FLink12--KeyByReduceApp

一、依赖

参考博文：https://www.cnblogs.com/robots2/p/16048648.html

二、代码

package net.xdclass.class9;

import java.util.Date;

import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.functions.ReduceFunction;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;

import net.xdclass.model.VideoOrder;

/**
 * @desc reduce算子,和sum类似，sum做简单聚合，reduce做复杂聚合
 * aggregate支持更复杂聚合
 * @menu
 */
public class FLink12KeyByReduceApp {

    public static void main(String[] args) throws Exception{
        //WebUi方式运行
//        final StreamExecutionEnvironment env =
//                StreamExecutionEnvironment.createLocalEnvironmentWithWebUI(new Configuration());
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        //设置运行模式为流批一体
        env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
        //并行度
        env.setParallelism(1);
        //设置为自定义source
//        DataStream<VideoOrder> ds = env.addSource(new VideoOrderSourceV2());
        DataStream<VideoOrder> ds = env.fromElements(
                new VideoOrder("20190242812", "springboot教程", 10,1001, new Date()),
                new VideoOrder("20194350812", "微服务SpringCloud", 20,1001, new Date()),
                new VideoOrder("20190814232", "Redis教程", 30,1001, new Date()),
                new VideoOrder("20190523812", "⽹⻚开发教程", 40,1001, new Date()),
                new VideoOrder("201932324", "百万并发实战Netty", 50,1001, new Date()),
                new VideoOrder("20190242812", "springboot教程", 10,1001, new Date()),
                new VideoOrder("20190814232", "Redis教程", 30,1001, new Date()));


        KeyedStream<VideoOrder, Object> videoOrderObjectKeyedStream = ds.keyBy(new KeySelector<VideoOrder, Object>() {
            @Override
            public Object getKey(VideoOrder videoOrder) throws Exception {
                return videoOrder.getTitle();
            }
        });

        //reduce,做聚合。合并数据，返回新的聚合对象。大于两条才会触发
        SingleOutputStreamOperator<VideoOrder> reduceResult = videoOrderObjectKeyedStream.reduce(
                new ReduceFunction<VideoOrder>() {
                    @Override
                    public VideoOrder reduce(VideoOrder value1, VideoOrder value2) throws Exception {
                        VideoOrder reduceOrder = new VideoOrder();
                        reduceOrder.setTitle(value1.getTitle());
                        reduceOrder.setMoney(value1.getMoney() + value2.getMoney());
                        return reduceOrder;
                    }
                });

        reduceResult.print();

        //DataStream需要调用execute,可以取个名称
        env.execute("reduce map job");
    }
}