Flink - FlinkKafkaConsumer010

Properties properties = new Properties();
properties.setProperty("bootstrap.servers", "localhost:9092");
// only required for Kafka 0.8
properties.setProperty("zookeeper.connect", "localhost:2181");
properties.setProperty("group.id", "test");
DataStream<String> stream = env
    .addSource(new FlinkKafkaConsumer08<>("topic", new SimpleStringSchema(), properties));

使用方式如上，核心就是对SourceFunction的实现

FlinkKafkaConsumer010除了重写createFetcher外，大部分都是继承自FlinkKafkaConsumerBase

FlinkKafkaConsumerBase

    public abstract class FlinkKafkaConsumerBase<T> extends RichParallelSourceFunction<T> implements
        CheckpointListener,
        ResultTypeQueryable<T>,
        CheckpointedFunction,
        CheckpointedRestoring<HashMap<KafkaTopicPartition, Long>>

FlinkKafkaConsumerBase继承RichParallelSourceFunction，实现4个接口

RichFunction.open

先看看FlinkKafkaConsumerBase初始化，

    @Override
    public void open(Configuration configuration) {
        // determine the offset commit mode
        // offsetCommitMode有三种，ON_CHECKPOINTS，KAFKA_PERIODIC，DISABLED；如果打开checkpoint，offest会记录在snapshot中，否则offset会定期写回kafka topic
        offsetCommitMode = OffsetCommitModes.fromConfiguration(
                getIsAutoCommitEnabled(),
                enableCommitOnCheckpoints,
                ((StreamingRuntimeContext) getRuntimeContext()).isCheckpointingEnabled());

        // initialize subscribed partitions
        List<KafkaTopicPartition> kafkaTopicPartitions = getKafkaPartitions(topics); //获取topic的partition信息

        subscribedPartitionsToStartOffsets = new HashMap<>(kafkaTopicPartitions.size()); //Map<KafkaTopicPartition, Long>，用于记录每个partition的offset

        if (restoredState != null) { //如果有可恢复的state
            for (KafkaTopicPartition kafkaTopicPartition : kafkaTopicPartitions) {
                if (restoredState.containsKey(kafkaTopicPartition)) { //如果state中包含该partition
                    subscribedPartitionsToStartOffsets.put(kafkaTopicPartition, restoredState.get(kafkaTopicPartition)); //将snapshot中的offset恢复
                }
            }

        } else { //如果没有state，那么初始化subscribedPartitionsToStartOffsets
            initializeSubscribedPartitionsToStartOffsets(
                subscribedPartitionsToStartOffsets,
                kafkaTopicPartitions,
                getRuntimeContext().getIndexOfThisSubtask(),
                getRuntimeContext().getNumberOfParallelSubtasks(),
                startupMode,
                specificStartupOffsets);
            //startupMode，有下面几种，默认是GROUP_OFFSETS
            if (subscribedPartitionsToStartOffsets.size() != 0) {
                switch (startupMode) {
                    case EARLIEST: //从最早的开始读
                        LOG.info("Consumer subtask {} will start reading the following {} partitions from the earliest offsets: {}",
                            getRuntimeContext().getIndexOfThisSubtask(),
                            subscribedPartitionsToStartOffsets.size(),
                            subscribedPartitionsToStartOffsets.keySet());
                        break;
                    case LATEST: //从最新的开始读
                        LOG.info("Consumer subtask {} will start reading the following {} partitions from the latest offsets: {}",
                            getRuntimeContext().getIndexOfThisSubtask(),
                            subscribedPartitionsToStartOffsets.size(),
                            subscribedPartitionsToStartOffsets.keySet());
                        break;
                    case SPECIFIC_OFFSETS: //从特定的offset开始读
                        LOG.info("Consumer subtask {} will start reading the following {} partitions from the specified startup offsets {}: {}",
                            getRuntimeContext().getIndexOfThisSubtask(),
                            subscribedPartitionsToStartOffsets.size(),
                            specificStartupOffsets,
                            subscribedPartitionsToStartOffsets.keySet());

                        List<KafkaTopicPartition> partitionsDefaultedToGroupOffsets = new ArrayList<>(subscribedPartitionsToStartOffsets.size());
                        for (Map.Entry<KafkaTopicPartition, Long> subscribedPartition : subscribedPartitionsToStartOffsets.entrySet()) {
                            if (subscribedPartition.getValue() == KafkaTopicPartitionStateSentinel.GROUP_OFFSET) {
                                partitionsDefaultedToGroupOffsets.add(subscribedPartition.getKey());
                            }
                        }

