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自定义trigger的主要目的是为了等待数据到齐:
代码如下; flink版本1.6
public class WatermarkTest {
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
Properties properties = new Properties();
properties.setProperty("bootstrap.servers", GlobalConstants.KAFKA_BROKER);
properties.setProperty("group.id", "crm_stream_window");
properties.setProperty(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "latest");
DataStream<String> stream =
env.addSource(new FlinkKafkaConsumer011<>("test", new SimpleStringSchema(), properties));
DataStream<Tuple3<String, Long, Integer>> inputMap = stream.map(new MapFunction<String, Tuple3<String, Long, Integer>>() {
private static final long serialVersionUID = -8812094804806854937L;
@Override
public Tuple3<String, Long, Integer> map(String value) throws Exception {
return new Tuple3<>(value.split("\\W+")[0], Long.valueOf(value.split("\\W+")[1]), Integer.valueOf(value.split("\\W+")[2]));
}
});
DataStream<Tuple3<String, Long, Integer>> watermark =
inputMap.assignTimestampsAndWatermarks(new AssignerWithPeriodicWatermarks<Tuple3<String, Long, Integer>>() {
private static final long serialVersionUID = 8252616297345284790L;
Long currentMaxTimestamp = 0L;
Long maxOutOfOrderness = 2000L;//最大允许的乱序时间是10s
Watermark watermark = null;
SimpleDateFormat format = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss.SSS");
@Nullable
@Override
public Watermark getCurrentWatermark() {
watermark = new Watermark(currentMaxTimestamp - maxOutOfOrderness);
return watermark;
}
@Override
public long extractTimestamp(Tuple3<String, Long, Integer> element, long previousElementTimestamp) {
Long timestamp = element.f1;
currentMaxTimestamp = Math.max(timestamp, currentMaxTimestamp);
System.out.println("timestamp : " + element.f1 + "|" + format.format(element.f1) + " currentMaxTimestamp : " + currentMaxTimestamp + "|" + format.format(currentMaxTimestamp) + "," + " watermark : " + watermark.getTimestamp() + "|" + format.format(watermark.getTimestamp()));
return timestamp;
}
});
OutputTag<Tuple3<String, Long, Integer>> lateOutputTag = new OutputTag<Tuple3<String, Long, Integer>>("late-data") {
private static final long serialVersionUID = -1552769100986888698L;
};
SingleOutputStreamOperator<String> resultStream = watermark
.keyBy(0)
.window(TumblingEventTimeWindows.of(Time.seconds(3)))
.trigger(new Trigger<Tuple3<String, Long, Integer>, TimeWindow>() {
private static final long serialVersionUID = 2742133264310093792L;
ValueStateDescriptor<Integer> sumStateDescriptor = new ValueStateDescriptor<Integer>("sum", Integer.class);
@Override
public TriggerResult onElement(Tuple3<String, Long, Integer> element, long timestamp, TimeWindow window, TriggerContext ctx) throws Exception {
ValueState<Integer> sumState = ctx.getPartitionedState(sumStateDescriptor);
if (null == sumState.value()) {
sumState.update(0);
}
sumState.update(element.f2 + sumState.value());
if (sumState.value() >= 2) {
//这里可以选择手动处理状态
// 默认的trigger发送是TriggerResult.FIRE 不会清除窗口数据
return TriggerResult.FIRE_AND_PURGE;
}
return TriggerResult.CONTINUE;
}
@Override
public TriggerResult onProcessingTime(long time, TimeWindow window, TriggerContext ctx) throws Exception {
return TriggerResult.CONTINUE;
}
@Override
public TriggerResult onEventTime(long time, TimeWindow window, TriggerContext ctx) throws Exception {
return TriggerResult.CONTINUE;
}
@Override
public void clear(TimeWindow window, TriggerContext ctx) throws Exception {
System.out.println("清理窗口状态 窗口内保存值为" + ctx.getPartitionedState(sumStateDescriptor).value());
