xuanyuanking commented on a change in pull request #33433:
URL: https://github.com/apache/spark/pull/33433#discussion_r673150129
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File path: docs/structured-streaming-programming-guide.md
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@@ -1063,6 +1063,77 @@ then drops intermediate state of a window < watermark,
and appends the final
counts to the Result Table/sink. For example, the final counts of window
`12:00 - 12:10` is
appended to the Result Table only after the watermark is updated to `12:11`.
+#### Types of time windows
+
+Spark supports three types of time windows: tumbling (fixed), sliding and
session.
+
+
+
+Tumbling windows are a series of fixed-sized, non-overlapping and contiguous
time intervals. An input
+can only be bound to a single window.
+
+Sliding windows are similar to the tumbling windows from the point of being
"fixed-sized", but windows
+can overlap if the duration of slide is smaller than the duration of window,
and in this case an input
+can be bound to the multiple windows.
+
+Tumbling and sliding window use `window` function, which has been described on
above examples.
+
+Session windows have different characteristic compared to the previous two
types. Session window has a dynamic size
+of the window length, depending on the inputs. A session window starts with an
input, and expands itself
+if following input has been received within gap duration. A session window
closes when there's no input
+received within gap duration after receiving the latest input.
+
+Session window uses `session_window` function. The usage of the function is
similar to the `window` function.
Review comment:
Ah yea make sense, I only considered the SS scenario.
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