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07、Flink实战：如何自定义DataSource？

分类：Flink实战（3）    2024-03-14    阅读(117)

前言

上一节，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，

[*]nbsp_nbsp

[*]nbsp_nbsp 1

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>

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：

[*]nbsp_nbsp 2

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：

[*]nbsp_nbsp 3

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>

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。

[*]nbsp_nbsp 4

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：

[*]nbsp_nbsp 5

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。

[*]nbsp_nbsp 6

  • 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。

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