ctx.getPartitionedState(sumStateDescriptor).clear();
}
})
//如果使用allowedLateness会有重复计算的效果
//默认的trigger情况下
// 在event time>window_end_time+watermark+allowedLateness时会触发窗口的clear
// 后续数据如果属于该窗口而且数据的event_time>watermark-allowedLateness 会触发重新计算
//
//在使用自定义的trigger情况下
//同一个窗口内只要满足要求可以不停的触发窗口数据往下流
//在event time>window_end_time+watermark+allowedLateness时会触发窗口clear
//后续数据如果属于该窗口而且数据的event_time>watermark-allowedLateness 会触发重新计算
//
//窗口状态的clear只和时间有关与是否自定义trigger无关
.allowedLateness(Time.seconds(3))
.sideOutputLateData(lateOutputTag)
.apply(new WindowFunction<Tuple3<String, Long, Integer>, String, Tuple, TimeWindow>() {
private static final long serialVersionUID = 7813420265419629362L;
@Override
public void apply(Tuple tuple, TimeWindow window, Iterable<Tuple3<String, Long, Integer>> input, Collector<String> out) throws Exception {
for (Tuple3<String, Long, Integer> stringLongTuple2 : input) {
System.out.println(stringLongTuple2.f1);
}
SimpleDateFormat format = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss.SSS");
out.collect("window " + format.format(window.getStart()) + " window " + format.format(window.getEnd()));
}
});
resultStream.print();
// resultStream.getSideOutput(lateOutputTag).print();
env.execute("window test");
}比较了自定义trigger和默认的trigger在event time的前提下，watermark和allowedLateness对trigger的影响。
默认trigger加allowedLateness: 会导致窗口原来的数据也会触发
timestamp : 1461756862000|2016-04-27 19:34:22.000 currentMaxTimestamp : 1461756862000|2016-04-27 19:34:22.000, watermark : -2000|1970-01-01 07:59:58.000
timestamp : 1461756863000|2016-04-27 19:34:23.000 currentMaxTimestamp : 1461756863000|2016-04-27 19:34:23.000, watermark : 1461756860000|2016-04-27 19:34:20.000
timestamp : 1461756864000|2016-04-27 19:34:24.000 currentMaxTimestamp : 1461756864000|2016-04-27 19:34:24.000, watermark : 1461756861000|2016-04-27 19:34:21.000
timestamp : 1461756865000|2016-04-27 19:34:25.000 currentMaxTimestamp : 1461756865000|2016-04-27 19:34:25.000, watermark : 1461756862000|2016-04-27 19:34:22.000
timestamp : 1461756866000|2016-04-27 19:34:26.000 currentMaxTimestamp : 1461756866000|2016-04-27 19:34:26.000, watermark : 1461756863000|2016-04-27 19:34:23.000
1461756862000
1461756863000
8> window 2016-04-27 19:34:21.000 window 2016-04-27 19:34:24.000
timestamp : 1461756862000|2016-04-27 19:34:22.000 currentMaxTimestamp : 1461756866000|2016-04-27 19:34:26.000, watermark : 1461756864000|2016-04-27 19:34:24.000
1461756862000
1461756863000
1461756862000
8> window 2016-04-27 19:34:21.000 window 2016-04-27 19:34:24.000自定义trigger加allowedLateness: 会将落后的数据直接往下发送
timestamp : 1461756862000|2016-04-27 19:34:22.000 currentMaxTimestamp : 1461756862000|2016-04-27 19:34:22.000, watermark : -2000|1970-01-01 07:59:58.000
timestamp : 1461756863000|2016-04-27 19:34:23.000 currentMaxTimestamp : 1461756863000|2016-04-27 19:34:23.000, watermark : 1461756860000|2016-04-27 19:34:20.000
1461756862000
1461756863000
8> window 2016-04-27 19:34:21.000 window 2016-04-27 19:34:24.000
timestamp : 1461756864000|2016-04-27 19:34:24.000 currentMaxTimestamp : 1461756864000|2016-04-27 19:34:24.000, watermark : 1461756861000|2016-04-27 19:34:21.000
timestamp : 1461756865000|2016-04-27 19:34:25.000 currentMaxTimestamp : 1461756865000|2016-04-27 19:34:25.000, watermark : 1461756862000|2016-04-27 19:34:22.000
timestamp : 1461756866000|2016-04-27 19:34:26.000 currentMaxTimestamp : 1461756866000|2016-04-27 19:34:26.000, watermark : 1461756863000|2016-04-27 19:34:23.000
