kafka实践（十二）：生产者（KafkaProducer）源码详解和调试
本节对producer的源码解析以熟悉生产者数据发送过程，关于使用Idea对kafka源码编译和调试，可以翻看之前的博客：本地kafka源码的编译和调试，本次分析的版本是kafka-1.0.0；
一、环境准备
在前面已经完成win环境下zk（3.4.12版本）的运行，并对kafka源码编译， 参考：本地kafka源码的编译和调试，在idea的run-->debug-->中新增configuration来创建topic：yzg（3分区1备份），本地启动运行效果：
二、生产过程和KafkaProducer类解析
KafkaProducer在 org.apache.kafka.clients.producer的包下（所有关于生产者源码都在这包），在使用生产者类时要实例化KafkaProducer，其中定义了发送机制，KafkaProducer是Producer的子类，生产者实例（producer）通过实例化KafkaProducer类，并调用它的send()方法完成数据发送，梳理如下：
① 首先过一个拦截器；
② 调用KafkaProducer.send().doSend()方法，doSend首先把key和value按照指定的序列化器进行序列化；
③ partition()函数得到数据和序列化后的数据后，对数据进行分区；
④ 调用RecordAccumulator.append()方法，将处理后的数据扔进RecordAccumulator（缓存对象)的RecordAppendResult类属性中；
⑤ RecordAccumulator.append()方法首先将数据进行队列化放在Deque对象中，Deque包含多个ProducerBatch；
⑥ 上面流程完成后，调用this.sender.wakeup()唤醒sender线程，该线程就干一件事就是发数据，
KafkaProducer类的构造函数如下，在生产者实例传入集群config和序列化器后(暂未传入topic名称)，KafkaProducer实例化后完成所有相关属性的实例化，主要的对象有
private KafkaProducer(ProducerConfig config, Serializer keySerializer, Serializer valueSerializer) {
try {
Map userProvidedConfigs = config.originals();
this.producerConfig = config;
this.time = Time.SYSTEM;
String clientId = config.getString(ProducerConfig.CLIENT_ID_CONFIG);
if (clientId.length() <= 0)
clientId = "producer-" + PRODUCER_CLIENT_ID_SEQUENCE.getAndIncrement();
this.clientId = clientId;
String transactionalId = userProvidedConfigs.containsKey(ProducerConfig.TRANSACTIONAL_ID_CONFIG) ?
(String) userProvidedConfigs.get(ProducerConfig.TRANSACTIONAL_ID_CONFIG) : null;
LogContext logContext;
if (transactionalId == null)
logContext = new LogContext(String.format("[Producer clientId=%s] ", clientId));
else
logContext = new LogContext(String.format("[Producer clientId=%s, transactionalId=%s] ", clientId, transactionalId));
log = logContext.logger(KafkaProducer.class);
log.trace("Starting the Kafka producer");
Map metricTags = Collections.singletonMap("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)
.recordLevel(Sensor.RecordingLevel.forName(config.getString(ProducerConfig.METRICS_RECORDING_LEVEL_CONFIG)))
.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);
ProducerMetrics metricsRegistry = new ProducerMetrics(this.metrics);
this.partitioner = config.getConfiguredInstance(ProducerConfig.PARTITIONER_CLASS_CONFIG, Partitioner.class);
long retryBackoffMs = config.getLong(ProducerConfig.RETRY_BACKOFF_MS_CONFIG);
if (keySerializer == null) {
this.keySerializer = ensureExtended(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 = ensureExtended(keySerializer);
}
if (valueSerializer == null) {
this.valueSerializer = ensureExtended(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 = ensureExtended(valueSerializer);
}
// load interceptors and make sure they get clientId
userProvidedConfigs.put(ProducerConfig.CLIENT_ID_CONFIG, clientId);
List> interceptorList = (List) (new ProducerConfig(userProvidedConfigs, false)).getConfiguredInstances(ProducerConfig.INTERCEPTOR_CLASSES_CONFIG,
ProducerInterceptor.class);
this.interceptors = interceptorList.isEmpty() ? null : new ProducerInterceptors<>(interceptorList);
ClusterResourceListeners clusterResourceListeners = configureClusterResourceListeners(keySerializer, valueSerializer, interceptorList, reporters);
