深入分析KafkaProducer消息发送流程
一、Kafka生产者发送消息示例
注意：以下所用kafka版本为0.10.1.0 KafkaProducer是线程安全对象，建议可以将其封装成多线程共享一个实例，效率反而比多实例更高，在深入分析前，先简单看一个生产者生产消息的demo
package com.tanjie.kafka;
import java.util.List;
import java.util.Properties;
import java.util.UUID;
import java.util.concurrent.ExecutionException;
import org.apache.kafka.clients.producer.Callback;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.apache.kafka.clients.producer.RecordMetadata;
import org.apache.kafka.common.PartitionInfo;
public class ProducerDemo2 {
public static void main(String[] args) {
boolean isAsync = args.length == 0 || args[0].trim().equalsIgnoreCase("sync");
Properties props = new Properties();
props.put("bootstrap.servers", "192.168.80.132:9092");
props.put("client.id", "producerDemo2");
props.put("key.serializer", "org.apache.kafka.common.serialization.IntegerSerializer");
props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
KafkaProducer producer = new KafkaProducer<>(props);
List partitionInfos = producer.partitionsFor("kafka-topic-02");
for(final PartitionInfo partitionInfo : partitionInfos) {
System.out.println(partitionInfo.toString());
}
String topic = "kafka-topic-02";
for (int i = 0; i < 10; i++) {
String message = UUID.randomUUID().toString();
long startTime = System.currentTimeMillis();
if (isAsync) {
producer.send(new ProducerRecord(topic, i, message),
new ProducerAckCallback(startTime, i, message));
} else {
try {
/** 同步等待返回 */
producer.send(new ProducerRecord(topic, i, message)).get();
System.out.println("Send message : " + "key:" + i + ";value:" + message);
} catch (InterruptedException e) {
e.printStackTrace();
} catch (ExecutionException e) {
e.printStackTrace();
}
}
}
producer.close();
}
static class ProducerAckCallback implements Callback {
private final Long startTime;
private final int key;
private final String value;
public ProducerAckCallback(Long startTime, int key, String value) {
this.startTime = startTime;
this.key = key;
this.value = value;
}
@Override
public void onCompletion(RecordMetadata metadata, Exception e) {
long spendTime = System.currentTimeMillis() - startTime;
if (null != metadata) {
System.out.println("消息(" + key + "," + value + ")send to partition(" + metadata.partition()
+ " and offest " + metadata.offset() + " and spend " + spendTime + " ms");
}
}
}
} 首先新建一个topic
bin/kafka-topics.sh --create --zookeeper 192.168.80.132:2181 --replication-factor 3 --partitions 3 --topic kafka-topic-02运行代码如下
Partition(topic = kafka-topic-02, partition = 2, leader = 0, replicas = [0,1,2,], isr = [0,2,])
Partition(topic = kafka-topic-02, partition = 1, leader = 0, replicas = [0,1,2,], isr = [0,1,2,])
Partition(topic = kafka-topic-02, partition = 0, leader = 0, replicas = [0,1,2,], isr = [0,2,])
消息(2,c2f2d395-80af-4982-94fd-7ad517734cb9)send to partition(2 and offest 3 and spend 39 ms
消息(5,c55e3b06-ed35-421b-9a56-d02a4921a9ab)send to partition(2 and offest 4 and spend 37 ms
消息(6,1b4a6b27-a63e-499e-bf36-c83d3ad438ad)send to partition(2 and offest 5 and spend 37 ms
消息(0,8f670453-b31d-4b9f-b7c0-e80fbc06e26e)send to partition(1 and offest 4 and spend 106 ms
消息(3,c7015529-0b01-4bcb-8b07-2ebca01d2309)send to partition(1 and offest 5 and spend 39 ms
消息(4,03bc15b6-b8df-410f-a350-f7e205d378f9)send to partition(1 and offest 6 and spend 39 ms
消息(9,24285a99-f02f-4f98-80eb-8ffdfd61111a)send to partition(1 and offest 7 and spend 37 ms
消息(1,324d88cc-65e3-4dbe-8ea6-9aa09c362884)send to partition(0 and offest 3 and spend 43 ms
消息(7,ea6ab214-31ff-4438-985b-f06ff33dcbf7)send to partition(0 and offest 4 and spend 38 ms
消息(8,558b6b86-10d3-440d-9f9e-b6cf25fe90f3)send to partition(0 and offest 5 and spend 37 ms整个代码比较简单，当然也不是很优雅，只是为了演示这样的一个效果，下面我们先来看一下整个生产者生产的消息是如何流转到kafka server的
整个流程大概分为如下几步
1、构建一个KafkaProducer对象，初始化一些用到的组件，比如缓存区，Sender线程等
2、如果配置了拦截器，可用对发送的消息进行可定制化的拦截或更改
3、对Key,value进行序列化
4、根据传入的参数，为消息选择合适的分区，具体怎么选，后面分析
5、将消息按照分区发送到RecordAccmulator暂存，消息按照每个分区进行汇总
6、后台Sender线程被触发后从RecordAccmulator里面获取消息然后构建成ClientRequest，怎么构建后面分析
7、将ClientRequest封装成NetWorkClient准备发送
8、NetWorkClient将请求放入KafkaChannel准备发送，然后执行网络IO，最后发送到kafka server
二、深入分析KafkaProducer的实现
