Flink1.10实战：两种分流器Spilt-Select和Side-Outputs

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一、概述

    Flink两种分流器Split和Side-Outputs，新版本中Split分流接口已经被置为“deprecated”，Split只可以进行一级分流，不能进行二级分流，Flink新版本推荐使用Side-Outputs分流器，它支持多级分流。

二、分流器使用

我这里有一份演示数据，里面是人的一些籍贯信息，每条数据有5个字段，分别代表：姓名、所在省份、所在城市、年龄、身份证号码，这里一级分流主要是将不同省份的人进行分流、二级分流在一级分流的基础上对各个省份的人进行城市分流，这里先给大家画一个分流流程图：

1.数据准备,人员信息

lujisen1,shandong,jinan,18,370102198606431256

lujisen2,jiangsu,nanjing,19,330102198606431256

lujisen3,shandong,qingdao,20,370103198606431256

lujisen4,jiangsu,suzhou,21,330104198606431256

2.定义一个人员信息类PersonInfo,代码如下：

package com.hadoop.ljs.flink110.split;

/**

* @author: Created By lujisen

* @company ChinaUnicom Software JiNan

* @date: 2020-04-05 09:20

* @version: v1.0

* @description: com.hadoop.ljs.flink110.split

*/

public class PersonInfo {

    String name;

    String province;

    String city;

    int age;

    String idCard;

    public String getName() {

        return name;

    }

    public void setName(String name) {

        this.name = name;

    }

    public String getProvince() {

        return province;

    }

    public void setProvince(String province) {

        this.province = province;

    }

    public String getCity() {

        return city;

    }

    public void setCity(String city) {

        this.city = city;

    }

    public int getAge() {

        return age;

    }

    public void setAge(int age) {

        this.age = age;

    }

  public String getIdCard() {

        return idCard;

    }

    public void setIdCard(String idCard) {

        this.idCard = idCard;

    }

  public String toString(){

        return "name:"+name +" province:"+province+" city:"+city+" age:"+age+" idCard"+idCard;

    }

}

3.先用Split进行一级分流，代码如下：

package com.hadoop.ljs.flink110.split;

import org.apache.flink.api.common.functions.MapFunction;

import org.apache.flink.streaming.api.collector.selector.OutputSelector;

import org.apache.flink.streaming.api.datastream.DataStream;

import org.apache.flink.streaming.api.datastream.SplitStream;

import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;

import java.util.ArrayList;

import java.util.List;

/**

* @author: Created By lujisen

* @company ChinaUnicom Software JiNan

* @date: 2020-04-05 09:14

* @version: v1.0

* @description: com.hadoop.ljs.flink110

*/

public class SplitSelectTest {

    public static void main(String[] args) throws Exception {

        StreamExecutionEnvironment senv= StreamExecutionEnvironment.getExecutionEnvironment();

        /*为方便测试 这里把并行度设置为1*/

        senv.setParallelism(1);

        DataStream sourceData = senv.readTextFile("D:\\projectData\\sideOutputTest.txt");

        DataStream personStream = sourceData.map(new MapFunction() {

            @Override

            public PersonInfo map(String s) throws Exception {

                String[] lines = s.split(",");

                PersonInfo personInfo = new PersonInfo();

                personInfo.setName(lines[0]);

                personInfo.setProvince(lines[1]);

                personInfo.setCity(lines[2]);

                personInfo.setAge(Integer.valueOf(lines[3]));

                personInfo.setIdCard(lines[4]);

                return personInfo;

            }

        });

        //这里是用spilt-slect进行一级分流

        SplitStream splitProvinceStream = personStream.split(new OutputSelector() {

            @Override

            public Iterable select(PersonInfo personInfo) {

                List split = new ArrayList<>();

                if ("shandong".equals(personInfo.getProvince())) {

                    split.add("shandong");

                } else if ("jiangsu".equals(personInfo.getProvince())) {

                    split.add("jiangsu");

                }

                return split;