this.metadata = new Metadata(retryBackoffMs, config.getLong(ProducerConfig.METADATA_MAX_AGE_CONFIG),
true, true, clusterResourceListeners);
this.maxRequestSize = config.getInt(ProducerConfig.MAX_REQUEST_SIZE_CONFIG);
this.totalMemorySize = config.getLong(ProducerConfig.BUFFER_MEMORY_CONFIG);
this.compressionType = CompressionType.forName(config.getString(ProducerConfig.COMPRESSION_TYPE_CONFIG));
this.maxBlockTimeMs = config.getLong(ProducerConfig.MAX_BLOCK_MS_CONFIG);
this.requestTimeoutMs = config.getInt(ProducerConfig.REQUEST_TIMEOUT_MS_CONFIG);
this.transactionManager = configureTransactionState(config, logContext, log);
int retries = configureRetries(config, transactionManager != null, log);
int maxInflightRequests = configureInflightRequests(config, transactionManager != null);
short acks = configureAcks(config, transactionManager != null, log);
this.apiVersions = new ApiVersions();
this.accumulator = new RecordAccumulator(logContext,
config.getInt(ProducerConfig.BATCH_SIZE_CONFIG),
this.totalMemorySize,
this.compressionType,
config.getLong(ProducerConfig.LINGER_MS_CONFIG),
retryBackoffMs,
metrics,
time,
apiVersions,
transactionManager);
List addresses = ClientUtils.parseAndValidateAddresses(config.getList(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG));
this.metadata.update(Cluster.bootstrap(addresses), Collections.emptySet(), time.milliseconds());
ChannelBuilder channelBuilder = ClientUtils.createChannelBuilder(config);
Sensor throttleTimeSensor = Sender.throttleTimeSensor(metricsRegistry.senderMetrics);
NetworkClient client = new NetworkClient(
new Selector(config.getLong(ProducerConfig.CONNECTIONS_MAX_IDLE_MS_CONFIG),
this.metrics, time, "producer", channelBuilder, logContext),
this.metadata,
clientId,
maxInflightRequests,
config.getLong(ProducerConfig.RECONNECT_BACKOFF_MS_CONFIG),
config.getLong(ProducerConfig.RECONNECT_BACKOFF_MAX_MS_CONFIG),
config.getInt(ProducerConfig.SEND_BUFFER_CONFIG),
config.getInt(ProducerConfig.RECEIVE_BUFFER_CONFIG),
this.requestTimeoutMs,
time,
true,
apiVersions,
throttleTimeSensor,
logContext);
this.sender = new Sender(logContext,
client,
this.metadata,
this.accumulator,
maxInflightRequests == 1,
config.getInt(ProducerConfig.MAX_REQUEST_SIZE_CONFIG),
acks,
retries,
metricsRegistry.senderMetrics,
Time.SYSTEM,
this.requestTimeoutMs,
config.getLong(ProducerConfig.RETRY_BACKOFF_MS_CONFIG),
this.transactionManager,
apiVersions);
String ioThreadName = NETWORK_THREAD_PREFIX + " | " + clientId;
this.ioThread = new KafkaThread(ioThreadName, this.sender, true);
this.ioThread.start();
this.errors = this.metrics.sensor("errors");
config.logUnused();
AppInfoParser.registerAppInfo(JMX_PREFIX, clientId, metrics);
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);
}
}
1、数据预处理（拦截器、序列化、分区器、缓存）
① 生产者producer在拿到props后实例化KafkaProducer，然后多线程调用send()，KafkaProducer如果没有定义拦截器interceptors（ProducerInterceptors类的实例）数据record保持不变，若定义了interceptors就调用拦截器的ProducerInterceptors.onSend()方法过滤数据record，这个拦截器就是用来自定义的，源码里面没有过滤方法；
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);
} ② 接下来调用doSend方法，方法体内首先调用partition()方法，入参是原始的record数据，以及key和value序列化结果；
// 确认topic和集群信息正确
ClusterAndWaitTime clusterAndWaitTime = waitOnMetadata(record.topic(), record.partition(), maxBlockTimeMs);