根据上面的demo，我们首先来看kafkaProducer的初始化过程，首先看一下kafkaProducer的属性
public class KafkaProducer implements Producer {
private static final Logger log = LoggerFactory.getLogger(KafkaProducer.class);
private static final AtomicInteger PRODUCER_CLIENT_ID_SEQUENCE = new AtomicInteger(1);
private static final String JMX_PREFIX = "kafka.producer";
private String clientId; //客户端的一个标识
private final Partitioner partitioner; //分区选择器，根据传入的参数，决定该条消息被放到哪个分区
private final int maxRequestSize; //客户端最大的消息大小
private final long totalMemorySize; //单个消息的缓存区大小
private final Metadata metadata; //kafka 元数据维护
private final RecordAccumulator accumulator; //消息暂存区
private final Sender sender; //发送消息的sender任务
private final Metrics metrics; //一些统计信息
private final Thread ioThread; //执行Sender任务发送消息的线程
private final CompressionType compressionType; //消息的压缩策略
private final Sensor errors; //
private final Time time;
private final Serializer keySerializer; //key序列化
private final Serializer valueSerializer; //value序列化
private final ProducerConfig producerConfig; //生产者相关配置信息
private final long maxBlockTimeMs; //在等待metadata更新的最大等待时间
private final int requestTimeoutMs; //消息的超时时间
private final ProducerInterceptors interceptors; //消息拦截器 再看构造函数，我一般看源码习惯关注主干，不是很纠结细节，所以下面标注一些重要的东西
private KafkaProducer(ProducerConfig config, Serializer keySerializer, Serializer valueSerializer) {
try {
log.trace("Starting the Kafka producer");
Map userProvidedConfigs = config.originals();
this.producerConfig = config;
this.time = new SystemTime();
clientId = config.getString(ProducerConfig.CLIENT_ID_CONFIG);
if (clientId.length() <= 0)
clientId = "producer-" + PRODUCER_CLIENT_ID_SEQUENCE.getAndIncrement();
Map metricTags = new LinkedHashMap();
metricTags.put("client-id", clientId);
MetricConfig metricConfig = new MetricConfig().samples(config.getInt(ProducerConfig.METRICS_NUM_SAMPLES_CONFIG))
.timeWindow(config.getLong(ProducerConfig.METRICS_SAMPLE_WINDOW_MS_CONFIG), TimeUnit.MILLISECONDS)
.tags(metricTags);
List reporters = config.getConfiguredInstances(ProducerConfig.METRIC_REPORTER_CLASSES_CONFIG,
MetricsReporter.class);
reporters.add(new JmxReporter(JMX_PREFIX));
this.metrics = new Metrics(metricConfig, reporters, time);
//初始化分区选择器
this.partitioner = config.getConfiguredInstance(ProducerConfig.PARTITIONER_CLASS_CONFIG, Partitioner.class);
long retryBackoffMs = config.getLong(ProducerConfig.RETRY_BACKOFF_MS_CONFIG);
//序列化key
if (keySerializer == null) {
this.keySerializer = config.getConfiguredInstance(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG,
Serializer.class);
this.keySerializer.configure(config.originals(), true);
} else {
config.ignore(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG);
this.keySerializer = keySerializer;
}
//序列化value
if (valueSerializer == null) {
this.valueSerializer = config.getConfiguredInstance(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG,
Serializer.class);
this.valueSerializer.configure(config.originals(), false);
} else {
config.ignore(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG);
this.valueSerializer = valueSerializer;
}
// load interceptors and make sure they get clientId
userProvidedConfigs.put(ProducerConfig.CLIENT_ID_CONFIG, clientId);
List> interceptorList = (List) (new ProducerConfig(userProvidedConfigs)).getConfiguredInstances(ProducerConfig.INTERCEPTOR_CLASSES_CONFIG,
ProducerInterceptor.class);
this.interceptors = interceptorList.isEmpty() ? null : new ProducerInterceptors<>(interceptorList);
ClusterResourceListeners clusterResourceListeners = configureClusterResourceListeners(keySerializer, valueSerializer, interceptorList, reporters);
//初始集群元数据
this.metadata = new Metadata(retryBackoffMs, config.getLong(ProducerConfig.METADATA_MAX_AGE_CONFIG), true, clusterResourceListeners);
//初始最大的消息长度
this.maxRequestSize = config.getInt(ProducerConfig.MAX_REQUEST_SIZE_CONFIG);
//初始消息缓冲区大小
this.totalMemorySize = config.getLong(ProducerConfig.BUFFER_MEMORY_CONFIG);
//初始压缩策略
this.compressionType = CompressionType.forName(config.getString(ProducerConfig.COMPRESSION_TYPE_CONFIG));
/* check for user defined settings.
