Flink（三）Flink自定义Data Source

柔光的暖阳◎ 2022-02-19 03:27 795阅读 0赞

## 一、Flink Kafka source：Flink默认实现的自定义Data Source ##

> 准备工作

我们先来看下 Flink 从 Kafka topic 中获取数据的 demo，首先你需要安装好了 Flink 和 Kafka 。

运行启动 Flink、Zookepeer、Kafka

[![006tNbRwly1fwuuz88g2oj31kw0d5gt5.jpg][]][006tNbRwly1fwuuz88g2oj31kw0d5gt5.jpg]

[![006tNbRwly1fwuv1xdaq5j31kw08zq7u.jpg][]][006tNbRwly1fwuv1xdaq5j31kw08zq7u.jpg]

好了，都启动了！

> maven 依赖

<dependency>
    	<groupId>org.apache.flink</groupId>
    	<artifactId>flink-java</artifactId>
    	<version>${flink.version}</version>
    	<scope>provided</scope>
    </dependency>
    <dependency>
    	<groupId>org.apache.flink</groupId>
    	<artifactId>flink-streaming-java_${scala.binary.version}</artifactId>
    	<version>${flink.version}</version>
    	<scope>provided</scope>
    </dependency>
    
    <dependency>
    	<groupId>org.slf4j</groupId>
    	<artifactId>slf4j-log4j12</artifactId>
    	<version>1.7.7</version>
    	<scope>runtime</scope>
    </dependency>
    <dependency>
    	<groupId>log4j</groupId>
    	<artifactId>log4j</artifactId>
    	<version>1.2.17</version>
    	<scope>runtime</scope>
    </dependency>
    
    <dependency>
    	<groupId>org.apache.flink</groupId>
    	<artifactId>flink-connector-kafka-0.11_${scala.binary.version}</artifactId>
    	<version>${flink.version}</version>
    </dependency>
    
    <dependency>
    	<groupId>com.alibaba</groupId>
    	<artifactId>fastjson</artifactId>
    	<version>1.2.51</version>
    </dependency>

> 测试发送数据到 kafka topic
> 
> 实体类，Metric.java

package com.zhisheng.flink.model;
    
    import java.util.Map;
    
    /**
     * Desc:
     * weixi: zhisheng_tian
     * blog: http://www.54tianzhisheng.cn/
     */
    public class Metric {
        public String name;
        public long timestamp;
        public Map<String, Object> fields;
        public Map<String, String> tags;
    
        public Metric() {
        }
    
        public Metric(String name, long timestamp, Map<String, Object> fields, Map<String, String> tags) {
            this.name = name;
            this.timestamp = timestamp;
            this.fields = fields;
            this.tags = tags;
        }
    
        @Override
        public String toString() {
            return "Metric{" +
                    "name='" + name + '\'' +
                    ", timestamp='" + timestamp + '\'' +
                    ", fields=" + fields +
                    ", tags=" + tags +
                    '}';
        }
    
        public String getName() {
            return name;
        }
    
        public void setName(String name) {
            this.name = name;
        }
    
        public long getTimestamp() {
            return timestamp;
        }
    
        public void setTimestamp(long timestamp) {
            this.timestamp = timestamp;
        }
    
        public Map<String, Object> getFields() {
            return fields;
        }
    
        public void setFields(Map<String, Object> fields) {
            this.fields = fields;
        }
    
        public Map<String, String> getTags() {
            return tags;
        }
    
        public void setTags(Map<String, String> tags) {
            this.tags = tags;
        }
    }

> 往 kafka 中写数据工具类：KafkaUtils.java

import com.alibaba.fastjson.JSON;
    import com.zhisheng.flink.model.Metric;
    import org.apache.kafka.clients.producer.KafkaProducer;
    import org.apache.kafka.clients.producer.ProducerRecord;
    
    import java.util.HashMap;
    import java.util.Map;
    import java.util.Properties;
    
    /**
     * 往kafka中写数据
     * 可以使用这个main函数进行测试一下
     * weixin: zhisheng_tian 
     * blog: http://www.54tianzhisheng.cn/
     */
    public class KafkaUtils {
        public static final String broker_list = "localhost:9092";
        public static final String topic = "metric";  // kafka topic，Flink 程序中需要和这个统一 
    
        public static void writeToKafka() throws InterruptedException {
            Properties props = new Properties();
            props.put("bootstrap.servers", broker_list);
            props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer"); //key 序列化
            props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer"); //value 序列化
            KafkaProducer producer = new KafkaProducer<String, String>(props);
    
            Metric metric = new Metric();
            metric.setTimestamp(System.currentTimeMillis());
            metric.setName("mem");
            Map<String, String> tags = new HashMap<>();
            Map<String, Object> fields = new HashMap<>();
    
            tags.put("cluster", "zhisheng");
            tags.put("host_ip", "101.147.022.106");
    
            fields.put("used_percent", 90d);
            fields.put("max", 27244873d);
            fields.put("used", 17244873d);
            fields.put("init", 27244873d);
    
            metric.setTags(tags);
            metric.setFields(fields);
    
