Kafka3.0源码分析-生产者的实现细节原创
1年前
3409117
引言
在 Kafka 中，生产者（Producer）负责将消息发送到 Kafka 集群，是实现高效数据流动的关键组件之一。本文将从源码层面分析 Kafka 生产者的实现细节，帮助读者更好地理解 Kafka 生产者的工作原理和性能特征。
注明:
本次源码分析基于kafka的3版本
0.10.2 的 Kafka 中，其 Client 端是由 Java 实现，Server 端是由 Scala 来实现的
能学到什么
- Kafka 生产者是如何实现消息的发送和分发的？
- Kafka 生产者的代码实现中有哪些值得我们注意的细节和技巧？
名词解释
- Producer Metadata——管理生产者所需的元数据：集群中的主题和分区、充当分区领导者的代理节点等。
- Partitioner——计算给定记录的分区。
- 序列化器——记录键和值序列化器。序列化程序将对象转换为字节数组。
- 生产者拦截器——可能改变记录的拦截器。
- Record Accumulator——累积记录并按主题分区将它们分组为批次。
- 事务管理器——管理事务并维护必要的状态以确保幂等生产。
- Sender——向 Kafka 集群发送数据的后台线程。
架构图
从上图,我们了解到:
- kafka的生产者采用生产者-消费者模式,生产者发送消息的过程可以分为两个阶段：
- 第一个阶段是将待发送的消息缓存到 RecordAccumulator(记录叠加器)中
- 第二个阶段是从 RecordAccumulator 中取出消息进行网络发送。
- kafka的生产者其实分为三部分
- kafkaProducer主线程
- RecordAccumulator
- sender线程
开始分析
生产者的例子
public class Producer {
private final KafkaProducer<Integer, String> producer;
public Producer(final String topic,
final String transactionalId,
final boolean enableIdempotency,
final int transactionTimeoutMs
) {
Properties props = new Properties();
props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, KafkaProperties.KAFKA_SERVER_URL + ":" + KafkaProperties.KAFKA_SERVER_PORT);
props.put(ProducerConfig.CLIENT_ID_CONFIG, "DemoProducer");
props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, IntegerSerializer.class.getName());
props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName());
if (transactionTimeoutMs > 0) {
props.put(ProducerConfig.TRANSACTION_TIMEOUT_CONFIG, transactionTimeoutMs);
}
if (transactionalId != null) {
props.put(ProducerConfig.TRANSACTIONAL_ID_CONFIG, transactionalId);
}
props.put(ProducerConfig.ENABLE_IDEMPOTENCE_CONFIG, enableIdempotency);
// 重点 分析1
producer = new KafkaProducer<>(props);
}
private void sendAsync(final int messageKey, final String messageStr, final long currentTimeMs) {
// 重点 分析2
this.producer.send(new ProducerRecord<>(topic,
messageKey,
messageStr),
new DemoCallBack(currentTimeMs, messageKey, messageStr));
}
}
class DemoCallBack implements Callback {
private final long startTime;
private final int key;
private final String message;
public DemoCallBack(long startTime, int key, String message) {
this.startTime = startTime;
this.key = key;
this.message = message;
}
public void onCompletion(RecordMetadata metadata, Exception exception) {
long elapsedTime = System.currentTimeMillis() - startTime;
if (metadata != null) {
System.out.println(
"message(" + key + ", " + message + ") sent to partition(" + metadata.partition() +
"), " +
"offset(" + metadata.offset() + ") in " + elapsedTime + " ms");
} else {
exception.printStackTrace();
}
}
}
说明:
- 上面的例子，最关键的两个地方（尤其是send消息的）:
- KafkaProducer的构造方法
- producer.send消息
KafkaProducer的介绍
组成部分
说明
