本文转自个人微信公众号，原文链接。
如 上篇 所述，Flink 里时间包括Event Time、Processing Time 和 Ingestion Time 三种类型。
- Processing Time：Processing Time 是算子处理某个数据时到系统时间。Processing Time 是最简单的时间，提供了最好的性能和最低的延迟，但是，在分布式环境中，Processing Time具有不确定性，多次运行的结果可能出现不一致。
- Ingestion Time：Ingestion Time 是数据进入Flink 集群的时间，Source Operator 给数据加上时间戳。
- Event Time：Event Time是数据在设备上产生时的时间，一般都嵌入到了数据记录中，相比于其他两种，Event Time 更具有业务意义， 取决于数据而不是系统。举例来说，重跑历史数据时，如果根据Processing Time 重跑，可能会造成结果不一致，而根据Event Time 重跑，结果是一致的。
由于Event Time 更能表达业务需求，所以，Event Time 应用更为广泛，但使用Event Time 也会存在一些问题。
1. 问题：乱序与延迟
乱序与延迟是实时系统中最常见的问题。比如说，在实时系统中广泛使用的消息队列，很难保证端到端的全局有序，从而导致进入 Flink 集群的数据是无序的；然后，由于洪峰的存在，比如秒杀或者重跑历史数据，很容易造成数据在消息队列堆积，从而造成延迟。
2. 解决方案
采用Event Time的流计算处理器，需要评估Event Time进展，比如当窗口结束时，需要通知 Operator 关闭窗口并开始计算。
2.1 Watermark
Apache Flink 采用watermark来处理，watermark 带有一个时间戳，作为数据流的一部分随数据流流动，Watermark(t) 表示event time 小于等于 t 的都已经到达，如下图所示。
2.1.1 生成Watermark
2.1.1.1 方法1 Source 中生成
在source中，直接生成watermark，不过，source生成的watermark 优先级比较低，可以被方法2中的覆盖掉。具体的定义在一篇讲Source & Sink 时详述。
2.1.1.2 方法2 Timestamp Assigner
Timestamp Assigner 输入数据流，产生一个新的数据流，新数据流带有产生的watermark，如果原数据流本身就有watermark，则覆盖原watermark。Timestamp Assigner 一般紧跟在source后，但不是必须的，但是必须在第一个event time 操作前。
Timestamp Assigner 分两种：
- Periodic： 周期性（一定时间间隔或一定数据量）产生watermark。
- Punctuated： 间断的 watermark，一般根据event 决定是否产生新watermark。
Periodic
直接看源码(注释太明白，不舍得删)。
/**
* A {@code TimestampAssigner} assigns event time timestamps to elements.
* These timestamps are used by all functions that operate on event time,
* for example event time windows.
*
* <p>Timestamps are represented in milliseconds since the Epoch
* (midnight, January 1, 1970 UTC).
*
* @param <T> The type of the elements to which this assigner assigns timestamps.
*/
public interface TimestampAssigner<T> extends Function {
/**
* Assigns a timestamp to an element, in milliseconds since the Epoch.
*
* <p>The method is passed the previously assigned timestamp of the element.
* That previous timestamp may have been assigned from a previous assigner,
* by ingestion time. If the element did not carry a timestamp before, this value is
* {@code Long.MIN_VALUE}.
*
* @param element The element that the timestamp will be assigned to.
* @param previousElementTimestamp The previous internal timestamp of the element,
* or a negative value, if no timestamp has been assigned yet.
* @return The new timestamp.
*/
long extractTimestamp(T element, long previousElementTimestamp);
}
/**
* The {@code AssignerWithPeriodicWatermarks} assigns event time timestamps to elements,
* and generates low watermarks that signal event time progress within the stream.
* These timestamps and watermarks are used by functions and operators that operate
* on event time, for example event time windows.
*
* <p>Use this class to generate watermarks in a periodical interval.
* At most every {@code i} milliseconds (configured via
* {@link ExecutionConfig#getAutoWatermarkInterval()}), the system will call the
* {@link #getCurrentWatermark()} method to probe for the next watermark value.
* The system will generate a new watermark, if the probed value is non-null
* and has a timestamp larger than that of the previous watermark (to preserve
* the contract of ascending watermarks).
*
* <p>The system may call the {@link #getCurrentWatermark()} method less often than every
* {@code i} milliseconds, if no new elements arrived since the last call to the
* method.
*
* <p>Timestamps and watermarks are defined as {@code longs} that represent the
* milliseconds since the Epoch (midnight, January 1, 1970 UTC).
* A watermark with a certain value {@code t} indicates that no elements with event
* timestamps {@code x}, where {@code x} is lower or equal to {@code t}, will occur any more.
*
* @param <T> The type of the elements to which this assigner assigns timestamps.
*
* @see org.apache.flink.streaming.api.watermark.Watermark
*/
public interface AssignerWithPeriodicWatermarks<T> extends TimestampAssigner<T> {
/**
* Returns the current watermark. This method is periodically called by the
* system to retrieve the current watermark. The method may return {@code null} to
* indicate that no new Watermark is available.