            ProducerRecord record = new ProducerRecord<String, String>(topic, null, null, JSON.toJSONString(metric));
            producer.send(record);
            System.out.println("发送数据: " + JSON.toJSONString(metric));
    
            producer.flush();
        }
    
        public static void main(String[] args) throws InterruptedException {
            while (true) {
                Thread.sleep(300);
                writeToKafka();
            }
        }
    }

运行：

[![006tNbRwly1fwuv8pz2dmj31kw0zk7lu.jpg][]][006tNbRwly1fwuv8pz2dmj31kw0zk7lu.jpg]

如果出现如上图标记的，即代表能够不断的往 kafka 发送数据的。

Flink 程序

> Main.java

package com.zhisheng.flink;
    
    import org.apache.flink.api.common.serialization.SimpleStringSchema;
    import org.apache.flink.streaming.api.datastream.DataStreamSource;
    import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
    import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer011;
    
    import java.util.Properties;
    
    /**
     * Desc:
     * weixi: zhisheng_tian
     * blog: http://www.54tianzhisheng.cn/
     */
    public class Main {
        public static void main(String[] args) throws Exception {
            final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
    
            Properties props = new Properties();
            props.put("bootstrap.servers", "localhost:9092");
            props.put("zookeeper.connect", "localhost:2181");
            props.put("group.id", "metric-group");
            props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");  //key 反序列化
            props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
            props.put("auto.offset.reset", "latest"); //value 反序列化
    
            DataStreamSource<String> dataStreamSource = env.addSource(new FlinkKafkaConsumer011<>(
                    "metric",  //kafka topic
                    new SimpleStringSchema(),  // String 序列化
                    props)).setParallelism(1);
    
            dataStreamSource.print(); //把从 kafka 读取到的数据打印在控制台
    
            env.execute("Flink add data source");
        }
    }

运行起来：

[![006tNbRwly1fwuvbauvvgj31kw0zkqso.jpg][]][006tNbRwly1fwuvbauvvgj31kw0zkqso.jpg]

## 二、自定义 Source ##

上面就是 Flink 自带的 Kafka source，那么接下来就模仿着写一个从 MySQL 中读取数据的 Source。

> 首先 pom.xml 中**添加 MySQL 依赖**：

<dependency>
    	<groupId>mysql</groupId>
    	<artifactId>mysql-connector-java</artifactId>
    	<version>5.1.34</version>
    </dependency>

> **数据库建表**如下：

DROP TABLE IF EXISTS `student`;
    CREATE TABLE `student` (
      `id` int(11) unsigned NOT NULL AUTO_INCREMENT,
      `name` varchar(25) COLLATE utf8_bin DEFAULT NULL,
      `password` varchar(25) COLLATE utf8_bin DEFAULT NULL,
      `age` int(10) DEFAULT NULL,
      PRIMARY KEY (`id`)
    ) ENGINE=InnoDB AUTO_INCREMENT=5 DEFAULT CHARSET=utf8 COLLATE=utf8_bin;

> **插入数据**：

INSERT INTO `student` VALUES ('1', 'zhisheng01', '123456', '18'), ('2', 'zhisheng02', '123', '17'), ('3', 'zhisheng03', '1234', '18'), ('4', 'zhisheng04', '12345', '16');
    COMMIT;

> **新建实体类**：Student.java

package com.zhisheng.flink.model;
    
    /**
     * Desc:
     * weixi: zhisheng_tian
     * blog: http://www.54tianzhisheng.cn/
     */
    public class Student {
        public int id;
        public String name;
        public String password;
        public int age;
    
        public Student() {
        }
    
        public Student(int id, String name, String password, int age) {
            this.id = id;
            this.name = name;
            this.password = password;
            this.age = age;
        }
    
        @Override
        public String toString() {
            return "Student{" +
                    "id=" + id +
                    ", name='" + name + '\'' +
                    ", password='" + password + '\'' +
                    ", age=" + age +
                    '}';
        }
    
        public int getId() {
            return id;
        }
    
        public void setId(int id) {
            this.id = id;
        }
    
        public String getName() {
            return name;
        }
    
        public void setName(String name) {
            this.name = name;
        }
    
        public String getPassword() {
            return password;
        }
    
        public void setPassword(String password) {
            this.password = password;
        }
    
        public int getAge() {
            return age;
        }
    
        public void setAge(int age) {
            this.age = age;
        }
    }

> **新建 Source 类** SourceFromMySQL.java，该类继承 RichSourceFunction ，实现里面的 open、close、run、cancel 方法：

package com.zhisheng.flink.source;
    
    import com.zhisheng.flink.model.Student;
    import org.apache.flink.configuration.Configuration;
    import org.apache.flink.streaming.api.functions.source.RichSourceFunction;
    
    import java.sql.Connection;
    import java.sql.DriverManager;
    import java.sql.PreparedStatement;
    import java.sql.ResultSet;
    