                        if (partitionsDefaultedToGroupOffsets.size() > 0) { //说明你有某些partitions没有指定offset，所以用了默认的GROUP_OFFSET
                            LOG.warn("Consumer subtask {} cannot find offsets for the following {} partitions in the specified startup offsets: {}" +
                                    "; their startup offsets will be defaulted to their committed group offsets in Kafka.",
                                getRuntimeContext().getIndexOfThisSubtask(),
                                partitionsDefaultedToGroupOffsets.size(),
                                partitionsDefaultedToGroupOffsets);
                        }
                        break;
                    default:
                    case GROUP_OFFSETS: //根据kafka group中的offset开始读
                        LOG.info("Consumer subtask {} will start reading the following {} partitions from the committed group offsets in Kafka: {}",
                            getRuntimeContext().getIndexOfThisSubtask(),
                            subscribedPartitionsToStartOffsets.size(),
                            subscribedPartitionsToStartOffsets.keySet());
                }
            }
        }
    }

    protected static void initializeSubscribedPartitionsToStartOffsets(
            Map<KafkaTopicPartition, Long> subscribedPartitionsToStartOffsets,
            List<KafkaTopicPartition> kafkaTopicPartitions,
            int indexOfThisSubtask,
            int numParallelSubtasks,
            StartupMode startupMode,
            Map<KafkaTopicPartition, Long> specificStartupOffsets) {

        for (int i = 0; i < kafkaTopicPartitions.size(); i++) {
            if (i % numParallelSubtasks == indexOfThisSubtask) { //如果这个partition会分配到该task；所以只有被分配到的partition会有offset数据，这里实际做了partition的分配
                if (startupMode != StartupMode.SPECIFIC_OFFSETS) { //如果不是SPECIFIC_OFFSETS，就把offset设为特定的常量值
                    subscribedPartitionsToStartOffsets.put(kafkaTopicPartitions.get(i), startupMode.getStateSentinel());
                } else {

                    KafkaTopicPartition partition = kafkaTopicPartitions.get(i);

                    Long specificOffset = specificStartupOffsets.get(partition);
                    if (specificOffset != null) {
                        // since the specified offsets represent the next record to read, we subtract
                        // it by one so that the initial state of the consumer will be correct
                        subscribedPartitionsToStartOffsets.put(partition, specificOffset - 1); //设置上你配置的partition对应的offset，注意需要减一
                    } else { //如果没有该partition的offset，就用默认的GROUP_OFFSET
                        subscribedPartitionsToStartOffsets.put(partition, KafkaTopicPartitionStateSentinel.GROUP_OFFSET);
                    }
                }
            }
        }
    }

初始化的工作主要是恢复和初始化，topic partition的offset

RichParallelSourceFunction

核心run函数，

    @Override
    public void run(SourceContext<T> sourceContext) throws Exception {

        // we need only do work, if we actually have partitions assigned
        if (!subscribedPartitionsToStartOffsets.isEmpty()) {

            // create the fetcher that will communicate with the Kafka brokers
            final AbstractFetcher<T, ?> fetcher = createFetcher(
                    sourceContext, //sourceContext，用于发送record和watermark
                    subscribedPartitionsToStartOffsets, //partition，offset对应关系
                       periodicWatermarkAssigner,
                    punctuatedWatermarkAssigner,
                    (StreamingRuntimeContext) getRuntimeContext(),
                    offsetCommitMode); //offsetCommitMode有三种，ON_CHECKPOINTS，KAFKA_PERIODIC，DISABLED

            // publish the reference, for snapshot-, commit-, and cancel calls
            // IMPORTANT: We can only do that now, because only now will calls to
            //            the fetchers 'snapshotCurrentState()' method return at least
            //            the restored offsets
            this.kafkaFetcher = fetcher;
            if (!running) {
                return;
            }

            // (3) run the fetcher' main work method
            fetcher.runFetchLoop();
        }
    }

主要就是创建Fetcher，并启动，Fetcher中做了具体的工作

创建Fetcher的参数都中大多比较容易理解， 除了，

periodicWatermarkAssigner

punctuatedWatermarkAssigner

这些是用来产生watermark的，参考 Flink - watermark

CheckpointedFunction 接口

主要实现，initializeState，snapshotState函数

initializeState，目的就是从stateBackend中把offset state恢复到restoredState；这个数据在open时候会被用到

    @Override
    public void initializeState(FunctionInitializationContext context) throws Exception {

