问题
I have a PySpark dataframe that keeps track of changes that occur in a product's price and status over months. This means that a new row is created only when a change occurred (in either status or price) compared to the previous month, like in the dummy data below
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|product_id| status | price| month |
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|1 | available | 5 | 2019-10|
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|1 | available | 8 | 2020-08|
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|1 | limited | 8 | 2020-10|
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|2 | limited | 1 | 2020-09|
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|2 | limited | 3 | 2020-10|
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I would like to create a dataframe that shows the values for each of the last 6 months. This means that I need to duplicate the records whenever there is a gap in the above dataframe. For example, if the last 6 months are 2020-07, 2020-08, ... 2020-12, then the result for the above dataframe should be
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|product_id| status | price| month |
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|1 | available | 5 | 2020-07|
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|1 | available | 8 | 2020-08|
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|1 | available | 8 | 2020-09|
----------------------------------------
|1 | limited | 8 | 2020-10|
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|1 | limited | 8 | 2020-11|
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|1 | limited | 8 | 2020-12|
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|2 | limited | 1 | 2020-09|
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|2 | limited | 3 | 2020-10|
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|2 | limited | 3 | 2020-11|
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|2 | limited | 3 | 2020-12|
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Notice that for product_id = 1 there was an older record from 2019-10 that was propagated until 2020-08 and then trimmed, whereas for product_id = 2 there were no records prior to 2020-09 and thus the months 2020-07, 2020-08 were not filled for it (as the product did not exist prior to 2020-09).
Since the dataframe consists of millions of records, a "brute-force" solution using for loops and checking for each product_id is rather slow. It seems that it should be possible to solve this using window functions, by creating another column next_month and then filling in the gaps based on that column, but I don't know how to achieve that.
回答1:
With Respect to the @jxc comment, I have prepared the answer for this use case.
Following is the code snippet.
Import the spark SQL functions
from pyspark.sql import functions as F, WindowPrepare the sample data
simpleData = ((1,"Available",5,"2020-07"),
(1,"Available",8,"2020-08"),
(1,"Limited",8,"2020-12"),
(2,"Limited",1,"2020-09"),
(2,"Limited",3,"2020-12")
)
columns= ["product_id", "status", "price", "month"]
Creating dataframe of sample data
df = spark.createDataFrame(data = simpleData, schema = columns)Add date column in dataframe to get proper formatted date
df0 = df.withColumn("date",F.to_date('month','yyyy-MM'))
df0.show()
+----------+---------+-----+-------+----------+
|product_id| status|price| month| date|
+----------+---------+-----+-------+----------+
| 1|Available| 5|2020-07|2020-07-01|
| 1|Available| 8|2020-08|2020-08-01|
| 1| Limited| 8|2020-12|2020-12-01|
| 2| Limited| 1|2020-09|2020-09-01|
| 2| Limited| 3|2020-12|2020-12-01|
+----------+---------+-----+-------+----------+
- Creating WinSpec w1 and use Window aggregate function lead to find the next date over(w1), convert it to the previous months to set up date sequences:
w1 = Window.partitionBy('product_id').orderBy('date')
df1 = df0.withColumn('end_date',F.coalesce(F.add_months(F.lead('date').over(w1),-1),'date'))
df1.show()
+----------+---------+-----+-------+----------+----------+
|product_id| status|price| month| date| end_date|
+----------+---------+-----+-------+----------+----------+
| 1|Available| 5|2020-07|2020-07-01|2020-07-01|
| 1|Available| 8|2020-08|2020-08-01|2020-11-01|
| 1| Limited| 8|2020-12|2020-12-01|2020-12-01|
| 2| Limited| 1|2020-09|2020-09-01|2020-11-01|
| 2| Limited| 3|2020-12|2020-12-01|2020-12-01|
+----------+---------+-----+-------+----------+----------+
- Using months_between(end_date, date) to calculate # of months between two dates, and use transform function to iterate through sequence(0, #months), create a named_struct with date=add_months(date,i) and price=IF(i=0,price,price), use inline_outer to explode the array of structs.