* If the BLOCK_ON_BUFFER_FULL is set to true,we do not honor METADATA_FETCH_TIMEOUT_CONFIG.
* This should be removed with release 0.9 when the deprecated configs are removed.
*/
if (userProvidedConfigs.containsKey(ProducerConfig.BLOCK_ON_BUFFER_FULL_CONFIG)) {
log.warn(ProducerConfig.BLOCK_ON_BUFFER_FULL_CONFIG + " config is deprecated and will be removed soon. " +
"Please use " + ProducerConfig.MAX_BLOCK_MS_CONFIG);
boolean blockOnBufferFull = config.getBoolean(ProducerConfig.BLOCK_ON_BUFFER_FULL_CONFIG);
if (blockOnBufferFull) {
this.maxBlockTimeMs = Long.MAX_VALUE;
} else if (userProvidedConfigs.containsKey(ProducerConfig.METADATA_FETCH_TIMEOUT_CONFIG)) {
log.warn(ProducerConfig.METADATA_FETCH_TIMEOUT_CONFIG + " config is deprecated and will be removed soon. " +
"Please use " + ProducerConfig.MAX_BLOCK_MS_CONFIG);
this.maxBlockTimeMs = config.getLong(ProducerConfig.METADATA_FETCH_TIMEOUT_CONFIG);
} else {
this.maxBlockTimeMs = config.getLong(ProducerConfig.MAX_BLOCK_MS_CONFIG);
}
} else if (userProvidedConfigs.containsKey(ProducerConfig.METADATA_FETCH_TIMEOUT_CONFIG)) {
log.warn(ProducerConfig.METADATA_FETCH_TIMEOUT_CONFIG + " config is deprecated and will be removed soon. " +
"Please use " + ProducerConfig.MAX_BLOCK_MS_CONFIG);
this.maxBlockTimeMs = config.getLong(ProducerConfig.METADATA_FETCH_TIMEOUT_CONFIG);
} else {
this.maxBlockTimeMs = config.getLong(ProducerConfig.MAX_BLOCK_MS_CONFIG);
}
/* check for user defined settings.
* If the TIME_OUT config is set use that for request timeout.
