《从0到1学习Flink》—— 如何自定义 Data Source ？_zhisheng的专栏文章_微信文章

前言

在《从0到1学习Flink》—— Data Source 介绍文章中，我给大家介绍了 Flink Data Source 以及简短的介绍了一下自定义 Data Source，这篇文章更详细的介绍下，并写一个 demo 出来让大家理解。

Flink Kafka 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，

好了，都启动了！

maven 依赖

<!--flink java-->
<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>${flink.version}</version>
	<scope>provided</scope>
</dependency>
<!--日志-->
<dependency>
	<groupId>org.slf4j</groupId>
	<artifactId>slf4j-log4j12</artifactId>
	<version>1.7.7</version>
	<scope>runtime</scope>
</dependency>
<dependency>
	<groupId>log4j</groupId>
	<artifactId>log4j</artifactId>
	<version>1.2.17</version>
	<scope>runtime</scope>
</dependency>
<!--flink kafka connector-->
<dependency>
	<groupId>org.apache.flink</groupId>
	<artifactId>flink-connector-kafka-0.11_${scala.binary.version}</artifactId>
	<version>${flink.version}</version>
</dependency>
<!--alibaba fastjson-->
<dependency>
	<groupId>com.alibaba</groupId>
	<artifactId>fastjson</artifactId>
	<version>1.2.51</version>
</dependency>
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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;
    }
}
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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();
        }
    }
}
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运行：

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");
    }
}
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运行起来：

看到没程序，Flink 程序控制台能够源源不断的打印数据呢。

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>
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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;
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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;
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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;
    }
}
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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
                            

                            






                            
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《从0到1学习Flink》—— 如何自定义 Data Source ？

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