count - pyspark:在一个窗口中计数不相同

Question

我只是尝试在一个窗口上做一个countDistinct并得到了这个错误:

AnalysisException: u'Distinct window functions are not supported: count(distinct color#1926)

有没有办法对pyspark中的窗口进行不同的计数？

这是一些示例代码:

from pyspark.sql.window import Window    
from pyspark.sql import functions as F

#function to calculate number of seconds from number of days
days = lambda i: i * 86400

df = spark.createDataFrame([(17, "2017-03-10T15:27:18+00:00", "orange"),
                    (13, "2017-03-15T12:27:18+00:00", "red"),
                    (25, "2017-03-18T11:27:18+00:00", "red")],
                    ["dollars", "timestampGMT", "color"])

df = df.withColumn('timestampGMT', df.timestampGMT.cast('timestamp'))

#create window by casting timestamp to long (number of seconds)
w = (Window.orderBy(F.col("timestampGMT").cast('long')).rangeBetween(-days(7), 0))

df = df.withColumn('distinct_color_count_over_the_last_week', F.countDistinct("color").over(w))

df.show()

这是我想看到的输出:

+-------+--------------------+------+---------------------------------------+
|dollars|        timestampGMT| color|distinct_color_count_over_the_last_week|
+-------+--------------------+------+---------------------------------------+
|     17|2017-03-10 15:27:...|orange|                                      1|
|     13|2017-03-15 12:27:...|   red|                                      2|
|     25|2017-03-18 11:27:...|   red|                                      1|
+-------+--------------------+------+---------------------------------------+

Answer 1

编辑:

正如noleto在下面的回答中提到的那样，自pyspark 2.1以来，现在有一个大约可在窗口上运行的函数。

原始答案

我发现可以结合使用collect_set和size函数来模拟窗口上countCounttinct的功能:

from pyspark.sql.window import Window
from pyspark.sql import functions as F

#function to calculate number of seconds from number of days
days = lambda i: i * 86400

#create some test data
df = spark.createDataFrame([(17, "2017-03-10T15:27:18+00:00", "orange"),
                    (13, "2017-03-15T12:27:18+00:00", "red"),
                    (25, "2017-03-18T11:27:18+00:00", "red")],
                    ["dollars", "timestampGMT", "color"])

#convert string timestamp to timestamp type             
df = df.withColumn('timestampGMT', df.timestampGMT.cast('timestamp'))

#create window by casting timestamp to long (number of seconds)
w = (Window.orderBy(F.col("timestampGMT").cast('long')).rangeBetween(-days(7), 0))

#use collect_set and size functions to perform countDistinct over a window
df = df.withColumn('distinct_color_count_over_the_last_week', F.size(F.collect_set("color").over(w)))

df.show()

这导致记录的前一周有不同的颜色计数:

+-------+--------------------+------+---------------------------------------+
|dollars|        timestampGMT| color|distinct_color_count_over_the_last_week|
+-------+--------------------+------+---------------------------------------+
|     17|2017-03-10 15:27:...|orange|                                      1|
|     13|2017-03-15 12:27:...|   red|                                      2|
|     25|2017-03-18 11:27:...|   red|                                      1|
+-------+--------------------+------+---------------------------------------+