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Time window functions

In stream processing, time windows are time intervals based on which we can divide events and perform data computations.

RisingWave supports two types of time windows:

  • Tumbling windows
  • Hopping windows

For each type of time window, there is a corresponding time windowing function (hereafter referred to as “time window function”) that creates a window of this type. For tumbling windows, the function is tumble(). For hopping windows, the function is hop().

In RisingWave, the result of a time window function is a table in which each row carries data for a time window. A time window function extends the schema of the original table with two new columns, window_start and window_end, which indicate the start and end of time windows respectively.

In RisingWave, time window functions are invoked in the FROM clause. See the sections below for the syntaxes of two time window functions.

tumble() time window function

Tumbling windows are contiguous time intervals.

The syntax of the tumble() window function is as follows:

SELECT [ ALL | DISTINCT ] [ * | expression [ AS output_name ] [, expression [ AS output_name ]...] ]
FROM TUMBLE ( table_or_source, start_time, window_size [, offset ] );
  • start_time can be in either timestamp or timestamp with time zone format.

    Example of timestamp with time zone format: 2022-01-01 10:00:00+00:00.

  • window_size is in the format of INTERVAL 'interval'.

    Example: INTERVAL '2 MINUTES'. The standard SQL format, which places time units outside of quotation marks (for example, INTERVAL '2' MINUTE), is also supported.

  • offset is an optional parameter that allows you to adjust the starting point of the tumbling windows.

    By default, tumbling windows are inclusive in the end of the window and exclusive in the beginning. By specifying offset, you can shift start_time by the specified duration.

Suppose that we have a table, taxi_trips, that consists of these columns: trip_id, taxi_id, completed_at, distance, and duration.

trip_idtaxi_idcompleted_atdistanceduration
110012022-07-01 22:00:0046
210022022-07-01 22:01:0069
310032022-07-01 22:02:0035
410042022-07-01 22:03:00715
510052022-07-01 22:05:0024
610062022-07-01 22:05:30817

Here is an example that uses the tumble window function.

SELECT trip_id, taxi_id, completed_at, window_start, window_end
FROM TUMBLE (taxi_trips, completed_at, INTERVAL '2 MINUTES');

The result looks like this:

trip_id | taxi_id   | completed_at          | window_start          | window_end 
--------+-----------+-----------------------+-----------------------+---------------------
1 | 1001 | 2022-07-01 22:00:00 | 2022-07-01 22:00:00 | 2022-07-01 22:02:00
2 | 1002 | 2022-07-01 22:01:00 | 2022-07-01 22:00:00 | 2022-07-01 22:02:00
3 | 1003 | 2022-07-01 22:02:10 | 2022-07-01 22:02:00 | 2022-07-01 22:04:00
4 | 1004 | 2022-07-01 22:03:00 | 2022-07-01 22:02:00 | 2022-07-01 22:04:00
5 | 1005 | 2022-07-01 22:05:00 | 2022-07-01 22:04:00 | 2022-07-01 22:06:00
6 | 1006 | 2022-07-01 22:06:00 | 2022-07-01 22:06:00 | 2022-07-01 22:08:00

hop() time window function

Hopping windows are scheduled time intervals. A hopping window consists of three time-related parameters: start time, hop size, and window size.

See below for the syntax of the hop() window function.

SELECT [ ALL | DISTINCT] [ * | expression [ AS output_name ] [, expression [ AS output_name ]...] ]
FROM HOP ( table_or_source, start_time, hop_size, window_size [, offset ]);
  • start_time can be in either timestamp or timestamp with time zone format.

    Example of timestamp with time zone format: 2022-01-01 10:00:00+00:00.

  • Both hop_size and window_size are in the format of INTERVAL '<interval>'.

    For example: INTERVAL '2 MINUTES'. The standard SQL format, which places time units outside of quotation marks (for example, INTERVAL '2' MINUTE), is also supported.

  • offset is an optional parameter that allows you to adjust the starting point of the hopping windows.

