实战理解之Flink Trigger

 1，Trigger基础：

Trigger窗口触发器自带的类型有：

EventTimeTrigger

ProcessTimeTrigger

CountTrigger 等等

如果不满足自己的业务要求，可以自定义实现触发器：

简单案例：

package com.learning.window;

import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.streaming.api.TimeCharacteristic;
import org.apache.flink.streaming.api.datastream.AllWindowedStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
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.TimeWindow;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer010;

import java.util.Properties;

/**
 * todo TriggerResult 的几个操作  CONTINUE(false, false),FIRE_AND_PURGE(true, true),FIRE(true, false), PURGE(false, true);
 * <p>
 * todo    1, CONTINUE  不触发，等待
 * todo    2， FIRE  触发 数据保留
 * todo    3，PURGE 不触发，数据清空
 * todo    4，FIRE_AND_PURGE 触发 清除数据
 */
public class TriggerDemo extends Trigger<Object, TimeWindow> {
    private static int flag = 0;

    // todo 进入窗口每个元素都触发
    @Override
    public TriggerResult onElement(Object element, long l, TimeWindow timeWindow, TriggerContext ctx) throws Exception {

        ctx.registerProcessingTimeTimer(timeWindow.maxTimestamp());

        if (flag > 9) {
            flag = 0;
            return TriggerResult.FIRE; //统计数量为10的时候 触发
        } else {
            flag++;
        }
        //进入的元素
        System.out.println("onElement : " + element);
        return TriggerResult.CONTINUE;
    }

    // todo 针对ProcessingTime进行触发操作
    @Override
    public TriggerResult onProcessingTime(long l, TimeWindow timeWindow, TriggerContext triggerContext) throws Exception {
        return TriggerResult.FIRE;
    }

    // todo 针对EventTime进行触发操作
    @Override
    public TriggerResult onEventTime(long l, TimeWindow timeWindow, TriggerContext triggerContext) throws Exception {
        return TriggerResult.CONTINUE;
    }

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

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

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

    public static void main(String[] args) throws Exception {
// set up the streaming execution environment
        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        env.setStreamTimeCharacteristic(TimeCharacteristic.ProcessingTime);
        Properties properties = new Properties();
        properties.setProperty("bootstrap.servers", "localhost:9093");
        properties.setProperty("group.id", "test");

        FlinkKafkaConsumer010<String> kafkaConsumer010 = new FlinkKafkaConsumer010<>("test",
                new SimpleStringSchema(),
                properties);

        AllWindowedStream<String, TimeWindow> stream = env
                .addSource(kafkaConsumer010)
                .timeWindowAll(org.apache.flink.streaming.api.windowing.time.Time.seconds(20))
                .trigger(TriggerDemo.create());

        stream.sum(0).print();


        env.execute("Flink Streaming Java API Skeleton");
    }
}

对了，这里做个笔记：

window.getEnd跟 window.maxTimestamp区别：

2，简单案例代码：

package com.learning.window;

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.TimeCharacteristic;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
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.TimeWindow;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

/**
 * todo flink 自定义触发器实现带超时时间的 CountWindow
 *
 */
public class CountTriggerWithTimeout<T> extends Trigger<T, TimeWindow> {
    private static Logger logger = LoggerFactory.getLogger(CountTriggerWithTimeout.class);

    /**
     * 窗口最大数据量
     */
    private int maxCount;
    /**
     * event time / process time
     */
    private TimeCharacteristic timeType;
    /**
     * 用于储存窗口当前数据量的状态对象
     */
    private ReducingStateDescriptor<Long> countStateDescriptor =
            new ReducingStateDescriptor("counter", new Sum(), LongSerializer.INSTANCE);


    public CountTriggerWithTimeout(int maxCount, TimeCharacteristic timeType) {

        this.maxCount = maxCount;
        this.timeType = timeType;
    }


    private TriggerResult fireAndPurge(TimeWindow window, TriggerContext ctx) throws Exception {
        clear(window, ctx);
        return TriggerResult.FIRE_AND_PURGE;
    }


    //进入窗口的每个元素都会调用该方法。
    @Override
    public TriggerResult onElement(T element, long timestamp, TimeWindow window, TriggerContext ctx) throws Exception {
        ReducingState<Long> countState = ctx.getPartitionedState(countStateDescriptor);
        countState.add(1L);

        if (countState.get() >= maxCount) {
            logger.info("fire with count: " + countState.get());
            return fireAndPurge(window, ctx);
        }
        if (timestamp >= window.getEnd()) {
            logger.info("fire with tiem: " + timestamp);
            return fireAndPurge(window, ctx);
        } else {
            return TriggerResult.CONTINUE;
        }
    }

    //ProcessingTime触发的时候会被调用。
    @Override
    public TriggerResult onProcessingTime(long time, TimeWindow window, TriggerContext ctx) throws Exception {
        if (timeType != TimeCharacteristic.ProcessingTime) {
            return TriggerResult.CONTINUE;
        }

        if (time >= window.getEnd()) {
            return TriggerResult.CONTINUE;
        } else {
            logger.info("fire with process tiem: " + time);
            return fireAndPurge(window, ctx);
        }
    }

    //EventTime 事件触发的时候被调用。
    @Override
    public TriggerResult onEventTime(long time, TimeWindow window, TriggerContext ctx) throws Exception {
        if (timeType != TimeCharacteristic.EventTime) {
            return TriggerResult.CONTINUE;
        }

        if (time >= window.getEnd()) {
            return TriggerResult.CONTINUE;
        } else {
            logger.info("fire with event tiem: " + time);
            return fireAndPurge(window, ctx);
        }
    }

    @Override
    public void clear(TimeWindow window, TriggerContext ctx) throws Exception {
        ReducingState<Long> countState = ctx.getPartitionedState(countStateDescriptor);
        countState.clear();
    }

    /**
     * 计数方法
     */
    class Sum implements ReduceFunction<Long> {

        @Override
        public Long reduce(Long value1, Long value2) throws Exception {
            return value1 + value2;
        }
    }

    //todo main方法执行
    public static void main(String[] args) {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        DataStreamSource<String> streams = env.fromElements("aaa");

        streams.timeWindowAll(Time.seconds(10))
                .trigger(
                        new CountTriggerWithTimeout(1000, TimeCharacteristic.ProcessingTime)
                )
                //todo 做业务操作
                .process(new XxxxWindowProcessFunction())
                //todo 输出
                .addSink(new XxxSinkFunction())
                .name("Xxx");
        try {
            env.execute("aaa");
        } catch (Exception e) {
            e.printStackTrace();
        }

    }
}

最后

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