package com.hao.chapter06;

import com.hao.chapter05.ClickSource;
import com.hao.chapter05.Event;
import org.apache.flink.api.common.eventtime.SerializableTimestampAssigner;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.common.functions.AggregateFunction;
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.assigners.TumblingEventTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;


import java.sql.Timestamp;
import java.time.Duration;

public class WindowAggregateTest {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        // 读取数据，并提取时间戳、生成水位线
        DataStream<Event> stream = env.addSource(new ClickSource())
                .assignTimestampsAndWatermarks(WatermarkStrategy.<Event>forBoundedOutOfOrderness(Duration.ZERO)
                        .withTimestampAssigner(new SerializableTimestampAssigner<Event>() {
                            @Override
                            public long extractTimestamp(Event element, long recordTimestamp) {
                                return element.timestamp;
                            }
                        }));

        stream.keyBy(data -> data.user)
                .window(TumblingEventTimeWindows.of(Time.seconds(10)))
                //举例，想要知道当前每一个用户他访问的那个网站的时间戳的平均数。
                .aggregate(new AggregateFunction<Event, Tuple2<Long,Integer>, String>() {
                    @Override
                    public Tuple2<Long, Integer> createAccumulator() {
                        // 创建累加器
                        return Tuple2.of(0L,0);
                    }

                    @Override
                    public Tuple2<Long, Integer> add(Event value, Tuple2<Long, Integer> accumulator) {
                        // 累加器相加
                        // 属于本窗口的数据来一条累加一次，并返回累加器
                        return Tuple2.of(accumulator.f0 + value.timestamp,accumulator.f1 + 1);
                    }

                    @Override
                    public String getResult(Tuple2<Long, Integer> accumulator) {
                        // 窗口闭合时，增量聚合结束，将计算结果发送到下游
                        Timestamp timestamp = new Timestamp(accumulator.f0 / accumulator.f1);
                        return timestamp.toString();
                    }

                    @Override
                    public Tuple2<Long, Integer> merge(Tuple2<Long, Integer> a, Tuple2<Long, Integer> b) {
                        return Tuple2.of(a.f0 + b.f0 , a.f1 + b.f1);
                    }
                })
                .print();

        env.execute();

    }
}