spark sql的agg函数,作用：在整体DataFrame不分组聚合

、agg(expers:column*) 返回dataframe类型 ，同数学计算求值
df.agg(max("age"), avg("salary"))
df.groupBy().agg(max("age"), avg("salary"))
、 agg(exprs: Map[String, String])  返回dataframe类型 ，同数学计算求值 map类型的
df.agg(Map("age" -> "max", "salary" -> "avg"))
df.groupBy().agg(Map("age" -> "max", "salary" -> "avg"))
、 agg(aggExpr: (String, String), aggExprs: (String, String)*)  返回dataframe类型 ，同数学计算求值
df.agg(Map("age" -> "max", "salary" -> "avg"))
df.groupBy().agg(Map("age" -> "max", "salary" -> "avg"))
例子1：
scala> spark.version
res2: String = 2.0.
 
scala> case class Test(bf: Int, df: Int, duration: Int, tel_date: Int)
defined class Test
 
scala> val df = Seq(Test(,,,), Test(,,,), Test(,,,), Test(,,,), Test(,,,), Test(,,,)).toDF
df: org.apache.spark.sql.DataFrame = [bf: int, df: int ...  more fields]
 
scala> df.show
+---+---+--------+--------+
| bf| df|duration|tel_date|
+---+---+--------+--------+
|  |  |       |       |
|  |  |       |       |
|  |  |       |       |
|  |  |       |       |
|  |  |       |       |
|  |  |       |       |
+---+---+--------+--------+
 
 
scala> df.groupBy("bf", "df").agg(("duration","sum"),("tel_date","min"),("tel_date","max")).show()
+---+---+-------------+-------------+-------------+
| bf| df|sum(duration)|min(tel_date)|max(tel_date)|
+---+---+-------------+-------------+-------------+
|  |  |            |            |            |
|  |  |            |            |            |

+---+---+-------------+-------------+-------------+
注意：此处df已经少了列duration和tel_date，只有groupby的key和agg中的字段

例子2：
import pyspark.sql.functions as func
agg(func.max("event_time").alias("max_event_tm"),func.min("event_time").alias("min_event_tm"))