Flink 自定义trigger 同时按照计数和时间触发窗口计算

自定义窗口 实现同时按照计数和时间（processing-time）触发计算

Triggers

A Trigger determines when a window (as formed by the window assigner) is ready to be processed by the window function. Each WindowAssigner comes with a default Trigger. If the default trigger does not fit your needs, you can specify a custom trigger using trigger(...).

The trigger interface has five methods that allow a Trigger to react to different events:

• The onElement() method is called for each element that is added to a window.
• The onEventTime() method is called when a registered event-time timer fires.
• The onProcessingTime() method is called when a registered processing-time timer fires.
• The onMerge() method is relevant for stateful triggers and merges the states of two triggers when their corresponding windows merge, e.g. when using session windows.
• Finally the clear() method performs any action needed upon removal of the corresponding window.

Two things to notice about the above methods are:

1) The first three decide how to act on their invocation event by returning a TriggerResult. The action can be one of the following:

• CONTINUE: do nothing,
• FIRE: trigger the computation,
• PURGE: clear the elements in the window, and
• FIRE_AND_PURGE: trigger the computation and clear the elements in the window afterwards.

2) Any of these methods can be used to register processing- or event-time timers for future actions.

trigger 接口有5个方法如下：

       onElement()方法,每个元素被添加到窗口时调用
　　onEventTime()方法,当一个已注册的事件时间计时器启动时调用
　　onProcessingTime()方法,当一个已注册的处理时间计时器启动时调用
　　onMerge()方法，与状态性触发器相关，当使用会话窗口时，两个触发器对应的窗口合并时，合并两个触发器的状态。
　　*最后一个clear()方法执行任何需要清除的相应窗口

TriggerResult返回的操作:

CONTINUE:什么也不做
FIRE:触发计算
PURGE:清除窗口中的数据
FIRE_AND_PURGE:触发计算并清除窗口中的数据

按照计数触发

@Override
    public TriggerResult onElement(Object element, long timestamp, TimeWindow window, TriggerContext ctx){
        ctx.registerEventTimeTimer(window.maxTimestamp());
        if (flag > 4) {
            flag = 0;
            return TriggerResult.FIRE;
        }else{
            flag ++;
        }
        System.out.println("onElement: " + element);
        return TriggerResult.CONTINUE;
    }

按照时间（processing-time）触发：

@Override
public TriggerResult onProcessingTime(long time, TimeWindow window, TriggerContext ctx) throws Exception {
    return TriggerResult.FIRE;
}

SocketWindowWordCount.java

package com.flink.test;

import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.api.windowing.triggers.Trigger;
import org.apache.flink.streaming.api.windowing.triggers.TriggerResult;
import org.apache.flink.streaming.api.windowing.windows.GlobalWindow;
import org.apache.flink.streaming.api.windowing.windows.TimeWindow;
import org.apache.flink.util.Collector;

import java.awt.image.ImagingOpException;

public class SocketWindowWordCount {

    public static void main(String[] args) throws Exception {
        /***
         *   1:在192.168.3.101 服务器执行命令: nc -l 9000
         *   2: 启动该服务
         *   3: 在命令行发数据
         */

        // 创建 execution environment
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // 通过连接 socket 获取输入数据，这里连接到本地9000端口，如果9000端口已被占用，请换一个端口
        DataStream<String> text = env.socketTextStream("101.132.79.68", 9000, "n");

        // 解析数据，按 word 分组，开窗，聚合
        DataStream<Tuple2<String, Integer>> windowCounts = text
                .flatMap(new FlatMapFunction<String, Tuple2<String, Integer>>() {
                    @Override
                    public void flatMap(String value, Collector<Tuple2<String, Integer>> out) {
                        for (String word : value.split("\s")) {
                            out.collect(Tuple2.of(word, 1));
                        }
                    }
                })
                .keyBy(0)
                .timeWindow(Time.seconds(20))
                .trigger(CustomTrigger.create())
                .sum(1);

        windowCounts.print().setParallelism(1);

        env.execute("Socket Window WordCount");
    }
}

class CustomTrigger extends Trigger<Object, TimeWindow> {
    private static final long serialVersionUID = 1L;
    public CustomTrigger() {}

    private static int flag = 0;

    @Override
    public TriggerResult onElement(Object element, long timestamp, TimeWindow window, TriggerContext ctx){
        ctx.registerEventTimeTimer(window.maxTimestamp());
        if (flag > 4) {
            flag = 0;
            return TriggerResult.FIRE;
        }else{
            flag ++;
        }
        System.out.println("onElement: " + element);
        return TriggerResult.CONTINUE;
    }

    @Override
    public TriggerResult onEventTime(long time, TimeWindow window, TriggerContext ctx) throws Exception{
        return TriggerResult.CONTINUE;
    }

    @Override
    public TriggerResult onProcessingTime(long time, TimeWindow window, TriggerContext ctx) throws Exception {
        return TriggerResult.FIRE;
    }

    @Override
    public void clear(TimeWindow window, TriggerContext ctx) throws Exception{
        ctx.deleteProcessingTimeTimer(window.maxTimestamp());
    }

    @Override
    public String toString(){
        return "CustomTrigger";
    }

//    @Override
//    public void onMerge(TimeWindow window, OnMergeContext ctx) throws Exception {
//        long windowMaxTimestamp = window.maxTimestamp();
//        if (windowMaxTimestamp > ctx.getCurrentProcessingTime()) {
//            ctx.registerProcessingTimeTimer(windowMaxTimestamp);
//        }
//    }

    public static CustomTrigger create(){
       return new CustomTrigger();
    }
}

结果显示：

服务器发送数据

客户端接收：

其中计数（onElement）触发而打印

===========onElement: (hello,1)
===========onElement: (hello,1)
===========onElement: (hello,1)
===========onElement: (world,1)
===========onElement: (hadoop,1)
(java,1)

其中时间（onProcessingTime）触发而打印

(world,1)
(hadoop,1)
(hello,3)
(java,1)

参考：

flink官网：https://ci.apache.org/projects/flink/flink-docs-release-1.8/dev/stream/operators/windows.html

https://www.jianshu.com/p/1dacf2f84325

https://blog.csdn.net/rlnLo2pNEfx9c/article/details/88014778

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