聚合后如何包括未聚合的列? [英] How to include non-aggregated columns after aggregation?
本文介绍了聚合后如何包括未聚合的列?的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
我正在使用spark-sql-2.4.1v.在这里,我有下面的情况
I am using spark-sql-2.4.1v. Here I have scenario like below
val df = Seq(
(2010,"2018-11-24",71285,"USA","0.9192019", "0.1992019", "0.9955999"),
(2010,"2017-08-24",71286,"USA","0.9292018", "0.2992019", "0.99662018"),
(2010,"2019-02-24",71287,"USA","0.9392017", "0.3992019", "0.99772000")).toDF("seq_id","load_date","company_id","country_code","item1_value","item2_value","item3_value")
.withColumn("item1_value", $"item1_value".cast(DoubleType))
.withColumn("item2_value", $"item2_value".cast(DoubleType))
.withColumn("item3_value", $"item3_value".cast(DoubleType))
.withColumn("fiscal_year", year(col("load_date")).cast(IntegerType))
.withColumn("fiscal_quarter", quarter(col("load_date")).cast(IntegerType))
df.show()
val aggregateColumns = Seq("item1_value","item2_value","item3_value")
var aggDFs = aggregateColumns.map( c => {
df.groupBy("country_code").agg(lit(c).as("col_name"),sum(c).as("sum_of_column"))
})
var combinedDF = aggDFs.reduce(_ union _)
combinedDF.show
我得到的输出数据
Output data i am getting like
|country_code| col_name| sum_of_column|
| USA|item1_value| 2.7876054|
| USA|item2_value| 0.8976057|
| USA|item3_value|2.9899400800000002|
我需要在输出中添加其他列,即"seq_id","load_date"和"company_id"数据帧聚合操作后如何获取它们?
推荐答案
您可以使用Window函数显示未聚合的列,也可以说在每一行显示总和.
You can use Window functions to show non-aggregated columns or can say showing sum in each row.
如果有帮助,请参见下面的代码片段:
Please see below code snippet if it helps:
import org.apache.spark.sql.expressions.Window
val df = Seq(
(2010,"2018-11-24",71285,"USA","0.9192019", "0.1992019", "0.9955999"),
(2010,"2017-08-24",71286,"USA","0.9292018", "0.2992019", "0.99662018"),
(2010,"2019-02-24",71287,"USA","0.9392017", "0.3992019", "0.99772000")).
toDF("seq_id","load_date","company_id","country_code","item1_value","item2_value","item3_value").
withColumn("item1_value", $"item1_value".cast(DoubleType)).
withColumn("item2_value", $"item2_value".cast(DoubleType)).
withColumn("item3_value", $"item3_value".cast(DoubleType)).
withColumn("fiscal_year", year(col("load_date")).cast(IntegerType)).
withColumn("fiscal_quarter", quarter(col("load_date")).cast(IntegerType))
val byCountry = Window.partitionBy(col("country_code"))
val aggregateColumns = Seq("item1_value","item2_value","item3_value")
var aggDFs = aggregateColumns.map( c => {
df.withColumn("col_name",lit(c)).withColumn("sum_country", sum(c) over byCountry)
})
var combinedDF = aggDFs.reduce(_ union _)
combinedDF.
select("seq_id","load_date","company_id","country_code","col_name","sum_country").
distinct.show(100,false)
输出如下:
+------+----------+----------+------------+-----------+------------------+
|seq_id|load_date |company_id|country_code|col_name |sum_country |
+------+----------+----------+------------+-----------+------------------+
|2010 |2019-02-24|71287 |USA |item1_value|2.7876054 |
|2010 |2018-11-24|71285 |USA |item1_value|2.7876054 |
|2010 |2017-08-24|71286 |USA |item1_value|2.7876054 |
|2010 |2018-11-24|71285 |USA |item2_value|0.8976057000000001|
|2010 |2019-02-24|71287 |USA |item2_value|0.8976057000000001|
|2010 |2017-08-24|71286 |USA |item2_value|0.8976057000000001|
|2010 |2019-02-24|71287 |USA |item3_value|2.9899400800000002|
|2010 |2018-11-24|71285 |USA |item3_value|2.9899400800000002|
|2010 |2017-08-24|71286 |USA |item3_value|2.9899400800000002|
+------+----------+----------+------------+-----------+------------------+
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