在Apache Spark Java中获取整个数据集或仅列的摘要 [英] Getting the Summary of Whole Dataset or Only Columns in Apache Spark Java

问题描述

对于以下数据集，要获取 Col1 的总摘要值，我做了

For below Dataset, to get Total Summary values of Col1 , I did

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

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，我想要以下所示的Whole Dataset的摘要，它将Col1摘要作为总计，并且具有All Col1和Col2的数据. 必需.

Now I want summary of Whole Dataset as Below , which will have Col1 Summary as Total and has the Data of All Col1 and Col2. Required.

    +-----------+-------+-------+--------------+
    |   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 |
    +-----------+-------+-------+--------------+

我如何能够获得上述结果?

How Can I be able to achieve the above result?

推荐答案

从totaldf创建第三个​​数据帧为

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)

您应该具有最终要求 dataframe

已更新

如果订购对您而言很重要，则应执行以下操作

If ordering matters to you then you should be changing T of Total in Col2 column to t as total by doing the following

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)

您应该得到

+---------+-----+-----+------------+
|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而言确实很重要

If ordering really matters to you as mentioned in the comment

我希望总数据量能达到最高水平，所以我希望排在最前，这实际上是我的要求

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以

Then you can create another column for sorting as

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)

您应该获得

+---------+-----+-----+------------+
|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          |
+---------+-----+-----+------------+

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