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The following commit(s) were added to refs/heads/asf-site by this push: new 8b73df7 Publishing website 2019/10/04 16:46:34 at commit 37e8926 8b73df7 is described below commit 8b73df77765e8e735ff311eca177c38577f114fc Author: jenkins <bui...@apache.org> AuthorDate: Fri Oct 4 16:46:34 2019 +0000 Publishing website 2019/10/04 16:46:34 at commit 37e8926 --- .../documentation/programming-guide/index.html | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/website/generated-content/documentation/programming-guide/index.html b/website/generated-content/documentation/programming-guide/index.html index f775a5e..cee3367 100644 --- a/website/generated-content/documentation/programming-guide/index.html +++ b/website/generated-content/documentation/programming-guide/index.html @@ -3139,14 +3139,14 @@ windows.</p> <p>The simplest form of windowing is using <strong>fixed time windows</strong>: given a timestamped <code class="highlighter-rouge">PCollection</code> which might be continuously updating, each window -might capture (for example) all elements with timestamps that fall into a five -minute interval.</p> +might capture (for example) all elements with timestamps that fall into a 30 +second interval.</p> <p>A fixed time window represents a consistent duration, non overlapping time -interval in the data stream. Consider windows with a five-minute duration: all +interval in the data stream. Consider windows with a 30 second duration: all of the elements in your unbounded <code class="highlighter-rouge">PCollection</code> with timestamp values from -0:00:00 up to (but not including) 0:05:00 belong to the first window, elements -with timestamp values from 0:05:00 up to (but not including) 0:10:00 belong to +0:00:00 up to (but not including) 0:00:30 belong to the first window, elements +with timestamp values from 0:00:30 up to (but not including) 0:01:00 belong to the second window, and so on.</p> <p><img src="/images/fixed-time-windows.png" alt="Diagram of fixed time windows, 30s in duration" title="Fixed time windows, 30s in duration" /></p> @@ -3157,15 +3157,15 @@ the second window, and so on.</p> <p>A <strong>sliding time window</strong> also represents time intervals in the data stream; however, sliding time windows can overlap. For example, each window might -capture five minutes worth of data, but a new window starts every ten seconds. +capture 60 seconds worth of data, but a new window starts every 30 seconds. The frequency with which sliding windows begin is called the <em>period</em>. -Therefore, our example would have a window <em>duration</em> of five minutes and a -<em>period</em> of ten seconds.</p> +Therefore, our example would have a window <em>duration</em> of 60 seconds and a +<em>period</em> of 30 seconds.</p> <p>Because multiple windows overlap, most elements in a data set will belong to more than one window. This kind of windowing is useful for taking running averages of data; using sliding time windows, you can compute a running average -of the past five minutes’ worth of data, updated every ten seconds, in our +of the past 60 seconds’ worth of data, updated every 30 seconds, in our example.</p> <p><img src="/images/sliding-time-windows.png" alt="Diagram of sliding time windows, with 1 minute window duration and 30s window period" title="Sliding time windows, with 1 minute window duration and 30s window period" /></p>