sc: SparkContext,
getConnection: () => Connection,
sql: String,
lowerBound: Long,
upperBound: Long,
numPartitions: Int,
mapRow: (ResultSet) => T = JdbcRDD.resultSetToObjectArray _

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)

lowerBound 用于定义查询的下标upperBound 用于定义查询的上标numPartitions 用于定义查询的分区数这三个参数在实际的生产环境中，可能很有用，通过该三个参数定义每个分区查询的范数据围，这也是spark人员设计时一定加上该参数的原因。说明：本例仅仅是简单的去掉该三个参数，需要知道的是方式不止这一种，且由于把分区参数去掉了，本代码默认的是一个分区，可以在代码中手动的设置多个分区。修改JdbcRDD的源码，同时需要修改有NextIterator.scala（其实只是挪一下位置，源码不动的挪过来，由于源码是spark包下的private，所以不能引用在其他的包内），该文件就不再粘贴出来。修改后的JdbcRDD.scala改名为JDBCRDD.scala，NextIterator.scala放在与JDBCRDD.scala同一个包内。以下是JDBCRDD.scala源码
import java.sql.{Connection, ResultSet}

import scala.reflect.ClassTag
import org.apache.spark.{Partition, SparkContext, TaskContext}
import org.apache.spark.api.java.{JavaRDD, JavaSparkContext}
import org.apache.spark.api.java.function.{Function => JFunction}
import org.apache.spark.internal.Logging
import org.apache.spark.rdd.RDD

/**
  * Created by Administrator on 2017/9/8.
  */
class JDBCPartition(idx: Int) extends Partition {
  override def index: Int = idx
}

class JDBCRDD[T: ClassTag](
                            sc: SparkContext,
                            getConnection: () => Connection,
                            sql: String,
                            mapRow: (ResultSet) => T = JDBCRDD.resultSetToObjectArray _)
  extends RDD[T](sc, Nil) with Logging {
  override def getPartitions: Array[Partition] = {
    (0 to 1).map { i => new JDBCPartition(i) }.toArray
  }

  override def compute(thePart: Partition, context: TaskContext): Iterator[T] = new NextIterator[T] {
    context.addTaskCompletionListener { context => closeIfNeeded() }
    val part = thePart.asInstanceOf[JDBCPartition]
    val conn = getConnection()
    val stmt = conn.prepareStatement(sql, ResultSet.TYPE_FORWARD_ONLY, ResultSet.CONCUR_READ_ONLY)

    val url = conn.getMetaData.getURL
    if (url.startsWith("jdbc:mysql:")) {
      stmt.setFetchSize(Integer.MIN_VALUE)
    } else {
      stmt.setFetchSize(100)
    }

    logInfo(s"statement fetch size set to: ${stmt.getFetchSize}")

    val rs = stmt.executeQuery()

    override def getNext(): T = {
      if (rs.next()) {
        mapRow(rs)
      } else {
        finished = true
        null.asInstanceOf[T]
      }
    }

    override def close() {
      try {
        if (null != rs) {
          rs.close()
        }
      } catch {
        case e: Exception => logWarning("Exception closing resultset", e)
      }
      try {
        if (null != stmt) {
          stmt.close()
        }
      } catch {
        case e: Exception => logWarning("Exception closing statement", e)
      }
      try {
        if (null != conn) {
          conn.close()
        }
        logInfo("closed connection")
      } catch {
        case e: Exception => logWarning("Exception closing connection", e)
      }
    }
  }
}

object JDBCRDD {
  def resultSetToObjectArray(rs: ResultSet): Array[Object] = {
    Array.tabulate[Object](rs.getMetaData.getColumnCount)(i => rs.getObject(i + 1))
  }

  trait ConnectionFactory extends Serializable {
    @throws[Exception]
    def getConnection: Connection
  }

  def fakeClassTag[T]: ClassTag[T] = ClassTag.AnyRef.asInstanceOf[ClassTag[T]]

  def create[T](
                 sc: JavaSparkContext,
                 connectionFactory: ConnectionFactory,
                 sql: String,
                 mapRow: JFunction[ResultSet, T]): JavaRDD[T] = {

    val JDBCRDD = new JDBCRDD[T](
      sc.sc,
      () => connectionFactory.getConnection,
      sql,
      (resultSet: ResultSet) => mapRow.call(resultSet))(fakeClassTag)

    new JavaRDD[T](JDBCRDD)(fakeClassTag)
  }

  def create(
              sc: JavaSparkContext,
              connectionFactory: ConnectionFactory,
              sql: String
            ): JavaRDD[Array[Object]] = {

    val mapRow = new JFunction[ResultSet, Array[Object]] {
      override def call(resultSet: ResultSet): Array[Object] = {
        resultSetToObjectArray(resultSet)
      }
    }

    create(sc, connectionFactory, sql, mapRow)
  }
}

import java.sql.DriverManager

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

/**
  * Created by Administrator on 2017/9/8.
  */
object TestJDBC {
  def main(args: Array[String]): Unit = {

    val conf = new SparkConf().setAppName("TestJDBC").setMaster("local[2]")
    val sc = new SparkContext(conf)
    try {
      val connection = () => {
        Class.forName("com.mysql.jdbc.Driver").newInstance()
        DriverManager.getConnection("jdbc:mysql://192.168.0.4:3306/spark", "root", "root")
      }

      val JDBCRDD = new JDBCRDD(
        sc,
        connection,
        "SELECT * FROM result",
        r => {
          val id = r.getInt(1)
          val code = r.getString(2)
          (id, code)
        }
      )
      val jrdd = JDBCRDD.collect()
      println(JDBCRDD.collect().toBuffer)
      sc.stop()
    }
    catch {
      case e: Exception => println(e.printStackTrace())
    }
  }
}