producerConfig: 存储了Kafka Producer的配置信息，包括连接的Kafka集群地址、序列化器、确认机制等参数。metrics: 存储了生产者的指标数据，例如发送的消息数量、成功发送的消息数量、失败的消息数量等。sender: 负责将消息发送到Kafka集群的组件。它会将消息转换成Kafka可识别的格式，然后将其发送到指定的分区。recordAccumulator: 缓存待发送的消息。生产者将消息发送到recordAccumulator后，sender从recordAccumulator中获取消息并发送到Kafka集群。metadata: 存储了Kafka集群中所有主题和分区的元数据信息，包括分区的leader、ISR（in-sync replicas）列表等。interceptors: 消息拦截器列表。生产者可以配置多个拦截器，用于在消息发送前、发送后对消息进行处理，例如添加时间戳、打印日志等。bufferMemory: 缓存待发送消息的总大小。如果recordAccumulator中待发送消息的大小超过了bufferMemory，则生产者将等待sender将消息发送出去，以释放recordAccumulator中的空间。maxBlockMs: 生产者在发送消息时，如果recordAccumulator已满，会等待sender将消息发送出去。如果sender在指定的时间内无法发送消息，则生产者会抛出异常。maxBlockMs指定了等待sender的最大时间。requestTimeoutMs: 生产者等待Kafka Broker的响应的最大时间。如果在指定时间内没有收到Broker的响应，则生产者会重试发送消息或抛出异常。transactionManager: 支持事务的生产者需要配置transactionManager。transactionManager负责管理事务的状态、事务中发送的消息等信息。
构造方法
KafkaProducer(ProducerConfig config,
Serializer<K> keySerializer,
Serializer<V> valueSerializer,
ProducerMetadata metadata,
KafkaClient kafkaClient,
ProducerInterceptors<K, V> interceptors,
Time time) {
try {
// 生产者的配置项
this.producerConfig = config;
this.time = time;
// 事务id
String transactionalId = config.getString(ProducerConfig.TRANSACTIONAL_ID_CONFIG);
// 客户端id
this.clientId = config.getString(ProducerConfig.CLIENT_ID_CONFIG);
// 设置对应的分区器
this.partitioner = config.getConfiguredInstance(
ProducerConfig.PARTITIONER_CLASS_CONFIG,
Partitioner.class,
Collections.singletonMap(ProducerConfig.CLIENT_ID_CONFIG, clientId));
warnIfPartitionerDeprecated();
this.partitionerIgnoreKeys = config.getBoolean(ProducerConfig.PARTITIONER_IGNORE_KEYS_CONFIG);
// 失败重试的退避时间
long retryBackoffMs = config.getLong(ProducerConfig.RETRY_BACKOFF_MS_CONFIG);
// 序列化
if (keySerializer == null) {
this.keySerializer = config.getConfiguredInstance(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG,
Serializer.class);
this.keySerializer.configure(config.originals(Collections.singletonMap(ProducerConfig.CLIENT_ID_CONFIG, clientId)), true);
} else {
config.ignore(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG);
this.keySerializer = keySerializer;
}
if (valueSerializer == null) {
this.valueSerializer = config.getConfiguredInstance(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG,
Serializer.class);
this.valueSerializer.configure(config.originals(Collections.singletonMap(ProducerConfig.CLIENT_ID_CONFIG, clientId)), false);
} else {
config.ignore(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG);
this.valueSerializer = valueSerializer;
}
// 配置生产者的拦截器
List<ProducerInterceptor<K, V>> interceptorList = (List) config.getConfiguredInstances(
ProducerConfig.INTERCEPTOR_CLASSES_CONFIG,
ProducerInterceptor.class,
Collections.singletonMap(ProducerConfig.CLIENT_ID_CONFIG, clientId));
// 集群资源变更监听器
ClusterResourceListeners clusterResourceListeners = configureClusterResourceListeners(this.keySerializer,
this.valueSerializer, interceptorList, reporters);
//设置消息的最大的长度，默认1M，生产环境可以提高到10M
this.maxRequestSize = config.getInt(ProducerConfig.MAX_REQUEST_SIZE_CONFIG);
// 设置发送消息的缓冲区的大小
this.totalMemorySize = config.getLong(ProducerConfig.BUFFER_MEMORY_CONFIG);
// 压缩类型
this.compressionType = CompressionType.forName(config.getString(ProducerConfig.COMPRESSION_TYPE_CONFIG));
// 最大阻塞时间
this.maxBlockTimeMs = config.getLong(ProducerConfig.MAX_BLOCK_MS_CONFIG);
// 投递的超时时间
int deliveryTimeoutMs = configureDeliveryTimeout(config, log);
this.apiVersions = new ApiVersions();
// 事务管理器
this.transactionManager = configureTransactionState(config, logContext);
// 如果我们使用自定义分区器，则无需执行自适应分区所需的工作.
