python - Window timeseries with step in Spark/Scala -


i have input :

timestamp,user 1,a 2,b 5,c 9,e 12,f

the result wanted :

timestamprange,userlist 1 2,[a,b] 3 4,[] or null 5 6,[c] 7 8,[] or null 9 10,[e] 11 12,[f]

i tried using window, problem, doesn't include empty timestamp range.

any hints helpful.

don't know if widowing function cover gaps between ranges, can take following approach :

define dataframe, df_ranges:

 val ranges = list((1,2), (3,4), (5,6), (7,8), (9,10))  val df_ranges = sc.parallelize(ranges).todf("start", "end") +-----+---+ |start|end| +-----+---+ |    1|  2| |    3|  4| |    5|  6| |    7|  8| |    9| 10| +-----+---+

data timestamp column, df_data :

val data = list((1,"a"), (2,"b"), (5,"c"), (9,"e")) val df_data = sc.parallelize(data).todf("timestamp", "user") +---------+----+ |timestamp|user| +---------+----+ |        1|   a| |        2|   b| |        5|   c| |        9|   e| +---------+----+

join 2 dataframe on start, end, timestamp columns:

df_ranges.join(df_data, df_ranges.col("start").equalto(df_data.col("timestamp")).or(df_ranges.col("end").equalto(df_data.col("timestamp"))), "left")  +-----+---+---------+----+ |start|end|timestamp|user| +-----+---+---------+----+ |    1|  2|        1|   a| |    1|  2|        2|   b| |    5|  6|        5|   c| |    9| 10|        9|   e| |    3|  4|     null|null| |    7|  8|     null|null| +-----+---+---------+----+

now simple aggregation collect_list function :

 res4.groupby("start", "end").agg(collect_list("user")).orderby("start") +-----+---+------------------+ |start|end|collect_list(user)| +-----+---+------------------+ |    1|  2|            [a, b]| |    3|  4|                []| |    5|  6|               [c]| |    7|  8|                []| |    9| 10|               [e]| +-----+---+------------------+

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