* This should be removed with release 0.9
*/
if (userProvidedConfigs.containsKey(ProducerConfig.TIMEOUT_CONFIG)) {
log.warn(ProducerConfig.TIMEOUT_CONFIG + " config is deprecated and will be removed soon. Please use " +
ProducerConfig.REQUEST_TIMEOUT_MS_CONFIG);
this.requestTimeoutMs = config.getInt(ProducerConfig.TIMEOUT_CONFIG);
} else {
this.requestTimeoutMs = config.getInt(ProducerConfig.REQUEST_TIMEOUT_MS_CONFIG);
}
//初始缓冲区RecordAccmulator
this.accumulator = new RecordAccumulator(config.getInt(ProducerConfig.BATCH_SIZE_CONFIG),
this.totalMemorySize,
this.compressionType,
config.getLong(ProducerConfig.LINGER_MS_CONFIG),
retryBackoffMs,
metrics,
time);
List addresses = ClientUtils.parseAndValidateAddresses(config.getList(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG));
this.metadata.update(Cluster.bootstrap(addresses), time.milliseconds());
ChannelBuilder channelBuilder = ClientUtils.createChannelBuilder(config.values());
//构建NetworkClient和kafkaserver进行IO
NetworkClient client = new NetworkClient(
new Selector(config.getLong(ProducerConfig.CONNECTIONS_MAX_IDLE_MS_CONFIG), this.metrics, time, "producer", channelBuilder),
this.metadata,
clientId,
config.getInt(ProducerConfig.MAX_IN_FLIGHT_REQUESTS_PER_CONNECTION),
config.getLong(ProducerConfig.RECONNECT_BACKOFF_MS_CONFIG),
config.getInt(ProducerConfig.SEND_BUFFER_CONFIG),
config.getInt(ProducerConfig.RECEIVE_BUFFER_CONFIG),
this.requestTimeoutMs, time);
//初始发送消息的sender任务，这些任务在ioThread中运行
this.sender = new Sender(client,
this.metadata,
this.accumulator,
config.getInt(ProducerConfig.MAX_IN_FLIGHT_REQUESTS_PER_CONNECTION) == 1,
config.getInt(ProducerConfig.MAX_REQUEST_SIZE_CONFIG),
(short) parseAcks(config.getString(ProducerConfig.ACKS_CONFIG)),
config.getInt(ProducerConfig.RETRIES_CONFIG),
this.metrics,
new SystemTime(),
clientId,
this.requestTimeoutMs);
String ioThreadName = "kafka-producer-network-thread" + (clientId.length() > 0 ? " | " + clientId : "");
this.ioThread = new KafkaThread(ioThreadName, this.sender, true);
//启动线程
this.ioThread.start();
this.errors = this.metrics.sensor("errors");
config.logUnused();
AppInfoParser.registerAppInfo(JMX_PREFIX, clientId);
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(0, TimeUnit.MILLISECONDS, true);
// now propagate the exception
throw new KafkaException("Failed to construct kafka producer", t);
}
}
当KafkaProducer初始化好以后，调用Send进行发送，
@Override
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 == null ? record : this.interceptors.onSend(record);
return doSend(interceptedRecord, callback);
} private Future doSend(ProducerRecord record, Callback callback) {
TopicPartition tp = null;
try {
// first make sure the metadata for the topic is available
//等待kafka更新元数据信息
ClusterAndWaitTime clusterAndWaitTime = waitOnMetadata(record.topic(), record.partition(), maxBlockTimeMs);
long remainingWaitMs = Math.max(0, maxBlockTimeMs - clusterAndWaitTime.waitedOnMetadataMs);
//获取元数据里面的集群相关信息
Cluster cluster = clusterAndWaitTime.cluster;
//对key进行序列化
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");
}
//对value进行序列化
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");
}
//获取消息应该被发送到哪个分区
int partition = partition(record, serializedKey, serializedValue, cluster);
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);
//将要发送的消息追加到RecordAccmulator里面
RecordAccumulator.RecordAppendResult result = accumulator.append(tp, timestamp, serializedKey, serializedValue, interceptCallback, remainingWaitMs);
//如果消息追加到RecordAccmulator后, 最后一个RecordBatch满了或者队列里面不止一个RecordBatch
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);
//唤醒sender线程
this.sender.wakeup();
}
//返回最后的结果
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) {
// ..................省略
}