Cluster cluster = clusterAndWaitTime.cluster;
// 分区器
int partition = partition(record, serializedKey, serializedValue, cluster);
tp = new TopicPartition(record.topic(), partition);
//如果没有指定分区，就使用内置的分区器partitioner.partition()
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);
} ③ 接下来调用KafkaProducer类持有的RecordAccumulator对象的RecordAccumulator.append()方法，返回RecordAppendResult对象；
RecordAccumulator.RecordAppendResult result = accumulator.append(tp, timestamp, serializedKey,
serializedValue, headers, interceptCallback, remainingWaitMs);
// append方法的实现，返回RecordAppendResult 对象
public RecordAppendResult append(TopicPartition tp,
long timestamp,
byte[] key,
byte[] value,
Header[] headers,
Callback callback,
long maxTimeToBlock) throws InterruptedException {
appendsInProgress.incrementAndGet();
ByteBuffer buffer = null;
if (headers == null) headers = Record.EMPTY_HEADERS;
try {
Deque dq = getOrCreateDeque(tp);
synchronized (dq) {
if (closed)
throw new IllegalStateException("Cannot send after the producer is closed.");
RecordAppendResult appendResult = tryAppend(timestamp, key, value, headers, callback, dq);
if (appendResult != null)
return appendResult;
}
byte maxUsableMagic = apiVersions.maxUsableProduceMagic();
int size = Math.max(this.batchSize, AbstractRecords.estimateSizeInBytesUpperBound(maxUsableMagic, compression, key, value, headers));
log.trace("Allocating a new {} byte message buffer for topic {} partition {}", size, tp.topic(), tp.partition());
buffer = free.allocate(size, maxTimeToBlock);
synchronized (dq) {
if (closed)
throw new IllegalStateException("Cannot send after the producer is closed.");
RecordAppendResult appendResult = tryAppend(timestamp, key, value, headers, callback, dq);
if (appendResult != null) {
return appendResult;
}
MemoryRecordsBuilder recordsBuilder = recordsBuilder(buffer, maxUsableMagic);
ProducerBatch batch = new ProducerBatch(tp, recordsBuilder, time.milliseconds());
FutureRecordMetadata future = Utils.notNull(batch.tryAppend(timestamp, key, value, headers, callback, time.milliseconds()));
dq.addLast(batch);
incomplete.add(batch);
buffer = null;
return new RecordAppendResult(future, dq.size() > 1 || batch.isFull(), true);
}
} finally {
if (buffer != null)
free.deallocate(buffer);
appendsInProgress.decrementAndGet();
}
} ④ 继续上面的代码，生产者本地维护一个未发送数据的缓存池，也是一个后台IO线程用来将records转换为网络请求，这就是RecordAccumulator，RecordAccumulator持有RecordAppendResult对象，其future作为整个producer.send()方法的返回值；
public final static class RecordAppendResult {
public final FutureRecordMetadata future;
public final boolean batchIsFull;
public final boolean newBatchCreated;
public RecordAppendResult(FutureRecordMetadata future, boolean batchIsFull, boolean newBatchCreated) {
this.future = future;
this.batchIsFull = batchIsFull;
this.newBatchCreated = newBatchCreated;
}
}RecordAccumulator通过getOrCreateDeque(tp)得到deque队列（持有ProducerBatch对象），ProducerBatch是最小的发送数据实体，RecordAccumulator计算字节数并分配本地资源，不断往deque队列新增ProducerBatch对象；
Deque
dq = getOrCreateDeque(tp);
至此 KafkaProducer.send()方法的逻辑结束，也就是原始数据经过逻辑转换后放在本地的Deque队列中；
2、sender线程处理
在KafkaProducer实例化后sender也被实例化，KafkaProducer.send().doSend()会通过this.sender.wakeup()把线程方法启动，它持有一个NetworkClient实例，sender实例的run()方法包含对NetworkClient的处理逻辑，
// 网络请求的构造器
