Date difference between consecutive rows - Pyspark Dataframe

Question

I have a table with following structure

USER_ID     Tweet_ID                 Date
  1           1001       Thu Aug 05 19:11:39 +0000 2010
  1           6022          


        

Answer 1

Another way could be:

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 2

EDITED thanks to @cool_kid

@Joesemy answer is really good but didn't work for me since cast("bigint") threw an error. So I used the datediff function from the pyspark.sql.functions module this way and it worked :

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")‌​)))

Answer 3

Like this:

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""")