1.Spark SQL 基本操作
将下列 json 数据复制到你的 ubuntu 系统/usr/local/spark 下,并保存命名为 employee.json。
{ "id":1 ,"name":" Ella","age":36 }
{ "id":2,"name":"Bob","age":29 }
{ "id":3 ,"name":"Jack","age":29 }
{ "id":4 ,"name":"Jim","age":28 }
{ "id":5 ,"name":"Damon" }
{ "id":5 ,"name":"Damon" }
首先为
employee.json 创建 DataFrame,并写出 Scala 语句完成下列操作:
创建 DataFrame
![](https://img2018.cnblogs.com/i-beta/1279500/202002/1279500-20200214090418502-657440814.png)
查询 DataFrame 的所有数据:
![](https://img2018.cnblogs.com/i-beta/1279500/202002/1279500-20200214090433616-895224728.png)
查询所有数据,并去除重复的数据:
![](https://img2018.cnblogs.com/i-beta/1279500/202002/1279500-20200214090455891-61130048.png)
查询所有数据,打印时去除 id 字段:
![](https://img2018.cnblogs.com/i-beta/1279500/202002/1279500-20200214090528247-403271647.png)
筛选 age>20 的记录:
![](https://img2018.cnblogs.com/i-beta/1279500/202002/1279500-20200214090610945-1369807638.png)
将数据按 name 分组:
![](https://img2018.cnblogs.com/i-beta/1279500/202002/1279500-20200214090631278-157804599.png)
将数据按 name 升序排列:
![](https://img2018.cnblogs.com/i-beta/1279500/202002/1279500-20200214090653585-1372160929.png)
取出前 3 行数据:
![](https://img2018.cnblogs.com/i-beta/1279500/202002/1279500-20200214090717045-1563517644.png)
查询所有记录的 name 列,并为其取别名为 username:
![](https://img2018.cnblogs.com/i-beta/1279500/202002/1279500-20200214090734714-127365878.png)
查询年龄 age 的平均值:
![](https://img2018.cnblogs.com/i-beta/1279500/202002/1279500-20200214090758136-1759823899.png)
查询年龄 age 的最小值:
![](https://img2018.cnblogs.com/i-beta/1279500/202002/1279500-20200214090818156-1032907391.png)