2021年大数据Flink（十一）：流批一体API Source

2021-10-11 10:02:46 浏览数 (1)

Source

预定义Source

基于集合的Source

  • API

一般用于学习测试时编造数据时使用

1.env.fromElements(可变参数);

2.env.fromColletion(各种集合);

3.env.generateSequence(开始,结束);

4.env.fromSequence(开始,结束);

  • 代码演示:
代码语言：javascript复制
package cn.it.source;

import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;

import java.util.Arrays;

/**
 * Author lanson
 * Desc
 * 把本地的普通的Java集合/Scala集合变为分布式的Flink的DataStream集合!
 * 一般用于学习测试时编造数据时使用
 * 1.env.fromElements(可变参数);
 * 2.env.fromColletion(各种集合);
 * 3.env.generateSequence(开始,结束);
 * 4.env.fromSequence(开始,结束);
 */
public class SourceDemo01 {
    public static void main(String[] args) throws Exception {
        //1.env
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
        //2.source
        // * 1.env.fromElements(可变参数);
        DataStream<String> ds1 = env.fromElements("hadoop", "spark", "flink");
        // * 2.env.fromColletion(各种集合);
        DataStream<String> ds2 = env.fromCollection(Arrays.asList("hadoop", "spark", "flink"));
        // * 3.env.generateSequence(开始,结束);
        DataStream<Long> ds3 = env.generateSequence(1, 10);
        //* 4.env.fromSequence(开始,结束);
        DataStream<Long> ds4 = env.fromSequence(1, 10);
        //3.Transformation
        //4.sink
        ds1.print();
        ds2.print();
        ds3.print();
        ds4.print();
        //5.execute
        env.execute();
    }
}

​​​​​​​基于文件的Source

  • API

一般用于学习测试

env.readTextFile(本地/HDFS文件/文件夹);//压缩文件也可以

  • 代码演示:
代码语言：javascript复制
package cn.it.source;

import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;

/**
 * Author lanson
 * Desc
 * 1.env.readTextFile(本地/HDFS文件/文件夹);//压缩文件也可以
 */
public class SourceDemo02 {
    public static void main(String[] args) throws Exception {
        //1.env
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
        //2.source
        // * 1.env.readTextFile(本地文件/HDFS文件);//压缩文件也可以
        DataStream<String> ds1 = env.readTextFile("data/input/words.txt");
        DataStream<String> ds2 = env.readTextFile("data/input/dir");
        DataStream<String> ds3 = env.readTextFile("hdfs://node1:8020//wordcount/input/words.txt");
        DataStream<String> ds4 = env.readTextFile("data/input/wordcount.txt.gz");
        //3.Transformation
        //4.sink
        ds1.print();
        ds2.print();
        ds3.print();
        ds4.print();
        //5.execute
        env.execute();
    }
}

​​​​​​​基于Socket的Source

一般用于学习测试

  • 需求

1.在node1上使用nc -lk 9999 向指定端口发送数据

nc是netcat的简称，原本是用来设置路由器,我们可以利用它向某个端口发送数据

如果没有该命令可以下安装

代码语言：javascript复制
yum install -y nc

2.使用Flink编写流处理应用程序实时统计单词数量

  • 代码实现:
代码语言：javascript复制
package cn.it.source;

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

/**
 * Author lanson
 * Desc
 * SocketSource
 */
public class SourceDemo03 {
    public static void main(String[] args) throws Exception {
        //1.env
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
        //2.source
        DataStream<String> linesDS = env.socketTextStream("node1", 9999);