timestamp : 1461756862000|2016-04-27 19:34:22.000 currentMaxTimestamp : 1461756866000|2016-04-27 19:34:26.000, watermark : 1461756864000|2016-04-27 19:34:24.000
1461756862000
8> window 2016-04-27 19:34:21.000 window 2016-04-27 19:34:24.000可以发现两者的不同，默认的trigger会将之前窗口中的数据一起发出，但是自定义的trigger不会将之前的数据发送，而是单独将落后的数据往后发送了。避免数据的重复的问题(trigger发送数据的方式不同)。
自定义trigger加allowedLateness: 会导致窗口原来的数据也会触发
timestamp : 1461756861000|2016-04-27 19:34:21.000 currentMaxTimestamp : 1461756861000|2016-04-27 19:34:21.000, watermark : -2000|1970-01-01 07:59:58.000
timestamp : 1461756862000|2016-04-27 19:34:22.000 currentMaxTimestamp : 1461756862000|2016-04-27 19:34:22.000, watermark : 1461756859000|2016-04-27 19:34:19.000
timestamp : 1461756863000|2016-04-27 19:34:23.000 currentMaxTimestamp : 1461756863000|2016-04-27 19:34:23.000, watermark : 1461756860000|2016-04-27 19:34:20.000
timestamp : 1461756864000|2016-04-27 19:34:24.000 currentMaxTimestamp : 1461756864000|2016-04-27 19:34:24.000, watermark : 1461756861000|2016-04-27 19:34:21.000
timestamp : 1461756865000|2016-04-27 19:34:25.000 currentMaxTimestamp : 1461756865000|2016-04-27 19:34:25.000, watermark : 1461756862000|2016-04-27 19:34:22.000
timestamp : 1461756862000|2016-04-27 19:34:22.000 currentMaxTimestamp : 1461756865000|2016-04-27 19:34:25.000, watermark : 1461756863000|2016-04-27 19:34:23.000
1461756861000
1461756862000
1461756863000
1461756862000
8> window 2016-04-27 19:34:21.000 window 2016-04-27 19:34:24.000
timestamp : 1461756866000|2016-04-27 19:34:26.000 currentMaxTimestamp : 1461756866000|2016-04-27 19:34:26.000, watermark : 1461756863000|2016-04-27 19:34:23.000
timestamp : 1461756867000|2016-04-27 19:34:27.000 currentMaxTimestamp : 1461756867000|2016-04-27 19:34:27.000, watermark : 1461756864000|2016-04-27 19:34:24.000
timestamp : 1461756868000|2016-04-27 19:34:28.000 currentMaxTimestamp : 1461756868000|2016-04-27 19:34:28.000, watermark : 1461756865000|2016-04-27 19:34:25.000
timestamp : 1461756869000|2016-04-27 19:34:29.000 currentMaxTimestamp : 1461756869000|2016-04-27 19:34:29.000, watermark : 1461756866000|2016-04-27 19:34:26.000
清理窗口状态 窗口内保存值为4默认trigger clear()的调用时间: 在29s的时候触发的默认的clear方法，默认执行的类名(EventTimeTrigger)
timestamp : 146175682000|1974-08-20 04:21:22.000 currentMaxTimestamp : 146175682000|1974-08-20 04:21:22.000, watermark : -2000|1970-01-01 07:59:58.000
timestamp : 146175683000|1974-08-20 04:21:23.000 currentMaxTimestamp : 146175683000|1974-08-20 04:21:23.000, watermark : 146175680000|1974-08-20 04:21:20.000
timestamp : 146175684000|1974-08-20 04:21:24.000 currentMaxTimestamp : 146175684000|1974-08-20 04:21:24.000, watermark : 146175681000|1974-08-20 04:21:21.000
timestamp : 146175685000|1974-08-20 04:21:25.000 currentMaxTimestamp : 146175685000|1974-08-20 04:21:25.000, watermark : 146175682000|1974-08-20 04:21:22.000
timestamp : 146175686000|1974-08-20 04:21:26.000 currentMaxTimestamp : 146175686000|1974-08-20 04:21:26.000, watermark : 146175683000|1974-08-20 04:21:23.000
146175682000
146175683000
8> window 1974-08-20 04:21:21.000 window 1974-08-20 04:21:24.000
timestamp : 146175687000|1974-08-20 04:21:27.000 currentMaxTimestamp : 146175687000|1974-08-20 04:21:27.000, watermark : 146175684000|1974-08-20 04:21:24.000
timestamp : 146175688000|1974-08-20 04:21:28.000 currentMaxTimestamp : 146175688000|1974-08-20 04:21:28.000, watermark : 146175685000|1974-08-20 04:21:25.000
timestamp : 146175689000|1974-08-20 04:21:29.000 currentMaxTimestamp : 146175689000|1974-08-20 04:21:29.000, watermark : 146175686000|1974-08-20 04:21:26.000默认trigger clear()的调用时间:
