Time windows

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

RisingWave supports three types of time windows:

Tumble windows
Hop windows
Session windows

For tumbling and hopping windows, RisingWave supports them by time window functions, tumble() and hop() respectively. For session window, RisingWave supports it by a special type of window function frame, i.e. SESSION frame.

Time window function

Time window functions are used in the FROM clause. They take a table/source/materialized view, a time column and some other arguments as input, and assign each input row a time window by augmenting the row with two new columns: window_start and window_end. The two augmented columns represent the start and end of time windows respectively.

`tumble()` time window function

Tumbling windows are contiguous non-overlapping time intervals.

The syntax of the tumble() is as follows:

SELECT [ ALL | DISTINCT ] [ * | expression [ AS output_name ] [, expression [ AS output_name ]...] ]
FROM TUMBLE ( table_or_source, time_col, window_size [, offset ] );

table_or_source specifies the table/source/materialized view that needs to be assigned with time windows.
time_col specifies the column to determine time windows on. It 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 specifies the size of each time window. It should be a constant value of INTERVAL type.
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 shift the starting point of each time window.
Example: window_size = INTERVAL '10 MINUTES', offset = INTERVAL '2 MINUTES' will yield time window starts like 2022-01-01 00:12:00+00:00.

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

 trip_id | taxi_id | completed_at        | distance | duration
---------|---------|---------------------|----------|----------
     | 1001    | 2022-07-01 22:00:00 | 4        | 6
     | 1002    | 2022-07-01 22:01:00 | 6        | 9
     | 1003    | 2022-07-01 22:02:00 | 3        | 5
     | 1004    | 2022-07-01 22:03:00 | 7        | 15
     | 1005    | 2022-07-01 22:05:00 | 2        | 4
     | 1006    | 2022-07-01 22:05:30 | 8        | 17

Here is an example that uses the tumbling time 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
---------+---------+---------------------+---------------------+---------------------
     | 1001    | 2022-07-01 22:00:00 | 2022-07-01 22:00:00 | 2022-07-01 22:02:00
     | 1002    | 2022-07-01 22:01:00 | 2022-07-01 22:00:00 | 2022-07-01 22:02:00
     | 1003    | 2022-07-01 22:02:10 | 2022-07-01 22:02:00 | 2022-07-01 22:04:00
     | 1004    | 2022-07-01 22:03:00 | 2022-07-01 22:02:00 | 2022-07-01 22:04:00
     | 1005    | 2022-07-01 22:05:00 | 2022-07-01 22:04:00 | 2022-07-01 22:06:00
     | 1006    | 2022-07-01 22:06:00 | 2022-07-01 22:06:00 | 2022-07-01 22:08:00

`hop()` time window function

The hop() time window function also assigns each row a time window with a fixed size, which is very similar to tumble(), except that the assigned time windows may overlap.

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

SELECT [ ALL | DISTINCT] [ * | expression [ AS output_name ] [, expression [ AS output_name ]...] ]
FROM HOP ( table_or_source, time_col, hop_size, window_size [, offset ]);

table_or_source specifies the table/source/materialized view that needs to be assigned with time windows.
time_col specifies the column to determine time windows on. It 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.
hop_size specifies the size of each hop, window_size specifies the size of each time window. Both should be constant values of INTERVAL type.
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 shift the starting point of each time window.
Example: window_size = INTERVAL '10 MINUTES', offset = INTERVAL '2 MINUTES' will yield time window starts like 2022-01-01 00:12:00+00:00.

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');

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
---------+---------+---------------------+---------------------+---------------------
     | 1001    | 2022-07-01 22:00:00 | 2022-07-01 21:59:00 | 2022-07-01 22:01:00
     | 1001    | 2022-07-01 22:00:00 | 2022-07-01 22:00:00 | 2022-07-01 22:02:00
     | 1002    | 2022-07-01 22:01:00 | 2022-07-01 22:00:00 | 2022-07-01 22:02:00
     | 1002    | 2022-07-01 22:01:00 | 2022-07-01 22:01:00 | 2022-07-01 22:03:00
     | 1003    | 2022-07-01 22:02:10 | 2022-07-01 22:01:00 | 2022-07-01 22:03:00
     | 1003    | 2022-07-01 22:02:10 | 2022-07-01 22:02:00 | 2022-07-01 22:04:00
     | 1004    | 2022-07-01 22:03:00 | 2022-07-01 22:02:00 | 2022-07-01 22:04:00
     | 1004    | 2022-07-01 22:03:00 | 2022-07-01 22:03:00 | 2022-07-01 22:05:00
     | 1005    | 2022-07-01 22:05:00 | 2022-07-01 22:04:00 | 2022-07-01 22:06:00
     | 1005    | 2022-07-01 22:05:00 | 2022-07-01 22:05:00 | 2022-07-01 22:07:00
     | 1006    | 2022-07-01 22:06:00 | 2022-07-01 22:05:00 | 2022-07-01 22:07:00
     | 1006    | 2022-07-01 22:06:00 | 2022-07-01 22:06:00 | 2022-07-01 22:08:00