boolean enableAdaptivePartitioning = partitioner == null &&
config.getBoolean(ProducerConfig.PARTITIONER_ADPATIVE_PARTITIONING_ENABLE_CONFIG);
// 分区器的配置
RecordAccumulator.PartitionerConfig partitionerConfig = new RecordAccumulator.PartitionerConfig(
enableAdaptivePartitioning,
config.getLong(ProducerConfig.PARTITIONER_AVAILABILITY_TIMEOUT_MS_CONFIG)
);
// 按Kafka生产者配置配置大小。Size可以设置为0以显式禁用批处理，这实际上意味着使用批处理大小为1
int batchSize = Math.max(1, config.getInt(ProducerConfig.BATCH_SIZE_CONFIG));
// 消息记录累加器
this.accumulator = new RecordAccumulator(logContext,
batchSize,
this.compressionType,
lingerMs(config),
retryBackoffMs,
deliveryTimeoutMs,
partitionerConfig,
metrics,
PRODUCER_METRIC_GROUP_NAME,
time,
apiVersions,
transactionManager,
new BufferPool(this.totalMemorySize, batchSize, metrics, time, PRODUCER_METRIC_GROUP_NAME));
// 解析Broker地址
List<InetSocketAddress> addresses = ClientUtils.parseAndValidateAddresses(
config.getList(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG),
config.getString(ProducerConfig.CLIENT_DNS_LOOKUP_CONFIG));
// 生产端的元信息配置
if (metadata != null) {
this.metadata = metadata;
} else {
this.metadata = new ProducerMetadata(retryBackoffMs,
config.getLong(ProducerConfig.METADATA_MAX_AGE_CONFIG),
config.getLong(ProducerConfig.METADATA_MAX_IDLE_CONFIG),
logContext,
clusterResourceListeners,
Time.SYSTEM);
this.metadata.bootstrap(addresses);
}
// 记录失败的监控数据
this.errors = this.metrics.sensor("errors");
// 创建发送器
this.sender = newSender(logContext, kafkaClient, this.metadata);
String ioThreadName = NETWORK_THREAD_PREFIX + " | " + clientId;
this.ioThread = new KafkaThread(ioThreadName, this.sender, true);
this.ioThread.start();
config.logUnused();
// 注册mb的相关的
AppInfoParser.registerAppInfo(JMX_PREFIX, clientId, metrics, time.milliseconds());
log.debug("Kafka producer started");
} catch (Throwable t) {
// call close methods if internal objects are already constructed this is to prevent resource leak. see KAFKA-2121
close(Duration.ofMillis(0), true);
// now propagate the exception
throw new KafkaException("Failed to construct kafka producer", t);
}
}
说明：
- 配置生产者的监控
- 设置对应的分区器
- 配置发送失败的重试时间
- key和value的序列化
- 配置生产者的拦截器
- 分区器的配置
- 初始化累加器
- 解析Broker地址
- 生产端的元信息配置
- 创建发送器并且启动的IO线程
KafkaProducer发送消息
说明
- 生产者发送消息的过程可以分为两个阶段：
- 将发送的消息缓存到 RecordAccumulator(记录叠加器)中
- RecordAccumulator是如何存储消息的
- sender线程取出消息进行网络发送。
将消息缓存到记录叠加器
代码
send方法
public Future<RecordMetadata> send(ProducerRecord<K, V> record, Callback callback) {
ProducerRecord<K, V> interceptedRecord = this.interceptors.onSend(record);
return doSend(interceptedRecord, callback);
}
doSend方法
private Future < RecordMetadata > doSend(ProducerRecord < K, V > record, Callback callback) {
AppendCallbacks < K, V > appendCallbacks = new AppendCallbacks < K, V > (callback, this.interceptors, record);
try {
long nowMs = time.milliseconds();
ClusterAndWaitTime clusterAndWaitTime = waitOnMetadata(record.topic(), record.partition(), nowMs, maxBlockTimeMs);
nowMs += clusterAndWaitTime.waitedOnMetadataMs;
long remainingWaitMs = Math.max(0, maxBlockTimeMs - clusterAndWaitTime.waitedOnMetadataMs);
Cluster cluster = clusterAndWaitTime.cluster;
// key和value序列化
....
// 计算分区，但注意，在调用之后，它可以是RecordMetadata。UNKNOWN_PARTITION
// 这意味着RecordAccumulator将使用内置逻辑(可能会考虑代理负载，每个分区产生的数据量等)选择一个分区.
