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Flink/Spark/Hadoop/数仓，数据分析、面试，源码解读等干货学习资料
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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}.
*/
@PublicEvolving
public <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;
@Override
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
*/
@Override
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
*/
@Override
public void processElement2(StreamRecord<T2> record) throws Exception {
processElement(record, rightBuffer, leftBuffer, -upperBound, -lowerBound, false);
}
@SuppressWarnings("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。
@Override
public 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);
}
}
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