timestamp : 1461756862000|2016-04-27 19:34:22.000 currentMaxTimestamp : 1461756862000|2016-04-27 19:34:22.000, watermark : -2000|1970-01-01 07:59:58.000
timestamp : 1461756863000|2016-04-27 19:34:23.000 currentMaxTimestamp : 1461756863000|2016-04-27 19:34:23.000, watermark : 1461756860000|2016-04-27 19:34:20.000
1461756862000
1461756863000
timestamp : 1461756864000|2016-04-27 19:34:24.000 currentMaxTimestamp : 1461756864000|2016-04-27 19:34:24.000, watermark : 1461756861000|2016-04-27 19:34:21.000
timestamp : 1461756865000|2016-04-27 19:34:25.000 currentMaxTimestamp : 1461756865000|2016-04-27 19:34:25.000, watermark : 1461756862000|2016-04-27 19:34:22.000
timestamp : 1461756866000|2016-04-27 19:34:26.000 currentMaxTimestamp : 1461756866000|2016-04-27 19:34:26.000, watermark : 1461756863000|2016-04-27 19:34:23.000
timestamp : 1461756867000|2016-04-27 19:34:27.000 currentMaxTimestamp : 1461756867000|2016-04-27 19:34:27.000, watermark : 1461756864000|2016-04-27 19:34:24.000
timestamp : 1461756868000|2016-04-27 19:34:28.000 currentMaxTimestamp : 1461756868000|2016-04-27 19:34:28.000, watermark : 1461756865000|2016-04-27 19:34:25.000
timestamp : 1461756869000|2016-04-27 19:34:29.000 currentMaxTimestamp : 1461756869000|2016-04-27 19:34:29.000, watermark : 1461756866000|2016-04-27 19:34:26.000
清理窗口状态 窗口内保存值为2
通过自定义trigger和默认的trigger的比较，可以发现clear()方法的调用只和时间有关
当event time>window_end_time+watermark+allowedLateness时调用
进入event time默认的trigger看看:
@PublicEvolving
public class EventTimeTrigger extends Trigger<Object, TimeWindow> {
private static final long serialVersionUID = 1L;
private EventTimeTrigger() {}
@Override
public TriggerResult onElement(Object element, long timestamp, TimeWindow window, TriggerContext ctx) throws Exception {
if (window.maxTimestamp() <= ctx.getCurrentWatermark()) {
// if the watermark is already past the window fire immediately
return TriggerResult.FIRE;
} else {
// 注册一个事件时间的定时器，触发onEventTime
ctx.registerEventTimeTimer(window.maxTimestamp());
return TriggerResult.CONTINUE;
}
}
@Override
public TriggerResult onEventTime(long time, TimeWindow window, TriggerContext ctx) {
//可以看到当触发onEventTime方法时只是将数据往下发送了
return time == window.maxTimestamp() ?
TriggerResult.FIRE :
TriggerResult.CONTINUE;
}
@Override
public TriggerResult onProcessingTime(long time, TimeWindow window, TriggerContext ctx) throws Exception {
return TriggerResult.CONTINUE;
}
@Override
public void clear(TimeWindow window, TriggerContext ctx) throws Exception {
//删除事件时间的定时器
ctx.deleteEventTimeTimer(window.maxTimestamp());
}
@Override
public boolean canMerge() {
return true;
}
@Override
public void onMerge(TimeWindow window,
OnMergeContext ctx) {
// only register a timer if the watermark is not yet past the end of the merged window
// this is in line with the logic in onElement(). If the watermark is past the end of
// the window onElement() will fire and setting a timer here would fire the window twice.
long windowMaxTimestamp = window.maxTimestamp();
if (windowMaxTimestamp > ctx.getCurrentWatermark()) {
ctx.registerEventTimeTimer(windowMaxTimestamp);
}
}
@Override
public String toString() {
return "EventTimeTrigger()";
}
/**
* Creates an event-time trigger that fires once the watermark passes the end of the window.
*
* <p>Once the trigger fires all elements are discarded. Elements that arrive late immediately
* trigger window evaluation with just this one element.
*/
public static EventTimeTrigger create() {
return new EventTimeTrigger();
}
}
到此有些疑问窗口中元素的清除是在什么类中实现的？何时清除的？(自我理解:按理说是应该在调用clear()方法时清除窗口数据，因为此时窗口结束时间已经比watermark-allowedLateness小了)
努力吧，皮卡丘