Flink双流join的3种方式及IntervalJoin源码分析
概述
在数据库中的静态表上做 OLAP 分析时，两表 join 是非常常见的操作。同理，在流式处理作业中，有时也需要在两条流上做 join 以获得更丰富的信息。Flink DataStream API 为用户提供了3个算子来实现双流 join，分别是：
join()
coGroup()
intervalJoin()
join()
join() 算子提供的语义为"Window join"，即按照指定字段和（滚动/滑动/会话）窗口进行 inner join，支持处理时间和事件时间两种时间特征。
paymentInfo_ds.join(orderInfo__ds).where(_.order_id).equalTo(_.order_id).window(TumblingEventTimeWindows.of(Time.seconds(20))).apply(new JoinFunction[PaymentInfo,OrderInfo,PaymentWide]{override def join(first: PaymentInfo, second: OrderInfo): PaymentWide = {//处理逻辑new PaymentWide(first, second)}})
coGroup()
只有 inner join 肯定还不够，如何实现 left/right outer join 呢？答案就是利用 coGroup() 算子。
它的调用方式类似于 join() 算子，也需要开窗，但是 CoGroupFunction 比 JoinFunction 更加灵活，可以按照用户指定的逻辑匹配左流和/或右流的数据并输出。
paymentInfo_wm_ds.coGroup(orderInfo_ds).where(_.order_id).equalTo(_.order_id).window(TumblingEventTimeWindows.of(Time.seconds(20))).apply(new CoGroupFunction[PaymentInfo,OrderInfo,PaymentWide](){override def coGroup(first: lang.Iterable[PaymentInfo], second: lang.Iterable[OrderInfo], out: Collector[PaymentWide]): Unit = {val f = first.iterator()while (f.hasNext){//处理左流数据//处理右流数据}}})
intervalJoin()
join() 和 coGroup() 都是基于窗口做关联的。但是在某些情况下，两条流的数据步调未必一致。例如，订单流的数据有可能在点击流的购买动作发生之后很久才被写入，如果用窗口来圈定，很容易 join 不上。
所以 Flink 又提供了"Interval join"的语义，按照指定字段以及右流相对左流偏移的时间区间进行关联，即：right.timestamp ∈ [left.timestamp + lowerBound; left.timestamp + upperBound]
interval join 也是 inner join，虽然不需要开窗，但是需要用户指定偏移区间的上下界，并且只支持事件时间。注意在运行之前，需要分别在两个流上应用 assignTimestampsAndWatermarks() 方法获取事件时间戳和水印。
paymentInfo_ds.keyBy(_.order_id).intervalJoin(orderWide_wm_ds.keyBy(_.order_id)).between(Time.minutes(-15), Time.minutes(0)).process(new ProcessJoinFunction[PaymentInfo, OrderInfo, PaymentWide]() {override def processElement(in1: PaymentInfo,in2: OrderInfo,context: ProcessJoinFunction[PaymentInfo, OrderInfo, PaymentWide]#Context,collector: Collector[PaymentWide]): Unit = {collector.collect(new PaymentWide(in1, in2))}})
由上可见，interval join 与 window join 不同，是两个 KeyedStream 之上的操作，并且需要调用 between() 方法指定偏移区间的上下界。如果想令上下界是开区间，可以调用 upperBoundExclusive()/lowerBoundExclusive() 方法。
interval join 的实现原理及源码分析
org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator
/*** Completes the join operation with the given user function that is executed for each joined pair* of elements. This methods allows for passing explicit type information for the output type.** @param processJoinFunction The user-defined process join function.* @param outputType The type information for the output type.* @param <OUT> The output type.* @return The transformed {@link DataStream}.*/@PublicEvolvingpublic <OUT> SingleOutputStreamOperator<OUT> process(ProcessJoinFunction<IN1, IN2, OUT> processJoinFunction,TypeInformation<OUT> outputType) {Preconditions.checkNotNull(processJoinFunction);Preconditions.checkNotNull(outputType);final ProcessJoinFunction<IN1, IN2, OUT> cleanedUdf = left.getExecutionEnvironment().clean(processJoinFunction);final IntervalJoinOperator<KEY, IN1, IN2, OUT> operator =new IntervalJoinOperator<>(lowerBound,upperBound,lowerBoundInclusive,upperBoundInclusive,left.getType().createSerializer(left.getExecutionConfig()),right.getType().createSerializer(right.getExecutionConfig()),cleanedUdf);return left.connect(right).keyBy(keySelector1, keySelector2).transform("Interval Join", outputType, operator);}
可见是先对两条流执行 connect() 和 keyBy() 操作，然后利用 IntervalJoinOperator 算子进行转换。在 IntervalJoinOperator 中，会利用两个 MapState 分别缓存左流和右流的数据。
org.apache.flink.streaming.api.operators.co.IntervalJoinOperator#initializeState
private transient MapState<Long, List<BufferEntry<T1>>> leftBuffer;private transient MapState<Long, List<BufferEntry<T2>>> rightBuffer;public void initializeState(StateInitializationContext context) throws Exception {super.initializeState(context);this.leftBuffer = context.getKeyedStateStore().getMapState(new MapStateDescriptor<>(LEFT_BUFFER,LongSerializer.INSTANCE,new ListSerializer<>(new BufferEntrySerializer<>(leftTypeSerializer))));this.rightBuffer = context.getKeyedStateStore().getMapState(new MapStateDescriptor<>(RIGHT_BUFFER,LongSerializer.INSTANCE,new ListSerializer<>(new BufferEntrySerializer<>(rightTypeSerializer))));}
Long 表示事件时间戳
List表示该时刻到来的数据记录
当左流和右流有数据到达时，会分别调用 processElement1() 和 processElement2() 方法，它们都调用了 processElement() 方法，代码如下。