} 在分析waitOnMetadata之前，先说一下kafka集群的元数据，我们知道，每个topic有多个分区，每个分区有多个副本，而每个分区的副本里面都需要有一个Leader副本，其他副本只需要同步leader副本的数据即可，而Kafak的元数据就是记录了比如某个分区有哪些副本，leader副本在哪台机器上，follow副本在哪台机器上，哪些副本在ISR（可以理解为follower副本中数据和Leader副本数据相差不大的副本节点）里面 , 在kafka里面主要通过下面的几个类来进行元数据的维护
Metadata里面有个属性叫cluster, 而Cluster类主要维护了下面的关系
private final boolean isBootstrapConfigured;
//整个kafka集群的节点相关信息
private final List nodes;
private final Set unauthorizedTopics;
private final Set internalTopics;
//维护集群里面某个分区和具体的分区之间的映射关系
private final Map partitionsByTopicPartition;
//维护topic name和具体分区信息之间的映射关系
private final Map> partitionsByTopic;
//维护topic name和具体分区信息之间的映射关系，这个和上一个的区别是，这个里面的分区必须是有leader的，上一个可以没有
private final Map> availablePartitionsByTopic;
//维护节点node 和具体的分区信息，可以根据某个节点查询这个节点里的所有分区信息
private final Map> partitionsByNode;
//维护节点id和具体节点之间的映射关系
private final Map nodesById;
private final ClusterResource clusterResource; 关于Node，Topicpartition，PartitionInfo 字段的定义都比较简单，这里就不累赘了。
说完了Cluster，在Metadata里面持有对Cluster的引用，Metadata的主要字段含义如下
private final long refreshBackoffMs;
private final long metadataExpireMs;
//版本号，每更新一次元数据信息，版本号+1
private int version;
//上一次更新的时间戳
private long lastRefreshMs;
//上一次成功更新的时间戳
private long lastSuccessfulRefreshMs;
//记录kafka集群的元数据信息
private Cluster cluster;
//是否需要强制更新，如果为true,会触发后台sender线程去强制更新元数据信息
private boolean needUpdate;
/* Topics with expiry time */
//记录当前的topic信息
private final Map topics;
private final List listeners;
private final ClusterResourceListeners clusterResourceListeners;
private boolean needMetadataForAllTopics;
private final boolean topicExpiryEnabled; 接下来我们回到waitOnMetadata，来看一下元数据是如何更新的
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
metadata.add(topic);
//获取集群元数据信息
Cluster cluster = metadata.fetch();
//获取当前topic对应的分区个数
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
//假如topic对应的分区信息记录在metadata里面已经有了，就是说集群加载了分区相关信息,就直接返回元数据
if (partitionsCount != null && (partition == null || partition < partitionsCount))
return new ClusterAndWaitTime(cluster, 0);
long begin = time.milliseconds();
long remainingWaitMs = maxWaitMs;
long elapsed;
// Issue metadata requests until we have metadata for the topic or maxWaitTimeMs is exceeded.
// In case we already have cached metadata for the topic, but the requested partition is greater
// than expected, issue an update request only once. This is necessary in case the metadata
// is stale and the number of partitions for this topic has increased in the meantime.
do {
log.trace("Requesting metadata update for topic {}.", topic);
//将needUpdate设置为true,表示元数据需要更新
int version = metadata.requestUpdate();
//唤醒sender线程
sender.wakeup();
try {
//等待sender线程去更新元数据信息
metadata.awaitUpdate(version, remainingWaitMs);
} catch (TimeoutException ex) {
// Rethrow with original maxWaitMs to prevent logging exception with remainingWaitMs
throw new TimeoutException("Failed to update metadata after " + maxWaitMs + " ms.");
}
cluster = metadata.fetch();
elapsed = time.milliseconds() - begin;
if (elapsed >= maxWaitMs)
throw new TimeoutException("Failed to update metadata after " + maxWaitMs + " ms.");
if (cluster.unauthorizedTopics().contains(topic))
throw new TopicAuthorizationException(topic);
remainingWaitMs = maxWaitMs - elapsed;
partitionsCount = cluster.partitionCountForTopic(topic);
} while (partitionsCount == null);
if (partition != null && partition >= partitionsCount) {
throw new KafkaException(
String.format("Invalid partition given with record: %d is not in the range [0...%d).", partition, partitionsCount));
}
return new ClusterAndWaitTime(cluster, elapsed);
}
int partition = partition(record, serializedKey, serializedValue, cluster); private int partition(ProducerRecord record, byte[] serializedKey, byte[] serializedValue, Cluster cluster) {
Integer partition = record.partition();
return partition != null ?
