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()
本文举例说明它们的使用方法,顺便聊聊比较特殊的 interval join 的原理。
准备数据
从 Kafka 分别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));
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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。以下示例以 10 秒滚动窗口,将两个流通过商品 ID 关联,取得订单流中的售价相关字段。
640.png
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);
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简单易用。
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。
以下的例子就实现了点击流 left join 订单流的功能,是很朴素的 nested loop join 思想(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);
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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]
2.jpg
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。
示例代码如下。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。
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);
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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
以下是 KeyedStream.process(ProcessJoinFunction) 方法调用的重载方法的逻辑。
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);
}
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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。
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))
));
}
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其中 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。
@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);
}
}
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这段代码的思路是:
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 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);
}
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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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本文转载自简书,作者:LittleMagic 原文链接:
https://www.jianshu.com/p/45ec888332df
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