Windowing allows you to control how to group records which share the same key for stateful operations such as aggregations or join windows. Windows are tracked per record key. Lenses SQL has support for the full spectrum of windowing functionality available in the Kafka Streams API.
A record is discarded and will not be processed by the window if it arrives after the retention period has passed.
Following types of windowing are supported:
Hopping time windows. These are windows based on time intervals. They model fixed-sized, (possibly) overlapping windows. A hopping window is defined by two properties: the window’s size and its advance interval (aka “hop”). The advance interval specifies how much a window moves forward relative to the previous one. For example, you can configure a hopping window with a size of 5 minutes and an advance interval of 1 minute. Since hopping windows can overlap a data record may belong to more than one such windows.
... GROUP BY HOP(5,m,1,m) ...
Tumbling time windows. These are a special case of hopping time windows and, like the latter, are windows based on time intervals. They model fixed-size, non-overlapping, gap-less windows. A tumbling window is defined by a single property: the window’s size. A tumbling window is a hopping window whose window size is equal to its advance interval. Since tumbling windows never overlap, a data record will belong to one and only one window.
... GROUP BY tumble(1,m) ...
Sliding windows. These express fixed-size window that slides continuously over the time axis. Here, two data records are said to be included in the same window if the difference of their timestamps is within the window size. Thus, sliding windows are not aligned with the epoch, but on the data record timestamps.
... GROUP BY SLIDING(1,m) ...
Session windows. These are used to aggregate key-based events into sessions. Sessions represent a period of activity separated by a defined gap of inactivity. Any events processed that fall within the inactivity gap of any existing sessions are merged into the existing sessions. If the event falls outside of the session gap, then a new session will be created. Session windows are tracked independently across keys (e.g. windows of different keys typically have different start and end times) and their sizes vary (even windows for the same key typically have different sizes). As such session windows can’t be pre-computed and are instead derived from analyzing the timestamps of the data records.
... GROUP BY SESSION(10,m, 5, m) ...
All the window functions allow the user to specify the time unit. Supported time windows are