Flink

参考，Flink - Generating Timestamps / Watermarks

watermark，只有在有window的情况下才用到，所以在window operator前加上assignTimestampsAndWatermarks即可

不一定需要从source发出

1. 首先，source可以发出watermark

我们就看看kafka source的实现

    protected AbstractFetcher(
            SourceContext<T> sourceContext,
            List<KafkaTopicPartition> assignedPartitions,
            SerializedValue<AssignerWithPeriodicWatermarks<T>> watermarksPeriodic,  //在创建KafkaConsumer的时候assignTimestampsAndWatermarks
            SerializedValue<AssignerWithPunctuatedWatermarks<T>> watermarksPunctuated,
            ProcessingTimeService processingTimeProvider,
            long autoWatermarkInterval,  //env.getConfig().setAutoWatermarkInterval()
            ClassLoader userCodeClassLoader,
            boolean useMetrics) throws Exception
    {    
        //判断watermark的类型
        if (watermarksPeriodic == null) {
            if (watermarksPunctuated == null) {
                // simple case, no watermarks involved
                timestampWatermarkMode = NO_TIMESTAMPS_WATERMARKS;
            } else {
                timestampWatermarkMode = PUNCTUATED_WATERMARKS;
            }
        } else {
            if (watermarksPunctuated == null) {
                timestampWatermarkMode = PERIODIC_WATERMARKS;
            } else {
                throw new IllegalArgumentException("Cannot have both periodic and punctuated watermarks");
            }
        }
        
        // create our partition state according to the timestamp/watermark mode 
        this.allPartitions = initializePartitions(
                assignedPartitions,
                timestampWatermarkMode,
                watermarksPeriodic, watermarksPunctuated,
                userCodeClassLoader);
        
        // if we have periodic watermarks, kick off the interval scheduler
        if (timestampWatermarkMode == PERIODIC_WATERMARKS) { //如果是定期发出WaterMark
            KafkaTopicPartitionStateWithPeriodicWatermarks<?, ?>[] parts = 
                    (KafkaTopicPartitionStateWithPeriodicWatermarks<?, ?>[]) allPartitions;
            
            PeriodicWatermarkEmitter periodicEmitter= 
                    new PeriodicWatermarkEmitter(parts, sourceContext, processingTimeProvider, autoWatermarkInterval);
            periodicEmitter.start();
        }
    }

FlinkKafkaConsumerBase

    public FlinkKafkaConsumerBase<T> assignTimestampsAndWatermarks(AssignerWithPeriodicWatermarks<T> assigner) {
        checkNotNull(assigner);
        
        if (this.punctuatedWatermarkAssigner != null) {
            throw new IllegalStateException("A punctuated watermark emitter has already been set.");
        }
        try {
            ClosureCleaner.clean(assigner, true);
            this.periodicWatermarkAssigner = new SerializedValue<>(assigner);
            return this;
        } catch (Exception e) {
            throw new IllegalArgumentException("The given assigner is not serializable", e);
        }
    }

这个接口的核心函数，定义，如何提取Timestamp和生成Watermark的逻辑

public interface AssignerWithPeriodicWatermarks<T> extends TimestampAssigner<T> {
    Watermark getCurrentWatermark();
}

public interface TimestampAssigner<T> extends Function {
    long extractTimestamp(T element, long previousElementTimestamp);
}

如果在初始化KafkaConsumer的时候，没有assignTimestampsAndWatermarks，就不会产生watermark

可以看到watermark有两种，

PERIODIC_WATERMARKS，定期发送的watermark

PUNCTUATED_WATERMARKS，由element触发的watermark，比如有element的特征或某种类型的element来表示触发watermark，这样便于开发者来控制watermark

initializePartitions

case PERIODIC_WATERMARKS: {
    @SuppressWarnings("unchecked")
    KafkaTopicPartitionStateWithPeriodicWatermarks<T, KPH>[] partitions =
            (KafkaTopicPartitionStateWithPeriodicWatermarks<T, KPH>[])
                    new KafkaTopicPartitionStateWithPeriodicWatermarks<?, ?>[assignedPartitions.size()];

    int pos = 0;
    for (KafkaTopicPartition partition : assignedPartitions) {
        KPH kafkaHandle = createKafkaPartitionHandle(partition);

