1：map，flatMap，Filter

package com.atguigu.transfrom;

import org.apache.flink.api.common.functions.FilterFunction;
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;

import java.util.Arrays;

public class Test1 {

    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        String path ="D:\\大数据组件API\\Flink\\Flink01\\src\\main\\resources\\hello.txt";
        DataStreamSource<String> dataStream = env.readTextFile(path);


        //map,返回每行字符的长度
        SingleOutputStreamOperator<Integer> map = dataStream.map(new MapFunction<String, Integer>() {
            @Override
            public Integer map(String value) throws Exception {
                return value.length();
            }
        });

        //flatMap,获取是以h开头的单词
        SingleOutputStreamOperator<String> flatMap = dataStream.flatMap(new FlatMapFunction<String, String>() {
            @Override
            public void flatMap(String value, Collector<String> out) throws Exception {
                String[] split = value.split(" ");
                for (String s : split) {
                    if (s.startsWith("h")) {
                        out.collect(s);
                    }
                }
            }
        });

        //filter,过滤掉不是h开头的单词
        SingleOutputStreamOperator<String> filter = dataStream.filter(new FilterFunction<String>() {
            @Override
            public boolean filter(String value) throws Exception {

                    if (value.startsWith("h")) {
                        return true;
                    } else {
                        return false;
                    }
            }
        });

        flatMap.print();
        env.execute();


    }
}

2：KeyBy

DataStream → KeyedStream：逻辑地将一个流拆分成不相交的分区，每个分区包含具有相同 key 的元素，在内部以 hash 的形式实现的。

3：滚动聚合

这些算子可以针对 KeyedStream 的每一个支流做聚合。

? sum()

? min()

? max()

? minBy()

? maxBy()

package com.atguigu.transfrom;

import com.atguigu.bean.SensorReading;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.tuple.Tuple;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;

public class Test2 {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        String path ="D:\\大数据组件API\\Flink\\Flink01\\src\\main\\resources\\test.txt";
        DataStreamSource<String> dataStream = env.readTextFile(path);

        DataStream<SensorReading> map = dataStream.map( value -> {
            String[] split = value.split(",");
            return new SensorReading(split[0], new Long(split[1]), new Double(split[2]));
        });

        //分组
        KeyedStream<SensorReading, Tuple> keyedStream = map.keyBy("id");
//        KeyedStream<SensorReading, String> keyedStream1 = map.keyBy(SensorReading::getId);

        //滚动聚合
        //max仅更新temperature数据，其他数据和以前一样
        SingleOutputStreamOperator<SensorReading> temperature = keyedStream.max("temperature");
        //maxBy获取最大temperature数据，其他字段是最大temperature对应的数据
        SingleOutputStreamOperator<SensorReading> maxBy = keyedStream.maxBy("temperature");


        maxBy.print();
        env.execute();
    }
}

4：Reduce

KeyedStream → DataStream ：一个分组数据流的聚合操作，合并当前的元素和上次聚合的结果，产生一个新的值，返回的流中包含每一次聚合的结果，而不是只返回最后一次聚合的最终结果

package com.atguigu.transfrom;

import com.atguigu.bean.SensorReading;
import org.apache.flink.api.common.functions.ReduceFunction;
import org.apache.flink.api.java.tuple.Tuple;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;

public class Test3 {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        String path ="D:\\大数据组件API\\Flink\\Flink01\\src\\main\\resources\\test.txt";
        DataStreamSource<String> dataStream = env.readTextFile(path);

        DataStream<SensorReading> map = dataStream.map( value -> {
            String[] split = value.split(",");
            return new SensorReading(split[0], new Long(split[1]), new Double(split[2]));
        });

        //分组
        KeyedStream<SensorReading, Tuple> keyedStream = map.keyBy("id");

        SingleOutputStreamOperator<SensorReading> reduce = keyedStream.reduce(new ReduceFunction<SensorReading>() {
            @Override
            public SensorReading reduce(SensorReading value1, SensorReading value2) throws Exception {
                return new SensorReading(value1.getId(),value2.getTimestamp(),
                        Math.max(value1.getTemperature(),value2.getTemperature()));
            }
        });


        reduce.print();
        env.execute();
    }
}

[大数据]Flink流处理——Transform分组聚合

1：map，flatMap，Filter

2：KeyBy

3：滚动聚合

4：Reduce