kafka 源码分析 3 : Producer

Posted 2021-04-05 ImportNew

来源：刘正阳 ，

liuzhengyang.github.io/2017/12/31/kafka-source-3-kafka-producer/

Producer

Producer是生产者的接口定义

常用的方法有

public Future<RecordMetadata> send(ProducerRecord<K, V> record);

public Future<RecordMetadata> send(ProducerRecord<K, V> record, Callback callback);

public void flush();

public void close();

KafkaProducer是异步的，调用send方法后，kafka并没有立即发送给broker，而是先放在buffer缓冲池中就立即返回，后台的IO线程来负责把消息记录转换成请求发送给kafka集群。

buffer大小通过batch.size配置置顶，producer维护每个partition的没有发送记录的buffer。

默认情况下不满的buffer也是可以发送的，可以通过linger.ms来设置等待时间减少请求数量，跟TCP中的Nagle算法是一个道理。

producer的总的buffer大小可以通过buffer.memory控制，如果生产太快来不及发送超过了这个值则会block住，block的最大时间通过max.block.ms，超时后会抛出TimeoutException

key.serialize和value.serializer控制如何把Java对象转换成byte数组传输给kafka集群。

acks控制producer什么时候认为写成功了，数量是需要leader获得的ack的数量。acks=0时producer把消息记录放到socket buffer中就认为成功了;acks=1时，需要leader成功写到本地就返回，但是不需要等待follower的ack。acks=all是，需要所有的in-sync replica都返回ack才认为是发送成功，这样只要有一个in-sync replica存活消息就没有丢。

Partitioner负责决定将哪一个消息写入到哪一个partition, 有一些场景希望特定的key发送到特定的partition时可以指定自己实现的Paritioner。

默认的Partitioner是随机负载均衡的。

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) {

           int nextValue = nextValue(topic);

           List<PartitionInfo> availablePartitions = cluster.availablePartitionsForTopic(topic);

           if (!availablePartitions.isEmpty()) {

               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;

           }

       } else {

           // hash the keyBytes to choose a partition

           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();

   }

ProducerRecord

ProducerRecord包含了发送给Broker需要的内容

class ProducerRecord<K, V> {

    private final String topic;

    private final Integer partition;

    private final Headers headers;

    private final K key;

    private final V value;

    private final Long timestamp;

}

KafkaProducer构建过程

 

// 创建partitioner

this.partitioner = config.getConfiguredInstance(ProducerConfig.PARTITIONER_CLASS_CONFIG, Partitioner.class);

long retryBackoffMs = config.getLong(ProducerConfig.RETRY_BACKOFF_MS_CONFIG);

// 配置序列化

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<ProducerInterceptor<K, V>> 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);

int retries = configureRetries(config, transactionManager != null);

int maxInflightRequests = configureInflightRequests(config, transactionManager != null);

short acks = configureAcks(config, transactionManager != null);

this.apiVersions = new ApiVersions();

// RecordAccumulator中实现了累加和等待的逻辑

this.accumulator = new RecordAccumulator(config.getInt(ProducerConfig.BATCH_SIZE_CONFIG),

        this.totalMemorySize,

        this.compressionType,

        config.getLong(ProducerConfig.LINGER_MS_CONFIG),

        retryBackoffMs,

        metrics,

        time,

        apiVersions,

        transactionManager);

List<InetSocketAddress> addresses = ClientUtils.parseAndValidateAddresses(config.getList(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG));

this.metadata.update(Cluster.bootstrap(addresses), Collections.<String>emptySet(), time.milliseconds());

ChannelBuilder channelBuilder = ClientUtils.createChannelBuilder(config);

Sensor throttleTimeSensor = Sender.throttleTimeSensor(metrics);

// 高层的网络处理，封装了send、poll等接口

NetworkClient client = new NetworkClient(

        new Selector(config.getLong(ProducerConfig.CONNECTIONS_MAX_IDLE_MS_CONFIG),

                this.metrics, time, "producer", channelBuilder),

        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);

// 负责实际发送请求给kafka集群的后台线程

this.sender = new Sender(client,

        this.metadata,

        this.accumulator,

        maxInflightRequests == 1,

        config.getInt(ProducerConfig.MAX_REQUEST_SIZE_CONFIG),

        acks,

        retries,

        this.metrics,

        Time.SYSTEM,

        this.requestTimeoutMs,

        config.getLong(ProducerConfig.RETRY_BACKOFF_MS_CONFIG),

        this.transactionManager,

        apiVersions);

String ioThreadName = NETWORK_THREAD_PREFIX + (clientId.length() > 0 ? " | " + 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);

log.debug("Kafka producer started");

KafkaProducer#send

入口在doSend(interceptedRecord, callback);

// 获取cluster信息， 来得到对应topic的cluster节点信息

ClusterAndWaitTime clusterAndWaitTime = waitOnMetadata(record.topic(), record.partition(), maxBlockTimeMs);

long remainingWaitMs = Math.max(0, maxBlockTimeMs - clusterAndWaitTime.waitedOnMetadataMs);

   Cluster cluster = clusterAndWaitTime.cluster;

   byte[] serializedKey;

   try {

       serializedKey = keySerializer.serialize(record.topic(), record.headers(), 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");