    By default, hopping windows are inclusive in the end of the window and exclusive in the beginning. By specifying offset, you can shift start_time by the specified duration.

Here is an example.

SELECT trip_id, taxi_id, completed_at, window_start, window_end
FROM HOP (taxi_trips, completed_at, INTERVAL '1 MINUTE', INTERVAL '2 MINUTES')
ORDER BY window_start;

The result looks like the table below. Note that the number of rows in the result of a hop window function is N times the number of rows in the original table, where N is the window size divided by the hop size.

 trip_id | taxi_id  | completed_at          | window_start          | window_end 
---------+---------+------------------------+-----------------------+--------------------
1 | 1001 | 2022-07-01 22:00:00 | 2022-07-01 21:59:00 | 2022-07-01 22:01:00
2 | 1002 | 2022-07-01 22:01:00 | 2022-07-01 22:00:00 | 2022-07-01 22:02:00
1 | 1001 | 2022-07-01 22:00:00 | 2022-07-01 22:00:00 | 2022-07-01 22:02:00
3 | 1003 | 2022-07-01 22:02:10 | 2022-07-01 22:01:00 | 2022-07-01 22:03:00
2 | 1002 | 2022-07-01 22:01:00 | 2022-07-01 22:01:00 | 2022-07-01 22:03:00
4 | 1004 | 2022-07-01 22:03:00 | 2022-07-01 22:02:00 | 2022-07-01 22:04:00
3 | 1003 | 2022-07-01 22:02:10 | 2022-07-01 22:02:00 | 2022-07-01 22:04:00
4 | 1004 | 2022-07-01 22:03:00 | 2022-07-01 22:03:00 | 2022-07-01 22:05:00
5 | 1005 | 2022-07-01 22:05:00 | 2022-07-01 22:04:00 | 2022-07-01 22:06:00
6 | 1006 | 2022-07-01 22:06:00 | 2022-07-01 22:05:00 | 2022-07-01 22:07:00
5 | 1005 | 2022-07-01 22:05:00 | 2022-07-01 22:05:00 | 2022-07-01 22:07:00
6 | 1006 | 2022-07-01 22:06:00 | 2022-07-01 22:06:00 | 2022-07-01 22:08:00
(12 rows)

Window aggregations

Let’s see how we can perform time window aggregations.

Tumble window aggregations

Below is an example of tumble window aggregation. In this example, we want to get the number of trips and the total distance for each tumbling window (2 minutes).

SELECT window_start, window_end, count(trip_id) as no_of_trips, sum(distance) as total_distance 
FROM TUMBLE (taxi_trips, completed_at, INTERVAL '2 MINUTES')
GROUP BY window_start, window_end
ORDER BY window_start ASC;

The result looks like this:

 window_start        | window_end          | no_of_trips | total_distance 
---------------------+---------------------+-------------+----------------
2022-07-01 22:00:00 | 2022-07-01 22:02:00 | 2 | 10
2022-07-01 22:02:00 | 2022-07-01 22:04:00 | 2 | 10
2022-07-01 22:04:00 | 2022-07-01 22:06:00 | 1 | 2
2022-07-01 22:06:00 | 2022-07-01 22:08:00 | 1 | 8

Hop window aggregations

Below is an example of hopping window aggregation. In this example, we want to get the number of trips and the total distance within a two-minute window every minute.

SELECT window_start, window_end, count(trip_id) as no_of_trips, sum(distance) as total_distance 
FROM HOP (taxi_trips, completed_at, INTERVAL '1 MINUTES', INTERVAL '2 MINUTES')
GROUP BY window_start, window_end
ORDER BY window_start ASC;

The result looks like this:

 window_start        | window_end          | no_of_trips | total_distance 
---------------------+---------------------+-------------+----------------
2022-07-01 21:59:00 | 2022-07-01 22:01:00 | 1 | 4
2022-07-01 22:00:00 | 2022-07-01 22:02:00 | 2 | 10
2022-07-01 22:01:00 | 2022-07-01 22:03:00 | 2 | 9
2022-07-01 22:02:00 | 2022-07-01 22:04:00 | 2 | 10
2022-07-01 22:03:00 | 2022-07-01 22:05:00 | 1 | 7
2022-07-01 22:04:00 | 2022-07-01 22:06:00 | 1 | 2
2022-07-01 22:05:00 | 2022-07-01 22:07:00 | 2 | 10
2022-07-01 22:06:00 | 2022-07-01 22:08:00 | 1 | 8

Window joins

You can join a time window with a table, or another time window that is of the same type and has the same time attributes.