partition :
partitioner.partition(
record.topic(), record.key(), serializedKey, record.value(), serializedValue, cluster);
} 如果没有指定，调用partitioner.partition进行判断，kafka提供了默认的实现，当然你可以自己定制分发策略
public int partition(String topic, Object key, byte[] keyBytes, Object value, byte[] valueBytes, Cluster cluster) {
//根据指定topic获取所有分区信息
List partitions = cluster.partitionsForTopic(topic);
//获取分区个数
int numPartitions = partitions.size();
//如果没有指定消息key
if (keyBytes == null) {
int nextValue = counter.getAndIncrement();
//获取指定topic对应的可利用的分区信息，这些可利用是说副本有leader副本的，有些分区他是没有leader副本的，有可能因为一些原因导致
List availablePartitions = cluster.availablePartitionsForTopic(topic);
if (availablePartitions.size() > 0) {
// 对可利用的分区数取模获取下标
int part = Utils.toPositive(nextValue) % availablePartitions.size();
return availablePartitions.get(part).partition();
} else {
// no partitions are available, give a non-available partition
return Utils.toPositive(nextValue) % numPartitions;
}
//如果指定了消息key,直接对key进行hash后然后对分区数大小进行取模操作
} else {
// hash the keyBytes to choose a partition
return Utils.toPositive(Utils.murmur2(keyBytes)) % numPartitions;
}
} 总结一下：
1、如果你指定了分区，那么只会将这条消息发送到指定分区
2、如果你同时指定了分区和消息key，也是指发送到这个分区
3、如果没有指定分区，指定了消息key，那么对key进行hash后对当前分区数进行取模后得出消息应该放到哪个分区
4、如果没有分区，也没有指定key，则按照一定的轮询方式(counter和分区数取模，counter每次递增，确保消息不会发送到同一个分区里面)来获取分区数
说完如何获取消息发送的分区后，下一步就是将消息放到暂存区RecordAccumulator，我们先来看一下RecordAccumulator
从上面的3个类的关系可以看到，RecordAccmulator里面有一个ConcurrentMap，里面key是TopicPartition，value是一个双端队列，说明RecordAccmulator里面是按照每个分区进行消息缓存的，也就是相同分区的消息会放到一起，而每个RecordBatch里面持有MemoryRecords的引用，MemoryRecords才是真正放消息的地方，
MemoryRecords里面支持对消息的压缩，还有保存消息数据的java nio ByteBuffer。当我们往RecordAccmulator里面追加消息的时候，看append方法
public RecordAppendResult append(TopicPartition tp,
long timestamp,
byte[] key,
byte[] value,
Callback callback,
long maxTimeToBlock) throws InterruptedException {
// We keep track of the number of appending thread to make sure we do not miss batches in
// abortIncompleteBatches().
//统计当前正在像RecordAccmulator中追加的线程数
appendsInProgress.incrementAndGet();
try {
// check if we have an in-progress batch
//第一步：首先查找已有的batches,如果已经有该分区对应的队列信息则直接返回，否则从新建一个
Deque dq = getOrCreateDeque(tp);
//因为ArrayDeque是非线程安全的，所以这里加锁进行处理
synchronized (dq) {
if (closed)
throw new IllegalStateException("Cannot send after the producer is closed.");
//第二步：像Deque中最后一个RecordBatch追加MemoryRecord
RecordAppendResult appendResult = tryAppend(timestamp, key, value, callback, dq);
//如果追加成功了那么直接返回
if (appendResult != null)
return appendResult;
}
//如果上面那一步追加的时候没有追加成功，比如某个线程发送的消息太大了，在这个队列里的最后一个MemoryRecords空间不足以存这条消息
// we don't have an in-progress record batch try to allocate a new batch
//重新计算大小
int size = Math.max(this.batchSize, Records.LOG_OVERHEAD + Record.recordSize(key, value));
log.trace("Allocating a new {} byte message buffer for topic {} partition {}", size, tp.topic(), tp.partition());
//重新申请新的空间
ByteBuffer buffer = free.allocate(size, maxTimeToBlock);
synchronized (dq) {
// Need to check if producer is closed again after grabbing the dequeue lock.
if (closed)
throw new IllegalStateException("Cannot send after the producer is closed.");
//重新追加
RecordAppendResult appendResult = tryAppend(timestamp, key, value, callback, dq);
if (appendResult != null) {
//释放刚刚申请的空间
// Somebody else found us a batch, return the one we waited for! Hopefully this doesn't happen often...
free.deallocate(buffer);
return appendResult;
}
//如果重新追加也失败了，构建一个MemoryRecords
MemoryRecords records = MemoryRecords.emptyRecords(buffer, compression, this.batchSize);
//构建一个RecordBatch
RecordBatch batch = new RecordBatch(tp, records, time.milliseconds());
//在刚刚新建的RecordBatch中追加消息
FutureRecordMetadata future = Utils.notNull(batch.tryAppend(timestamp, key, value, callback, time.milliseconds()));
//将新建的RecordBatch追加到队列的尾部
dq.addLast(batch);
incomplete.add(batch);
return new RecordAppendResult(future, dq.size() > 1 || batch.records.isFull(), true);
}
} finally {
//释放
appendsInProgress.decrementAndGet();
}
} 这样，消息就放到了消息缓冲区中，接下来，看一下Sender线程是如何去获取消息和Kafka server通信的。
void run(long now) {
//获取集群元数据信息
Cluster cluster = metadata.fetch();
// get the list of partitions with data ready to send
//从缓存区中选择出可以向哪些Node节点发送消息,具体怎么选择的，下面会分析
RecordAccumulator.ReadyCheckResult result = this.accumulator.ready(cluster, now);
//如果返回的分区存在没有leader副本的情况,那么标记元数据需要更新
// if there are any partitions whose leaders are not known yet, force metadata update
if (!result.unknownLeaderTopics.isEmpty()) {
// The set of topics with unknown leader contains topics with leader election pending as well as
// topics which may have expired. Add the topic again to metadata to ensure it is included
// and request metadata update, since there are messages to send to the topic.