            }

        });

        DataStream shandong = splitProvinceStream.select("shandong");

        DataStream jiangsu = splitProvinceStream.select("jiangsu");

        /*一级分流结果*/

        shandong.map(new MapFunction() {

            @Override

            public String map(PersonInfo personInfo) throws Exception {

                return personInfo.toString();

            }

        }).print("山东分流结果:");

        /*一级分流结果*/

        jiangsu.map(new MapFunction() {

            @Override

            public String map(PersonInfo personInfo) throws Exception {

                return personInfo.toString();

            }

        }).print("江苏分流结果: ");

        senv.execute();

    }

}

    分流结果输出：

4.这里如果我们用Split对分流后的山东人进行二级分流，代码如下：

package com.hadoop.ljs.flink110.split;

import org.apache.flink.api.common.functions.MapFunction;

import org.apache.flink.streaming.api.collector.selector.OutputSelector;

import org.apache.flink.streaming.api.datastream.DataStream;

import org.apache.flink.streaming.api.datastream.SplitStream;

import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;

import java.util.ArrayList;

import java.util.List;

/**

* @author: Created By lujisen

* @company ChinaUnicom Software JiNan

* @date: 2020-04-05 09:14

* @version: v1.0

* @description: com.hadoop.ljs.flink110

*/

public class SplitSelectTest {

    public static void main(String[] args) throws Exception {

        StreamExecutionEnvironment senv= StreamExecutionEnvironment.getExecutionEnvironment();

        /*为方便测试 这里把并行度设置为1*/

        senv.setParallelism(1);

        DataStream sourceData = senv.readTextFile("D:\\projectData\\sideOutputTest.txt");

        DataStream personStream = sourceData.map(new MapFunction() {

            @Override

            public PersonInfo map(String s) throws Exception {

                String[] lines = s.split(",");

                PersonInfo personInfo = new PersonInfo();

                personInfo.setName(lines[0]);

                personInfo.setProvince(lines[1]);

                personInfo.setCity(lines[2]);

                personInfo.setAge(Integer.valueOf(lines[3]));

                personInfo.setIdCard(lines[4]);

                return personInfo;

            }

        });

        SplitStream splitProvinceStream = personStream.split(new OutputSelector() {

            @Override

            public Iterable select(PersonInfo personInfo) {

                List split = new ArrayList<>();

                if ("shandong".equals(personInfo.getProvince())) {

                    split.add("shandong");

                } else if ("jiangsu".equals(personInfo.getProvince())) {

                    split.add("jiangsu");

                }

                return split;

            }

        });

        //到这里一级分流没有问题

        DataStream shandong = splitProvinceStream.select("shandong");

        DataStream jiangsu = splitProvinceStream.select("jiangsu");

        //下面就是二级分流，由于split不支持二级分流，这里会报错

        SplitStream splitSDCityStream = shandong.split(new OutputSelector() {

            @Override

            public Iterable select(PersonInfo personInfo) {

                List split = new ArrayList<>();

                if ("jinan".equals(personInfo.getProvince())) {

                    split.add("jinan");

                } else if ("qingdao".equals(personInfo.getProvince())) {

                    split.add("qingdao");

                }

                return split;

            }

        });

        DataStream jinan = splitSDCityStream.select("jinan");

        DataStream qingdao = splitSDCityStream.select("qingdao");

        jinan.map(new MapFunction() {

            @Override

            public String map(PersonInfo personInfo) throws Exception {

                return personInfo.toString();

            }

        }).print("山东-济南二级分流结果:");

        qingdao.map(new MapFunction() {

            @Override

            public String map(PersonInfo personInfo) throws Exception {

                return personInfo.toString();

            }

        }).print("山东-青岛二级分流结果:");

        senv.execute();

