Flink基础系列15-Tranform之基本转换算子(map/flatMap/filter) 一.转换算子 二.代码 参考:

一.转换算子

1.1 map

从如下图解可以看到，map是一对一的操作，对dataStream中的计算，一对一输出

DataStream<Integer> mapStram = dataStream.map(new MapFunction<String, Integer>() {
            public Integer map(String value) throws Exception {
                return value.length();
            } 
        });

1.2 flatMap

flatMap是一个输入，多个输出，例如通过"，" 分隔符将

DataStream<String> flatMapStream = dataStream.flatMap(new FlatMapFunction<String, String>() {
            public void flatMap(String value, Collector<String> out) throws Exception {
                String[] fields = value.split(",");
                for( String field: fields )
                    out.collect(field);
            } 
        });

1.3 Filter

Filter可以理解为SQL语句中的where子句，过滤数据用的

DataStream<Interger> filterStream = dataStream.filter(new FilterFunction<String>() {
            public boolean filter(String value) throws Exception {
                return value == 1; 
            } 
        });

二.代码

数据准备:
sensor.txt
sensor_1 1547718199 35.8
sensor_6 1547718201, 15.4
sensor_7 1547718202, 6.7
sensor_10 1547718205 38.1

代码:

package org.flink.transform;

/**
 * @author 只是甲
 * @date   2021-08-31
 * @remark Flink 基础Transform  map、flatMap、filter
 */

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.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;


public class TransformTest1_Base {
    public static void main(String[] args) throws Exception{
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        // 从文件读取数据
        DataStream<String> inputStream = env.readTextFile("C:\\Users\\Administrator\\IdeaProjects\\FlinkStudy\\src\\main\\resources\\sensor.txt");

        // 1. map，把String转换成长度输出
        DataStream<Integer> mapStream = inputStream.map(new MapFunction<String, Integer>() {
            @Override
            public Integer map(String value) throws Exception {
                return value.length();
            }
        });

        // 2. flatmap，按逗号分字段
        DataStream<String> flatMapStream = inputStream.flatMap(new FlatMapFunction<String, String>() {
            @Override
            public void flatMap(String value, Collector<String> out) throws Exception {
                String[] fields = value.split(",");
                for( String field: fields )
                    out.collect(field);
            }
        });

        // 3. filter, 筛选sensor_1开头的id对应的数据
        DataStream<String> filterStream = inputStream.filter(new FilterFunction<String>() {
            @Override
            public boolean filter(String value) throws Exception {
                return value.startsWith("sensor_1");
            }
        });

        // 打印输出
        mapStream.print("map");
        flatMapStream.print("flatMap");
        filterStream.print("filter");

        env.execute();
    }
}

运行结果:

Flink是基于数据流的处理，所以是来一条处理一条，由于并行度是1所以3个算子计算一个就输出一个。

这里，我把并行度改为2，再来看输出，就可以看到输出不一样了。

参考:

1. https://www.bilibili.com/video/BV1qy4y1q728
2. https://ashiamd.github.io/docsify-notes/#/study/BigData/Flink/%E5%B0%9A%E7%A1%85%E8%B0%B7Flink%E5%85%A5%E9%97%A8%E5%88%B0%E5%AE%9E%E6%88%98-%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0?id=_521-%e4%bb%8e%e9%9b%86%e5%90%88%e8%af%bb%e5%8f%96%e6%95%b0%e6%8d%ae