        AssignerWithPeriodicWatermarks<T> assignerInstance =
                watermarksPeriodic.deserializeValue(userCodeClassLoader);
        
        partitions[pos++] = new KafkaTopicPartitionStateWithPeriodicWatermarks<>(
                partition, kafkaHandle, assignerInstance);
    }

    return partitions;
}

KafkaTopicPartitionStateWithPeriodicWatermarks

这个类里面最核心的函数，

    public long getTimestampForRecord(T record, long kafkaEventTimestamp) {
        return timestampsAndWatermarks.extractTimestamp(record, kafkaEventTimestamp);
    }
    
    public long getCurrentWatermarkTimestamp() {
        Watermark wm = timestampsAndWatermarks.getCurrentWatermark();
        if (wm != null) {
            partitionWatermark = Math.max(partitionWatermark, wm.getTimestamp());
        }
        return partitionWatermark;
    }

可以看到是调用你定义的AssignerWithPeriodicWatermarks来实现

PeriodicWatermarkEmitter

    private static class PeriodicWatermarkEmitter implements ProcessingTimeCallback {

        public void start() {
            timerService.registerTimer(timerService.getCurrentProcessingTime() + interval, this); //start定时器，定时触发
        }
        
        @Override
        public void onProcessingTime(long timestamp) throws Exception { //触发逻辑

            long minAcrossAll = Long.MAX_VALUE;
            for (KafkaTopicPartitionStateWithPeriodicWatermarks<?, ?> state : allPartitions) { //对于每个partitions
                
                // we access the current watermark for the periodic assigners under the state
                // lock, to prevent concurrent modification to any internal variables
                final long curr;
                //noinspection SynchronizationOnLocalVariableOrMethodParameter
                synchronized (state) {
                    curr = state.getCurrentWatermarkTimestamp(); //取出当前partition的WaterMark
                }
                
                minAcrossAll = Math.min(minAcrossAll, curr); //求min，以partition中最小的partition作为watermark
            }
            
            // emit next watermark, if there is one
            if (minAcrossAll > lastWatermarkTimestamp) {
                lastWatermarkTimestamp = minAcrossAll;
                emitter.emitWatermark(new Watermark(minAcrossAll)); //emit
            }
            
            // schedule the next watermark
            timerService.registerTimer(timerService.getCurrentProcessingTime() + interval, this); //重新设置timer
        }
    }

2. DataStream也可以设置定时发送Watermark

其实实现是加了个chain的TimestampsAndPeriodicWatermarksOperator

DataStream

   /**
     * Assigns timestamps to the elements in the data stream and periodically creates
     * watermarks to signal event time progress.
     * 
     * <p>This method creates watermarks periodically (for example every second), based
     * on the watermarks indicated by the given watermark generator. Even when no new elements
     * in the stream arrive, the given watermark generator will be periodically checked for
     * new watermarks. The interval in which watermarks are generated is defined in
     * {@link ExecutionConfig#setAutoWatermarkInterval(long)}.
     * 
     * <p>Use this method for the common cases, where some characteristic over all elements
     * should generate the watermarks, or where watermarks are simply trailing behind the
     * wall clock time by a certain amount.
     *
     * <p>For the second case and when the watermarks are required to lag behind the maximum
     * timestamp seen so far in the elements of the stream by a fixed amount of time, and this
     * amount is known in advance, use the
     * {@link BoundedOutOfOrdernessTimestampExtractor}.
     * 
     * <p>For cases where watermarks should be created in an irregular fashion, for example
     * based on certain markers that some element carry, use the
     * {@link AssignerWithPunctuatedWatermarks}.
     * 
     * @param timestampAndWatermarkAssigner The implementation of the timestamp assigner and
     *                                      watermark generator.   
     * @return The stream after the transformation, with assigned timestamps and watermarks.
     * 
     * @see AssignerWithPeriodicWatermarks
     * @see AssignerWithPunctuatedWatermarks
     * @see #assignTimestampsAndWatermarks(AssignerWithPunctuatedWatermarks) 
     */
    public SingleOutputStreamOperator<T> assignTimestampsAndWatermarks(
            AssignerWithPeriodicWatermarks<T> timestampAndWatermarkAssigner) {
        
