处理。
Sender（是一个Runnable，被包含在一个IO线程ioThread中，该线程不断从RecordAccumulator队列中的读取消息并通过Selector将数据发送给Broker）的wakeup方法，实际上是KafkaClient接口的wakeup方法，由NetworkClient类实现，采用了NIO，也就是java.nio.channels.Selector.wakeup()方法实现。
Sender的run中主要逻辑是不停执行准备消息和等待消息：
long pollTimeout = sendProducerData(now);//③
client.poll(pollTimeout, now);//④
③完成消息设置并保存到信道中，然后监听感兴趣的key，由KafkaChannel实现。
public void setSend(Send send) {
if (this.send != null)
throw new IllegalStateException("Attempt to begin a send operation with prior send operation still in progress, connection id is " + id);
this.send = send;
this.transportLayer.addInterestOps(SelectionKey.OP_WRITE);
}
// transportLayer的一种实现中的相关方法
public void addInterestOps(int ops) {
key.interestOps(key.interestOps() | ops);
}
④主要是Selector的poll，其select被wakeup唤醒：
public void poll(long timeout) throws IOException {
/* check ready keys */
long startSelect = time.nanoseconds();
int numReadyKeys = select(timeout);//wakeup使其停止阻塞
long endSelect = time.nanoseconds();
this.sensors.selectTime.record(endSelect - startSelect, time.milliseconds());
if (numReadyKeys > 0 || !immediatelyConnectedKeys.isEmpty() || dataInBuffers) {
Set<SelectionKey> readyKeys = this.nioSelector.selectedKeys();
// Poll from channels that have buffered data (but nothing more from the underlying socket)
if (dataInBuffers) {
keysWithBufferedRead.removeAll(readyKeys); //so no channel gets polled twice
Set<SelectionKey> toPoll = keysWithBufferedRead;
keysWithBufferedRead = new HashSet<>(); //poll() calls will repopulate if needed
pollSelectionKeys(toPoll, false, endSelect);
}
// Poll from channels where the underlying socket has more data
pollSelectionKeys(readyKeys, false, endSelect);
// Clear all selected keys so that they are included in the ready count for the next select
readyKeys.clear();
pollSelectionKeys(immediatelyConnectedKeys, true, endSelect);
immediatelyConnectedKeys.clear();
} else {
madeReadProgressLastPoll = true; //no work is also "progress"
}
long endIo = time.nanoseconds();
this.sensors.ioTime.record(endIo - endSelect, time.milliseconds());
}
其中pollSelectionKeys方法会调用如下方法完成消息发送：
public Send write() throws IOException {
Send result = null;
if (send != null && send(send)) {
result = send;
send = null;
}
return result;
}
private boolean send(Send send) throws IOException {
send.writeTo(transportLayer);
if (send.completed())
transportLayer.removeInterestOps(SelectionKey.OP_WRITE);
return send.completed();
}
Send是一次数据发包，一般由ByteBufferSend或者MultiRecordsSend实现，其writeTo调用transportLayer的write方法，一般由PlaintextTransportLayer或者SslTransportLayer实现，区分是否使用ssl：
public long writeTo(GatheringByteChannel channel) throws IOException {
long written = channel.write(buffers);
if (written < 0)
throw new EOFException("Wrote negative bytes to channel. This shouldn‘t happen.");
remaining -= written;
pending = TransportLayers.hasPendingWrites(channel);
return written;
}
public int write(ByteBuffer src) throws IOException {
return socketChannel.write(src);
}
到此就把Producer的业务相关逻辑处理和非业务相关的网络 2方面的主要流程梳理清楚了。其他额外的功能是通过一些配置保证的。
比如顺序保证就是max.in.flight.requests.per.connection，InFlightRequests的doSend会进行判断（由NetworkClient的canSendRequest调用），只要该参数设为1即可保证当前包未确认就不能发送下一个包从而实现有序性
public boolean canSendMore(String node) {
Deque<NetworkClient.InFlightRequest> queue = requests.get(node);
return queue == null || queue.isEmpty() ||
(queue.peekFirst().send.completed() && queue.size() < this.maxInFlightRequestsPerConnection);
}
再比如可靠性，通过设置acks，Sender中sendProduceRequest的clientRequest加入了回调函数：
RequestCompletionHandler callback = new RequestCompletionHandler() {
public void onComplete(ClientResponse response) {
handleProduceResponse(response, recordsByPartition, time.milliseconds());//调用completeBatch
}
};
/**
* 完成或者重试投递，这里如果acks不对就会重试
*
* @param batch The record batch
* @param response The produce response
* @param correlationId The correlation id for the request
* @param now The current POSIX timestamp in milliseconds
*/
private void completeBatch(ProducerBatch batch, ProduceResponse.PartitionResponse response, long correlationId,
long now, long throttleUntilTimeMs) {
}
public class ProduceResponse extends AbstractResponse {
/**
* Possible error code:
* INVALID_REQUIRED_ACKS (21)
*/
}
kafka源码一层一层包装很多，错综复杂，如有错误请大家不吝赐教。
高吞吐量的分布式发布订阅消息系统Kafka之Producer源码分析
标签：creat use buffered trace erro nec tco 时间戳 数据
原文地址：https://www.cnblogs.com/MonsterJ/p/12994418.html
