鸡肋的JdbcRDD

      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。 * 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 ，里面有不少源码博文，利于理解spark的内核。