        OperatorStateStore stateStore = context.getOperatorStateStore();
        offsetsStateForCheckpoint = stateStore.getSerializableListState(DefaultOperatorStateBackend.DEFAULT_OPERATOR_STATE_NAME); //从StateBackend读出state

        if (context.isRestored()) {
            if (restoredState == null) {
                restoredState = new HashMap<>();
                for (Tuple2<KafkaTopicPartition, Long> kafkaOffset : offsetsStateForCheckpoint.get()) {
                    restoredState.put(kafkaOffset.f0, kafkaOffset.f1); //将offsetsStateForCheckpoint数据恢复到restoredState
                }
            }
        }
    }

snapshotState，做snapshot的逻辑

    @Override
    public void snapshotState(FunctionSnapshotContext context) throws Exception {

        offsetsStateForCheckpoint.clear(); //transient ListState<Tuple2<KafkaTopicPartition, Long>> offsetsStateForCheckpoint

        final AbstractFetcher<?, ?> fetcher = this.kafkaFetcher;
        if (fetcher == null) {
            //...
        } else {
            HashMap<KafkaTopicPartition, Long> currentOffsets = fetcher.snapshotCurrentState(); //从fetcher snapshot最新的offset数据

            if (offsetCommitMode == OffsetCommitMode.ON_CHECKPOINTS) {
                // the map cannot be asynchronously updated, because only one checkpoint call can happen
                // on this function at a time: either snapshotState() or notifyCheckpointComplete()
                pendingOffsetsToCommit.put(context.getCheckpointId(), currentOffsets); //增加pendingOffset
            }

            for (Map.Entry<KafkaTopicPartition, Long> kafkaTopicPartitionLongEntry : currentOffsets.entrySet()) {
                offsetsStateForCheckpoint.add(
                        Tuple2.of(kafkaTopicPartitionLongEntry.getKey(), kafkaTopicPartitionLongEntry.getValue())); //把offset存入stateBackend
            }
        }

        if (offsetCommitMode == OffsetCommitMode.ON_CHECKPOINTS) {
            // truncate the map of pending offsets to commit, to prevent infinite growth
            while (pendingOffsetsToCommit.size() > MAX_NUM_PENDING_CHECKPOINTS) { //pending的太多，删掉老的
                pendingOffsetsToCommit.remove(0);
            }
        }
    }

CheckpointListener 接口

    @Override
    public void notifyCheckpointComplete(long checkpointId) throws Exception {

        final AbstractFetcher<?, ?> fetcher = this.kafkaFetcher;

        if (offsetCommitMode == OffsetCommitMode.ON_CHECKPOINTS) {
            // only one commit operation must be in progress
            try {
                final int posInMap = pendingOffsetsToCommit.indexOf(checkpointId); //在pendingOffsetsToCommit中找这个checkpoint
                if (posInMap == -1) {
                    LOG.warn("Received confirmation for unknown checkpoint id {}", checkpointId);
                    return;
                }

                @SuppressWarnings("unchecked")
                HashMap<KafkaTopicPartition, Long> offsets =
                    (HashMap<KafkaTopicPartition, Long>) pendingOffsetsToCommit.remove(posInMap); //移除该checkpoint

                // remove older checkpoints in map
                for (int i = 0; i < posInMap; i++) {
                    pendingOffsetsToCommit.remove(0); //把比这个checkpoint更老的也都删掉
                }

                if (offsets == null || offsets.size() == 0) {
                    LOG.debug("Checkpoint state was empty.");
                    return;
                }
                fetcher.commitInternalOffsetsToKafka(offsets);//将offset信息发给kafka的group
            }
        }
    }

Kafka010Fetcher

FlinkKafkaConsumer010也就重写createFetcher

不同的kafka版本就Fetcher是不一样的

public class Kafka010Fetcher<T> extends Kafka09Fetcher<T>

Kafka010Fetcher的不同，

    @Override
    protected void emitRecord(
            T record,
            KafkaTopicPartitionState<TopicPartition> partition,
            long offset,
            ConsumerRecord<?, ?> consumerRecord) throws Exception {

        // we attach the Kafka 0.10 timestamp here
        emitRecordWithTimestamp(record, partition, offset, consumerRecord.timestamp()); //0.10支持record中带timestap
    }

    /**
     * This method needs to be overridden because Kafka broke binary compatibility between 0.9 and 0.10,
     * changing binary signatures.
     */
    @Override
    protected KafkaConsumerCallBridge010 createCallBridge() {
        return new KafkaConsumerCallBridge010(); //CallBridge目的是在封装各个版本kafka consumer之间的差异
    }