Joins with tables

Let's see how you can join a time window with a table.

Suppose that you have a simple table taxi_simple that has the following data:

taxi_id        |company    
---------------+-------------------
1001 |'SAFE TAXI'
1002 |'SUPER TAXI'
1003 |'FAST TAXI'
1004 |'BEST TAXI'
1005 |'WEST TAXI'
1006 |'EAST TAXI'

You can join it with a time window:

SELECT trip.window_start, trip.window_end, trip.distance, taxi_simple.company
FROM TUMBLE (taxi_trips, completed_at, INTERVAL '2 MINUTES') as trip
JOIN taxi_simple
ON trip.taxi_id = taxi_simple.taxi_id
ORDER BY trip.window_start ASC;

The result looks like this:

 window_start        | window_end          | distance | company 
---------------------+---------------------+----------+------------
2022-07-01 22:00:00 | 2022-07-01 22:02:00 | 6 | SAFE TAXI
2022-07-01 22:00:00 | 2022-07-01 22:02:00 | 4 | SUPER TAXI
2022-07-01 22:02:00 | 2022-07-01 22:04:00 | 3 | FAST TAXI
2022-07-01 22:02:00 | 2022-07-01 22:04:00 | 7 | BEST TAXI
2022-07-01 22:04:00 | 2022-07-01 22:06:00 | 2 | WEST TAXI
2022-07-01 22:06:00 | 2022-07-01 22:08:00 | 8 | EAST TAXI

Window joins

You can join two tumble time windows to get both trip and fare information. The corresponding tables are taxi_trips and taxi_fare.

The taxi_fare table has the following data:

trip_id| completed_at | total_fare | payment_status
------+--------------+--------------+--------------
1 | 2022-07-01 22:00:00 | 8 | COMPLETED
2 | 2022-07-01 22:01:00 | 12 | PROCESSING
3 | 2022-07-01 22:02:10 | 5 | COMPLETED
4 | 2022-07-01 22:03:00 | 15 | COMPLETED
5 | 2022-07-01 22:06:00 | 5 | REJECTED
6 | 2022-07-01 22:06:00 | 20 | COMPLETED

You can join two time windows:

SELECT trip.window_start, trip.window_end, trip.distance, fare.total_fare, fare.payment_status
FROM TUMBLE (taxi_trips, completed_at, INTERVAL '2 MINUTES') as trip
JOIN TUMBLE (taxi_fare, completed_at, INTERVAL '2 MINUTES') as fare
ON trip.trip_id = fare.trip_id AND trip.window_start = fare.window_start
ORDER BY trip.window_start ASC;

The result looks like this.

 window_start        | window_end          | distance | total_fare | payment_status 
---------------------+---------------------+----------+------------+----------------
2022-07-01 22:00:00 | 2022-07-01 22:02:00 | 4 | 8 | COMPLETED
2022-07-01 22:00:00 | 2022-07-01 22:02:00 | 6 | 12 | PROCESSING
2022-07-01 22:02:00 | 2022-07-01 22:04:00 | 7 | 15 | COMPLETED
2022-07-01 22:02:00 | 2022-07-01 22:04:00 | 3 | 5 | COMPLETED
2022-07-01 22:04:00 | 2022-07-01 22:06:00 | 2 | 5 | REJECTED
2022-07-01 22:06:00 | 2022-07-01 22:08:00 | 8 | 20 | COMPLETED
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