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原文：http://www.itcast.cn/news/20151229/16012088060.shtml
已经比较过时的写法了，但是认可以作为参考实例。
Spark SQL通过JDBC连接MySQL读写数据
Spark SQL可以通过JDBC从关系型数据库中读取数据的方式创建DataFrame，通过对DataFrame一系列的计算后，还可以将数据再写回关系型数据库中。
一．从MySQL中加载数据（Spark Shell方式）
1.启动Spark Shell，必须指定mysql连接驱动jar包
/usr/local/spark-1.5.2-bin-hadoop2.6/bin/spark-shell \
--master spark://node1.itcast.cn:7077 \
--jars /usr/local/spark-1.5.2-bin-hadoop2.6/mysql-connector-java-5.1.35-bin.jar \
--driver-class-path /usr/local/spark-1.5.2-bin-hadoop2.6/mysql-connector-java-5.1.35-bin.jar
2.从mysql中加载数据
val jdbcDF = sqlContext.read.format("jdbc").options(Map("url" -> "jdbc:mysql://192.168.10.1:3306/bigdata", "driver" -> "com.mysql.jdbc.Driver", "dbtable" -> "person", "user" -> "root", "password" -> "123456")).load()
3.执行查询
jdbcDF.show()
1.编写Spark SQL程序
package cn.itcast.spark.sql
import java.util.Properties
import org.apache.spark.sql.{SQLContext, Row}
import org.apache.spark.sql.types.{StringType, IntegerType, StructField, StructType}
import org.apache.spark.{SparkConf, SparkContext}
object JdbcRDD {
def main(args: Array[String]) {
val conf = new SparkConf().setAppName("MySQL-Demo")
val sc = new SparkContext(conf)
val sqlContext = new SQLContext(sc)
//通过并行化创建RDD
val personRDD = sc.parallelize(Array("1 tom 5", "2 jerry 3", "3 kitty 6")).map(_.split(" "))
//通过StructType直接指定每个字段的schema
val schema = StructType(
List(
StructField("id", IntegerType, true),
StructField("name", StringType, true),
StructField("age", IntegerType, true)
)
)
//将RDD映射到rowRDD
val rowRDD = personRDD.map(p => Row(p(0).toInt, p(1).trim, p(2).toInt))
//将schema信息应用到rowRDD上
val personDataFrame = sqlContext.createDataFrame(rowRDD, schema)
//创建Properties存储数据库相关属性
val prop = new Properties()
prop.put("user", "root")
prop.put("password", "123456")
//将数据追加到数据库
personDataFrame.write.mode("append").jdbc("jdbc:mysql://192.168.10.1:3306/bigdata", "bigdata.person", prop)
//停止SparkContext
sc.stop()
}
}
2.用maven将程序打包
3.将Jar包提交到spark集群
/usr/local/spark-1.5.2-bin-hadoop2.6/bin/spark-submit \
--class cn.itcast.spark.sql.JdbcRDD \
--master spark://node1.itcast.cn:7077 \
--jars /usr/local/spark-1.5.2-bin-hadoop2.6/mysql-connector-java-5.1.35-bin.jar \
--driver-class-path /usr/local/spark-1.5.2-bin-hadoop2.6/mysql-connector-java-5.1.35-bin.jar \
/root/spark-mvn-1.0-SNAPSHOT.jar