        // match parallelism to input, otherwise dop=1 sources could lead to some strange
        // behaviour: the watermark will creep along very slowly because the elements
        // from the source go to each extraction operator round robin.
        final int inputParallelism = getTransformation().getParallelism();
        final AssignerWithPeriodicWatermarks<T> cleanedAssigner = clean(timestampAndWatermarkAssigner);
        
        TimestampsAndPeriodicWatermarksOperator<T> operator = 
                new TimestampsAndPeriodicWatermarksOperator<>(cleanedAssigner);
        
        return transform("Timestamps/Watermarks", getTransformation().getOutputType(), operator)
                .setParallelism(inputParallelism);
    }

TimestampsAndPeriodicWatermarksOperator

  public class TimestampsAndPeriodicWatermarksOperator<T>
        extends AbstractUdfStreamOperator<T, AssignerWithPeriodicWatermarks<T>>
        implements OneInputStreamOperator<T, T>, Triggerable {
    
    private transient long watermarkInterval;
    private transient long currentWatermark;

    public TimestampsAndPeriodicWatermarksOperator(AssignerWithPeriodicWatermarks<T> assigner) {
        super(assigner); //AbstractUdfStreamOperator(F userFunction)
        this.chainingStrategy = ChainingStrategy.ALWAYS; //一定是chain
    }

    @Override
    public void open() throws Exception {
        super.open();

        currentWatermark = Long.MIN_VALUE;
        watermarkInterval = getExecutionConfig().getAutoWatermarkInterval();
        
        if (watermarkInterval > 0) {
            registerTimer(System.currentTimeMillis() + watermarkInterval, this); //注册到定时器
        }
    }

    @Override
    public void processElement(StreamRecord<T> element) throws Exception {
        final long newTimestamp = userFunction.extractTimestamp(element.getValue(), //由element中基于AssignerWithPeriodicWatermarks提取时间戳
                element.hasTimestamp() ? element.getTimestamp() : Long.MIN_VALUE);
        
        output.collect(element.replace(element.getValue(), newTimestamp)); //更新element的时间戳，再次发出
    }

    @Override
    public void trigger(long timestamp) throws Exception { //定时器触发trigger
        // register next timer
        Watermark newWatermark = userFunction.getCurrentWatermark(); //取得watermark
        if (newWatermark != null && newWatermark.getTimestamp() > currentWatermark) {
            currentWatermark = newWatermark.getTimestamp();
            // emit watermark
            output.emitWatermark(newWatermark); //发出watermark
        }

        registerTimer(System.currentTimeMillis() + watermarkInterval, this); //重新注册到定时器
    }

    @Override
    public void processWatermark(Watermark mark) throws Exception {
        // if we receive a Long.MAX_VALUE watermark we forward it since it is used
        // to signal the end of input and to not block watermark progress downstream
        if (mark.getTimestamp() == Long.MAX_VALUE && currentWatermark != Long.MAX_VALUE) {
            currentWatermark = Long.MAX_VALUE;
            output.emitWatermark(mark); //forward watermark
        }
    }

可以看到在processElement会调用AssignerWithPeriodicWatermarks.extractTimestamp提取event time

然后更新StreamRecord的时间

然后在Window Operator中，

@Override
    public void processElement(StreamRecord<IN> element) throws Exception {
        final Collection<W> elementWindows = windowAssigner.assignWindows(
            element.getValue(), element.getTimestamp(), windowAssignerContext);

会在windowAssigner.assignWindows时以element的timestamp作为assign时间

对于watermark的处理，参考，Flink – window operator