df2 = df1.selectExpr("product_id", "status", inline_outer( transform( sequence(0,int(months_between(end_date, date)),1), i -> (add_months(date,i) as date, IF(i=0,price,price) as price) ) ) )
df2.show()
+----------+---------+----------+-----+
|product_id| status| date|price|
+----------+---------+----------+-----+
| 1|Available|2020-07-01| 5|
| 1|Available|2020-08-01| 8|
| 1|Available|2020-09-01| 8|
| 1|Available|2020-10-01| 8|
| 1|Available|2020-11-01| 8|
| 1| Limited|2020-12-01| 8|
| 2| Limited|2020-09-01| 1|
| 2| Limited|2020-10-01| 1|
| 2| Limited|2020-11-01| 1|
| 2| Limited|2020-12-01| 3|
+----------+---------+----------+-----+
- Partitioning the dataframe on
product_idand adding a rank column indf3to get row number for each row. Then, Storing the maximum ofrankcolumn value with new columnmax_rankfor eachproduct_idand storingmax_rankin todf4
w2 = Window.partitionBy('product_id').orderBy('date')
df3 = df2.withColumn('rank',F.row_number().over(w2))
Schema: DataFrame[product_id: bigint, status: string, date: date, price: bigint, rank: int]
df3.show()
+----------+---------+----------+-----+----+
|product_id| status| date|price|rank|
+----------+---------+----------+-----+----+
| 1|Available|2020-07-01| 5| 1|
| 1|Available|2020-08-01| 8| 2|
| 1|Available|2020-09-01| 8| 3|
| 1|Available|2020-10-01| 8| 4|
| 1|Available|2020-11-01| 8| 5|
| 1| Limited|2020-12-01| 8| 6|
| 2| Limited|2020-09-01| 1| 1|
| 2| Limited|2020-10-01| 1| 2|
| 2| Limited|2020-11-01| 1| 3|
| 2| Limited|2020-12-01| 3| 4|
+----------+---------+----------+-----+----+
df4 = df3.groupBy("product_id").agg(F.max('rank').alias('max_rank'))
Schema: DataFrame[product_id: bigint, max_rank: int]
df4.show()
+----------+--------+
|product_id|max_rank|
+----------+--------+
| 1| 6|
| 2| 4|
+----------+--------+
- Joining
df3anddf4dataframes onproduct_idgetmax_rank
df5 = df3.join(df4,df3.product_id == df4.product_id,"inner") \
.select(df3.product_id,df3.status,df3.date,df3.price,df3.rank,df4.max_rank)
Schema: DataFrame[product_id: bigint, status: string, date: date, price: bigint, rank: int, max_rank: int]
df5.show()
+----------+---------+----------+-----+----+--------+
|product_id| status| date|price|rank|max_rank|
+----------+---------+----------+-----+----+--------+
| 1|Available|2020-07-01| 5| 1| 6|
| 1|Available|2020-08-01| 8| 2| 6|
| 1|Available|2020-09-01| 8| 3| 6|
| 1|Available|2020-10-01| 8| 4| 6|
| 1|Available|2020-11-01| 8| 5| 6|
| 1| Limited|2020-12-01| 8| 6| 6|
| 2| Limited|2020-09-01| 1| 1| 4|
| 2| Limited|2020-10-01| 1| 2| 4|
| 2| Limited|2020-11-01| 1| 3| 4|
| 2| Limited|2020-12-01| 3| 4| 4|
+----------+---------+----------+-----+----+--------+
- Then finally filtering the
df5dataframe usingbetweenfunction to get the latest 6 months data.