NetworkClient client = new NetworkClient(
new Selector(config.getLong(ProducerConfig.CONNECTIONS_MAX_IDLE_MS_CONFIG),
this.metrics, time, "producer", channelBuilder, logContext),
this.metadata,
clientId,
maxInflightRequests,
config.getLong(ProducerConfig.RECONNECT_BACKOFF_MS_CONFIG),
config.getLong(ProducerConfig.RECONNECT_BACKOFF_MAX_MS_CONFIG),
config.getInt(ProducerConfig.SEND_BUFFER_CONFIG),
config.getInt(ProducerConfig.RECEIVE_BUFFER_CONFIG),
this.requestTimeoutMs,
time,
true,
apiVersions,
throttleTimeSensor,
logContext);
// run方法中对于网络请求的逻辑
void run(long now) {
if (transactionManager != null) {
try {
if (transactionManager.shouldResetProducerStateAfterResolvingSequences())
// Check if the previous run expired batches which requires a reset of the producer state.
transactionManager.resetProducerId();
if (!transactionManager.isTransactional()) {
// this is an idempotent producer, so make sure we have a producer id
maybeWaitForProducerId();
} else if (transactionManager.hasUnresolvedSequences() && !transactionManager.hasFatalError()) {
transactionManager.transitionToFatalError(new KafkaException("The client hasn't received acknowledgment for " +
"some previously sent messages and can no longer retry them. It isn't safe to continue."));
} else if (transactionManager.hasInFlightTransactionalRequest() || maybeSendTransactionalRequest(now)) {
// as long as there are outstanding transactional requests, we simply wait for them to return
client.poll(retryBackoffMs, now);
return;
}
// do not continue sending if the transaction manager is in a failed state or if there
// is no producer id (for the idempotent case).
if (transactionManager.hasFatalError() || !transactionManager.hasProducerId()) {
RuntimeException lastError = transactionManager.lastError();
if (lastError != null)
maybeAbortBatches(lastError);
client.poll(retryBackoffMs, now);
return;
} else if (transactionManager.hasAbortableError()) {
accumulator.abortUndrainedBatches(transactionManager.lastError());
}
} catch (AuthenticationException e) {
// This is already logged as error, but propagated here to perform any clean ups.
log.trace("Authentication exception while processing transactional 三、生产者demo使用和调试
源码编译运行后相当于本地搭建了kafka集群，在源码examples包下 producer类来了解数据发送流程，首先定义kafka提供的KafkaProducer类，再调用它的send(）方法发送数据；很多工作是在KafkaProducer类实例化的时候已经做了；
producer类需定义key和value、topic名称、同步或者异步，然后构造器指定kafka集群地址，生产者id（可选），序列化器
producer线程类的执行方法（while死循环），判断是异步还是同步发送配置（高版本默认都异步），调用send方法发送数据,send方法的第1个参数是ProducerRecord，第2个是messageNo记录发送批次，第3个是数据record，DemoCallBack是回执类（不是函数）
callback类有开始时间、自增的messageno、messageStr字符串三个参数，并重写onCompletion方法来定义异常；
稍微修改这个实现类进行调试，集群是idea在运行的本地集群（127.0.0.1:9092），指定的topic是yezonggang，异步发送，producer.send()方法写在线程方法内调用，如下：
package demo;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerRecord;
import java.util.Properties;
import java.util.concurrent.ExecutionException;
public class DemoForProducer extends Thread{
public static void main(String[] args) {
//System.out.println("hello");
DemoForProducer dfp=new DemoForProducer("yezonggang",true);
dfp.run();
}
private final KafkaProducer producer;
private final String topic;
private final Boolean isAsync;
public DemoForProducer(String topic, Boolean isAsync) {
Properties props = new Properties();