Session windows

In RisingWave, session windows are supported by a special type of window function frame: SESSION frame. You can refer to Window functions (OVER clause) for detailed syntax.

note

Currently, SESSION frame is only supported in batch mode and emit-on-window-close streaming mode.

When using session windows, you can achieve the effect that is very similar to tumble() and hop() time window functions, that is, to assign each row a time window by augmenting it with window_start and window_end. Here is an example:

Given the following table data:

 user_id | product_id | viewed_at
---------+------------+---------------------
     | 1001       | 2022-07-01 22:00:00
     | 1002       | 2022-07-01 22:01:00
     | 1001       | 2022-07-01 22:03:00
     | 1003       | 2022-07-01 22:10:00
     | 1003       | 2022-07-01 22:05:00
     | 1006       | 2022-07-01 22:05:30

And the following query:

SELECT
    user_id, product_id, viewed_at,
    first_value(viewed_at) OVER (
        PARTITION BY user_id ORDER BY viewed_at
        SESSION WITH GAP INTERVAL '5 MINUTES'
    ) AS window_start,
    last_value(viewed_at) OVER (
        PARTITION BY user_id ORDER BY viewed_at
        SESSION WITH GAP INTERVAL '5 MINUTES'
    ) AS window_end
FROM user_views

The result looks like this:

 user_id | product_id | viewed_at           | window_start        | window_end
---------+------------+---------------------+---------------------+---------------------
     | 1001       | 2022-07-01 22:00:00 | 2022-07-01 22:00:00 | 2022-07-01 22:03:00
     | 1002       | 2022-07-01 22:01:00 | 2022-07-01 22:00:00 | 2022-07-01 22:03:00
     | 1001       | 2022-07-01 22:03:00 | 2022-07-01 22:00:00 | 2022-07-01 22:03:00
     | 1003       | 2022-07-01 22:10:00 | 2022-07-01 22:10:00 | 2022-07-01 22:10:00
     | 1003       | 2022-07-01 22:05:00 | 2022-07-01 22:05:00 | 2022-07-01 22:05:30
     | 1006       | 2022-07-01 22:05:30 | 2022-07-01 22:05:00 | 2022-07-01 22:05:30

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;

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;

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

Session window aggregations

Below is an example of aggregation over session windows. In this example, we want to get the number of unique products viewed by each user in session gapped by 5 minutes interval, based on the example data in previous Session windows section.

SELECT
    user_id, window_start,
    count(DISTINCT product_id) AS n_viewed_product
FROM (
    SELECT
        *,
        first_value(viewed_at) OVER (
            PARTITION BY user_id ORDER BY viewed_at
            SESSION WITH GAP INTERVAL '5 MINUTES'
        ) AS window_start
    FROM user_views
)
GROUP BY user_id, window_start
ORDER BY user_id, window_start;

The result looks like this:

 user_id | window_start        | n_viewed_product
---------+---------------------+-----------------
 1       | 2022-07-01 22:00:00 | 2
 1       | 2022-07-01 22:10:00 | 1
 2       | 2022-07-01 22:05:00 | 2

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
---------+------------
  | SAFE TAXI
  | SUPER TAXI
  | FAST TAXI
  | BEST TAXI
  | WEST TAXI
  | 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;

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
---------+---------------------+------------+----------------
     | 2022-07-01 22:00:00 | 8          | COMPLETED
     | 2022-07-01 22:01:00 | 12         | PROCESSING
     | 2022-07-01 22:02:10 | 5          | COMPLETED
     | 2022-07-01 22:03:00 | 15         | COMPLETED
     | 2022-07-01 22:06:00 | 5          | REJECTED
     | 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;

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

Time window function​

tumble() time window function​

hop() time window function​

Session windows​

Window aggregations​

Tumble window aggregations​

Hop window aggregations​

Session window aggregations​

Window joins​

Joins with tables​

Window joins​

Was this page helpful?

Time window function

`tumble()` time window function

`hop()` time window function

Session windows

Window aggregations

Tumble window aggregations

Hop window aggregations

Session window aggregations

Window joins

Joins with tables

Window joins