FinalResultDF = df5.filter(F.col('rank') \
.between(F.when((F.col('max_rank') > 5),(F.col('max_rank')-6)).otherwise(0),F.col('max_rank'))) \
.select(df5.product_id,df5.status,df5.date,df5.price)
FinalResultDF.show(truncate=False)
+----------+---------+----------+-----+
|product_id|status |date |price|
+----------+---------+----------+-----+
|1 |Available|2020-07-01|5 |
|1 |Available|2020-08-01|8 |
|1 |Available|2020-09-01|8 |
|1 |Available|2020-10-01|8 |
|1 |Available|2020-11-01|8 |
|1 |Limited |2020-12-01|8 |
|2 |Limited |2020-09-01|1 |
|2 |Limited |2020-10-01|1 |
|2 |Limited |2020-11-01|1 |
|2 |Limited |2020-12-01|3 |
+----------+---------+----------+-----+
回答2:
Using spark-sql:
Given input dataframe:
val df = spark.sql(""" with t1 (
select 1 c1, 'available' c2, 5 c3, '2019-10' c4 union all
select 1 c1, 'available' c2, 8 c3, '2020-08' c4 union all
select 1 c1, 'limited' c2, 8 c3, '2020-10' c4 union all
select 2 c1, 'limited' c2, 1 c3, '2020-09' c4 union all
select 2 c1, 'limited' c2, 3 c3, '2020-10' c4
) select c1 product_id, c2 status , c3 price, c4 month from t1
""")
df.createOrReplaceTempView("df")
df.show(false)
+----------+---------+-----+-------+
|product_id|status |price|month |
+----------+---------+-----+-------+
|1 |available|5 |2019-10|
|1 |available|8 |2020-08|
|1 |limited |8 |2020-10|
|2 |limited |1 |2020-09|
|2 |limited |3 |2020-10|
+----------+---------+-----+-------+
Filter on the date window i.e 6 months from 2020-07 to 2020-12 and store them in df1
val df1 = spark.sql("""
select * from df where month > '2020-07' and month < '2020-12'
""")
df1.createOrReplaceTempView("df1")
df1.show(false)
+----------+---------+-----+-------+
|product_id|status |price|month |
+----------+---------+-----+-------+
|1 |available|8 |2020-08|
|1 |limited |8 |2020-10|
|2 |limited |1 |2020-09|
|2 |limited |3 |2020-10|
+----------+---------+-----+-------+
Lower boundary - Get the maximum when the month <='2020-07'. Overwrite the month as '2020-07'
val df2 = spark.sql("""
select product_id, status, price, '2020-07' month from df where (product_id,month) in
( select product_id, max(month) from df where month <= '2020-07' group by 1 )
""")
df2.createOrReplaceTempView("df2")
df2.show(false)
+----------+---------+-----+-------+
|product_id|status |price|month |
+----------+---------+-----+-------+
|1 |available|5 |2020-07|
+----------+---------+-----+-------+
Upper boundary - Get the maximum using <='2020-12'. Overwrite the month as '2020-12'
val df3 = spark.sql("""
select product_id, status, price, '2020-12' month from df where (product_id, month) in
( select product_id, max(month) from df where month <= '2020-12' group by 1 )
""")
df3.createOrReplaceTempView("df3")
df3.show(false)
+----------+-------+-----+-------+
|product_id|status |price|month |
+----------+-------+-----+-------+
|1 |limited|8 |2020-12|
|2 |limited|3 |2020-12|
+----------+-------+-----+-------+
Now union all the 3 and store it in df4
val df4 = spark.sql("""
select product_id, status, price, month from df1 union all
select product_id, status, price, month from df2 union all
select product_id, status, price, month from df3
order by product_id, month
""")
df4.createOrReplaceTempView("df4")
df4.show(false)
+----------+---------+-----+-------+
|product_id|status |price|month |
+----------+---------+-----+-------+
|1 |available|5 |2020-07|
|1 |available|8 |2020-08|
|1 |limited |8 |2020-10|
|1 |limited |8 |2020-12|
|2 |limited |1 |2020-09|
|2 |limited |3 |2020-10|
|2 |limited |3 |2020-12|
+----------+---------+-----+-------+
Result: Use sequence(date1,date2, interval 1 month) to generate date array for the missing months. Explode the array and you get the results.
spark.sql("""
select product_id, status, price, month, explode(dt) res_month from
(
select t1.*,
case when months_between(lm||'-01',month||'-01')=1.0 then array(month||'-01')
when month='2020-12' then array(month||'-01')
else sequence(to_date(month||'-01'), add_months(to_date(lm||'-01'),-1), interval 1 month )
end dt
from (
select product_id, status, price, month,
lead(month) over(partition by product_id order by month) lm
from df4
) t1
) t2
order by product_id, res_month
""")
.show(false)
+----------+---------+-----+-------+----------+
|product_id|status |price|month |res_month |
+----------+---------+-----+-------+----------+
|1 |available|5 |2020-07|2020-07-01|
|1 |available|8 |2020-08|2020-08-01|
|1 |available|8 |2020-08|2020-09-01|
|1 |limited |8 |2020-10|2020-10-01|
|1 |limited |8 |2020-10|2020-11-01|
|1 |limited |8 |2020-12|2020-12-01|
|2 |limited |1 |2020-09|2020-09-01|
|2 |limited |3 |2020-10|2020-10-01|
|2 |limited |3 |2020-10|2020-11-01|
|2 |limited |3 |2020-12|2020-12-01|
+----------+---------+-----+-------+----------+
来源：https://stackoverflow.com/questions/65246883/duplicating-records-between-date-gaps-within-a-selected-time-interval-in-a-pyspa