Flink RichSourceFunction应用实践（MySQ-MySQL）

0. 前言

Flink被誉为第四代大数据计算引擎组件，即可以用作基于离线分布式计算，也可以应用于实时计算。Flink的核心是转化为流进行计算。Flink三个核心：Source,Transformation,Sink。其中Source即为Flink计算的数据源，Transformation即为进行分布式流式计算的算子，也是计算的核心，Sink即为计算后的数据输出端。Flink Source原生支持包括Kafka,ES,RabbitMQ等一些通用的消息队列组件或基于文本的高性能非关系型数据库。而Flink Sink写原生也只支持类似Redis，Kafka,ES,RabbitMQ等一些通用的消息队列组件或基于文本的高性能非关系型数据库。而对于写入关系型数据库或Flink不支持的组件中，需要借助RichSourceFunction去实现，但这部分性能是比原生的差些，虽然Flink不建议这么做，但在大数据处理过程中，由于业务或技术架构的复杂性，有些特定的场景还是需要这样做，本篇博客就是介绍如何通过Flink RichSourceFunction来写关系型数据库，这里以写mysql为例。

1. 引入依赖的jar包

flink基础包
flink-jdbc包
mysql-jdbc包

2. 继承RichSourceFunction包将jdbc封装读mysql

package com.run;import java.sql.DriverManager;
import java.sql.ResultSet;import org.apache.flink.api.java.tuple.Tuple10;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.functions.source.RichSourceFunction;import com.mysql.jdbc.Connection;
import com.mysql.jdbc.PreparedStatement;public class Flink2JdbcReader extendsRichSourceFunction<Tuple10<String, String, String, String, String, String, String, String, String, String>> {private static final long serialVersionUID = 3334654984018091675L;private Connection connect = null;private PreparedStatement ps = null;/** (non-Javadoc)* * @see org.apache.flink.api.common.functions.AbstractRichFunction#open(org.* apache.flink.configuration.Configuration) to use open database connect*/@Overridepublic void open(Configuration parameters) throws Exception {super.open(parameters);Class.forName("com.mysql.jdbc.Driver");connect = (Connection) DriverManager.getConnection("jdbc:mysql://192.168.21.11:3306", "root", "flink");ps = (PreparedStatement) connect.prepareStatement("select col1,col2,col3,col4,col5,col6,col7,col8,col9,col10 from flink.test_tb");}/** (non-Javadoc)* * @see* org.apache.flink.streaming.api.functions.source.SourceFunction#run(org.* apache.flink.streaming.api.functions.source.SourceFunction.SourceContext)* to use excuted sql and return result*/@Overridepublic void run(SourceContext<Tuple10<String, String, String, String, String, String, String, String, String, String>> collect)throws Exception {ResultSet resultSet = ps.executeQuery();while (resultSet.next()) {Tuple10<String, String, String, String, String, String, String, String, String, String> tuple = new Tuple10<String, String, String, String, String, String, String, String, String, String>();tuple.setFields(resultSet.getString(1), resultSet.getString(2), resultSet.getString(3),resultSet.getString(4), resultSet.getString(5), resultSet.getString(6), resultSet.getString(7),resultSet.getString(8), resultSet.getString(9), resultSet.getString(10));collect.collect(tuple);}}/** (non-Javadoc)* * @see* org.apache.flink.streaming.api.functions.source.SourceFunction#cancel()* colse database connect*/@Overridepublic void cancel() {try {super.close();if (connect != null) {connect.close();}if (ps != null) {ps.close();}} catch (Exception e) {e.printStackTrace();}}
}

3. 继承RichSourceFunction包将jdbc封装写mysql

package com.run;import java.sql.DriverManager;import org.apache.flink.api.java.tuple.Tuple10;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.functions.sink.RichSinkFunction;import com.mysql.jdbc.Connection;
import com.mysql.jdbc.PreparedStatement;public class Flink2JdbcWriter extendsRichSinkFunction<Tuple10<String, String, String, String, String, String, String, String, String, String>> {private static final long serialVersionUID = -8930276689109741501L;private Connection connect = null;private PreparedStatement ps = null;/** (non-Javadoc)* * @see org.apache.flink.api.common.functions.AbstractRichFunction#open(org.* apache.flink.configuration.Configuration) get database connect*/@Overridepublic void open(Configuration parameters) throws Exception {super.open(parameters);Class.forName("com.mysql.jdbc.Driver");connect = (Connection) DriverManager.getConnection("jdbc:mysql://192.168.21.11:3306", "root", "flink");ps = (PreparedStatement) connect.prepareStatement("insert into flink.test_tb1 values (?,?,?,?,?,?,?,?,?,?)");}/** (non-Javadoc)* * @see* org.apache.flink.streaming.api.functions.sink.SinkFunction#invoke(java.* lang.Object,* org.apache.flink.streaming.api.functions.sink.SinkFunction.Context) read* data from flink DataSet to database*/@Overridepublic void invoke(Tuple10<String, String, String, String, String, String, String, String, String, String> value,Context context) throws Exception {ps.setString(1, value.f0);ps.setString(2, value.f1);ps.setString(3, value.f2);ps.setString(4, value.f3);ps.setString(5, value.f4);ps.setString(6, value.f5);ps.setString(7, value.f6);ps.setString(8, value.f7);ps.setString(9, value.f8);ps.setString(10, value.f9);ps.executeUpdate();}/** (non-Javadoc)* * @see org.apache.flink.api.common.functions.AbstractRichFunction#close()* close database connect*/@Overridepublic void close() throws Exception {try {super.close();if (connect != null) {connect.close();}if (ps != null) {ps.close();}} catch (Exception e) {e.printStackTrace();}}
}

4. 代码解释

对于Flink2JdbcReader的读
里面有三个方法open，run，cancel，其中open方法是建立与关系型数据库的链接，这里其实就是普通的jdbc链接及mysql的地址，端口，库等信息。run方法是读取mysql数据转化为Flink独有的Tuple集合类型，可以根据代码看出其中的规律和Tuple8，Tuple9，Tuple10代表什么含义。cancel就很简单了关闭数据库连接

对于Flink2JdbcWriter的写
里面有三个方法open，invoke，close，其中open方法是建立与关系型数据库的链接，这里其实就是普通的jdbc链接及mysql的地址，端口，库等信息。invoke方法是将flink的数据类型插入到mysql，这里的写法与在web程序中写jdbc插入数据不太一样，因为flink独有的Tuple，可以根据代码看出其中的规律和Tuple8，Tuple9，Tuple10代表什么含义。close关闭数据库连接

5. 测试:读mysql数据并继续写入mysql

package com.run;import java.util.Date;import org.apache.flink.api.java.tuple.Tuple10;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;public class FlinkReadDbWriterDb {public static void main(String[] args) throws Exception {。final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();DataStream<Tuple10<String, String, String, String, String, String, String, String, String, String>> dataStream = env.addSource(new Flink2JdbcReader());// tranfomat     dataStream.addSink(new Flink2JdbcWriter());env.execute("Flink cost DB data to write Database") ;    }
}

6. 总结

从测试代码中可以很清晰的看出Flink的逻辑:Source->Transformation->Sink,可以在addSource到addSink之间加入我们的业务逻辑算子。同时这里必须注意env.execute("Flink cost DB data to write Database");这个必须有而且必须要放到结尾，否则整个代码是不会执行的，至于为什么在后续的博客会讲