Tumbling window

A strategy for processing (stream) data by specific limited frames, usually time periods. It is used to process the last entries of a data stream. A tumbling window divides the data stream into fixed-size, non-overlapping time intervals. Each window collects and processes a fixed number of data items or a fixed duration of data, after which the window is closed and a new window is opened.

What's the difference between a tumbling window and a sliding window?

The difference between a tumbling window and a sliding window is whether the fixed-size time intervals overlap. Tumbling windows are intervals that do not overlap. Sliding windows are intervals that do overlap.

For realtime monitoring you would usually prefer a sliding window over tumbling ones as the latter cut the data in non-overlapping parts: a wrong cut could prevent it from detecting the pattern you are looking for.

How can I perform a tumbling window in Python?

You can use Pathway to perform tumbling window operations on your data:

>>> import pathway as pw
>>> t = pw.debug.table_from_markdown(
... '''
...        | shard | t
...    1   | 0     |  12
...    2   | 0     |  13
...    3   | 0     |  14
...    4   | 0     |  15
...    5   | 0     |  16
...    6   | 0     |  17
...    7   | 1     |  12
...    8   | 1     |  13
... ''')
>>> result = t.windowby(
...     t.t, window=pw.window.tumbling(duration=5), shard=t.shard
... ).reduce(
...   pw.this.window,
...   min_t=pw.reducers.min(pw.this.t),
...   max_t=pw.reducers.max(pw.this.t),
...   count=pw.reducers.count(pw.this.t),
... )
>>> pw.debug.compute_and_print(result, include_id=False)
 window      | min_t | max_t | count
(0, 10, 15) | 12    | 14    | 3
(0, 15, 20) | 15    | 17    | 3
(1, 10, 15) | 12    | 13    | 2
"""