CODEWITHSUNDEEP
apache-sparkapache-spark-sqlpyspark
Adil Blanco
Adil Blanco

Reputation: 666

How to aggregate using window instead of Pyspark groupBy

I have trouble using the window function instead GroupBy to aggregate each user, in my case 110 and 220 user id:

1- count rows for each p_uuid

2- create new columns with min and max timestamp for each p_uuid

df = spark.createDataFrame([(1, 110, 'aaa', 'walk', 'work', '2019-09-28 13:40:19-04:00'),
                        (2, 110, 'aaa', 'walk', 'work', '2019-09-28 13:40:19-04:01'),
                        (3, 110, 'aaa', 'walk', 'work', '2019-09-28 13:40:19-04:02'),
                        (4, 110, 'aaa', 'metro', 'work', '2019-09-28 13:41:19-04:00'),
                        (5, 110, 'aaa', 'metro', 'work', '2019-09-28 13:41:19-04:01'),
                        (6, 110, 'aaa', 'walk', 'work', '2019-09-28 13:42:19-04:00'),
                        (7, 110, 'aaa', 'walk', 'work', '2019-09-28 13:42:19-04:01'),
                        (8, 110, 'bbb', 'bike', 'home', '2019-09-17 14:40:19-04:00'),
                        (9, 110, 'bbb', 'bus', 'home', '2019-09-17 14:41:19-04:00'),
                        (10, 110, 'bbb', 'walk', 'home', '2019-09-17 14:43:19-04:00'),
                        (16, 110, 'ooo', None, None, '2019-08-29 16:01:19-04:00'),
                        (17, 110, 'ooo', None, None, '2019-08-29 16:02:19-04:00'),
                        (18, 110, 'ooo', None, None, '2019-08-29 16:02:19-04:00'),
                        (19, 222, 'www', 'car', 'work', '2019-09-28 08:00:19-04:00'),
                        (20, 222, 'www', 'metro', 'work', '2019-09-28 08:01:19-04:00'),
                        (21, 222, 'www', 'walk', 'work', '2019-09-28 08:02:19-04:00'),
                        (22, 222, 'xxx', 'walk', 'friend', '2019-09-17 08:40:19-04:00'),
                        (23, 222, 'xxx', 'bike', 'friend', '2019-09-17 08:42:19-04:00'),
                        (24, 222, 'xxx', 'bus', 'friend', '2019-09-17 08:43:19-04:00'),
                        (30, 222, 'ooo', None, None, '2019-08-29 10:00:19-04:00'),
                        (31, 222, 'ooo', None, None, '2019-08-29 10:01:19-04:00'),
                        (32, 222, 'ooo', None, None, '2019-08-29 10:02:19-04:00')],
                    ['idx', 'u_uuid', 'p_uuid', 'mode', 'place', 'timestamp']
                )
 df.show(30, False)

enter image description here

I used

 win = Window.partitionBy("u_uuid", "p_uuid").orderBy("timestamp")
 df.withColumn("count_", F.count('p_uuid').over(win))
 df.withColumn("max_timestamp", F.max("timestamp").over(win))
 df.withColumn("min_timestamp", F.min("timestamp").over(win))

It doesn't seem to work (ex: get max_)

remarque: forget trip_id, subtrip_id and track_id columns

enter image description here

Upvotes: 1

Views: 119

Answers (2)

blackbishop
blackbishop

Reputation: 32660

You have to extend the window to the entire frame using rowsBetween: 

win = Window.partitionBy("u_uuid", "p_uuid").orderBy("timestamp").rowsBetween(Window.unboundedPreceding, Window.unboundedFollowing)

Upvotes: 1

notNull
notNull

Reputation: 31490

You need to use unboundedPreceding,unboundedFollowing with .partitionBy clause by default value is unboundedPreceding,currentRow if we provide orderBy clause.

Add .rowsBetween in your window spec and run again.

win = Window.partitionBy("u_uuid", "p_uuid").orderBy("timestamp").rowsBetween(Window.unboundedPreceding, Window.unboundedFollowing)

Example:

df.withColumn("max_timestamp", max("timestamp").over(win)).show(10,False)
+---+------+------+-----+------+-------------------------+-------------------------+
|idx|u_uuid|p_uuid|mode |place |timestamp                |max_timestamp            |
+---+------+------+-----+------+-------------------------+-------------------------+
|8  |110   |bbb   |bike |home  |2019-09-17 14:40:19-04:00|2019-09-17 14:43:19-04:00|
|9  |110   |bbb   |bus  |home  |2019-09-17 14:41:19-04:00|2019-09-17 14:43:19-04:00|
|10 |110   |bbb   |walk |home  |2019-09-17 14:43:19-04:00|2019-09-17 14:43:19-04:00|
|16 |110   |ooo   |null |null  |2019-08-29 16:01:19-04:00|2019-08-29 16:02:19-04:00|
|17 |110   |ooo   |null |null  |2019-08-29 16:02:19-04:00|2019-08-29 16:02:19-04:00|
|18 |110   |ooo   |null |null  |2019-08-29 16:02:19-04:00|2019-08-29 16:02:19-04:00|
+---+------+------+-----+------+-------------------------+-------------------------+

Upvotes: 1

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