int partition = partition(record, serializedKey, serializedValue, cluster);
// 将记录追加到累加器
RecordAccumulator.RecordAppendResult result = accumulator.append(record.topic(), partition, timestamp, serializedKey,
serializedValue, headers, appendCallbacks, remainingWaitMs, abortOnNewBatch, nowMs, cluster);
// 在累加器成功追加分区后，将其添加到事务中（如果正在进行）。我们不能在此之前执行此操作，因为该分区可能是未知的，
// 或者当批处理关闭时初始选择的分区可能会更改（如“abortForNewBatch”所示）。请注意，“发送方”将拒绝从累加器中出队批次，直到它们被添加到事务中。
if (transactionManager != null) {
transactionManager.maybeAddPartition(appendCallbacks.topicPartition());
}
// 如果累加器满了或者新创建的批次
if (result.batchIsFull || result.newBatchCreated) {
log.trace("Waking up the sender since topic {} partition {} is either full or getting a new batch", record.topic(), appendCallbacks.getPartition());
// 唤醒发送器线程
this.sender.wakeup();
}
return result.future;
} catch (ApiException e) {
//处理异常并记录错误 对于 API 异常，将它们返回Future，对于其他异常直接抛出
if (callback != null) {
TopicPartition tp = appendCallbacks.topicPartition();
callback.onCompletion(new RecordMetadata(tp, -1, -1, RecordBatch.NO_TIMESTAMP, -1, -1), e);
}
this.errors.record();
this.interceptors.onSendError(record, appendCallbacks.topicPartition(), e);
if (transactionManager != null) {
transactionManager.maybeTransitionToErrorState(e);
}
return new FutureFailure(e);
} catch (InterruptedException e) {
this.errors.record();
this.interceptors.onSendError(record, appendCallbacks.topicPartition(), e);
throw new InterruptException(e);
} catch (KafkaException e) {
this.errors.record();
this.interceptors.onSendError(record, appendCallbacks.topicPartition(), e);
throw e;
} catch (Exception e) {
this.interceptors.onSendError(record, appendCallbacks.topicPartition(), e);
throw e;
}
}
说明
RecordAccumulator
RecordAccumulator是Kafka消息传输机制的核心组件之一，主要功能是将多个ProducerRecord对象批量打包成RecordBatch，并将RecordBatch添加到RecordBatchBuilder中等待发送。
成员变量
/**
* 用于存储正在等待发送的RecordBatch
*/
private final AtomicInteger flushesInProgress;
/**
* 已经发送完成但还未被确认的RecordBatch
*/
private final AtomicInteger appendsInProgress;
/**
* 批次大小
*/
private final int batchSize;
/**
* RecordAccumulator可以使用LZ4和Gzip等压缩方式对RecordBatch进行压缩，以减少数据传输时的带宽占用和网络延迟。
*/
private final CompressionType compression;
/**
* 消息 batch 延迟多久再发送的时间
*/
private final int lingerMs;
/**
* 重试 间隔时间
*/
private final long retryBackoffMs;
private final int deliveryTimeoutMs;
/**
* 缓冲池
*/
private final BufferPool free;
private final Time time;
private final ApiVersions apiVersions;
// topic的缓存
private final ConcurrentMap<String /*topic*/, TopicInfo> topicInfoMap = new CopyOnWriteMap<>();
// node的状态
private final ConcurrentMap<Integer /*nodeId*/, NodeLatencyStats> nodeStats = new CopyOnWriteMap<>();
// 未完成的批次
private final IncompleteBatches incomplete;
// The following variables are only accessed by the sender thread, so we don't need to protect them.
private final Set<TopicPartition> muted;
private final Map<String, Integer> nodesDrainIndex;
private final TransactionManager transactionManager;
如何追加消息的流程
public RecordAppendResult append(String topic,
int partition,
long timestamp,
byte[] key,
byte[] value,
Header[] headers,
AppendCallbacks callbacks,
long maxTimeToBlock,
boolean abortOnNewBatch,
long nowMs,
Cluster cluster) throws InterruptedException {
// 创建或获取指定主题的 `TopicInfo` 对象，`TopicInfo` 用于跟踪与指定主题相关的信息，如分区信息、分区内的批次
TopicInfo topicInfo = topicInfoMap.computeIfAbsent(topic, k -> new TopicInfo(logContext, k, batchSize));
// 跟踪追加线程的数量，以确保在abortIncompleteBatches()中不会遗漏批次.
appendsInProgress.incrementAndGet();
ByteBuffer buffer = null;
if (headers == null) headers = Record.EMPTY_HEADERS;
try {
// 循环-在遇到分区器的竞态条件时重试.
while (true) {
// 根据TopicPartition获取或新建Deque双端队列
Deque<ProducerBatch> dq = topicInfo.batches.computeIfAbsent(effectivePartition, k -> new ArrayDeque<>());
synchronized (dq) {
// 获取锁后，验证分区没有更改，然后重试.
if (partitionChanged(topic, topicInfo, partitionInfo, dq, nowMs, cluster))
continue;
// 尝试将消息加入到缓冲区中
RecordAppendResult appendResult = tryAppend(timestamp, key, value, headers, callbacks, dq, nowMs);
if (appendResult != null) {
// 追加成功
boolean enableSwitch = allBatchesFull(dq);
topicInfo.builtInPartitioner.updatePartitionInfo(partitionInfo, appendResult.appendedBytes, cluster, enableSwitch);
return appendResult;
}
}
// 我们没有正在进行的记录批处理，请尝试分配一个新批处理
if (abortOnNewBatch) {
return new RecordAppendResult(null, false, false, true, 0);
}
// 分配缓存区
if (buffer == null) {
byte maxUsableMagic = apiVersions.maxUsableProduceMagic();
// 取16k和消息大小的最大值
int size = Math.max(this.batchSize, AbstractRecords.estimateSizeInBytesUpperBound(maxUsableMagic, compression, key, value, headers));
// 如果耗尽缓冲区空间，重新分配，此调用可能阻塞.
