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您可以创建一个窗口来计算过去 7 天内记录发生的次数。但是，如果您尝试查看记录在毫秒级别上出现的次数，它就会崩溃。

简而言之，下面的函数df.timestamp.astype('Timestamp').cast("long")只将时间戳转换为一秒到一个长。它忽略毫秒。您如何将整个时间戳（包括毫秒）转换为 long。您需要将值设置为 long 以便它可以与窗口一起使用。

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

df = sqlContext.createDataFrame([
        ("a", "u8u", "2018-02-02 05:46:41.438357"),
        ("a", "u8u", "2018-02-02 05:46:41.439377"),
        ("a", "a3a", "2018-02-02 09:48:34.081818"),
        ("a", "a3a", "2018-02-02 09:48:34.095586"),
        ("a", "g8g", "2018-02-02 09:48:56.006206"),
        ("a", "g8g", "2018-02-02 09:48:56.007974"),
        ("a", "9k9", "2018-02-02 12:50:48.000000"),
        ("a", "9k9", "2018-02-02 12:50:48.100000"),
], ["person_id", "session_id", "timestamp"])


df = df.withColumn('unix_ts',df.timestamp.astype('Timestamp').cast("long"))
df = df.withColumn("DayOfWeek",F.date_format(df.timestamp, 'EEEE'))

w = Window.partitionBy('person_id','DayOfWeek').orderBy('unix_ts').rangeBetween(-86400*7,-1)
df = df.withColumn('count',F.count('unix_ts').over(w))
df.sort(df.unix_ts).show(20,False)


+---------+----------+--------------------------+----------+---------+-----+
|person_id|session_id|timestamp                 |unix_ts   |DayOfWeek|count|
+---------+----------+--------------------------+----------+---------+-----+
|a        |u8u       |2018-02-02 05:46:41.438357|1517572001|Friday   |0    |
|a        |u8u       |2018-02-02 05:46:41.439377|1517572001|Friday   |0    |
|a        |a3a       |2018-02-02 09:48:34.081818|1517586514|Friday   |2    |
|a        |a3a       |2018-02-02 09:48:34.095586|1517586514|Friday   |2    |
|a        |g8g       |2018-02-02 09:48:56.006206|1517586536|Friday   |4    |
|a        |g8g       |2018-02-02 09:48:56.007974|1517586536|Friday   |4    |
|a        |9k9       |2018-02-02 12:50:48.000000|1517597448|Friday   |6    |
|a        |9k9       |2018-02-02 12:50:48.100000|1517597448|Friday   |6    |
+---------+----------+--------------------------+----------+---------+-----+

计数应该是 0,1,2,3,4,5... 而不是 0,0,2,2,4,4,...

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1 回答 1

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您可以使用pyspark.sql.functions.unix_timestamp()将字符串列转换为时间戳，而不是转换为long.

import pyspark.sql.functions as F
df.select(
    "timestamp",
    F.unix_timestamp(F.col("timestamp"), "yyyy-MM-dd HH:mm:ss.SSSSSS").alias("unix_ts")
).show(truncate=False)
#+--------------------------+----------+
#|timestamp                 |unix_ts   |
#+--------------------------+----------+
#|2018-02-02 05:46:41.438357|1517568839|
#|2018-02-02 05:46:41.439377|1517568840|
#|2018-02-02 09:48:34.081818|1517582995|
#|2018-02-02 09:48:34.095586|1517583009|
#|2018-02-02 09:48:56.006206|1517582942|
#|2018-02-02 09:48:56.007974|1517582943|
#|2018-02-02 12:50:48.862644|1517594710|
#|2018-02-02 12:50:49.981848|1517594830|
#+--------------------------+----------+

第二个参数unix_timestamp()是格式字符串。在您的情况下，使用"yyyy-MM-dd HH:mm:ss.SSSSSS".

应用于您的代码的相应更改将是：

df = df.withColumn(
    'unix_ts',
    F.unix_timestamp(F.col("timestamp"), "yyyy-MM-dd HH:mm:ss.SSSSSS")
)
df = df.withColumn("DayOfWeek", F.date_format(df.timestamp, 'EEEE'))

w = Window.partitionBy('person_id','DayOfWeek').orderBy('unix_ts').rangeBetween(-86400*7,-1)
df = df.withColumn('count',F.count('unix_ts').over(w))
df.sort(df.unix_ts).show(20,False)
#+---------+----------+--------------------------+----------+---------+-----+
#|person_id|session_id|timestamp                 |unix_ts   |DayOfWeek|count|
#+---------+----------+--------------------------+----------+---------+-----+
#|a        |u8u       |2018-02-02 05:46:41.438357|1517568839|Friday   |0    |
#|a        |u8u       |2018-02-02 05:46:41.439377|1517568840|Friday   |1    |
#|a        |g8g       |2018-02-02 09:48:56.006206|1517582942|Friday   |2    |
#|a        |g8g       |2018-02-02 09:48:56.007974|1517582943|Friday   |3    |
#|a        |a3a       |2018-02-02 09:48:34.081818|1517582995|Friday   |4    |
#|a        |a3a       |2018-02-02 09:48:34.095586|1517583009|Friday   |5    |
#|a        |9k9       |2018-02-02 12:50:48.862644|1517594710|Friday   |6    |
#|a        |9k9       |2018-02-02 12:50:49.981848|1517594830|Friday   |7    |
#+---------+----------+--------------------------+----------+---------+-----+
于 2018-06-08T17:53:03.663 回答