如何在 Spark SQL 中聚合两列

Question

现在我有一张桌子，上面有以下任务：

1. 按 DepartmentID 和 EmployeeID 上的函数分组
2. 在每个组中，我需要按 (ArrivalDate, ArrivalTime) 排序并选择第一个。因此，如果两个日期不同，请选择较新的日期。如果两个日期相同，请选择较新的时间。

我正在尝试这种方法：

input.select("DepartmenId","EmolyeeID", "ArrivalDate", "ArrivalTime", "Word")
  .agg(here will be the function that handles logic from 2)
  .show()

这里聚合的语法是什么？

提前谢谢你。

// +-----------+---------+-----------+-----------+--------+
// |DepartmenId|EmolyeeID|ArrivalDate|ArrivalTime|   Word |
// +-----------+---------+-----------+-----------+--------+
// |     D1    |   E1    |  20170101 |    0730   |  "YES" |
// +-----------+---------+-----------+-----------+--------+
// |     D1    |   E1    |  20170102 |    1530   |  "NO"  |
// +-----------+---------+-----------+-----------+--------+
// |     D1    |   E2    |  20170101 |    0730   |  "ZOO" |
// +-----------+---------+-----------+-----------+--------+
// |     D1    |   E2    |  20170102 |    0330   |  "BOO" |
// +-----------+---------+-----------+-----------+--------+
// |     D2    |   E1    |  20170101 |    0730   |  "LOL" |
// +-----------+---------+-----------+-----------+--------+
// |     D2    |   E1    |  20170101 |    1830   |  "ATT" |
// +-----------+---------+-----------+-----------+--------+
// |     D2    |   E2    |  20170105 |    1430   |  "UNI" |
// +-----------+---------+-----------+-----------+--------+


// output should be

// +-----------+---------+-----------+-----------+--------+
// |DepartmenId|EmolyeeID|ArrivalDate|ArrivalTime|   Word |
// +-----------+---------+-----------+-----------+--------+
// |     D1    |   E1    |  20170102 |    1530   |  "NO"  |
// +-----------+---------+-----------+-----------+--------+
// |     D1    |   E2    |  20170102 |    0330   |  "BOO" |
// +-----------+---------+-----------+-----------+--------+
// |     D2    |   E1    |  20170101 |    1830   |  "ATT" |
// +-----------+---------+-----------+-----------+--------+
// |     D2    |   E2    |  20170105 |    1430   |  "UNI" |
// +-----------+---------+-----------+-----------+--------+

Answer 1

您可以在包含所有非分组列的新结构列上使用max，首先使用ArrivalData，然后使用ArrivalTime：该新列的排序将符合您的要求（最新日期优先；最新相似日期之间的第一个小时），因此获得最大值将产生您所追求的记录。

然后，您可以使用select 操作将结构“拆分”回单独的列。

import spark.implicits._
import org.apache.spark.sql.functions._

df.groupBy($"DepartmentID", $"EmployeeID")
  .agg(max(struct("ArrivalDate", "ArrivalTime", "Word")) as "struct")
  .select($"DepartmentID", $"EmployeeID",
    $"struct.ArrivalDate" as "ArrivalDate",
    $"struct.ArrivalTime" as "ArrivalTime",
    $"struct.Word" as "Word"
  )

Answer 2

一种方法是使用 Spark Window 功能：

val df = Seq(
  ("D1", "E1", "20170101", "0730", "YES"),
  ("D1", "E1", "20170102", "1530", "NO"),
  ("D1", "E2", "20170101", "0730", "ZOO"),
  ("D1", "E2", "20170102", "0330", "BOO"),
  ("D2", "E1", "20170101", "0730", "LOL"),
  ("D2", "E1", "20170101", "1830", "ATT"),
  ("D2", "E2", "20170105", "1430", "UNI")
).toDF(
  "DepartmenId", "EmolyeeID", "ArrivalDate", "ArrivalTime", "Word"
)

import org.apache.spark.sql.expressions.Window

val df2 = df.withColumn("rowNum", row_number().over(
    Window.partitionBy("DepartmenId", "EmolyeeID").
      orderBy($"ArrivalDate".desc, $"ArrivalTime".desc)
  )).
  select("DepartmenId", "EmolyeeID", "ArrivalDate", "ArrivalTime","Word").
  where($"rowNum" === 1).
  orderBy("DepartmenId", "EmolyeeID")

df2.show
+-----------+---------+-----------+-----------+----+
|DepartmenId|EmolyeeID|ArrivalDate|ArrivalTime|Word|
+-----------+---------+-----------+-----------+----+
|         D1|       E1|   20170102|       1530|  NO|
|         D1|       E2|   20170102|       0330| BOO|
|         D2|       E1|   20170101|       1830| ATT|
|         D2|       E2|   20170105|       1430| UNI|
+-----------+---------+-----------+-----------+----+