for (String topic : result.unknownLeaderTopics)
this.metadata.add(topic);
this.metadata.requestUpdate();
}
// remove any nodes we aren't ready to send to
Iterator iter = result.readyNodes.iterator();
long notReadyTimeout = Long.MAX_VALUE;
while (iter.hasNext()) {
Node node = iter.next();
//针对返回的结果，检查网络IO是否符合发送条件，如果有不符合的node节点就将其移除
if (!this.client.ready(node, now)) {
iter.remove();
notReadyTimeout = Math.min(notReadyTimeout, this.client.connectionDelay(node, now));
}
}
// 获取代发送的消息集合,key为哪个节点，value为对应的所有消息
// create produce requests
Map> batches = this.accumulator.drain(cluster,
result.readyNodes,
this.maxRequestSize,
now);
if (guaranteeMessageOrder) {
// Mute all the partitions drained
for (List batchList : batches.values()) {
for (RecordBatch batch : batchList)
this.accumulator.mutePartition(batch.topicPartition);
}
}
List expiredBatches = this.accumulator.abortExpiredBatches(this.requestTimeout, now);
// update sensors
for (RecordBatch expiredBatch : expiredBatches)
this.sensors.recordErrors(expiredBatch.topicPartition.topic(), expiredBatch.recordCount);
sensors.updateProduceRequestMetrics(batches);
//将发送的消息构建成clietnRequest,每个node节点只会构建一个clientRequest
List 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;
//发送消息到服务端
this.client.poll(pollTimeout, now);
} 在run方法里面，有几个方法很关键，下面我们一个一个来分析，第一个就是ready方法，ready方法从RecordAccmulator里面获取已经准备好的待发送的消息集合，那么怎样才算是准备好了呢？看下面方法
public ReadyCheckResult ready(Cluster cluster, long nowMs) {
Set readyNodes = new HashSet<>();
long nextReadyCheckDelayMs = Long.MAX_VALUE;
Set unknownLeaderTopics = new HashSet<>();
//判断缓冲池是否被耗尽
boolean exhausted = this.free.queued() > 0;
//遍历所有的队列
for (Map.Entry> entry : this.batches.entrySet()) {
TopicPartition part = entry.getKey();
Deque deque = entry.getValue();
//获取某个topicPartition的Leader broker
Node leader = cluster.leaderFor(part);
synchronized (deque) {
//如果某个分区没有leader并且队列不为null(也就是虽然该分区没有Leader但是里面有消息，消息是可用的)
if (leader == null && !deque.isEmpty()) {
// This is a partition for which leader is not known, but messages are available to send.
// Note that entries are currently not removed from batches when deque is empty.
unknownLeaderTopics.add(part.topic());
//如果待发送的set集合里面不包含当前待发送的node,且这个topic partition不是处于正在发送中(保证单个分区消息的有序性)
} else if (!readyNodes.contains(leader) && !muted.contains(part)) {
RecordBatch batch = deque.peekFirst();
if (batch != null) {
//判断当前待发送的batch是否处于重发中，也就是再等待下次进行重发
boolean backingOff = batch.attempts > 0 && batch.lastAttemptMs + retryBackoffMs > nowMs;
//计算等待时间
long waitedTimeMs = nowMs - batch.lastAttemptMs;
//需要等待的时间
long timeToWaitMs = backingOff ? retryBackoffMs : lingerMs;
//还需要等待的时间
long timeLeftMs = Math.max(timeToWaitMs - waitedTimeMs, 0);
//batch是否已经满了
boolean full = deque.size() > 1 || batch.records.isFull();
//是否超时了，也就是等待时间太长了
boolean expired = waitedTimeMs >= timeToWaitMs;
boolean sendable = full || expired || exhausted || closed || flushInProgress();
//bathc已满或者 batch已经在缓存池待了足够长时间或者缓冲池被关闭了，那么就加入待发送set里面
if (sendable && !backingOff) {
readyNodes.add(leader);
} else {
// Note that this results in a conservative estimate since an un-sendable partition may have
// a leader that will later be found to have sendable data. However, this is good enough
// since we'll just wake up and then sleep again for the remaining time.
