概述
SparkSql将RDD封装成一个DataFrame对象，这个对象类似于关系型数据库中的表。
创建DataFrame对象
DataFrame就相当于数据库的一张表。它是个只读的表，不能在运算过程再往里加元素。
RDD.toDF("列名")
scala> val rdd = sc.parallelize(List(1,2,3,4,5,6))
rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[0] at parallelize at <console>:21
scala> rdd.toDF("id")
res0: org.apache.spark.sql.DataFrame = [id: int]
scala> res0.show#默认只显示20条数据
+---+
| id|
+---+
| 1|
| 2|
| 3|
| 4|
| 5|
| 6|
+---+
scala> res0.printSchema #查看列的类型等属性
root
|-- id: integer (nullable = true)
创建多列DataFrame对象
DataFrame就相当于数据库的一张表。
scala> sc.parallelize(List( (1,"beijing"),(2,"shanghai") ) )
res3: org.apache.spark.rdd.RDD[(Int, String)] = ParallelCollectionRDD[5] at parallelize at <console>:22
scala> res3.toDF("id","name")
res4: org.apache.spark.sql.DataFrame = [id: int, name: string]
scala> res4.show
+---+--------+
| id| name|
+---+--------+
| 1| beijing|
| 2|shanghai|
+---+--------+
例如3列的
scala> sc.parallelize(List( (1,"beijing",100780),(2,"shanghai",560090),(3,"xi'an",600329)))
res6: org.apache.spark.rdd.RDD[(Int, String, Int)] = ParallelCollectionRDD[10] at parallelize at <console>:22
scala> res6.toDF("id","name","postcode")
res7: org.apache.spark.sql.DataFrame = [id: int, name: string, postcode: int]
scala> res7.show
+---+--------+--------+
| id| name|postcode|
+---+--------+--------+
| 1| beijing| 100780|
| 2|shanghai| 560090|
| 3| xi'an| 600329|
+---+--------+--------+
可以看出，需要构建几列，tuple就有几个内容。
由外部文件构造DataFrame对象
1）txt文件
txt文件不能直接转换成，先利用RDD转换为tuple。然后toDF()转换为DataFrame。
scala> val rdd = sc.textFile("/root/words.txt")
.map( x => (x,1) )
.reduceByKey( (x,y) => x+y )
rdd: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[18] at reduceByKey at <console>:21
scala> rdd.toDF("word","count")
res9: org.apache.spark.sql.DataFrame = [word: string, count: int]
scala> res9.show
+------+-----+
| word|count|
+------+-----+
| spark| 3|
| hive| 1|
|hadoop| 2|
| big| 2|
| scla| 1|
| data| 1|
+------+-----+
2）json文件
文件代码：
{"id":1, "name":"leo", "age":18}
{"id":2, "name":"jack", "age":19}
{"id":3, "name":"marry", "age":17}
代码：
import org.apache.spark.sql.SQLContext
scala>val sqc=new SQLContext(sc)
scala> val tb4=sqc.read.json("/home/software/people.json")
scala> tb4.show
3）jdbc读取
实现步骤：
1）将mysql 的驱动jar上传到spark的jars目录下
2）重启spark服务
3）进入spark客户端
4）执行代码，比如在Mysql数据库下，有一个test库，在test库下有一张表为tabx
执行代码：
import org.apache.spark.sql.SQLContext
scala> val sqc = new SQLContext(sc);
scala> val prop = new java.util.Properties
scala> prop.put("user","root")
scala> prop.put("password","root")
scala>val flow=sqc.read.jdbc("jdbc:mysql://hadoop01:3306/test","flow",prop)
scala> flow.show
注：如果报权限不足，则进入mysql，执行：
grant all privileges on *.* to 'root'@'hadoop01' identified by 'root' with grant option;
然后执行：
flush privileges;