GlobalWindow之自定义Trigger (一)

前言

之前看到GlobalWindow需要自己定义trigger，写了个测试用例简单实现了下。

背景

前面文章讲到了窗口，在窗口中我们一般都会去使用api中定义好的滑动滚动窗口等等。但在一些特殊场景下，我们需要自定义去实现窗口的定义以及窗口的触发。
举个例子：如何去实现1min窗口的每10s输出一次该窗口的值。比如在10：00-10：10中每隔10s输出这个窗口的总和。

Trigger

今天主要讲下以下三个方法：

	/**
	 * Called for every element that gets added to a pane. The result of this will determine
	 * whether the pane is evaluated to emit results.
	 *
	 * @param element The element that arrived.
	 * @param timestamp The timestamp of the element that arrived.
	 * @param window The window to which the element is being added.
	 * @param ctx A context object that can be used to register timer callbacks.
	 */
	public abstract TriggerResult onElement(T element, long timestamp, W window, TriggerContext ctx) throws Exception;
		/**
	 * Called when a processing-time timer that was set using the trigger context fires.
	 *
	 * @param time The timestamp at which the timer fired.
	 * @param window The window for which the timer fired.
	 * @param ctx A context object that can be used to register timer callbacks.
	 */
	public abstract TriggerResult onProcessingTime(long time, W window, TriggerContext ctx) throws Exception;

	/**
	 * Called when an event-time timer that was set using the trigger context fires.
	 *
	 * @param time The timestamp at which the timer fired.
	 * @param window The window for which the timer fired.
	 * @param ctx A context object that can be used to register timer callbacks.
	 */
	public abstract TriggerResult onEventTime(long time, W window, TriggerContext ctx) throws Exception;

onElement： 每条数据进来处理一下。参数 timestamp: 应该是assignTimestampsAndWatermarks设置的时间戳，没设置的话不能用哈
onProcessingTime：触发processtimer进来，time为触发的时间 。
onEventTime：同上。

测试代码：

package com.realtime.flink.trigger;

import com.realtime.flink.dto.OrderDto;
import org.apache.flink.api.common.functions.ReduceFunction;
import org.apache.flink.api.common.state.ReducingState;
import org.apache.flink.api.common.state.ReducingStateDescriptor;
import org.apache.flink.api.common.typeutils.base.LongSerializer;
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.Window;

// 实现在1min中窗口内，每10s输出一次结果
public class MyTrigger extends Trigger<OrderDto,Window> {
   
    // 1min窗口
    private Long windowStep = 60000L;
    // 10s触发
    private Long outputStep = 10000L;
    private final ReducingStateDescriptor<Long> stateDesc =
            new ReducingStateDescriptor<>("count", new Sum(), LongSerializer.INSTANCE);
    // 每条数据进来
    public TriggerResult onElement(OrderDto element, long timestamp, Window window, TriggerContext ctx) throws Exception {
   
        timestamp = element.getOrderTime();
        ReducingState<Long> reducerState = ctx.getPartitionedState(stateDesc);
        if(reducerState.get()==null){
   
            // 第一次进入,设置start为起始的触发时间
            long start = timestamp - timestamp%outputStep;
            // nextfire为下一次触发时间
            long nextFire = outputStep+start;
            reducerState.add(nextFire);
            System.out.println("SSSSSSSSSSSSSS"+timestamp+"-->"+nextFire);
            ctx.registerProcessingTimeTimer(nextFire);
            return TriggerResult.CONTINUE;
        }
        return TriggerResult.CONTINUE;
    }
    // 触发eventtime窗口
    public TriggerResult onProcessingTime(long time, Window window, TriggerContext ctx) throws Exception {
   
        ReducingState<Long> reducerState = ctx.getPartitionedState(stateDesc);
        if(time==reducerState.get()){
   
            long nextFire = outputStep+time;
            reducerState.add(outputStep);
            ctx.registerProcessingTimeTimer(nextFire);
            System.out.println("KKKKKKKKKKKKKKKK"+nextFire+"-->"+time%windowStep);
            // 判断是否需要清空窗口，比如到了10:01:00时需要清空触发并窗口
            if(time%windowStep==0){
   
                // 触发并清空窗口数据
                return TriggerResult