nextReadyCheckDelayMs = Math.min(timeLeftMs, nextReadyCheckDelayMs);
}
}
}
}
}
return new ReadyCheckResult(readyNodes, nextReadyCheckDelayMs, unknownLeaderTopics);
} 将这些已经准备好的node返回后，接下来看一下drain方法，drain方法的作用就是，根据上一步返回的给定节点信息，从这个已经准备好消息发送的节点里面，从缓冲区中抽取节点下作为leader的topic partition的batch集合。
public Map> drain(Cluster cluster,
Set nodes,
int maxSize,
long now) {
if (nodes.isEmpty())
return Collections.emptyMap();
Map> batches = new HashMap<>();
//遍历节点
for (Node node : nodes) {
int size = 0;
//获取某个节点下所有的有Leader的分区信息
List parts = cluster.partitionsForNode(node.id());
List ready = new ArrayList<>();
/* to make starvation less likely this loop doesn't start at 0 */
int start = drainIndex = drainIndex % parts.size();
do {
PartitionInfo part = parts.get(drainIndex);
TopicPartition tp = new TopicPartition(part.topic(), part.partition());
// Only proceed if the partition has no in-flight batches.
if (!muted.contains(tp)) {
//从缓存区中根据分区topic,分区id，找到对应的消息队列
Deque deque = getDeque(new TopicPartition(part.topic(), part.partition()));
if (deque != null) {
synchronized (deque) {
//获取队列的第一个batch
RecordBatch first = deque.peekFirst();
if (first != null) {
boolean backoff = first.attempts > 0 && first.lastAttemptMs + retryBackoffMs > now;
// Only drain the batch if it is not during backoff period.
if (!backoff) {
if (size + first.records.sizeInBytes() > maxSize && !ready.isEmpty()) {
// there is a rare case that a single batch size is larger than the request size due
// to compression; in this case we will still eventually send this batch in a single
// request
break;
} else {
RecordBatch batch = deque.pollFirst();
batch.records.close();
size += batch.records.sizeInBytes();
ready.add(batch);
batch.drainedMs = now;
}
}
}
}
}
}
this.drainIndex = (this.drainIndex + 1) % parts.size();
} while (start != drainIndex);
//最后返回的key为某个node,value是这个node上所有需要发送的消息batch
batches.put(node.id(), ready);
}
return batches;
} 下一个重点是createProduceRequests，这个方法是Sender用来将待发送的消息封装成ClientRequest
List requests = createProduceRequests(batches, now); private List createProduceRequests(Map> collated, long now) {
List requests = new ArrayList(collated.size());
for (Map.Entry> entry : collated.entrySet())
//每个node节点只会构造一个ClientRequest
requests.add(produceRequest(now, entry.getKey(), acks, requestTimeout, entry.getValue()));
return requests;
} private ClientRequest produceRequest(long now, int destination, short acks, int timeout, List batches) {
//一个分区和该分区的
Map produceRecordsByPartition = new HashMap(batches.size());
//某个分区和对应该分区的RecordBatch映射
final Map recordsByPartition = new HashMap(batches.size());
//将RecordBatch列表按照partition进行分类，整理为上面2个Map的key,value
for (RecordBatch batch : batches) {
TopicPartition tp = batch.topicPartition;
produceRecordsByPartition.put(tp, batch.records.buffer());
recordsByPartition.put(tp, batch);
}
ProduceRequest request = new ProduceRequest(acks, timeout, produceRecordsByPartition);
RequestSend send = new RequestSend(Integer.toString(destination),
this.client.nextRequestHeader(ApiKeys.PRODUCE),
request.toStruct());
//创建回调函数
RequestCompletionHandler callback = new RequestCompletionHandler() {
public void onComplete(ClientResponse response) {
handleProduceResponse(response, recordsByPartition, time.milliseconds());
}
};
return new ClientRequest(now, acks != 0, send, callback);
} 至于ClientRequest后面如何和kafka server进行IO的，鉴于篇幅原因，本文到此截止，基本上大流程已经清楚了，后续会分几个小节分别对涉及的类进行细节分析，敬请期待。