在大数据开发场景中，数据同步有很多种工具可以实现，其中包括DataX、FlinkCDC、Spark、Canal、sqoop等等，这边文章主要是描述Spark做多种数据源的同步，原理是基于jdbc驱动。

1 Scala版本代码

pom.xml

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>

    <groupId>com.greenplum.spark</groupId>
    <artifactId>greenplum-spark-test</artifactId>
    <version>1.0-SNAPSHOT</version>

    <properties>
        <spark.version>3.2.1</spark.version>
        <scala.version>2.12</scala.version>
    </properties>
    <dependencies>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-hive_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-mllib_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>io.pivotal.greenplum.spark</groupId>
            <artifactId>greenplum-spark_${scala.version}</artifactId>
            <version>2.1.0</version>
        </dependency>
        <dependency>
            <groupId>com.pivotal</groupId>
            <artifactId>greenplum-jdbc</artifactId>
            <version>5.1.4</version>
        </dependency>
        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <version>8.0.27</version>
        </dependency>
        <dependency>
            <groupId>org.postgresql</groupId>
            <artifactId>postgresql</artifactId>
            <version>9.3-1102-jdbc4</version>
        </dependency>
    </dependencies>
    <build>
        <plugins>
            <plugin>
                <groupId>org.scala-tools</groupId>
                <artifactId>maven-scala-plugin</artifactId>
                <version>2.15.2</version>
                <executions>
                    <execution>
                    </execution>
                </executions>
            </plugin>
        </plugins>
    </build>
</project>

JDBCSource

package com.greenplum.spark

import org.apache.spark.sql.{DataFrame, SparkSession}

//MysqlDriver:"com.mysql.jdbc.Driver"
//OracleDriver:"oracle.jdbc.driver.OracleDriver"
//GreenplumDriver:"com.pivotal.jdbc.GreenplumDriver"
//postgresqlDriver:"org.postgresql.Driver"

//MysqlURL:"jdbc:mysql://localhost:3306/databaseName"
//OracleURL:"jdbc:oracle:thin:@//localhost:1521:databaseName"
//GreenplumURL:"jdbc:pivotal:greenplum://localhost:15432;DatabaseName=databaseName"
//postgresqlURL:"jdbc:postgresql://localhost:5432/databaseName"

object JDBCSource {

  def createDF(session: SparkSession, param: Map[String, AnyRef]): DataFrame = {
    session.sqlContext.read
      .format("jdbc")
      .option("driver", param.get("driver").get.toString)
      .option("url", param.get("url").get.toString)
      .option("dbtable", param.get("dbtable").get.toString)
      .option("user", param.get("user").get.toString)
      .option("password", param.get("password").get.toString)
      .load()
  }
}

JDBCSink 

package com.greenplum.spark

import org.apache.spark.sql.DataFrame

//MysqlDriver:"com.mysql.jdbc.Driver"
//OracleDriver:"oracle.jdbc.driver.OracleDriver"
//GreenplumDriver:"com.pivotal.jdbc.GreenplumDriver"
//postgresqlDriver:"org.postgresql.Driver"

//MysqlURL:"jdbc:mysql://localhost:3306/databaseName"
//OracleURL:"jdbc:oracle:thin:@//localhost:1521:databaseName"
//GreenplumURL:"jdbc:pivotal:greenplum://localhost:15432;DatabaseName=databaseName"
//postgresqlURL:"jdbc:postgresql://localhost:5432/databaseName"

//saveMode
// - `overwrite`: overwrite the existing data.
// - `append`: append the data.
// - `ignore`: ignore the operation (i.e. no-op).
// - `error` or `errorifexists`: default option, throw an exception at runtime.

object JDBCSink {

  def save(df: DataFrame, saveMode: String, param: Map[String, AnyRef]): Unit = {
    df.write
      .mode(saveMode)
      .format("jdbc")
      .option("driver", param.get("driver").get.toString)
      .option("url", param.get("url").get.toString)
      .option("dbtable", param.get("dbtable").get.toString)
      .option("user", param.get("user").get.toString)
      .option("password", param.get("password").get.toString)
      .save()
  }
}

测试类JDBCMainTest，测试场景 MySQL->Greenplum，其他数据源同理 

package com.greenplum.spark

import org.apache.spark.sql.{DataFrame, SaveMode, SparkSession}
import org.apache.spark.{SparkConf, SparkContext}

/**
 * @Description: TODO 
 * @Author: chenweifeng
 * @Date: 2022年08月17日 下午3:27 
 **/
object JDBCMainTest {

  def main(args: Array[String]): Unit = {

    val conf = new SparkConf().setAppName("greenplum_spark_test").setMaster("local[*]")
    val sc = new SparkContext(conf)
    sc.setLogLevel("WARN")
    val spark = SparkSession.builder().config(conf).getOrCreate()

    // MySQL / Oracle / PostgreSQL / Greenplum
    // 以mysql为例，Oracle、 PostgreSQL、Greenplum只需更改相应参数即可

    //从mysql数据库中读取数据并加载成DF
    val mysqlReadOptionMap = Map(
      "driver" -> "com.mysql.cj.jdbc.Driver",
      "url" -> "jdbc:mysql://100.89.9.20:3306/test_datax_gp?useSSL=false&allowPublicKeyRetrieval=true&useUnicode=true&characterEncoding=UTF-8&serverTimezone=Asia/Shanghai",
      "user" -> "root",
      "password" -> "MySql8_Root",
      "dbtable" -> "test_datax_gp_spark"
    )

    val mysqlDataFrame: DataFrame = JDBCSource.createDF(spark, mysqlReadOptionMap)
    mysqlDataFrame.show()

    // spark将结果写入greenplum数据库中
    val greenplumWriteOptionMap = Map(
      //      "driver" -> "org.postgresql.Driver",
      //      "url" -> "jdbc:postgresql://10.254.21.3:54432/pgbenchdb",
      "driver" -> "com.pivotal.jdbc.GreenplumDriver",
      "url" -> "jdbc:pivotal:greenplum://10.254.21.3:54432;DatabaseName=pgbenchdb",
      "user" -> "gpadmin",
      "password" -> "changeme",
      "dbschema" -> "public",
      "dbtable" -> "test_datax_gp_spark",
    )

    JDBCSink.save(mysqlDataFrame, SaveMode.Overwrite.name(), greenplumWriteOptionMap)

    sc.stop()
  }
}

2 Java版本代码

待更新。。。