在数据库中的动态表上做 OLAP 剖析时，两表 join 是十分常见的操作。同理，在流式解决作业中，有时也须要在两条流上做 join 以取得更丰盛的信息。Flink DataStream API 为用户提供了3个算子来实现双流 join，别离是：

● join()

● coGroup()

● intervalJoin()

本文举例说明它们的应用办法，顺便聊聊比拟非凡的 interval join 的原理。

筹备数据

从 Kafka 别离接入点击流和订单流，并转化为 POJO。

DataStream<String> clickSourceStream = env  .addSource(new FlinkKafkaConsumer011<>(    "ods_analytics_access_log",    new SimpleStringSchema(),    kafkaProps  ).setStartFromLatest());DataStream<String> orderSourceStream = env  .addSource(new FlinkKafkaConsumer011<>(    "ods_ms_order_done",    new SimpleStringSchema(),    kafkaProps  ).setStartFromLatest());DataStream<AnalyticsAccessLogRecord> clickRecordStream = clickSourceStream  .map(message -> JSON.parseObject(message, AnalyticsAccessLogRecord.class));DataStream<OrderDoneLogRecord> orderRecordStream = orderSourceStream  .map(message -> JSON.parseObject(message, OrderDoneLogRecord.class));

join()

join() 算子提供的语义为"Window join"，即依照指定字段和（滚动/滑动/会话）窗口进行 inner join，反对解决工夫和事件工夫两种工夫特色。以下示例以10秒滚动窗口，将两个流通过商品 ID 关联，获得订单流中的售价相干字段。

clickRecordStream  .join(orderRecordStream)  .where(record -> record.getMerchandiseId())  .equalTo(record -> record.getMerchandiseId())  .window(TumblingProcessingTimeWindows.of(Time.seconds(10)))  .apply(new JoinFunction<AnalyticsAccessLogRecord, OrderDoneLogRecord, String>() {    @Override    public String join(AnalyticsAccessLogRecord accessRecord, OrderDoneLogRecord orderRecord) throws Exception {      return StringUtils.join(Arrays.asList(        accessRecord.getMerchandiseId(),        orderRecord.getPrice(),        orderRecord.getCouponMoney(),        orderRecord.getRebateAmount()      ), '\t');    }  })  .print().setParallelism(1);

简略易用。

coGroup()

只有 inner join 必定还不够，如何实现 left/right outer join 呢？答案就是利用 coGroup() 算子。它的调用形式相似于 join() 算子，也须要开窗，然而 CoGroupFunction 比 JoinFunction 更加灵便，能够依照用户指定的逻辑匹配左流和/或右流的数据并输入。

以下的例子就实现了点击流 left join 订单流的性能，是很奢侈的 nested loop join 思维（二重循环）。

clickRecordStream  .coGroup(orderRecordStream)  .where(record -> record.getMerchandiseId())  .equalTo(record -> record.getMerchandiseId())  .window(TumblingProcessingTimeWindows.of(Time.seconds(10)))  .apply(new CoGroupFunction<AnalyticsAccessLogRecord, OrderDoneLogRecord, Tuple2<String, Long>>() {    @Override    public void coGroup(Iterable<AnalyticsAccessLogRecord> accessRecords, Iterable<OrderDoneLogRecord> orderRecords, Collector<Tuple2<String, Long>> collector) throws Exception {      for (AnalyticsAccessLogRecord accessRecord : accessRecords) {        boolean isMatched = false;        for (OrderDoneLogRecord orderRecord : orderRecords) {          // 右流中有对应的记录          collector.collect(new Tuple2<>(accessRecord.getMerchandiseName(), orderRecord.getPrice()));          isMatched = true;        }        if (!isMatched) {          // 右流中没有对应的记录          collector.collect(new Tuple2<>(accessRecord.getMerchandiseName(), null));        }      }    }  })  .print().setParallelism(1);

intervalJoin()

join() 和 coGroup() 都是基于窗口做关联的。然而在某些状况下，两条流的数据步调未必统一。例如，订单流的数据有可能在点击流的购买动作产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，依照指定字段以及右流绝对左流偏移的工夫区间进行关联，即：

right.timestamp ∈ [left.timestamp + lowerBound; left.timestamp + upperBound]

interval join 也是 inner join，尽管不须要开窗，然而须要用户指定偏移区间的上下界，并且只反对事件工夫。

示例代码如下。留神在运行之前，须要别离在两个流上利用 assignTimestampsAndWatermarks() 办法获取事件工夫戳和水印。

clickRecordStream  .keyBy(record -> record.getMerchandiseId())  .intervalJoin(orderRecordStream.keyBy(record -> record.getMerchandiseId()))  .between(Time.seconds(-30), Time.seconds(30))  .process(new ProcessJoinFunction<AnalyticsAccessLogRecord, OrderDoneLogRecord, String>() {    @Override    public void processElement(AnalyticsAccessLogRecord accessRecord, OrderDoneLogRecord orderRecord, Context context, Collector<String> collector) throws Exception {      collector.collect(StringUtils.join(Arrays.asList(        accessRecord.getMerchandiseId(),        orderRecord.getPrice(),        orderRecord.getCouponMoney(),        orderRecord.getRebateAmount()      ), '\t'));    }  })  .print().setParallelism(1);

由上可见，interval join 与 window join 不同，是两个 KeyedStream 之上的操作，并且须要调用 between() 办法指定偏移区间的上下界。如果想令上下界是开区间，能够调用 upperBoundExclusive()/lowerBoundExclusive() 办法。

interval join 的实现原理

以下是 KeyedStream.process(ProcessJoinFunction) 办法调用的重载办法的逻辑。

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，而后利用 IntervalJoinOperator 算子进行转换。在 IntervalJoinOperator 中，会利用两个 MapState 别离缓存左流和右流的数据。

private transient MapState<Long, List<BufferEntry<T1>>> leftBuffer;private transient MapState<Long, List<BufferEntry<T2>>> rightBuffer;@Overridepublic 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))    ));}

其中 Long 示意事件工夫戳，List> 示意该时刻到来的数据记录。当左流和右流有数据达到时，会别离调用 processElement1() 和 processElement2() 办法，它们都调用了 processElement() 办法，代码如下。

@Overridepublic void processElement1(StreamRecord<T1> record) throws Exception {    processElement(record, leftBuffer, rightBuffer, lowerBound, upperBound, true);}@Overridepublic 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);    }}

这段代码的思路是：

1.获得以后流 StreamRecord 的工夫戳，调用 isLate() 办法判断它是否是早退数据（即工夫戳小于以后水印值），如是则抛弃。

2.调用 addToBuffer() 办法，将工夫戳和数据一起插入以后流对应的 MapState。

3.遍历另外一个流的 MapState，如果数据满足前述的工夫区间条件，则调用 collect() 办法将该条数据投递给用户定义的 ProcessJoinFunction 进行解决。collect() 办法的代码如下，留神后果对应的工夫戳是左右流工夫戳里较大的那个。

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);}

4.调用 TimerService.registerEventTimeTimer() 注册工夫戳为 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，避免数据沉积。留神左右流的定时器所属的 namespace 是不同的，具体逻辑则位于 onEventTime() 办法中。

@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);    }}

本文转载自简书，作者：LittleMagic原文链接：

https://www.jianshu.com/p/45e...