buffer = free.allocate(size, maxTimeToBlock);
nowMs = time.milliseconds();
}
synchronized (dq) {
if (partitionChanged(topic, topicInfo, partitionInfo, dq, nowMs, cluster)) continue;
//
RecordAppendResult appendResult = appendNewBatch(topic, effectivePartition, dq, timestamp, key, value, headers, callbacks, buffer, nowMs);
if (appendResult.newBatchCreated)
buffer = null;
boolean enableSwitch = allBatchesFull(dq);
topicInfo.builtInPartitioner.updatePartitionInfo(partitionInfo, appendResult.appendedBytes, cluster, enableSwitch);
return appendResult;
}
}
} finally {
free.deallocate(buffer);
appendsInProgress.decrementAndGet();
}
}
tryAppend
public FutureRecordMetadata tryAppend(long timestamp, byte[] key, byte[] value, Header[] headers, Callback callback, long now) {
if (!recordsBuilder.hasRoomFor(timestamp, key, value, headers)) {
return null;
} else {
// 重点是这里
this.recordsBuilder.append(timestamp, key, value, headers);
this.maxRecordSize = Math.max(this.maxRecordSize, AbstractRecords.estimateSizeInBytesUpperBound(magic(), recordsBuilder.compressionType(), key, value, headers));
this.lastAppendTime = now;
FutureRecordMetadata future = new FutureRecordMetadata(this.produceFuture, this.recordCount,
timestamp,
key == null ? -1 : key.length,
value == null ? -1 : value.length,
Time.SYSTEM);
thunks.add(new Thunk(callback, future));
this.recordCount++;
return future;
}
}
- recordsBuilder.append的方法实际上是调用MemoryRecordsBuilder#appendWithOffset方法，代码如下
MemoryRecordsBuilder#appendWithOffset
private void appendWithOffset(long offset, boolean isControlRecord, long timestamp, ByteBuffer key,
ByteBuffer value, Header[] headers) {
try {
// 检查是否可以将控制记录追加到控制批次中
if (isControlRecord != isControlBatch) {
throw new IllegalArgumentException("Control records can only be appended to control batches");
}
// 检查新记录的偏移量是否合法
if (lastOffset != null && offset <= lastOffset) {
throw new IllegalArgumentException(String.format("Illegal offset %s following previous offset %s " +
"(Offsets must increase monotonically).", offset, lastOffset));
}
// 检查时间戳是否合法
if (timestamp < 0 && timestamp != RecordBatch.NO_TIMESTAMP) {
throw new IllegalArgumentException("Invalid negative timestamp " + timestamp);
}
// 检查是否支持记录头
if (magic < RecordBatch.MAGIC_VALUE_V2 && headers != null && headers.length > 0) {
throw new IllegalArgumentException("Magic v" + magic + " does not support record headers");
}
// 设置基准时间戳
if (baseTimestamp == null) {
baseTimestamp = timestamp;
}
// 根据不同的魔数调用不同的追加记录方法
if (magic > RecordBatch.MAGIC_VALUE_V1) {
appendDefaultRecord(offset, timestamp, key, value, headers);
} else {
appendLegacyRecord(offset, timestamp, key, value, magic);
}
} catch (IOException e) {
throw new KafkaException("I/O exception when writing to the append stream, closing", e);
}
}
根据RecordBatch类中的定义 byte CURRENT_MAGIC_VALUE = MAGIC_VALUE_V2;所以我们直接看appendLegacyRecord方法的实现
appendLegacyRecord
private long appendLegacyRecord(long offset, long timestamp, ByteBuffer key, ByteBuffer value, byte magic) throws IOException {
// 检查消息追加器是否已经打开，如果未打开则抛出异常
ensureOpenForRecordAppend();
// 如果消息的压缩类型为NONE，时间戳类型为LOG_APPEND_TIME，则将时间戳设置为当前追加时间
if (compressionType == CompressionType.NONE && timestampType == TimestampType.LOG_APPEND_TIME) {
timestamp = logAppendTime;
}
// 计算记录的大小
int size = LegacyRecord.recordSize(magic, key, value);
// 向追加流中写入记录头
AbstractLegacyRecordBatch.writeHeader(appendStream, toInnerOffset(offset), size);
// 如果时间戳类型为LOG_APPEND_TIME，则将时间戳设置为当前追加时间
if (timestampType == TimestampType.LOG_APPEND_TIME) {
timestamp = logAppendTime;
}
// 调用遗留记录的写入方法写入记录，并返回记录的CRC校验码
long crc = LegacyRecord.write(appendStream, magic, timestamp, key, value, CompressionType.NONE, timestampType);