    
    /**
     * Desc:
     * weixi: zhisheng_tian
     * blog: http://www.54tianzhisheng.cn/
     */
    public class SourceFromMySQL extends RichSourceFunction<Student> {
    
        PreparedStatement ps;
        private Connection connection;
    
        /**
         * open() 方法中建立连接，这样不用每次 invoke 的时候都要建立连接和释放连接。
         *
         * @param parameters
         * @throws Exception
         */
        @Override
        public void open(Configuration parameters) throws Exception {
            super.open(parameters);
            connection = getConnection();
            String sql = "select * from Student;";
            ps = this.connection.prepareStatement(sql);
        }
    
        /**
         * 程序执行完毕就可以进行，关闭连接和释放资源的动作了
         *
         * @throws Exception
         */
        @Override
        public void close() throws Exception {
            super.close();
            if (connection != null) { //关闭连接和释放资源
                connection.close();
            }
            if (ps != null) {
                ps.close();
            }
        }
    
        /**
         * DataStream 调用一次 run() 方法用来获取数据
         *
         * @param ctx
         * @throws Exception
         */
        @Override
        public void run(SourceContext<Student> ctx) throws Exception {
            ResultSet resultSet = ps.executeQuery();
            while (resultSet.next()) {
                Student student = new Student(
                        resultSet.getInt("id"),
                        resultSet.getString("name").trim(),
                        resultSet.getString("password").trim(),
                        resultSet.getInt("age"));
                ctx.collect(student);
            }
        }
    
        @Override
        public void cancel() {
        }
    
        private static Connection getConnection() {
            Connection con = null;
                try {
                    Class.forName("com.mysql.jdbc.Driver");
                    con = DriverManager.getConnection("jdbc:mysql://localhost:3306/test?useUnicode=true&characterEncoding=UTF-8", "root", "root123456");
                } catch (Exception e) {
                    System.out.println("-----------mysql get connection has exception , msg = "+ e.getMessage());
                }
            return con;
        }
    }

> **Flink 程序**：

package com.zhisheng.flink;
    
    import com.zhisheng.flink.source.SourceFromMySQL;
    import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
    
    /**
     * Desc:
     * weixi: zhisheng_tian
     * blog: http://www.54tianzhisheng.cn/
     */
    public class Main2 {
        public static void main(String[] args) throws Exception {
            final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
    
            env.addSource(new SourceFromMySQL()).print();
    
            env.execute("Flink add data sourc");
        }
    }

> 运行 Flink 程序，控制台日志中可以看见打印的 student 信息。

[![006tNbRwly1fwuxfzfmvyj31kw0zknll.jpg][]][006tNbRwly1fwuxfzfmvyj31kw0zknll.jpg]

## 三、RichSourceFunction ##

从上面自定义的 Source 可以看到我们继承的就是这个 RichSourceFunction 类，那么来了解一下：

[![006tNbRwly1fwuxklp906j31ki122n3n.jpg][]][006tNbRwly1fwuxklp906j31ki122n3n.jpg]

一个抽象类，继承自 AbstractRichFunction。为实现一个 Rich SourceFunction 提供基础能力。该类的子类有三个，两个是抽象类，在此基础上提供了更具体的实现，另一个是 ContinuousFileMonitoringFunction。

[![006tNbRwly1fwuy2kb4w1j31kw0tcdpj.jpg][]][006tNbRwly1fwuy2kb4w1j31kw0tcdpj.jpg]

*  MessageAcknowledgingSourceBase ：它针对的是数据源是消息队列的场景并且提供了基于 ID 的应答机制。
 *  MultipleIdsMessageAcknowledgingSourceBase ： 在 MessageAcknowledgingSourceBase 的基础上针对 ID 应答机制进行了更为细分的处理，支持两种 ID 应答模型：session id 和 unique message id。
 *  ContinuousFileMonitoringFunction：这是单个（非并行）监视任务，它接受 FileInputFormat，并且根据 FileProcessingMode 和 FilePathFilter，它负责监视用户提供的路径；决定应该进一步读取和处理哪些文件；创建与这些文件对应的 FileInputSplit 拆分，将它们分配给下游任务以进行进一步处理。

原创地址为：[http://www.54tianzhisheng.cn/2018/10/28/flink-sources/][http_www.54tianzhisheng.cn_2018_10_28_flink-sources]

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[http_www.54tianzhisheng.cn_2018_10_28_flink-sources]: http://www.54tianzhisheng.cn/2018/10/28/flink-sources/