/*** Process a {@link StreamRecord} from the left stream. Whenever an {@link StreamRecord}* arrives at the left stream, it will get added to the left buffer. Possible join candidates* for that element will be looked up from the right buffer and if the pair lies within the* user defined boundaries, it gets passed to the {@link ProcessJoinFunction}.** @param record An incoming record to be joined* @throws Exception Can throw an Exception during state access*/public void processElement1(StreamRecord<T1> record) throws Exception {processElement(record, leftBuffer, rightBuffer, lowerBound, upperBound, true);}/*** Process a {@link StreamRecord} from the right stream. Whenever a {@link StreamRecord}* arrives at the right stream, it will get added to the right buffer. Possible join candidates* for that element will be looked up from the left buffer and if the pair lies within the user* defined boundaries, it gets passed to the {@link ProcessJoinFunction}.** @param record An incoming record to be joined* @throws Exception Can throw an exception during state access*/public void processElement2(StreamRecord<T2> record) throws Exception {processElement(record, rightBuffer, leftBuffer, -upperBound, -lowerBound, false);}("unchecked")private <THIS, OTHER> void processElement(final StreamRecord<THIS> record,final MapState<Long, List<IntervalJoinOperator.BufferEntry<THIS>>> ourBuffer,final MapState<Long, List<IntervalJoinOperator.BufferEntry<OTHER>>> otherBuffer,final long relativeLowerBound,final long relativeUpperBound,final boolean isLeft) throws Exception {final THIS ourValue = record.getValue();final long ourTimestamp = record.getTimestamp();if (ourTimestamp == Long.MIN_VALUE) {throw new FlinkException("Long.MIN_VALUE timestamp: Elements used in " +"interval stream joins need to have timestamps meaningful timestamps.");}if (isLate(ourTimestamp)) {return;}addToBuffer(ourBuffer, ourValue, ourTimestamp);for (Map.Entry<Long, List<BufferEntry<OTHER>>> bucket: otherBuffer.entries()) {final long timestamp = bucket.getKey();if (timestamp < ourTimestamp + relativeLowerBound ||timestamp > ourTimestamp + relativeUpperBound) {continue;}for (BufferEntry<OTHER> entry: bucket.getValue()) {if (isLeft) {collect((T1) ourValue, (T2) entry.element, ourTimestamp, timestamp);} else {collect((T1) entry.element, (T2) ourValue, timestamp, ourTimestamp);}}}long cleanupTime = (relativeUpperBound > 0L) ? ourTimestamp + relativeUpperBound : ourTimestamp;if (isLeft) {internalTimerService.registerEventTimeTimer(CLEANUP_NAMESPACE_LEFT, cleanupTime);} else {internalTimerService.registerEventTimeTimer(CLEANUP_NAMESPACE_RIGHT, cleanupTime);}}
取得当前流 StreamRecord 的时间戳，调用 isLate() 方法判断它是否是迟到数据（即时间戳小于当前水印值），如是则丢弃。
调用 addToBuffer() 方法，将时间戳和数据一起插入当前流对应的 MapState。
遍历另外一个流的 MapState，如果数据满足前述的时间区间条件，则调用 collect() 方法将该条数据投递给用户定义的 ProcessJoinFunction 进行处理。collect() 方法的代码如下，注意结果对应的时间戳是左右流时间戳里较大的那个。
private boolean isLate(long timestamp) {long currentWatermark = internalTimerService.currentWatermark();return currentWatermark != Long.MIN_VALUE && timestamp < currentWatermark;}private void collect(T1 left, T2 right, long leftTimestamp, long rightTimestamp) throws Exception {final long resultTimestamp = Math.max(leftTimestamp, rightTimestamp);collector.setAbsoluteTimestamp(resultTimestamp);context.updateTimestamps(leftTimestamp, rightTimestamp, resultTimestamp);userFunction.processElement(left, right, context, collector);}private static <T> void addToBuffer(final MapState<Long, List<IntervalJoinOperator.BufferEntry<T>>> buffer,final T value,final long timestamp) throws Exception {List<BufferEntry<T>> elemsInBucket = buffer.get(timestamp);if (elemsInBucket == null) {elemsInBucket = new ArrayList<>();}elemsInBucket.add(new BufferEntry<>(value, false));buffer.put(timestamp, elemsInBucket);}
调用 TimerService.registerEventTimeTimer() 注册时间戳为 timestamp + relativeUpperBound 的定时器，该定时器负责在水印超过区间的上界时执行状态的清理逻辑，防止数据堆积。注意左右流的定时器所属的 namespace 是不同的，具体逻辑则位于 onEventTime() 方法中。
@Overridepublic void onEventTime(InternalTimer<K, String> timer) throws Exception {long timerTimestamp = timer.getTimestamp();String namespace = timer.getNamespace();logger.trace("onEventTime @ {}", timerTimestamp);switch (namespace) {case CLEANUP_NAMESPACE_LEFT: {long timestamp = (upperBound <= 0L) ? timerTimestamp : timerTimestamp - upperBound;logger.trace("Removing from left buffer @ {}", timestamp);leftBuffer.remove(timestamp);break;}case CLEANUP_NAMESPACE_RIGHT: {long timestamp = (lowerBound <= 0L) ? timerTimestamp + lowerBound : timerTimestamp;logger.trace("Removing from right buffer @ {}", timestamp);rightBuffer.remove(timestamp);break;}default:throw new RuntimeException("Invalid namespace " + namespace);}}