KafkaConsumerCallBridge010封装0.10版本中做assignPartitions，seek上API和其他版本的差异性

public class KafkaConsumerCallBridge010 extends KafkaConsumerCallBridge {

    @Override
    public void assignPartitions(KafkaConsumer<?, ?> consumer, List<TopicPartition> topicPartitions) throws Exception {
        consumer.assign(topicPartitions);
    }

    @Override
    public void seekPartitionToBeginning(KafkaConsumer<?, ?> consumer, TopicPartition partition) {
        consumer.seekToBeginning(Collections.singletonList(partition));
    }

    @Override
    public void seekPartitionToEnd(KafkaConsumer<?, ?> consumer, TopicPartition partition) {
        consumer.seekToEnd(Collections.singletonList(partition));
    }
}

Kafka09Fetcher

关键是runFetchLoop，启动KafkaConsumerThread

并从handover中取出records，然后封装发出去

    @Override
    public void runFetchLoop() throws Exception {
        try {
            final Handover handover = this.handover; //handover用于在Fetcher线程和consumer线程间传递数据

            // kick off the actual Kafka consumer
            consumerThread.start(); //new KafkaConsumerThread，真正的consumer线程

            while (running) {
                // this blocks until we get the next records
                // it automatically re-throws exceptions encountered in the fetcher thread
                final ConsumerRecords<byte[], byte[]> records = handover.pollNext(); //从handover中拿出数据

                // get the records for each topic partition
                for (KafkaTopicPartitionState<TopicPartition> partition : subscribedPartitionStates()) {

                    List<ConsumerRecord<byte[], byte[]>> partitionRecords =
                            records.records(partition.getKafkaPartitionHandle());//ConsumerRecords中的结构是Map<TopicPartition, List<ConsumerRecord<K, V>>> records

                    for (ConsumerRecord<byte[], byte[]> record : partitionRecords) {
                        final T value = deserializer.deserialize(
                                record.key(), record.value(),
                                record.topic(), record.partition(), record.offset());

                        if (deserializer.isEndOfStream(value)) {
                            // end of stream signaled
                            running = false;
                            break;
                        }

                        // emit the actual record. this also updates offset state atomically
                        // and deals with timestamps and watermark generation
                        emitRecord(value, partition, record.offset(), record);
                    }
                }
            }
        }

这里有个重要的结构是，subscribedPartitionStates

AbstractFetcher

        // create our partition state according to the timestamp/watermark mode
        this.subscribedPartitionStates = initializeSubscribedPartitionStates(
                assignedPartitionsWithInitialOffsets,
                timestampWatermarkMode,
                watermarksPeriodic, watermarksPunctuated,
                userCodeClassLoader);

可以看到，把这些信息都合并放到subscribedPartitionStates，尤其是assignedPartitionsWithInitialOffsets

/**
     * Utility method that takes the topic partitions and creates the topic partition state
     * holders. If a watermark generator per partition exists, this will also initialize those.
     */
    private KafkaTopicPartitionState<KPH>[] initializeSubscribedPartitionStates(
            Map<KafkaTopicPartition, Long> assignedPartitionsToInitialOffsets,
            int timestampWatermarkMode,
            SerializedValue<AssignerWithPeriodicWatermarks<T>> watermarksPeriodic,
            SerializedValue<AssignerWithPunctuatedWatermarks<T>> watermarksPunctuated,
            ClassLoader userCodeClassLoader) throws IOException, ClassNotFoundException {
        switch (timestampWatermarkMode) {

            case NO_TIMESTAMPS_WATERMARKS: {
                 //.......

            case PERIODIC_WATERMARKS: {
                @SuppressWarnings("unchecked")
                KafkaTopicPartitionStateWithPeriodicWatermarks<T, KPH>[] partitions = //KafkaTopicPartitionStateWithPeriodicWatermarks是KafkaTopicPartitionState的子类
                        (KafkaTopicPartitionStateWithPeriodicWatermarks<T, KPH>[])
                                new KafkaTopicPartitionStateWithPeriodicWatermarks<?, ?>[assignedPartitionsToInitialOffsets.size()]; //大小和assignedPartitionsToInitialOffsets一样

                int pos = 0;
                for (Map.Entry<KafkaTopicPartition, Long> partition : assignedPartitionsToInitialOffsets.entrySet()) {
                    KPH kafkaHandle = createKafkaPartitionHandle(partition.getKey()); //生成kafkaHandle，这个就是TopicPartition信息的抽象，为了屏蔽版本间结构的差异

