kafka producer 源码总结
kafka producer可以总体上分为两个部分:
- producer调用send方法,将消息存放到内存中
- sender线程轮询的从内存中将消息通过NIO发送到网络中
1 调用send方法
其实在调用new KafkaProducer初始化一个producer实例的时候,已经初始化了一个sender线程在后台轮询,不过为了方便理解,我们先分析send方法,即producer如何将消息放到内存队列中的。
1.1 producer存储结构
producer的整体存储结构如下图
![proudcer](https://images2018.cnblogs.com/blog/944831/201804/944831-20180421150157213-865863116.png)
1.2 整体流程
kafka在发送消息的时候,首先会连接一台broker来获取metadata信息,从metadata中可以知道要发送的topic一共有几个partiton、partiton的leader所在broker等信息。获取到metadata信息后,会通过获取到metadata信息并通过消息的key来计算消息被分配到哪个partiton(注意:消息被分配到哪个partiton是在客户端被计算好的)。然后会将消息按照partiton分组,放到对应的RecordBatch中,如果RecordBatch大于batch.size的大小,则新建一个RecordBatch放在list末尾。
doSend流程
private Future<RecordMetadata> doSend(ProducerRecord<K, V> record, Callback callback) {
TopicPartition tp = null;
// 阻塞并唤醒sender线程,等待sender线程获取到metadata
long waitedOnMetadataMs = waitOnMetadata(record.topic(), this.maxBlockTimeMs);
long remainingWaitMs = Math.max(0, this.maxBlockTimeMs - waitedOnMetadataMs);
byte[] serializedKey;
// 序列化key和value
serializedKey = keySerializer.serialize(record.topic(), record.key());
byte[] serializedValue;
serializedValue = valueSerializer.serialize(record.topic(), record.value());
// 计算消息应该放在哪个partition分区,如1.3详细介绍
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();
Callback interceptCallback = this.interceptors == null ? callback : new InterceptorCallback<>(callback, this.interceptors, tp);
// 根据计算到的分区,将消息追加到对应分区所在的Deque<RecordBatch>中,如1.4详细介绍
RecordAccumulator.RecordAppendResult result = accumulator.append(tp, timestamp, serializedKey, serializedValue, interceptCallback, remainingWaitMs);
// 如果是新创建batch或者batch满了,那么就唤醒sender线程。
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
//...
}
1.3 选择分区
在doSend方法中,调用partition 来计算消息的分区。如果没有特别指定的话,会使用默认的分区方法:
- 如果消息含有key,则计算方式是 key的绝对值 % (partiton个数 - 1)
- 如果不包含key的话,则采用round-robin方式发送。
1.4 消息放到内存中
accumulator#append(..)中会首先尝试将消息放到分区所对应的Deque<RecordBatch> 的最后一个batch中,如果添加失败(比如RecordBatch已经满了),则会使用BufferPool从内存中申请一块大小为batch.size的内存出来(如果消息体大于batch.size,则申请消息体大小的内存),将消息放到新的batch中,并将新的batch添加到Deque<RecordBatch>中。
append
public RecordAppendResult append(TopicPartition tp,
long timestamp,
byte[] key,
byte[] value,
Callback callback,
long maxTimeToBlock) throws InterruptedException {
appendsInProgress.incrementAndGet();
try {
Deque<RecordBatch> dq = getOrCreateDeque(tp);
// 注意,这里操作是加锁的。加锁的原因是
// 1.producer是可以多线程访问的
// 2.sender线程也会操作Deque<RecordBatch>
synchronized (dq) {
if (closed)
throw new IllegalStateException("Cannot send after the producer is closed.");
RecordBatch last = dq.peekLast();
if (last != null) {
FutureRecordMetadata future = last.tryAppend(timestamp, key, value, callback, time.milliseconds());
if (future != null)
return new RecordAppendResult(future, dq.size() > 1 || last.records.isFull(), false);
}
}
// 申请内存,大小为消息体和batch.size的最大值,另外buffer中其实只缓存batch.size大小的内存,只有batch.size大小的内存申请才会从buffer中获取,大于batch.size会重新开辟空间,
// 所以合理规划batch.size和消息体大小可以有效提供客户端内存使用效率
