Flink 從 0 到 1 學習 —— 如何自定義 Data Sink ？

時間 2020-07-17

標籤 flink 學習如何自定義 data sink 简体版

原文原文鏈接

前言

前篇文章《從0到1學習Flink》—— Data Sink 介紹介紹了 Flink Data Sink，也介紹了 Flink 自帶的 Sink，那麼如何自定義本身的 Sink 呢？這篇文章將寫一個 demo 教你們將從 Kafka Source 的數據 Sink 到 MySQL 中去。html

java

準備工做

咱們先來看下 Flink 從 Kafka topic 中獲取數據的 demo，首先你須要安裝好了 FLink 和 Kafka 。mysql

運行啓動 Flink、Zookepeer、Kafka，git

好了，都啓動了！github

數據庫建表

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;

實體類

Student.javasql

package com.zhisheng.flink.model;

/**
 * Desc:
 * weixin: 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;
    }
}

工具類

工具類往 kafka topic student 發送數據數據庫

import com.alibaba.fastjson.JSON;
import com.zhisheng.flink.model.Metric;
import com.zhisheng.flink.model.Student;
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中寫數據
 * 可使用這個main函數進行測試一下
 * weixin: zhisheng_tian
 * blog: http://www.54tianzhisheng.cn/
 */
public class KafkaUtils2 {
    public static final String broker_list = "localhost:9092";
    public static final String topic = "student";  //kafka topic 須要和 flink 程序用同一個 topic

    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");
        props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
        KafkaProducer producer = new KafkaProducer<String, String>(props);

        for (int i = 1; i <= 100; i++) {
            Student student = new Student(i, "zhisheng" + i, "password" + i, 18 + i);
            ProducerRecord record = new ProducerRecord<String, String>(topic, null, null, JSON.toJSONString(student));
            producer.send(record);
            System.out.println("發送數據: " + JSON.toJSONString(student));
        }
        producer.flush();
    }

    public static void main(String[] args) throws InterruptedException {
        writeToKafka();
    }
}

SinkToMySQL

該類就是 Sink Function，繼承了 RichSinkFunction ，而後重寫了裏面的方法。在 invoke 方法中將數據插入到 MySQL 中。apache

package com.zhisheng.flink.sink;

import com.zhisheng.flink.model.Student;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.functions.sink.RichSinkFunction;

import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.PreparedStatement;

/**
 * Desc:
 * weixin: zhisheng_tian
 * blog: http://www.54tianzhisheng.cn/
 */
public class SinkToMySQL extends RichSinkFunction<Student> {
    PreparedStatement ps;
    private Connection connection;

    /**
     * open() 方法中創建鏈接，這樣不用每次 invoke 的時候都要創建鏈接和釋放鏈接
     *
     * @param parameters
     * @throws Exception
     */
    @Override
    public void open(Configuration parameters) throws Exception {
        super.open(parameters);
        connection = getConnection();
        String sql = "insert into Student(id, name, password, age) values(?, ?, ?, ?);";
        ps = this.connection.prepareStatement(sql);
    }

    @Override
    public void close() throws Exception {
        super.close();
        //關閉鏈接和釋放資源
        if (connection != null) {
            connection.close();
        }
        if (ps != null) {
            ps.close();
        }
    }

    /**
     * 每條數據的插入都要調用一次 invoke() 方法
     *
     * @param value
     * @param context
     * @throws Exception
     */
    @Override
    public void invoke(Student value, Context context) throws Exception {
        //組裝數據，執行插入操做
        ps.setInt(1, value.getId());
        ps.setString(2, value.getName());
        ps.setString(3, value.getPassword());
        ps.setInt(4, value.getAge());
        ps.executeUpdate();
    }

    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

這裏的 source 是從 kafka 讀取數據的，而後 Flink 從 Kafka 讀取到數據（JSON）後用阿里 fastjson 來解析成 student 對象，而後在 addSink 中使用咱們建立的 SinkToMySQL，這樣就能夠把數據存儲到 MySQL 了。json

package com.zhisheng.flink;

import com.alibaba.fastjson.JSON;
import com.zhisheng.flink.model.Student;
import com.zhisheng.flink.sink.SinkToMySQL;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.sink.PrintSinkFunction;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer011;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer011;

import java.util.Properties;

/**
 * Desc:
 * weixin: zhisheng_tian
 * blog: http://www.54tianzhisheng.cn/
 */
public class Main3 {
    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");
        props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        props.put("auto.offset.reset", "latest");

        SingleOutputStreamOperator<Student> student = env.addSource(new FlinkKafkaConsumer011<>(
                "student",   //這個 kafka topic 須要和上面的工具類的 topic 一致
                new SimpleStringSchema(),
                props)).setParallelism(1)
                .map(string -> JSON.parseObject(string, Student.class)); //Fastjson 解析字符串成 student 對象

        student.addSink(new SinkToMySQL()); //數據 sink 到 mysql

        env.execute("Flink add sink");
    }
}

結果

運行 Flink 程序，而後再運行 KafkaUtils2.java 工具類，這樣就能夠了。bootstrap

若是數據插入成功了，那麼咱們查看下咱們的數據庫：

數據庫中已經插入了 100 條咱們從 Kafka 發送的數據了。證實咱們的 SinkToMySQL 起做用了。是否是很簡單？

項目結構

怕你們不知道個人項目結構，這裏發個截圖看下：

最後

本文主要利用一個 demo，告訴你們如何自定義 Sink Function，將從 Kafka 的數據 Sink 到 MySQL 中，若是你項目中有其餘的數據來源，你也能夠換成對應的 Source，也有可能你的 Sink 是到其餘的地方或者其餘不一樣的方式，那麼依舊是這個套路：繼承 RichSinkFunction 抽象類，重寫 invoke 方法。