        //3.处理数据-transformation
        //3.1每一行数据按照空格切分成一个个的单词组成一个集合
        DataStream<String> wordsDS = linesDS.flatMap(new FlatMapFunction<String, String>() {
            @Override
            public void flatMap(String value, Collector<String> out) throws Exception {
                //value就是一行行的数据
                String[] words = value.split(" ");
                for (String word : words) {
                    out.collect(word);//将切割处理的一个个的单词收集起来并返回
                }
            }
        });
        //3.2对集合中的每个单词记为1
        DataStream<Tuple2<String, Integer>> wordAndOnesDS = wordsDS.map(new MapFunction<String, Tuple2<String, Integer>>() {
            @Override
            public Tuple2<String, Integer> map(String value) throws Exception {
                //value就是进来一个个的单词
                return Tuple2.of(value, 1);
            }
        });

        //3.3对数据按照单词(key)进行分组
        //KeyedStream<Tuple2<String, Integer>, Tuple> groupedDS = wordAndOnesDS.keyBy(0);
        KeyedStream<Tuple2<String, Integer>, String> groupedDS = wordAndOnesDS.keyBy(t -> t.f0);
        //3.4对各个组内的数据按照数量(value)进行聚合就是求sum
        DataStream<Tuple2<String, Integer>> result = groupedDS.sum(1);

        //4.输出结果-sink
        result.print();

        //5.触发执行-execute
        env.execute();
    }
}

自定义Source

随机生成数据

  • API

一般用于学习测试,模拟生成一些数据

Flink还提供了数据源接口,我们实现该接口就可以实现自定义数据源，不同的接口有不同的功能，分类如下：

SourceFunction:非并行数据源(并行度只能=1)

RichSourceFunction:多功能非并行数据源(并行度只能=1)

ParallelSourceFunction:并行数据源(并行度能够>=1)

RichParallelSourceFunction:多功能并行数据源(并行度能够>=1)--后续学习的Kafka数据源使用的就是该接口

  • 需求

每隔1秒随机生成一条订单信息(订单ID、用户ID、订单金额、时间戳)

要求:

- 随机生成订单ID(UUID)

- 随机生成用户ID(0-2)

- 随机生成订单金额(0-100)

- 时间戳为当前系统时间

  • 代码实现
代码语言：javascript复制
package cn.it.source;

import lombok.AllArgsConstructor;
import lombok.Data;
import lombok.NoArgsConstructor;
import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.source.RichParallelSourceFunction;

import java.util.Random;
import java.util.UUID;

/**
 * Author lanson
 * Desc
 *需求
 * 每隔1秒随机生成一条订单信息(订单ID、用户ID、订单金额、时间戳)
 * 要求:
 * - 随机生成订单ID(UUID)
 * - 随机生成用户ID(0-2)
 * - 随机生成订单金额(0-100)
 * - 时间戳为当前系统时间
 *
 * API
 * 一般用于学习测试,模拟生成一些数据
 * Flink还提供了数据源接口,我们实现该接口就可以实现自定义数据源，不同的接口有不同的功能，分类如下：
 * SourceFunction:非并行数据源(并行度只能=1)
 * RichSourceFunction:多功能非并行数据源(并行度只能=1)
 * ParallelSourceFunction:并行数据源(并行度能够>=1)
 * RichParallelSourceFunction:多功能并行数据源(并行度能够>=1)--后续学习的Kafka数据源使用的就是该接口
 */
public class SourceDemo04_Customer {
    public static void main(String[] args) throws Exception {
        //1.env
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
        //2.Source
        DataStream<Order> orderDS = env
                .addSource(new MyOrderSource())
                .setParallelism(2);

        //3.Transformation

        //4.Sink
        orderDS.print();
        //5.execute
        env.execute();
    }
    @Data
    @NoArgsConstructor
    @AllArgsConstructor
    public static class Order {
        private String id;
        private Integer userId;
        private Integer money;
        private Long createTime;
    }
    public static class MyOrderSource extends RichParallelSourceFunction<Order> {
        private Boolean flag = true;
        @Override
        public void run(SourceContext<Order> ctx) throws Exception {
            Random random = new Random();
            while (flag){
                Thread.sleep(1000);
                String id = UUID.randomUUID().toString();
                int userId = random.nextInt(3);
                int money = random.nextInt(101);
                long createTime = System.currentTimeMillis();
                ctx.collect(new Order(id,userId,money,createTime));
            }
        }
        //取消任务/执行cancle命令的时候执行
        @Override
        public void cancel() {
            flag = false;
        }
    }
}

