连续行之间的日期差异 - Pyspark Dataframe

Question

我有一个 table 具有以下结构

USER_ID     Tweet_ID                 Date
  1           1001       Thu Aug 05 19:11:39 +0000 2010
  1           6022       Mon Aug 09 17:51:19 +0000 2010
  1           1041       Sun Aug 19 11:10:09 +0000 2010
  2           9483       Mon Jan 11 10:51:23 +0000 2012
  2           4532       Fri May 21 11:11:11 +0000 2012
  3           4374       Sat Jul 10 03:21:23 +0000 2013
  3           4334       Sun Jul 11 04:53:13 +0000 2013

基本上我想做的是有一个 PysparkSQL 查询来计算具有相同 user_id 数字的连续记录的日期差异（以秒为单位）。预期结果将是：

1      Sun Aug 19 11:10:09 +0000 2010 - Mon Aug 09 17:51:19 +0000 2010     839930
1      Mon Aug 09 17:51:19 +0000 2010 - Thu Aug 05 19:11:39 +0000 2010     340780
2      Fri May 21 11:11:11 +0000 2012 - Mon Jan 11 10:51:23 +0000 2012     1813212
3      Sun Jul 11 04:53:13 +0000 2013 - Sat Jul 10 03:21:23 +0000 2013     5510

Answer 1

像这样：

df.registerTempTable("df")

sqlContext.sql("""
     SELECT *, CAST(date AS bigint) - CAST(lag(date, 1) OVER (
              PARTITION BY user_id ORDER BY date) AS bigint) 
     FROM df""")

Answer 2

另一种方式可能是：

from pyspark.sql.functions import lag
from pyspark.sql.window import Window

df.withColumn("time_intertweet",(df.date.cast("bigint") - lag(df.date.cast("bigint"), 1)
.over(Window.partitionBy("user_‌id")
.orderBy("date")‌))
.cast("bigint"))

Answer 3

已编辑感谢@cool_kid

@Joesemy 的回答非常好，但对我不起作用，因为 cast("bigint") 抛出了一个错误。所以我以这种方式使用了 pyspark.sql.functions 模块 中的 datediff 函数并且它起作用了：

from pyspark.sql.functions import *
from pyspark.sql.window import Window

df.withColumn("time_intertweet", datediff(df.date, lag(df.date, 1)
    .over(Window.partitionBy("user_‌id")
    .orderBy("date")‌)))

连续行之间的日期差异 - Pyspark Dataframe

Date difference between consecutive rows - Pyspark Dataframe

python

apache-spark

pyspark

pyspark-sql