*
* <p>The returned watermark will be emitted only if it is non-null and its timestamp
* is larger than that of the previously emitted watermark (to preserve the contract of
* ascending watermarks). If the current watermark is still
* identical to the previous one, no progress in event time has happened since
* the previous call to this method. If a null value is returned, or the timestamp
* of the returned watermark is smaller than that of the last emitted one, then no
* new watermark will be generated.
*
* <p>The interval in which this method is called and Watermarks are generated
* depends on {@link ExecutionConfig#getAutoWatermarkInterval()}.
*
* @see org.apache.flink.streaming.api.watermark.Watermark
* @see ExecutionConfig#getAutoWatermarkInterval()
*
* @return {@code Null}, if no watermark should be emitted, or the next watermark to emit.
*/
@Nullable
Watermark getCurrentWatermark();
}
可以看出，自定义的Assigner 需要实现AssignerWithPeriodicWatermarks 接口，其中getCurrentWatermark 产生新的watermark，如果返回非空且大于原来的watermark，则生成了新的watermark；另外，extractTimestamp 用于给数据加上时间戳，这个时间戳在后续所有基于event time的计算中使用。以下面的代码为例，假设数据可能乱序，但最多延迟3.5秒。
/**
* This generator generates watermarks assuming that elements arrive out of order,
* but only to a certain degree. The latest elements for a certain timestamp t will arrive
* at most n milliseconds after the earliest elements for timestamp t.
*/
class BoundedOutOfOrdernessGenerator extends AssignerWithPeriodicWatermarks[MyEvent] {
val maxOutOfOrderness = 3500L // 3.5 seconds
var currentMaxTimestamp: Long = _
override def extractTimestamp(element: MyEvent, previousElementTimestamp: Long): Long = {
element.getCreationTime()
}
override def getCurrentWatermark(): Watermark = {
// return the watermark as current highest timestamp minus the out-of-orderness bound
new Watermark(currentMaxTimestamp - maxOutOfOrderness)
}
}
ExecutionConfig.setAutoWatermarkInterval(...) 定义了watermark产生的时间间隔，单位是毫秒。
Punctuated
根据event来确定是否需要产生新的watermark，定义Punctuated Assigner 需要实现AssignerWithPunctuatedWatermarks接口，包括函数extractTimestamp，checkAndGetNextWatermark，其中extractTimestamp 同Periodic Assigner，首先调用；然后调用checkAndGetNextWatermark ，用于确定是否需要产生新的watermark，当checkAndGetNextWatermark 产生一个非空且大于上一个watermark时就产生了新的watermark。举个例子如下：
class PunctuatedAssigner extends AssignerWithPunctuatedWatermarks[MyEvent] {
override def extractTimestamp(element: MyEvent, previousElementTimestamp: Long): Long = {
element.getCreationTime
}
override def checkAndGetNextWatermark(lastElement: MyEvent, extractedTimestamp: Long): Watermark = {
if (lastElement.hasWatermarkMarker()) new Watermark(extractedTimestamp) else null
}
}
2.1.2 Flink 预定义Timestamp Assigner
为了便于使用，Apache Flink 提供了两种预定义的Timestamp Assigner：
-
AscendingTimestampExtractor： 这是
AssignerWithPeriodicWatermarks的最简单的情况，数据流是按时间戳升序到达Flink的，这种情况下，数据里的时间戳就可以作为watermarkval withTimestampsAndWatermarks = stream.assignAscendingTimestamps( _.getCreationTime ) -
BoundedOutOfOrdernessTimestampExtractor: 这也是一个
AssignerWithPeriodicWatermarks的实现，表示已知数据的最大延迟。val withTimestampsAndWatermarks = stream.assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor[MyEvent](Time.seconds(10))( _.getCreationTime ))
这两种Timestamp Assigner 一是可以直接使用，二是可以作为学习的代码示例。
Latency
即使采用watermark 技术，对于watermark(t) 也可能存在时间戳小于t却没有到达的数据，在现实中，延迟可能是无上限的，这种情况下，不可能无限等待下去；另外，即使延迟有限，但如果让watermark 延迟太多也不好，因为延迟太多可能就失去了实时的意义。所以，必须要作出选择。
默认情况下，延迟超过watermark的数据会被丢弃，但 Flink 允许在窗口操作上指定最大延迟，我们用N表示支持的最大延迟（N默认为0），对于窗口 [start_time, end_time)] ，数据迟于 watermark(t) 但先于end_time+N到达的，仍然会添加到窗口中再次触发计算。为了支持这种情况，Flink 需要保持这个窗口state 到时间戳 end_time + N ，当时间到达end_time+N后，Flink 删除窗口和state。
stream
.keyBy(<key selector>)
.window(<window assigner>)
.allowedLateness(<time>)
.<windowed transformation>(<window function>)
3. 总结
本文主要介绍Flink 中Event Time 和Watermark。由于Event Time 具有业务意义，且具有确定性，所以Event Time 应用广泛，但由于在现实中存在延迟和乱序问题，Flink 采用了 Watermark 来解决这个问题。
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