获取 Apache Spark 中整个数据集或仅列的摘要 Java

Question

对于下面的数据集，为了获得 Col1 的 Total Summary 值，我做了

import org.apache.spark.sql.functions._
val totaldf = df.groupBy("Col1").agg(lit("Total").as("Col2"), sum("price").as("price"), sum("displayPrice").as("displayPrice"))

然后与

合并

df.union(totaldf).orderBy(col("Col1"), col("Col2").desc).show(false)

df.

+-----------+-------+--------+--------------+
|   Col1    | Col2  | price  | displayPrice |
+-----------+-------+--------+--------------+
| Category1 | item1 |     15 |           14 |
| Category1 | item2 |     11 |           10 |
| Category1 | item3 |     18 |           16 |
| Category2 | item1 |     15 |           14 |
| Category2 | item2 |     11 |           10 |
| Category2 | item3 |     18 |           16 |
+-----------+-------+--------+--------------+

合并后

+-----------+-------+-------+--------------+
|   Col1    | Col2  | price | displayPrice |
+-----------+-------+-------+--------------+
| Category1 | Total |    44 |           40 |
| Category1 | item1 |    15 |           14 |
| Category1 | item2 |    11 |           10 |
| Category1 | item3 |    18 |           16 |
| Category2 | Total |    46 |           44 |
| Category2 | item1 |    16 |           15 |
| Category2 | item2 |    11 |           10 |
| Category2 | item3 |    19 |           17 |
+-----------+-------+-------+--------------+

现在我想要如下所示的整个数据集的摘要，其中 Col1 摘要作为 Total，并且包含所有 Col1 和 Col2 的数据。 必填。

    +-----------+-------+-------+--------------+
    |   Col1    | Col2  | price | displayPrice |
    +-----------+-------+-------+--------------+
    | Total     | Total |    90 |           84 |
    | Category1 | Total |    44 |           40 |
    | Category1 | item1 |    15 |           14 |
    | Category1 | item2 |    11 |           10 |
    | Category1 | item3 |    18 |           16 |
    | Category2 | Total |    46 |           44 |
    | Category2 | item1 |    16 |           15 |
    | Category2 | item2 |    11 |           10 |
    | Category2 | item3 |    19 |           17 |
    +-----------+-------+-------+--------------+

我怎样才能达到上面的结果？

Answer 1

从 totaldf 创建 第三个数据帧 作为

val finalTotalDF= totaldf.select(lit("Total").as("Col1"), lit("Total").as("Col2"), sum("price").as("price"), sum("displayPrice").as("displayPrice"))

然后将其用于 union 作为

df.union(totaldf).union(finalTotalDF).orderBy(col("Col1"), col("Col2").desc).show(false)

你应该期末考试dataframe

已更新

如果订购对您很重要，那么您应该通过执行以下操作将 Col2 列中 Total 的 T 更改为 t 作为 total

import org.apache.spark.sql.functions._
val totaldf = df.groupBy("Col1").agg(lit("total").as("Col2"), sum("price").as("price"), sum("displayPrice").as("displayPrice"))
val finalTotalDF= totaldf.select(lit("Total").as("Col1"), lit("total").as("Col2"), sum("price").as("price"), sum("displayPrice").as("displayPrice"))
df.union(totaldf).union(finalTotalDF).orderBy(col("Col1").desc, col("Col2").desc).show(false)

你应该得到

+---------+-----+-----+------------+
|Col1     |Col2 |price|displayPrice|
+---------+-----+-----+------------+
|Total    |total|90   |82          |
|Category2|total|46   |42          |
|Category2|item3|19   |17          |
|Category2|item2|11   |10          |
|Category2|item1|16   |15          |
|Category1|total|44   |40          |
|Category1|item3|18   |16          |
|Category1|item2|11   |10          |
|Category1|item1|15   |14          |
+---------+-----+-----+------------+

如果评论中提到的订购对您来说真的很重要

I want the total Data as prioirity,So I want that to be at the Top, which is actuall the requirement for me

然后您可以创建另一列用于排序

import org.apache.spark.sql.functions._
val totaldf = df.groupBy("Col1").agg(lit("Total").as("Col2"), sum("price").as("price"), sum("displayPrice").as("displayPrice"), lit(1).as("sort"))
val finalTotalDF= totaldf.select(lit("Total").as("Col1"), lit("Total").as("Col2"), sum("price").as("price"), sum("displayPrice").as("displayPrice"), lit(0).as("sort"))
finalTotalDF.union(totaldf).union(df.withColumn("sort", lit(2))).orderBy(col("sort"), col("Col1"), col("Col2")).drop("sort").show(false)

你应该得到

+---------+-----+-----+------------+
|Col1     |Col2 |price|displayPrice|
+---------+-----+-----+------------+
|Total    |Total|90   |82          |
|Category1|Total|44   |40          |
|Category2|Total|46   |42          |
|Category1|item1|15   |14          |
|Category1|item2|11   |10          |
|Category1|item3|18   |16          |
|Category2|item1|16   |15          |
|Category2|item2|11   |10          |
|Category2|item3|19   |17          |
+---------+-----+-----+------------+

获取 Apache Spark 中整个数据集或仅列的摘要 Java

Getting the Summary of Whole Dataset or Only Columns in Apache Spark Java

apache-spark

apache-spark-sql

apache-spark-dataset