// 更新已写入的记录数和字节数
recordWritten(offset, timestamp, size + Records.LOG_OVERHEAD);
return crc;
}
说明
- RecordAccumulator采用了分区级别的缓冲机制，即每个分区都有一个对应的缓冲区。这样可以避免多个分区之间的竞争，提高发送消息的效率
- RecordAccumulator会对消息进行压缩，但是不会立即进行压缩操作，而是会等待一段时间后再进行压缩。这样可以让更多的消息被累积到一个批次中，从而提高压缩的效率。
- RecordAccumulator会将多个批次中的消息合并成一个更大的批次进行发送。这样可以减少网络I/O操作的次数，从而提高发送消息的效率。
- RecordAccumulator会根据当前发送消息的速度动态调整批次的大小。如果发送速度很快，就会增加批次的大小；如果发送速度很慢，就会减小批次的大小。这样可以保证发送消息的效率和稳定性
sender线程取出消息进行网络发送
说明
回忆下在kafkaProducer的构造方法里面会初始化sender线程：
public static final String NETWORK_THREAD_PREFIX = "kafka-producer-network-thread";
// 创建发送器
this.sender = newSender(logContext, kafkaClient, this.metadata);
String ioThreadName = NETWORK_THREAD_PREFIX + " | " + clientId;
this.ioThread = new KafkaThread(ioThreadName, this.sender, true);
this.ioThread.start();
- newSender方法其实就是构建一个 Sender对象
Sender对象的组成
sender类实现了Runnable接口,那么我们直接看run方法
Run
public void run() {
// main loop, runs until close is called
while (running) {
try {
runOnce();
} catch (Exception e) {
log.error("Uncaught error in Kafka producer I/O thread: ", e);
}
}
... // 删除其他代码
}
runOnce
void runOnce() {
if (transactionManager != null) {
try {
transactionManager.maybeResolveSequences();
// 如果transaction manager处于失败状态，不再发送消息
if (transactionManager.hasFatalError()) {
RuntimeException lastError = transactionManager.lastError();
if (lastError != null)
maybeAbortBatches(lastError);
client.poll(retryBackoffMs, time.milliseconds());
return;
}
// 检查是否需要一个新的producerId，如果需要，则发送一个InitProducerId请求
transactionManager.bumpIdempotentEpochAndResetIdIfNeeded();
if (maybeSendAndPollTransactionalRequest()) {
return;
}
} catch (AuthenticationException e) {
transactionManager.authenticationFailed(e);
}
}
long currentTimeMs = time.milliseconds();
long pollTimeout = sendProducerData(currentTimeMs);
client.poll(pollTimeout, currentTimeMs);
}
说明:
- 这里我们只看 sendProducerData和poll的方法
sendProducerData
private long sendProducerData(long now) {
// 获取当前集群的所有数据
Cluster cluster = metadata.fetch();
// 当前可发送数据的分区列表
RecordAccumulator.ReadyCheckResult result = this.accumulator.ready(cluster, now);
//如果存在未知领导者（Leader），则将其添加到元数据中，并请求更新元数据
if (!result.unknownLeaderTopics.isEmpty()) {
for (String topic : result.unknownLeaderTopics) {
this.metadata.add(topic, now);
}
unknownLeaderTopics);
this.metadata.requestUpdate();
}
Iterator<Node> iter = result.readyNodes.iterator();
long notReadyTimeout = Long.MAX_VALUE;
while (iter.hasNext()) {
Node node = iter.next();
//删除未准备好发送到的所有节点，并更新节点的延迟统计数据。
if (!this.client.ready(node, now)) {
this.accumulator.updateNodeLatencyStats(node.id(), now, false);
iter.remove();
notReadyTimeout = Math.min(notReadyTimeout, this.client.pollDelayMs(node, now));
} else {
this.accumulator.updateNodeLatencyStats(node.id(), now, true);
}
}
// 将可发送的批次添加到正在进行中的批次列表中。
Map<Integer, List<ProducerBatch>> batches = this.accumulator.drain(cluster, result.readyNodes, this.maxRequestSize, now);
addToInflightBatches(batches);
// 保证发送消息的顺序
if (guaranteeMessageOrder) {
for (List<ProducerBatch> batchList : batches.values()) {
for (ProducerBatch batch : batchList) {
this.accumulator.mutePartition(batch.topicPartition);
}
}
}
accumulator.resetNextBatchExpiryTime();
// 将所有已过期的批次删除，并标记为失败
List<ProducerBatch> expiredInflightBatches = getExpiredInflightBatches(now);
List<ProducerBatch> expiredBatches = this.accumulator.expiredBatches(now);
expiredBatches.addAll(expiredInflightBatches);
if (!expiredBatches.isEmpty()) {
log.trace("Expired {} batches in accumulator", expiredBatches.size());
}
for (ProducerBatch expiredBatch : expiredBatches) {
String errorMessage = "Expiring " + expiredBatch.recordCount + " record(s) for " + expiredBatch.topicPartition
