依賴

 <properties>
        <maven.compiler.source>1.7</maven.compiler.source>
        <maven.compiler.target>1.7</maven.compiler.target>
        <encoding>UTF-8</encoding>
        <scala.version>2.10.6</scala.version>
        <scala.compat.version>
 
2.10</scala.compat.version>
    </properties>

    <dependencies>
        <dependency>
            <groupId>org.scala-lang</groupId>
            <artifactId>scala-library</artifactId>
            <version>${scala.version}</version>
        </dependency
 
>

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.10</artifactId>
            <version>1.5.2</version>
        </dependency>

        <dependency>
            <groupId>org.apache.spark</groupId
 
>
            <artifactId>spark-streaming_2.10</artifactId>
            <version>1.5.2</version>
        </dependency>

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_2.10</artifactId>
            <version>1.5.2</version>
        </dependency>

        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>2.6.2</version>
        </dependency>

        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <version>5.1.38</version>
        </dependency>
    </dependencies>

1 使用RDD和Case Class關聯進行表操作（推斷模式）

package cn.itcast.spark.sql

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

object InferringSchema {
  def main(args: Array[String]) {

    //建立SparkConf()並設定App名稱
    val conf = new SparkConf().setAppName("SQL-1")
    //SQLContext要依賴SparkContext
    val sc = new SparkContext(conf)
    //建立SQLContext
    val sqlContext = new SQLContext(sc)

    //從指定的地址建立RDD
    val lineRDD = sc.textFile(args(0)).map(_.split(" "))

    //建立case class
    //將RDD和case class關聯
    val personRDD = lineRDD.map(x => Person(x(0).toInt, x(1), x(2).toInt))
    //匯入隱式轉換，如果不到人無法將RDD轉換成DataFrame
    //將RDD轉換成DataFrame
    import sqlContext.implicits._
    val personDF = personRDD.toDF
    //登錄檔
    personDF.registerTempTable("t_person")
    //傳入SQL
    val df = sqlContext.sql("select * from t_person order by age desc limit 2")
    //將結果以JSON的方式儲存到指定位置
    df.write.json(args(1))
    //停止Spark Context
    sc.stop()

  }
}
//case class一定要放到外面
case class Person(id: Int, name: String, age: Int)

2 RDD對映到rowRDD並指定schema（指定模式）

package cn.itcast.spark.sql

import org.apache.spark.sql.{SaveMode, Row, SQLContext}
import org.apache.spark.sql.types._
import org.apache.spark.{SparkContext, SparkConf}

object SpecifyingSchema {
  def main(args: Array[String]) {
    //建立SparkConf()並設定App名稱
    val conf = new SparkConf().setAppName("SQL-2")
    //SQLContext要依賴SparkContext
    val sc = new SparkContext(conf)
    //建立SQLContext
    val sqlContext = new SQLContext(sc)
    //從指定的地址建立RDD
    val personRDD = sc.textFile(args(0)).map(_.split(" "))
    //通過StructType直接指定每個欄位的schema
    val schema = StructType(
      List(
        StructField("id", IntegerType, true),
        StructField("name", StringType, true),
        StructField("age", IntegerType, true)
      )
    )
    //將RDD對映到rowRDD
    val rowRDD = personRDD.map(p => Row(p(0).toInt, p(1).trim, p(2).toInt))
    //將schema資訊應用到rowRDD上
    val personDataFrame = sqlContext.createDataFrame(rowRDD, schema)
    //登錄檔
    personDataFrame.registerTempTable("t_person")
    //執行SQL
    val df = sqlContext.sql("select * from t_person order by age desc limit 4")
    //將結果以JSON的方式儲存到指定位置
    df.write.json(args(1))
    //停止Spark Context
    sc.stop()
  }
}

3 DataFrame的JDBC操作

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)
    //通過並行化建立RDD
    val personRDD = sc.parallelize(Array("1 tom 5", "2 jerry 3", "3 kitty 6")).map(_.split(" "))
    //通過StructType直接指定每個欄位的schema
    val schema = StructType(
      List(
        StructField("id", IntegerType, true),
        StructField("name", StringType, true),
        StructField("age", IntegerType, true)
      )
    )
    //將RDD對映到rowRDD
    val rowRDD = personRDD.map(p => Row(p(0).toInt, p(1).trim, p(2).toInt))
    //將schema資訊應用到rowRDD上
    val personDataFrame = sqlContext.createDataFrame(rowRDD, schema)
    //建立Properties儲存資料庫相關屬性
    val prop = new Properties()
    prop.put("user", "root")
    prop.put("password", "123456")
    //將資料追加到資料庫
    personDataFrame.write.mode("append").jdbc("jdbc:mysql://localhost:3306/bigdata", "bigdata.person", prop)
    //停止SparkContext
    sc.stop()
  }
}