   }

   byte[] serializedValue;

   try {

       serializedValue = valueSerializer.serialize(record.topic(), record.headers(), 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");

   }

   // 找到对应的partition

   int partition = partition(record, serializedKey, serializedValue, cluster);

   tp = new TopicPartition(record.topic(), partition);

   setReadOnly(record.headers());

   Header[] headers = record.headers().toArray();

   int serializedSize = AbstractRecords.estimateSizeInBytesUpperBound(apiVersions.maxUsableProduceMagic(),

           compressionType, serializedKey, serializedValue, headers);

   ensureValidRecordSize(serializedSize);

   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);

   if (transactionManager != null && transactionManager.isTransactional())

       transactionManager.maybeAddPartitionToTransaction(tp);

   // 追加到RecordAccumulator中

   RecordAccumulator.RecordAppendResult result = accumulator.append(tp, timestamp, serializedKey,

           serializedValue, headers, interceptCallback, remainingWaitMs);

   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;

RecordAccumulator

使用双端队列Deque保存ProducerBatch

// We keep track of the number of appending thread to make sure we do not miss batches in

   // abortIncompleteBatches().

   appendsInProgress.incrementAndGet();

   ByteBuffer buffer = null;

   if (headers == null) headers = Record.EMPTY_HEADERS;

   try {

       // check if we have an in-progress batch

       // 获取或创建对应TopicPartition的队列

       Deque<ProducerBatch> dq = getOrCreateDeque(tp);

       synchronized (dq) {

           if (closed)

               throw new IllegalStateException("Cannot send after the producer is closed.");

           // 如果最后一个节点能加入就加入返回

           RecordAppendResult appendResult = tryAppend(timestamp, key, value, headers, callback, dq);

           if (appendResult != null)

               return appendResult;

       }

       // 加入不了就要新申请一个

       // we don't have an in-progress record batch try to allocate a new batch

       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) {

           // 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.");

           // 这两个同步块间可能有其他线程已经创建了下一个Batch

           RecordAppendResult appendResult = tryAppend(timestamp, key, value, headers, callback, dq);

           if (appendResult != null) {

               // Somebody else found us a batch, return the one we waited for! Hopefully this doesn't happen often...

               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);

           // Don't deallocate this buffer in the finally block as it's being used in the record batch

           buffer = null;

           return new RecordAppendResult(future, dq.size() > 1 || batch.isFull(), true);

       }

   } finally {

       if (buffer != null)

           free.deallocate(buffer);

       appendsInProgress.decrementAndGet();

   }

Sender

Sender是一个后台线程, 不考虑事务的话，只分为senProducerDat和poll, poll中等待处理返回结果

void run(long now) {

       if (transactionManager != null) {

           if (!transactionManager.isTransactional()) {

               // this is an idempotent producer, so make sure we have a producer id

               maybeWaitForProducerId();

           } else if (transactionManager.hasInFlightRequest() || 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());

           }

       }

       long pollTimeout = sendProducerData(now);

       client.poll(pollTimeout, now);

   }

```

private long sendProducerData(long now) {

//

Cluster cluster = metadata.fetch();

// 获取准备好发送的数据，包括各个TopicParition的队列，其中队列长度大于1、第一个batch满了、没有缓存buffer空间了、正在关闭、在调用flush都会刷新待发送数据。

// get the list of partitions with data ready to send

RecordAccumulator.ReadyCheckResult result = this.accumulator.ready(cluster, now);

 

    // 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<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));

        }

    }

 

    // 从队列中取出

    // create produce requests

    Map<Integer, List<ProducerBatch>> batches = this.accumulator.drain(cluster, result.readyNodes,

            this.maxRequestSize, now);

    if (guaranteeMessageOrder) {

        // Mute all the partitions drained

        for (List<ProducerBatch> batchList : batches.values()) {

            for (ProducerBatch batch : batchList)

                this.accumulator.mutePartition(batch.topicPartition);

        }

    }

 

    List<ProducerBatch> expiredBatches = this.accumulator.expiredBatches(this.requestTimeout, now);

    boolean needsTransactionStateReset = false;

    // Reset the producer id if an expired batch has previously been sent to the broker. Also update the metrics

    // for expired batches. see the documentation of @TransactionState.resetProducerId to understand why

    // we need to reset the producer id here.

    if (!expiredBatches.isEmpty())

        log.trace("Expired {} batches in accumulator", expiredBatches.size());

    for (ProducerBatch expiredBatch : expiredBatches) {

        failBatch(expiredBatch, -1, NO_TIMESTAMP, expiredBatch.timeoutException());

        if (transactionManager != null && expiredBatch.inRetry()) {

            needsTransactionStateReset = true;

        }

        this.sensors.recordErrors(expiredBatch.topicPartition.topic(), expiredBatch.recordCount);

    }

 

    if (needsTransactionStateReset) {

        transactionManager.resetProducerId();

        return 0;

    }

 

    sensors.updateProduceRequestMetrics(batches);

 

    // 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.isEmpty()) {

        log.trace("Nodes with data ready to send: {}", result.readyNodes);

        // 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;

        pollTimeout = 0;

    }

    sendProduceRequests(batches, now);

 

    return pollTimeout;

}

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