今天准备将mysql的数据倒腾到RDD，很早以前就知道有一个JdbcRDD，就想着使用一下，结果发现却是鸡肋一个。

首先，看看JdbcRDD的定义：

* An RDD that executes an SQL query on a JDBC connection and reads results.

* For usage example, see test case JdbcRDDSuite.

*

* @param getConnection a function that returns an open Connection.

* The RDD takes care of closing the connection.

* @param sql the text of the query.

* The query must contain two ? placeholders for parameters used to partition the results.

* E.g. "select title, author from books where ? <= id and id <= ?"

* @param lowerBound the minimum value of the first placeholder

* @param upperBound the maximum value of the second placeholder

* The lower and upper bounds are inclusive.

* @param numPartitions the number of partitions.

* Given a lowerBound of 1, an upperBound of 20, and a numPartitions of 2,

* the query would be executed twice, once with (1, 10) and once with (11, 20)

* @param mapRow a function from a ResultSet to a single row of the desired result type(s).

* This should only call getInt, getString, etc; the RDD takes care of calling next.

* The default maps a ResultSet to an array of Object.

*/

class JdbcRDD[T: ClassTag](

sc: SparkContext,

getConnection: () => Connection,

sql: String,

lowerBound: Long,

upperBound: Long,

numPartitions: Int,

mapRow: (ResultSet) => T = JdbcRDD.resultSetToObjectArray _)

附上个例子：

package test

import java.sql.{Connection, DriverManager, ResultSet}

import org.apache.spark.rdd.JdbcRDD

import org.apache.spark.{SparkConf, SparkContext}

object spark_mysql {

def main(args: Array[String]) {

//val conf = new SparkConf().setAppName("spark_mysql").setMaster("local")

val sc = new SparkContext("local","spark_mysql")

def createConnection() = {

Class.forName("com.mysql.jdbc.Driver").newInstance()

DriverManager.getConnection("jdbc:mysql://192.168.0.15:3306/wsmall", "root", "passwd")

}

def extractValues(r: ResultSet) = {

(r.getString(1), r.getString(2))

}

val data = new JdbcRDD(sc, createConnection, "SELECT id,aa FROM bbb where ? <= ID AND ID <= ?", lowerBound = 3, upperBound =5, numPartitions = 1, mapRow = extractValues)

println(data.collect().toList)

sc.stop()

}

}

使用的MySQL表的数据如下：

运行结果如下：

可以看出：JdbcRDD的

sql参数要带有两个？的占位符，而这两个占位符是给参数

lowerBound和参数

upperBound定义where语句的边界的，如果仅仅是这样的话，还可以接受；但悲催的是

参数

lowerBound和

参数

upperBound都是Long类型的，

，不知道现在作为关键字或做查询的字段有多少long类型呢？不过参照JdbcRDD的源代码，用户还是可以写出符合自己需求的JdbcRDD，这算是不幸中之大幸了。

最近一直忙于炼数成金的spark课程，没多少时间整理博客。特意给想深入了解spark的朋友推荐一位好友的博客

http://www.cnblogs.com/cenyuhai/ ，里面有不少源码博文，利于理解spark的内核。