1、 agg(expers:column*) 返回dataframe類型 ，同數學計算求值
df.agg(max("age"), avg("salary"))
df.groupBy().agg(max("age"), avg("salary"))
2、 agg(exprs: Map[String, String]) 返回dataframe類型 ，同數學計算求值 map類型的
df.agg(Map("age" -> "max", "salary" -> "avg"))
df.groupBy().agg(Map("age" -> "max", "salary" -> "avg"))
3、 agg(aggExpr: (String, String), aggExprs: (String, String)*) 返回dataframe類型 ，同數學計算求值
df.agg(Map("age" -> "max", "salary" -> "avg"))
df.groupBy().agg(Map("age" -> "max", "salary" -> "avg"))
例子1：
scala> spark.version
res2: String = 2.0.2
scala> case class Test(bf: Int, df: Int, duration: Int, tel_date: Int)
defined class Test
scala> val df = Seq(Test(1,1,1,1), Test(1,1,2,2), Test(1,1,3,3), Test(2,2,3,3), Test(2,2,2,2), Test(2,2,1,1)).toDF
df: org.apache.spark.sql.DataFrame = [bf: int, df: int ... 2 more fields]
scala> df.show
+---+---+--------+--------+
| bf| df|duration|tel_date|
+---+---+--------+--------+
| 1| 1| 1| 1|
| 1| 1| 2| 2|
| 1| 1| 3| 3|
| 2| 2| 3| 3|
| 2| 2| 2| 2|
| 2| 2| 1| 1|
+---+---+--------+--------+
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)|
+---+---+-------------+-------------+-------------+
| 2| 2| 6| 1| 3|
| 1| 1| 6| 1| 3|
+---+---+-------------+-------------+-------------+
注意：此處df已經少了列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"))