Flink之JDBC Sink

这里介绍一下Flink Sink中jdbc sink的使用方法,以mysql为例,这里代码分为两种,事务和非事务

• 非事务代码

import org.apache.flink.connector.jdbc.JdbcConnectionOptions;
import org.apache.flink.connector.jdbc.JdbcExecutionOptions;
import org.apache.flink.connector.jdbc.JdbcSink;
import org.apache.flink.connector.jdbc.JdbcStatementBuilder;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.sink.SinkFunction;

import java.sql.PreparedStatement;
import java.sql.SQLException;

/**
 * @Author: J
 * @Version: 1.0
 * @CreateTime: 2023/8/2
 * @Description: 测试
 **/
public class FlinkJdbcSink {
    public static void main(String[] args) throws Exception {
        // 构建流环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        // 这里使用的是自定义数据源CustomizeBean(name,age,gender,hobbit),为了方便测试,换成任何数据源都可,只要和最后的要写入的表结构匹配即可
        DataStreamSource<CustomizeBean> customizeSource = env.addSource(new CustomizeSource());
        // 构建jdbc sink
        SinkFunction<CustomizeBean> jdbcSink = JdbcSink.sink(
                "insert into t_user(`name`, `age`, `gender`, `hobbit`) values(?, ?, ?, ?)", // 数据插入sql语句
                new JdbcStatementBuilder<CustomizeBean>() {
                    @Override
                    public void accept(PreparedStatement pStmt, CustomizeBean customizeBean) throws SQLException {
                        pStmt.setString(1, customizeBean.getName());
                        pStmt.setInt(2, customizeBean.getAge());
                        pStmt.setString(3, customizeBean.getGender());
                        pStmt.setString(4, customizeBean.getHobbit());
                    }
                }, // 字段映射配置,这部分就和常规的java api差不多了
                JdbcExecutionOptions.builder()
                        .withBatchSize(10) // 批次大小,条数
                        .withBatchIntervalMs(5000) // 批次最大等待时间
                        .withMaxRetries(1) // 重复次数
                        .build(), // 写入参数配置
                new JdbcConnectionOptions.JdbcConnectionOptionsBuilder()
                        .withDriverName("com.mysql.jdbc.Driver")
                        .withUrl("jdbc:mysql://lx01:3306/test_db?useSSL=false")
                        .withUsername("root")
                        .withPassword("password")
                        .build() // jdbc信息配置
        );
        // 添加jdbc sink
        customizeSource.addSink(jdbcSink);
        env.execute();
    }
}

• 事务代码

import com.mysql.cj.jdbc.MysqlXADataSource;
import org.apache.flink.connector.jdbc.JdbcExactlyOnceOptions;
import org.apache.flink.connector.jdbc.JdbcExecutionOptions;
import org.apache.flink.connector.jdbc.JdbcSink;
import org.apache.flink.connector.jdbc.JdbcStatementBuilder;
import org.apache.flink.streaming.api.CheckpointingMode;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.CheckpointConfig;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.sink.SinkFunction;
import org.apache.flink.util.function.SerializableSupplier;

import javax.sql.XADataSource;

/**
 * @Author: J
 * @Version: 1.0
 * @CreateTime: 2023/8/2
 * @Description: 测试
 **/
public class FlinkJdbcSink {
    public static void main(String[] args) throws Exception {
        // 构建流环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        // 这里使用的是自定义数据源CustomizeBean(name,age,gender,hobbit),为了方便测试,换成任何数据源都可,只要和最后的要写入的表结构匹配即可
        DataStreamSource<CustomizeBean> customizeSource = env.addSource(new CustomizeSource());

        // 每20秒作为checkpoint的一个周期
        env.enableCheckpointing(20000);
        // 两次checkpoint间隔最少是10秒
        env.getCheckpointConfig().setMinPauseBetweenCheckpoints(10000);
        // 程序取消或者停止时不删除checkpoint
        env.getCheckpointConfig().setExternalizedCheckpointCleanup(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION);
        // checkpoint必须在60秒结束,否则将丢弃
        env.getCheckpointConfig().setCheckpointTimeout(60000);
        // 同一时间只能有一个checkpoint
        env.getCheckpointConfig().setMaxConcurrentCheckpoints(1);
        // 设置EXACTLY_ONCE语义,默认就是这个
        env.getCheckpointConfig().setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE);
        // checkpoint存储位置
        env.getCheckpointConfig().setCheckpointStorage("file:///Users/xxx/data/testData/checkpoint");
        // 构建ExactlyOne sink,要注意使用exactlyOnceSink需要开启checkpoint
        SinkFunction<CustomizeBean> exactlyOneJdbcSink = JdbcSink.exactlyOnceSink(
                "insert into t_user(`name`, `age`, `gender`, `hobbit`) values(?, ?, ?, ?)", // 数据插入sql语句
                (JdbcStatementBuilder<CustomizeBean>) (pStmt, customizeBean) -> {
                    pStmt.setString(1, customizeBean.getName());
                    pStmt.setInt(2, customizeBean.getAge());
                    pStmt.setString(3, customizeBean.getGender());
                    pStmt.setString(4, customizeBean.getHobbit());
                }, // 字段映射配置,这部分就和常规的java api差不多了
                JdbcExecutionOptions.builder()
                        .withMaxRetries(0) // 设置重复次数
                        .withBatchSize(25) // 设置批次大小,数据条数
                        .withBatchIntervalMs(1000) // 批次最大等待时间
                        .build(),
                JdbcExactlyOnceOptions.builder()
                        // 这里使用的mysql,所以要将这个参数设置为true,因为mysql不支持一个连接上开启多个事务,oracle是支持的
                        .withTransactionPerConnection(true)
                        .build(),
                (SerializableSupplier<XADataSource>) () -> {
                    // XADataSource 就是JDBC连接,不同的是它是支持分布式事务的连接
                    MysqlXADataSource mysqlXADataSource = new MysqlXADataSource();
                    mysqlXADataSource.setUrl("jdbc:mysql://lx01:3306/test_db?useSSL=false"); // 设置url
                    mysqlXADataSource.setUser("root"); // 设置用户
                    mysqlXADataSource.setPassword("password"); // 设置密码
                    return mysqlXADataSource;
                }
        );
        // 添加jdbc sink
        customizeSource.addSink(exactlyOneJdbcSink);
        env.execute();
    }
}

• pom依赖

        
        
        
        <dependency>
            <groupId>org.apache.flinkgroupId>
            <artifactId>flink-connector-jdbcartifactId>
            <version>${flink.version}version>
        dependency>

        
        <dependency>
            <groupId>mysqlgroupId>
            <artifactId>mysql-connector-javaartifactId>
            <version>8.0.28version>
        dependency>

• 结果
  
  jdbc sink的具体使用方式大概就这些内容,还是比较简单的,具体应用还要结合实际业务场景.