Flink Window那些事——AggregateFunction窗口函数

AggregateFunction 比 ReduceFunction 更加的通用，它有三个参数:输入类型(IN)、累加器类型(ACC)和输出类型(OUT)。

输入类型是输入流中的元素类型，AggregateFunction有一个add方
法可以将一个输入元素添加到一个累加器中。该接口还具有创建初始累加器(createAccumulator方法)、将两个累加器合并到一个累加器(merge方法)以及从累加器中提取输出(类型为OUT)的方法。

package com.lynch.stream.window;

import org.apache.flink.api.common.functions.AggregateFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.tuple.Tuple3;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;

/**
 * 测试AggFunction——求各个班级英语成绩平均分
 *
 */
public class TestAggFunctionOnWindow {
    public static void main(String[] args) throws Exception {
        // 获取执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // 读取数据
        DataStream<Tuple3<String, String, Long>> input = env.fromElements(ENGLISH);

        // 求各个班级英语成绩平均分
        DataStream<Double> avgScore = input.keyBy(0).countWindow(3).aggregate(new AverageAggrate());

        avgScore.print();

        env.execute("TestAggFunctionOnWindow");

    }

    public static final Tuple3[] ENGLISH = new Tuple3[] { 
            Tuple3.of("class1", "张三", 100L),
            Tuple3.of("class1", "李四", 40L), 
            Tuple3.of("class1", "王五", 60L), 
            Tuple3.of("class2", "赵六", 20L),
            Tuple3.of("class2", "小七", 30L), 
            Tuple3.of("class2", "小八", 50L), 
    };

    //Tuple3<String, String, Long> 输入类型
    //Tuple2<Long, Long> 累加器ACC类型，保存中间状态
    //Double 输出类型
    public static class AverageAggrate implements AggregateFunction<Tuple3<String, String, Long>, Tuple2<Long, Long>, Double> {
        /**
         * 创建累加器保存中间状态（sum count）
         * 
         * sum 英语总成绩
         * count 学生个数
         * 
         * @return
         */
        @Override
        public Tuple2<Long, Long> createAccumulator() {
            return new Tuple2<>(0L, 0L);
        }

        /**
         * 将元素添加到累加器并返回新的累加器
         * 
         * @param value 输入类型
         * @param acc 累加器ACC类型
         * 
         * @return 返回新的累加器
         */
        @Override
        public Tuple2<Long, Long> add(Tuple3<String, String, Long> value, Tuple2<Long, Long> acc) {
            //acc.f0 总成绩 
            //value.f2 表示成绩
            //acc.f1 人数
            return new Tuple2<>(acc.f0 + value.f2, acc.f1 + 1L);
        }

        /**
         * 从累加器提取结果
         * 
         * @param longLongTuple2
         * @return
         */
        @Override
        public Double getResult(Tuple2<Long, Long> acc) {
            return ((double) acc.f0) / acc.f1;
        }

        /**
         * 累加器合并
         * 
         * @param longLongTuple2
         * @param acc1
         * @return
         */
        @Override
        public Tuple2<Long, Long> merge(Tuple2<Long, Long> acc1, Tuple2<Long, Long> acc2) {
            return new Tuple2<>(acc1.f0 + acc2.f0, acc1.f1 + acc2.f1);
        }
    }

}

标签：acc,return,Flink,累加器,Tuple2,AggregateFunction,Window,Tuple3,public
来源： https://www.cnblogs.com/linjiqin/p/12591710.html