    }

}

    这里用Split进行二级分流会报错，报错信息如下，建议用side-outputs进行分流：

5.鉴于Spilt不能进行二级分流，我们用Side-Outputs进行二级分流，代码如下：

package com.hadoop.ljs.flink110.split;

import org.apache.flink.api.common.functions.MapFunction;

import org.apache.flink.streaming.api.collector.selector.OutputSelector;

import org.apache.flink.streaming.api.datastream.DataStream;

import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;

import org.apache.flink.streaming.api.datastream.SplitStream;

import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;

import org.apache.flink.streaming.api.functions.ProcessFunction;

import org.apache.flink.util.Collector;

import org.apache.flink.util.OutputTag;

import java.util.ArrayList;

import java.util.List;

/**

* @author: Created By lujisen

* @company ChinaUnicom Software JiNan

* @date: 2020-04-05 09:14

* @version: v1.0

* @description: com.hadoop.ljs.flink110

*/

public class SideOutputTest {

    public static void main(String[] args) throws Exception {

        StreamExecutionEnvironment senv= StreamExecutionEnvironment.getExecutionEnvironment();

        /*为方便测试 这里把并行度设置为1*/

        senv.setParallelism(1);

        DataStream sourceData = senv.readTextFile("D:\\projectData\\sideOutputTest.txt");

        DataStream personStream = sourceData.map(new MapFunction() {

            @Override

            public PersonInfo map(String s) throws Exception {

                String[] lines = s.split(",");

                PersonInfo personInfo = new PersonInfo();

                personInfo.setName(lines[0]);

                personInfo.setProvince(lines[1]);

                personInfo.setCity(lines[2]);

                personInfo.setAge(Integer.valueOf(lines[3]));

                personInfo.setIdCard(lines[4]);

                return personInfo;

            }

        });

        //定义流分类标识  进行一级分流

        OutputTag shandongTag = new OutputTag("shandong") {};

        OutputTag jiangsuTag = new OutputTag("jiangsu") {};

        SingleOutputStreamOperator splitProvinceStream = personStream.process(new ProcessFunction() {

            @Override

            public void processElement(PersonInfo person, Context context, Collector collector)

                    throws Exception {

                if ("shandong".equals(person.getProvince())) {

                    context.output(shandongTag, person);

                } else if ("jiangsu".equals(person.getProvince())) {

                    context.output(jiangsuTag, person);

                }

            }

        });

        DataStream shandongStream = splitProvinceStream.getSideOutput(shandongTag);

        DataStream jiangsuStream = splitProvinceStream.getSideOutput(jiangsuTag);

        /*下面对数据进行二级分流，我这里只对山东的这个数据流进行二级分流，江苏流程也一样*/

        OutputTag jinanTag = new OutputTag("jinan") {};

        OutputTag qingdaoTag = new OutputTag("qingdao") {};

        SingleOutputStreamOperator cityStream = shandongStream.process(new ProcessFunction() {

            @Override

            public void processElement(PersonInfo person, Context context, Collector collector)

                    throws Exception {

                if ("jinan".equals(person.getCity())) {

                    context.output(jinanTag, person);

                } else if ("qingdao".equals(person.getCity())) {

                    context.output(qingdaoTag, person);

                }

            }

        });

        DataStream jinan = cityStream.getSideOutput(jinanTag);

        DataStream qingdao = cityStream.getSideOutput(qingdaoTag);

        jinan.map(new MapFunction() {

            @Override

            public String map(PersonInfo personInfo) throws Exception {

                return personInfo.toString();

            }

        }).print("山东-济南二级分流结果:");

        qingdao.map(new MapFunction() {

            @Override

            public String map(PersonInfo personInfo) throws Exception {

                return personInfo.toString();

            }

        }).print("山东-青岛二级分流结果:");

        senv.execute();

    }

}

    分流结果如下图所示：

至此，分流演示完毕，我们知道Split-Select只能进行一级分流，二Side-Ouputs可以进行二级及以上分流，这里多级分流我就不再演示，道理是一样的，平时我们也经常用Fliter进行分流，那个比较简单，有空自己实操下就行，感谢关注！！！

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