+ ":" + (now - expiredBatch.createdMs) + " ms has passed since batch creation";
failBatch(expiredBatch, new TimeoutException(errorMessage), false);
if (transactionManager != null && expiredBatch.inRetry()) {
transactionManager.markSequenceUnresolved(expiredBatch);
}
}
//计算poll的超时时间
long pollTimeout = Math.min(result.nextReadyCheckDelayMs, notReadyTimeout);
pollTimeout = Math.min(pollTimeout, this.accumulator.nextExpiryTimeMs() - now);
pollTimeout = Math.max(pollTimeout, 0);
if (!result.readyNodes.isEmpty()) {
pollTimeout = 0;
}
// 发送请求到Kafka集群
sendProduceRequests(batches, now);
return pollTimeout;
}
sendProduceRequests
private void sendProduceRequest(long now, int destination, short acks, int timeout, List<ProducerBatch> batches) {
if (batches.isEmpty())
return;
final Map<TopicPartition, ProducerBatch> recordsByPartition = new HashMap<>(batches.size());
// 找到创建记录集时使用的最小版本
byte minUsedMagic = apiVersions.maxUsableProduceMagic();
for (ProducerBatch batch : batches) {
if (batch.magic() < minUsedMagic)
minUsedMagic = batch.magic();
}
ProduceRequestData.TopicProduceDataCollection tpd = new ProduceRequestData.TopicProduceDataCollection();
for (ProducerBatch batch : batches) {
TopicPartition tp = batch.topicPartition;
MemoryRecords records = batch.records();
if (!records.hasMatchingMagic(minUsedMagic))
records = batch.records().downConvert(minUsedMagic, 0, time).records();
ProduceRequestData.TopicProduceData tpData = tpd.find(tp.topic());
if (tpData == null) {
tpData = new ProduceRequestData.TopicProduceData().setName(tp.topic());
tpd.add(tpData);
}
tpData.partitionData().add(new ProduceRequestData.PartitionProduceData()
.setIndex(tp.partition())
.setRecords(records));
recordsByPartition.put(tp, batch);
}
String transactionalId = null;
if (transactionManager != null && transactionManager.isTransactional()) {
transactionalId = transactionManager.transactionalId();
}
// 将ProducerBatch转换为ProduceRequest
ProduceRequest.Builder requestBuilder = ProduceRequest.forMagic(minUsedMagic,
new ProduceRequestData()
.setAcks(acks)
.setTimeoutMs(timeout)
.setTransactionalId(transactionalId)
.setTopicData(tpd));
RequestCompletionHandler callback = response -> handleProduceResponse(response, recordsByPartition, time.milliseconds());
// 将ProduceRequest转换为clientRequest
ClientRequest clientRequest = client.newClientRequest(Integer.toString(destination), requestBuilder, now, acks != 0,
requestTimeoutMs, callback);
// 调用NetworkClient将消息写入网络发送出去
client.send(clientRequest, now);
}
- client.send 是调用NetworkClient#doSend的方法来发送数据的
NetworkClient#doSend
private void doSend(ClientRequest clientRequest, boolean isInternalRequest, long now) {
// 校验是否可用
ensureActive();
// 获取目的地的node节点
String nodeId = clientRequest.destination();
if (!isInternalRequest) {
if (!canSendRequest(nodeId, now))
throw new IllegalStateException("Attempt to send a request to node " + nodeId + " which is not ready.");
}
AbstractRequest.Builder<?> builder = clientRequest.requestBuilder();
try {
NodeApiVersions versionInfo = apiVersions.get(nodeId);
short version;
if (versionInfo == null) {
version = builder.latestAllowedVersion();
} else {
version = versionInfo.latestUsableVersion(clientRequest.apiKey(),
builder.oldestAllowedVersion(),
builder.latestAllowedVersion());
}
// 真正的发送
doSend(clientRequest, isInternalRequest, now, builder.build(version));
} catch (UnsupportedVersionException unsupportedVersionException) {
ClientResponse clientResponse = new ClientResponse(clientRequest.makeHeader(builder.latestAllowedVersion()),
clientRequest.callback(),
clientRequest.destination(), now, now, false,
unsupportedVersionException, null, null);