                    AssignerWithPeriodicWatermarks<T> assignerInstance = //AssignerWithPeriodicWatermarks
                            watermarksPeriodic.deserializeValue(userCodeClassLoader);

                    partitions[pos] = new KafkaTopicPartitionStateWithPeriodicWatermarks<>( //对于PUNCTUATED_WATERMARKS，这里是KafkaTopicPartitionStateWithPunctuatedWatermarks
                            partition.getKey(), kafkaHandle, assignerInstance); //对于NO_TIMESTAMPS_WATERMARKS，这里没有assignerInstance参数
                    partitions[pos].setOffset(partition.getValue()); //设置offset

                    pos++;
                }

                return partitions;
            }

            case PUNCTUATED_WATERMARKS: {
                //......
            }

subscribedPartitionStates，中包含该TopicPartition的offset和watermark的提取逻辑

KafkaConsumerThread

@Override
    public void run() {

        // this is the means to talk to FlinkKafkaConsumer's main thread
        final Handover handover = this.handover; //线程间数据交换结构

        // This method initializes the KafkaConsumer and guarantees it is torn down properly.
        // This is important, because the consumer has multi-threading issues,
        // including concurrent 'close()' calls.
        final KafkaConsumer<byte[], byte[]> consumer;
        try {
            consumer = new KafkaConsumer<>(kafkaProperties); //初始化kafka consumer
        }
        catch (Throwable t) {
            handover.reportError(t);
            return;
        }

        // from here on, the consumer is guaranteed to be closed properly
        try {
            // The callback invoked by Kafka once an offset commit is complete
            final OffsetCommitCallback offsetCommitCallback = new CommitCallback(); //这个callback，只是commitInProgress = false，表示commit结束

            // offsets in the state may still be placeholder sentinel values if we are starting fresh, or the
            // checkpoint / savepoint state we were restored with had not completely been replaced with actual offset
            // values yet; replace those with actual offsets, according to what the sentinel value represent.
            for (KafkaTopicPartitionState<TopicPartition> partition : subscribedPartitionStates) {
                if (partition.getOffset() == KafkaTopicPartitionStateSentinel.EARLIEST_OFFSET) { //先把kafka group offset强制设为earliest或latest，然后用kafka上最新的offset更新当前的offset
                    consumerCallBridge.seekPartitionToBeginning(consumer, partition.getKafkaPartitionHandle());
                    partition.setOffset(consumer.position(partition.getKafkaPartitionHandle()) - 1);
                } else if (partition.getOffset() == KafkaTopicPartitionStateSentinel.LATEST_OFFSET) {
                    consumerCallBridge.seekPartitionToEnd(consumer, partition.getKafkaPartitionHandle());
                    partition.setOffset(consumer.position(partition.getKafkaPartitionHandle()) - 1);
                } else if (partition.getOffset() == KafkaTopicPartitionStateSentinel.GROUP_OFFSET) {
                    // the KafkaConsumer by default will automatically seek the consumer position
                    // to the committed group offset, so we do not need to do it.

                    partition.setOffset(consumer.position(partition.getKafkaPartitionHandle()) - 1); //如果GROUP_OFFSET，就直接读取kafka group上的offset
                } else {
                    consumer.seek(partition.getKafkaPartitionHandle(), partition.getOffset() + 1); //其他情况，就用partition自带的offset，比如从state中恢复出来的offset
                }
            }

            // from now on, external operations may call the consumer
            this.consumer = consumer;

            // the latest bulk of records. may carry across the loop if the thread is woken up
            // from blocking on the handover
            ConsumerRecords<byte[], byte[]> records = null;

            // main fetch loop
            while (running) {

                // check if there is something to commit
                if (!commitInProgress) { //同时只能有一个commit
                    // get and reset the work-to-be committed, so we don't repeatedly commit the same
                    final Map<TopicPartition, OffsetAndMetadata> toCommit = nextOffsetsToCommit.getAndSet(null); //checkpoint的时候会snapshot fetcher的offset，并通过fetcher.commitInternalOffsetsToKafka设置

                    if (toCommit != null) {

                        // also record that a commit is already in progress
                        // the order here matters! first set the flag, then send the commit command.
                        commitInProgress = true;
                        consumer.commitAsync(toCommit, offsetCommitCallback); //异步commit offset
                    }
                }

                // get the next batch of records, unless we did not manage to hand the old batch over
                if (records == null) {
                    try {
                        records = consumer.poll(pollTimeout); //从kafka读取数据
                    }
                    catch (WakeupException we) {
                        continue;
                    }
                }

                try {
                    handover.produce(records); //放入handover
                    records = null;
                }
                catch (Handover.WakeupException e) {
                    // fall through the loop
                }
            }
            // end main fetch loop
        }
    }