​​​​​​​MySQL

  • 需求:

实际开发中,经常会实时接收一些数据,要和MySQL中存储的一些规则进行匹配,那么这时候就可以使用Flink自定义数据源从MySQL中读取数据

那么现在先完成一个简单的需求:

从MySQL中实时加载数据

要求MySQL中的数据有变化,也能被实时加载出来

  • 准备数据
代码语言：javascript复制
CREATE TABLE `t_student` (

    `id` int(11) NOT NULL AUTO_INCREMENT,

    `name` varchar(255) DEFAULT NULL,

    `age` int(11) DEFAULT NULL,

    PRIMARY KEY (`id`)

) ENGINE=InnoDB AUTO_INCREMENT=7 DEFAULT CHARSET=utf8;



INSERT INTO `t_student` VALUES ('1', 'jack', '18');

INSERT INTO `t_student` VALUES ('2', 'tom', '19');

INSERT INTO `t_student` VALUES ('3', 'rose', '20');

INSERT INTO `t_student` VALUES ('4', 'tom', '19');

INSERT INTO `t_student` VALUES ('5', 'jack', '18');

INSERT INTO `t_student` VALUES ('6', 'rose', '20');
  • 代码实现:
代码语言：javascript复制
package cn.it.source;

import lombok.AllArgsConstructor;
import lombok.Data;
import lombok.NoArgsConstructor;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.source.RichParallelSourceFunction;

import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.PreparedStatement;
import java.sql.ResultSet;
import java.util.concurrent.TimeUnit;

/**
 * Author lansnon
 * Desc
 * 需求:
 * 实际开发中,经常会实时接收一些数据,要和MySQL中存储的一些规则进行匹配,那么这时候就可以使用Flink自定义数据源从MySQL中读取数据
 * 那么现在先完成一个简单的需求:
 * 从MySQL中实时加载数据
 * 要求MySQL中的数据有变化,也能被实时加载出来
 */
public class SourceDemo05_Customer_MySQL {
    public static void main(String[] args) throws Exception {
        //1.env
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        //2.Source
        DataStream<Student> studentDS = env.addSource(new MySQLSource()).setParallelism(1);

        //3.Transformation
        //4.Sink
        studentDS.print();

        //5.execute
        env.execute();
    }

    @Data
    @NoArgsConstructor
    @AllArgsConstructor
    public static class Student {
        private Integer id;
        private String name;
        private Integer age;
    }

    public static class MySQLSource extends RichParallelSourceFunction<Student> {
        private Connection conn = null;
        private PreparedStatement ps = null;

        @Override
        public void open(Configuration parameters) throws Exception {
            //加载驱动,开启连接
            //Class.forName("com.mysql.jdbc.Driver");
            conn = DriverManager.getConnection("jdbc:mysql://localhost:3306/bigdata", "root", "root");
            String sql = "select id,name,age from t_student";
            ps = conn.prepareStatement(sql);
        }

        private boolean flag = true;

        @Override
        public void run(SourceContext<Student> ctx) throws Exception {
            while (flag) {
                ResultSet rs = ps.executeQuery();
                while (rs.next()) {
                    int id = rs.getInt("id");
                    String name = rs.getString("name");
                    int age = rs.getInt("age");
                    ctx.collect(new Student(id, name, age));
                }
                TimeUnit.SECONDS.sleep(5);
            }
        }
        @Override
        public void cancel() {
            flag = false;
        }
        @Override
        public void close() throws Exception {
            if (conn != null) conn.close();
            if (ps != null) ps.close();
        }
    }
}
数据库 云数据库SQLServer sql flink api

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