如何使用pyspark数据框窗口功能

Question

I have a dataframe like below

I want to get a dataframe which will have the most recent version with the latest date.The first filter criteria will be latest version and then latest date The resulting dataframe should look like below

I am using window function to achieve this.I have written below piece of code.

wind = Window.partitionBy("id")
data = data.withColumn("maxVersion", F.max("version").over(wind)) \
               .withColumn("maxDt", F.max("dt").over(wind)) \
               .where(F.col("version") == F.col("maxVersion")) \
               .where(F.col("maxDt") == F.col("dt")) \
               .drop(F.col("maxVersion")) \
               .drop(F.col("maxDt"))

I am not sure where I am missing out.I am only getting one output with id 100. Please help me to solve this

Answer 1

As you mentioned there is an order in your operatin : first version then dt Basically, you need to select only maximum version (removing everything else) and then select maximum dt and removing everything else. You just have to switch 2 lines as this :

wind = Window.partitionBy("id")
data = data.withColumn("maxVersion", F.max("version").over(wind)) \
               .where(F.col("version") == F.col("maxVersion")) \
               .withColumn("maxDt", F.max("dt").over(wind)) \
               .where(F.col("maxDt") == F.col("dt")) \
               .drop(F.col("maxVersion")) \
               .drop(F.col("maxDt"))

The reason why you got only one row for id 100 is because in that case maximum version and maximum dt is happening on the same row (you got lucky). But it is not true for id 200.

Answer 2

Basically there are couple of issues with your formulation. First you need to change the date from string to it's proper date format. Then Window in pyspark allows you to specify the ordering of the columns one after the other. Then there is rank() function which allows you to rank the results over the Window. Finally all that remains is to select the first rank.

from pyspark.sql.types import *
from pyspark import SparkContext, SQLContext
import pyspark.sql.functions as F
from pyspark.sql import Window

sc = SparkContext('local')
sqlContext = SQLContext(sc)

data1 = [
        (100,1,"2020-03-19","Nil1"),
        (100,2,"2020-04-19","Nil2"),
        (100,2,"2020-04-19","Nil2"),
        (100,2,"2020-05-19","Ni13"),
        (200,1,"2020-09-19","Jay1"),
        (200,2,"2020-07-19","Jay2"),
        (200,2,"2020-08-19","Jay3"),

      ]

df1Columns = ["id", "version", "dt",  "Name"]
df1 = sqlContext.createDataFrame(data=data1, schema = df1Columns)
df1 = df1.withColumn("dt",F.to_date(F.to_timestamp("dt", 'yyyy-MM-dd')).alias('dt'))
print("Schema.")
df1.printSchema()
print("Actual initial data")
df1.show(truncate=False)

wind = Window.partitionBy("id").orderBy(F.desc("version"), F.desc("dt"))

df1 = df1.withColumn("rank", F.rank().over(wind))
print("Ranking over the window spec specified")
df1.show(truncate=False)

final_df = df1.filter(F.col("rank") == 1).drop("rank")
print("Filtering the final result by applying the rank == 1 condition")
final_df.show(truncate=False)

Output :

Schema.
root
 |-- id: long (nullable = true)
 |-- version: long (nullable = true)
 |-- dt: date (nullable = true)
 |-- Name: string (nullable = true)

Actual initial data
+---+-------+----------+----+
|id |version|dt        |Name|
+---+-------+----------+----+
|100|1      |2020-03-19|Nil1|
|100|2      |2020-04-19|Nil2|
|100|2      |2020-04-19|Nil2|
|100|2      |2020-05-19|Ni13|
|200|1      |2020-09-19|Jay1|
|200|2      |2020-07-19|Jay2|
|200|2      |2020-08-19|Jay3|
+---+-------+----------+----+

Ranking over the window spec specified
+---+-------+----------+----+----+
|id |version|dt        |Name|rank|
+---+-------+----------+----+----+
|100|2      |2020-05-19|Ni13|1   |
|100|2      |2020-04-19|Nil2|2   |
|100|2      |2020-04-19|Nil2|2   |
|100|1      |2020-03-19|Nil1|4   |
|200|2      |2020-08-19|Jay3|1   |
|200|2      |2020-07-19|Jay2|2   |
|200|1      |2020-09-19|Jay1|3   |
+---+-------+----------+----+----+

Filtering the final result by applying the rank == 1 condition
+---+-------+----------+----+
|id |version|dt        |Name|
+---+-------+----------+----+
|100|2      |2020-05-19|Ni13|
|200|2      |2020-08-19|Jay3|
+---+-------+----------+----+

Answer 3

A neater way is perhaps to do the following:

w = Window.partitionBy("id").orderBy(F.col('version').desc(), F.col('dt').desc())
df1.withColumn('maximum', F.row_number().over(w)).filter('maximum = 1').drop('maximum').show()