int size = Math.max(this.batchSize, Records.LOG_OVERHEAD + Record.recordSize(key, value));
// 从池子中申请
ByteBuffer buffer = free.allocate(size, maxTimeToBlock);
// 注意,这里又用到锁,不将两个锁合并成一个锁原因是减少锁的粒度
synchronized (dq) {
if (closed)
throw new IllegalStateException("Cannot send after the producer is closed.");
RecordBatch last = dq.peekLast();
if (last != null) {
FutureRecordMetadata future = last.tryAppend(timestamp, key, value, callback, time.milliseconds());
if (future != null) {
free.deallocate(buffer);
return new RecordAppendResult(future, dq.size() > 1 || last.records.isFull(), false);
}
}
MemoryRecords records = MemoryRecords.emptyRecords(buffer, compression, this.batchSize);
RecordBatch batch = new RecordBatch(tp, records, time.milliseconds());
FutureRecordMetadata future = Utils.notNull(batch.tryAppend(timestamp, key, value, callback, time.milliseconds()));
dq.addLast(batch);
incomplete.add(batch);
return new RecordAppendResult(future, dq.size() > 1 || batch.records.isFull(), true);
}
} finally {
appendsInProgress.decrementAndGet();
}
}
2 sender线程
sender线程在new出一个Kafka Producer实例后就已经开始运行了.
消息放到内存中是按照partiton来进行分组的,但是sender线程发送的时候是按照broker的node节点来发送,这点需要注意。
sender线程的整体逻辑如下:
void run(long now) {
Cluster cluster = metadata.fetch();
// ready用来获取已经ready的节点,注意是节点,所谓ready节点是指partiton满足以下条件之一后,partition的leader所在的节点为ready
// 1. Deque<RecordBatch> size大于1,说明已经有一个Batch满了,可以发送
// 2. 内存池已经耗尽,这时候需要发送写消息,来释放内存
// 3. linger.ms 时间到了,表示可以发送了
// 4. 调用了close方法,需要将内存中消息发送出去
// 如果partiton满足以上条件之一,那么parttion所在的leader节点就算准备好了
RecordAccumulator.ReadyCheckResult result = this.accumulator.ready(cluster, now);
if (result.unknownLeadersExist)
this.metadata.requestUpdate();
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));
}
}
// 由于RecordBatch是按照partiton来组织的,而sender线程是按照节点来发送的,所以drain的作用就是将RecordBatch转换为按照节点来组织的方式。drain只会获取每个分区的第一个BatchRecord,而不是将一个分区的所有BatchRecord都发送,主要是避免饥饿
Map<Integer, List<RecordBatch>> batches = this.accumulator.drain(cluster,
result.readyNodes,
this.maxRequestSize,
now);
if (guaranteeMessageOrder) {
for (List<RecordBatch> batchList : batches.values()) {
for (RecordBatch batch : batchList)
this.accumulator.mutePartition(batch.topicPartition);
}
}
List<RecordBatch> expiredBatches = this.accumulator.abortExpiredBatches(this.requestTimeout, now);
for (RecordBatch expiredBatch : expiredBatches)
this.sensors.recordErrors(expiredBatch.topicPartition.topic(), expiredBatch.recordCount);
sensors.updateProduceRequestMetrics(batches);
// 一个节点只会产生一个request
List<ClientRequest> requests = createProduceRequests(batches, now);
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);
// 真正的发送
this.client.poll(pollTimeout, now);
}
3 一些细节总结
- batch.size和linger.ms满足其中之一,sender线程便会被激活进行发送消息
- sender每次只拿出一个partiton的一个RecordBatch进行发送,即便该partiton已经有多个RecordBatch满了,这样做主要为了避免其他parttion饥饿, 详见RecordAccumulator#drain(..)
- RecordAccumulator#drain(..)后,被drain的RecordBatch会被close,不可写;同时从相应的Deque<RecordBatch>中移除。