props.put("bootstrap.servers", "127.0.0.1:9092");
props.put("client.id", "DemoForProducer");
props.put("key.serializer", "org.apache.kafka.common.serialization.IntegerSerializer");
props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
producer = new KafkaProducer<>(props);
this.topic = topic;
this.isAsync = isAsync;
}
public DemoForProducer(KafkaProducer producer, String topic, Boolean isAsync) {
this.producer = producer;
this.topic = topic;
this.isAsync = isAsync;
}
// 线程类的执行方法（while死循环），判断是异步还是同步发送配置（高版本默认都异步），调用send方法发送数据,send方法的第1个参数是ProducerRecord，第2个是messageNo记录发送批次，DemoCallBack是回执函数
public void run() {
int messageNo = 1;
while (true) {
String messageStr = "Message_" + messageNo;
long startTime = System.currentTimeMillis();
if (isAsync) { // Send asynchronously
producer.send(new ProducerRecord<>(topic,
messageNo,
messageStr), new DemoForCallBack(startTime, messageNo, messageStr));
} else { // Send synchronously
try {
producer.send(new ProducerRecord<>(topic,
messageNo,
messageStr)).get();
System.out.println("Sent message: (" + messageNo + ", " + messageStr + ")");
} catch (InterruptedException | ExecutionException e) {
e.printStackTrace();
}
}
++messageNo;
}
}
}
在 KafkaProducer类实例化后，idea中运行的本地kafka集群就已经拿到了producerconfig设置，如下，client.id=DemoForProducer已经说明该生产者已经被kafka集群捕获，即使当前send()方法还未启动；
ProducerRecord(topic=yezonggang, partition=null, headers=RecordHeaders(headers = [], isReadOnly = false), key=1, value=Message_1, timestamp=null)
INFO ProducerConfig values:
acks = 1
batch.size = 16384
bootstrap.servers = [127.0.0.1:9092]
buffer.memory = 33554432
client.id = DemoForProducer
compression.type = none
connections.max.idle.ms = 540000
enable.idempotence = false
interceptor.classes = null
key.serializer = class org.apache.kafka.common.serialization.IntegerSerializer
linger.ms = 0
max.block.ms = 60000
max.in.flight.requests.per.connection = 5
max.request.size = 1048576
metadata.max.age.ms = 300000
metric.reporters = []
metrics.num.samples = 2
metrics.recording.level = INFO
metrics.sample.window.ms = 30000
partitioner.class = class org.apache.kafka.clients.producer.internals.DefaultPartitioner
receive.buffer.bytes = 32768
reconnect.backoff.max.ms = 1000
reconnect.backoff.ms = 50
request.timeout.ms = 30000
retries = 0
retry.backoff.ms = 100
sasl.jaas.config = null
sasl.kerberos.kinit.cmd = /usr/bin/kinit
sasl.kerberos.min.time.before.relogin = 60000
sasl.kerberos.service.name = null
sasl.kerberos.ticket.renew.jitter = 0.05
sasl.kerberos.ticket.renew.window.factor = 0.8
sasl.mechanism = GSSAPI
security.protocol = PLAINTEXT
send.buffer.bytes = 131072
ssl.cipher.suites = null
ssl.enabled.protocols = [TLSv1.2, TLSv1.1, TLSv1]
ssl.endpoint.identification.algorithm = null
ssl.key.password = null
ssl.keymanager.algorithm = SunX509
ssl.keystore.location = null
ssl.keystore.password = null
ssl.keystore.type = JKS
ssl.protocol = TLS
ssl.provider = null
ssl.secure.random.implementation = null
ssl.trustmanager.algorithm = PKIX
ssl.truststore.location = null
ssl.truststore.password = null
ssl.truststore.type = JKS
transaction.timeout.ms = 60000
transactional.id = null
value.serializer = class org.apache.kafka.common.serialization.StringSerializer(org.apache.kafka.clients.producer.ProducerConfig)