SparkSession新的起始点

在老的版本中，SparkSQL提供两种SQL查询起始点：一个叫SQLContext，用于Spark自己提供的SQL查询；一个叫HiveContext，用于连接Hive的查询。
SparkSession是Spark最新的SQL查询起始点，实质上是SQLContext和HiveContext的组合，所以在SQLContext和HiveContext上可用的API在SparkSession上同样是可以使用的。SparkSession内部封装了sparkContext，所以计算实际上是由sparkContext完成的

DataFrame创建

在Spark SQL中SparkSession是创建DataFrame和执行SQL的入口，创建DataFrame有三种方式：通过Spark的数据源进行创建；从一个存在的RDD进行转换；还可以从Hive Table进行查询返回。

从Spark数据源进行创建

查看Spark数据源进行创建的文件格式

scala> spark.read.
csv   format   jdbc   json   load   option   options   orc   parquet   schema   table   text   textFile

读取json文件创建DataFrame

scala> val df = spark.read.json("/opt/module/spark/examples/src/main/resources/people.json")
df: org.apache.spark.sql.DataFrame = [age: bigint, name: string]

展示结果

scala> df.show
+----+-------+
| age|   name|
+----+-------+
|null|Michael|
|  30|   Andy|
|  19| Justin|
+----+-------+

SQL风格语法

创建一个DataFrame

scala> val df = spark.read.json("/opt/module/spark/examples/src/main/resources/people.json")
df: org.apache.spark.sql.DataFrame = [age: bigint, name: string]

对DataFrame创建一个临时表

scala> df.createOrReplaceTempView("people")

通过SQL语句实现查询全表

scala> val sqlDF = spark.sql("SELECT * FROM people")
sqlDF: org.apache.spark.sql.DataFrame = [age: bigint, name: string]

结果展示

scala> sqlDF.show
+----+-------+
| age|   name|
+----+-------+
|null|Michael|
|  30|   Andy|
|  19| Justin|
+----+-------+

注意：临时表是Session范围内的，Session退出后，表就失效了。如果想应用范围内有效，可以使用全局表。注意使用全局表时需要全路径访问，如：global_temp.people

对于DataFrame创建一个全局表

scala> df.createGlobalTempView("people")

通过SQL语句实现查询全表

scala> spark.sql("SELECT * FROM global_temp.people").show()
+----+-------+
| age|   name|
+----+-------+
|null|Michael|
|  30|   Andy|
|  19| Justin|
+----+-------+
scala> spark.newSession().sql("SELECT * FROM global_temp.people").show()
+----+-------+
| age|   name|
+----+-------+
|null|Michael|
|  30|   Andy|
|  19| Justin|
+----+-------+

DSL风格语法

创建一个DateFrame

scala> spark.read.
csv   format   jdbc   json   load   option   options   orc   parquet   schema   table   text   textFile

查看DataFrame的Schema信息

scala> df.printSchema
root
 |-- age: long (nullable = true)
 |-- name: string (nullable = true)

只查看”name”列数据

scala> df.select("name").show()
+-------+
|   name|
+-------+
|Michael|
|   Andy|
| Justin|
+-------+

查看”name”列数据以及”age+1”数据

scala> df.select($"name", $"age" + 1).show()
+-------+---------+
|   name|(age + 1)|
+-------+---------+
|Michael|     null|
|   Andy|       31|
| Justin|       20|
+-------+---------+

查看”age”大于”21”的数据

scala> df.filter($"age" > 21).show()
+---+----+
|age|name|
+---+----+
| 30|Andy|
+---+----+

按照”age”分组，查看数据条数

scala> df.groupBy("age").count().show()
+----+-----+
| age|count|
+----+-----+
|  19|     1|
|null|     1|
|  30|     1|
+----+-----+

RDD转换为DateFrame

注意：如果需要RDD与DF或者DS之间操作，那么都需要引入 import spark.implicits._ 【spark不是包名，而是sparkSession对象的名称】
前置条件：导入隐式转换并创建一个RDD

scala> import spark.implicits._
import spark.implicits._
scala> val peopleRDD = sc.textFile("examples/src/main/resources/people.txt")
peopleRDD: org.apache.spark.rdd.RDD[String] = examples/src/main/resources/people.txt MapPartitionsRDD[3] at textFile at <console>:27

通过手动确定转换

scala> peopleRDD.map{x=>val para = x.split(",");(para(0),para(1).trim.toInt)}.toDF("name","age")
res1: org.apache.spark.sql.DataFrame = [name: string, age: int]

通过反射确定（需要用到样例类）

1. 创建一个样例类

scala> case class People(name:String, age:Int)

2. 根据样例类将RDD转换为DataFrame

scala> peopleRDD.map{ x => val para = x.split(",");People(para(0),para(1).trim.toInt)}.toDF
res2: org.apache.spark.sql.DataFrame = [name: string, age: int]

通过编程的方式

1. 导入所需的类型

scala> import org.apache.spark.sql.types._
import org.apache.spark.sql.types._

2. 创建Schema

scala> val structType: StructType = StructType(StructField("name", StringType) :: StructField("age", IntegerType) :: Nil)
structType: org.apache.spark.sql.types.StructType = StructType(StructField(name,StringType,true), StructField(age,IntegerType,true))

3. 导入所需的类型

scala> import org.apache.spark.sql.Row
import org.apache.spark.sql.Row

4. 根据给定的类型创建二元组RDD

scala> val data = peopleRDD.map{ x => val para = x.split(",");Row(para(0),para(1).trim.toInt)}
data: org.apache.spark.rdd.RDD[org.apache.spark.sql.Row] = MapPartitionsRDD[6] at map at <console>:33

5. 根据数据及给定的schema创建DataFrame

scala> val dataFrame = spark.createDataFrame(data, structType)
dataFrame: org.apache.spark.sql.DataFrame = [name: string, age: int]

DateFrame转换为RDD

直接调用rdd即可

创建一个DataFrame

scala> val df = spark.read.json("/opt/module/spark/examples/src/main/resources/people.json")
df: org.apache.spark.sql.DataFrame = [age: bigint, name: string]

将DataFrame转换为RDD

scala> val dfToRDD = df.rdd
dfToRDD: org.apache.spark.rdd.RDD[org.apache.spark.sql.Row] = MapPartitionsRDD[19] at rdd at <console>:29

打印RDD

scala> dfToRDD.collect
res13: Array[org.apache.spark.sql.Row] = Array([Michael, 29], [Andy, 30], [Justin, 19])

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