package com.kk.sparksql
import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
import org.apache.spark.sql.Encoder
import org.apache.spark.sql.Row
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.types._
import org.apache.spark.sql._
import org.apache.spark.sql.types._
object Emps {
  def main(args: Array[String]) {
    // 创建一个SparkSession
    val spark = SparkSession.builder().master("local").appName("sql").getOrCreate()
    import spark.sqlContext.implicits._ 
    // 导入emp.csv文件（导入数据）
    val lines = spark.sparkContext.textFile("G:/emp.csv").map(_.split(","));
    val myschema = StructType(List(StructField("empno", DataTypes.IntegerType), 
        StructField("ename", DataTypes.StringType), 
        StructField("job", DataTypes.StringType), 
        StructField("mgr", DataTypes.StringType), 
        StructField("hiredate", DataTypes.StringType), 
        StructField("sal", DataTypes.IntegerType), 
        StructField("comm", DataTypes.StringType), 
        StructField("deptno", DataTypes.IntegerType)))
    // 把读入的每一行数据映射成一个个Row 
    val rowRDD = lines.map(x => Row(x(0).toInt, x(1), x(2), x(3), x(4), x(5).toInt, x(6), x(7).toInt))
    val df = spark.createDataFrame(rowRDD,myschema)
    // 操作表(DataFrame)：支持两种语言
    
    // (1) DSL语句：不常用
    // 查询所有的员工姓名 
    println(df.select("ename").show)
    // 运行结果
	+------+
	| ename|
	+------+
	| SMITH|
	| ALLEN|
	|  WARD|
	| JONES|
	|MARTIN|
	| BLAKE|
	| CLARK|
	| SCOTT|
	|  KING|
	|TURNER|
	| ADAMS|
	| JAMES|
	|  FORD|
	|MILLER|
	+------+
    
    // 查询所有的员工姓名和薪水，并给薪水加100块钱
    println(df.select($"ename",$"sal",$"sal"+100).show())
    // 运行结果
	+------+----+-----------+
	| ename| sal|(sal + 100)|
	+------+----+-----------+
	| SMITH| 800|        900|
	| ALLEN|1600|       1700|
	|  WARD|1250|       1350|
	| JONES|2975|       3075|
	|MARTIN|1250|       1350|
	| BLAKE|2850|       2950|
	| CLARK|2450|       2550|
	| SCOTT|3000|       3100|
	|  KING|5000|       5100|
	|TURNER|1500|       1600|
	| ADAMS|1100|       1200|
	| JAMES| 950|       1050|
	|  FORD|3000|       3100|
	|MILLER|1300|       1400|
	+------+----+-----------+

    // 查询工资大于2000的员工
    println(df.filter($"sal" > 2000).show())
    // 运行结果
	+-----+-----+---------+----+----------+----+----+------+
	|empno|ename|      job| mgr|  hiredate| sal|comm|deptno|
	+-----+-----+---------+----+----------+----+----+------+
	| 7566|JONES|  MANAGER|7839|  1981/4/2|2975|    |    20|
	| 7698|BLAKE|  MANAGER|7839|  1981/5/1|2850|    |    30|
	| 7782|CLARK|  MANAGER|7839|  1981/6/9|2450|    |    10|
	| 7788|SCOTT|  ANALYST|7566| 1987/4/19|3000|    |    20|
	| 7839| KING|PRESIDENT|    |1981/11/17|5000|    |    10|
	| 7902| FORD|  ANALYST|7566| 1981/12/3|3000|    |    20|
	+-----+-----+---------+----+----------+----+----+------+
   
    // 分组: empDF.groupBy($"deptno").count.show
    println(df.groupBy($"deptno").count.show())
     // 运行结果
   	|deptno|count|
	+------+-----+
	|    20|    5|
	|    10|    3|
	|    30|    6|
	+------+-----+
    
    // (2) SQL语句: 前提条件：需要把DataFrame注册成是一个Table或者View
    df.createTempView("emp")
    println(spark.sql("select ename from emp").show());
     // 运行结果
 	+------+
	| SMITH|
	| ALLEN|
	|  WARD|
	| JONES|
	|MARTIN|
	| BLAKE|
	| CLARK|
	| SCOTT|
	|  KING|
	|TURNER|
	| ADAMS|
	| JAMES|
	|  FORD|
	|MILLER|
	+------+
    
    println(spark.sql("select ename,sal,sal + 100 from emp").show())
     // 运行结果
 	+------+----+-----------+
	| ename| sal|(sal + 100)|
	+------+----+-----------+
	| SMITH| 800|        900|
	| ALLEN|1600|       1700|
	|  WARD|1250|       1350|
	| JONES|2975|       3075|
	|MARTIN|1250|       1350|
	| BLAKE|2850|       2950|
	| CLARK|2450|       2550|
	| SCOTT|3000|       3100|
	|  KING|5000|       5100|
	|TURNER|1500|       1600|
	| ADAMS|1100|       1200|
	| JAMES| 950|       1050|
	|  FORD|3000|       3100|
	|MILLER|1300|       1400|
	+------+----+-----------+
    
    println(spark.sql("select deptno,sum(sal) from emp group by deptno").show())
    // 运行结果
    +------+--------+
	|deptno|sum(sal)|
	+------+--------+
	|    20|   10875|
	|    10|    8750|
	|    30|    9400|
	+------+--------+
  }
}