共计 8376 个字符，预计需要花费 21 分钟才能阅读完成。
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;
@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))
));
}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。
@Override
public void processElement1(StreamRecord<T1> record) throws Exception {processElement(record, leftBuffer, rightBuffer, lowerBound, upperBound, true);
}
@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);
}
}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。
@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);
}
}本文转载自简书，作者：LittleMagic 原文链接：
https://www.jianshu.com/p/45e…