if (!isInternalRequest)
abortedSends.add(clientResponse);
else if (clientRequest.apiKey() == ApiKeys.METADATA)
metadataUpdater.handleFailedRequest(now, Optional.of(unsupportedVersionException));
}
}
private void doSend(ClientRequest clientRequest, boolean isInternalRequest, long now, AbstractRequest request) {
String destination = clientRequest.destination();
RequestHeader header = clientRequest.makeHeader(request.version());
Send send = request.toSend(header);
InFlightRequest inFlightRequest = new InFlightRequest(
clientRequest,
header,
isInternalRequest,
request,
send,
now
);
// InFlightRequest(飞行队列)表示请求已经发送，但是还没有得到响应
this.inFlightRequests.add(inFlightRequest);
selector.send(new NetworkSend(destination, send));
}
selector.send
/**
* 主要实现了 Kafka 客户端的网络请求的排队功能，能够将网络请求加入到发送队列中，等待后续的 poll 方法进行发送
*
* @param send The request to send
*/
public void send(NetworkSend send) {
// 获取目标连接的连接 ID
String connectionId = send.destinationId();
//获得 KafkaChannel 对象，
KafkaChannel channel = openOrClosingChannelOrFail(connectionId);
//若连接正在关闭，则将连接 ID 添加到 failedSends 队列中
if (closingChannels.containsKey(connectionId)) {
this.failedSends.add(connectionId);
} else {
try {
//将网络请求交给 KafkaChannel 对象处理，暂存数据预发送，并没有真正的发送
channel.setSend(send);
} catch (Exception e) {
// 如果 KafkaChannel 对象在处理过程中抛出异常，将连接状态设置为 FAILED_SEND，并将连接 ID 添加到 failedSends 队列中，然后关闭连接，并将异常向上抛出，以便上层代码处理
channel.state(ChannelState.FAILED_SEND);
this.failedSends.add(connectionId);
close(channel, CloseMode.DISCARD_NO_NOTIFY);
if (!(e instanceof CancelledKeyException)) {
throw e;
}
}
}
}
poll
public void poll(long timeout) throws IOException {
// 超时时间是否小于 0
if (timeout < 0) {
throw new IllegalArgumentException("timeout should be >= 0");
}
boolean madeReadProgressLastCall = madeReadProgressLastPoll;
clear();
boolean dataInBuffers = !keysWithBufferedRead.isEmpty();
if (!immediatelyConnectedKeys.isEmpty() || (madeReadProgressLastCall && dataInBuffers)) {
timeout = 0;
}
//检查内存是否已经不足
if (!memoryPool.isOutOfMemory() && outOfMemory) {
for (KafkaChannel channel : channels.values()) {
if (channel.isInMutableState() && !explicitlyMutedChannels.contains(channel)) {
channel.maybeUnmute();
}
}
outOfMemory = false;
}
/* 检测已经准备好的keys */
long startSelect = time.nanoseconds();
//Java NIO 库提供的 select 方法
int numReadyKeys = select(timeout);
long endSelect = time.nanoseconds();
this.sensors.selectTime.record(endSelect - startSelect, time.milliseconds(), false);
if (numReadyKeys > 0 || !immediatelyConnectedKeys.isEmpty() || dataInBuffers) {
Set<SelectionKey> readyKeys = this.nioSelector.selectedKeys();
// 从缓冲了数据的通道进行轮询(但不再从底层套接字进行轮询)
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);
}
// 从底层套接字拥有更多数据的通道进行轮询
pollSelectionKeys(readyKeys, false, endSelect);
// 清除所有选定的键，使它们从下一次选择的就绪计数中排除
readyKeys.clear();
pollSelectionKeys(immediatelyConnectedKeys, true, endSelect);
immediatelyConnectedKeys.clear();
} else {
madeReadProgressLastPoll = true;
}
long endIo = time.nanoseconds();
this.sensors.ioTime.record(endIo - endSelect, time.milliseconds(), false);
// 关闭被延迟的通道，现在可以关闭了
completeDelayedChannelClose(endIo);
// 我们使用select末尾的时间来确保不会关闭pollSelectionKeys中刚刚处理的任何连接
maybeCloseOldestConnection(endSelect);
}
总结
Kafka 生产者的设计具有多个精妙之处，其中包括：
- 高效的异步发送：Kafka 生产者使用 RecordAccumulator 进行消息缓存，并利用 Sender 线程异步发送消息，这种设计可以提高消息发送的吞吐量。
- 批量发送：Kafka 生产者可以将多个消息批量发送，从而减少网络开销和服务端的负载压力。
- 可靠的重试机制：Kafka 生产者使用重试机制来保证消息能够成功发送，当消息发送失败时，生产者会自动进行重试，直到消息发送成功或者达到最大重试次数。
- 动态分区分配：Kafka 生产者可以根据生产者和分区的数量动态分配分区，从而实现负载均衡和优化网络使用。
- 可配置的消息压缩：Kafka 生产者支持多种消息压缩算法，可以根据实际需求进行配置，从而减少网络传输的数据量。
遗留
- RecordAccumulator对内存的操作逻辑没有分析透彻
- selector#poll底层的逻辑也没有分析透彻
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