[FLINK-15999][doc] Move time and windows section from programming-mod…

…el.md to timely-stream-processing.md

docs/concepts/programming-model.md

@@ -62,44 +62,6 @@ Flink offers different levels of abstraction to develop streaming/batch applicat
  expressiveness, but represents programs as SQL query expressions.
  The [SQL](../dev/table/index.html#sql) abstraction closely interacts with the Table API, and SQL queries can be executed over tables defined in the *Table API*.
 
-
-## Windows
-
-Aggregating events (e.g., counts, sums) works differently on streams than in batch processing.
-For example, it is impossible to count all elements in a stream,
-because streams are in general infinite (unbounded). Instead, aggregates on streams (counts, sums, etc),
-are scoped by **windows**, such as *"count over the last 5 minutes"*, or *"sum of the last 100 elements"*.
-
-Windows can be *time driven* (example: every 30 seconds) or *data driven* (example: every 100 elements).
-One typically distinguishes different types of windows, such as *tumbling windows* (no overlap),
-*sliding windows* (with overlap), and *session windows* (punctuated by a gap of inactivity).
-
-<img src="{{ site.baseurl }}/fig/windows.svg" alt="Time- and Count Windows" class="offset" width="80%" />
-
-More window examples can be found in this [blog post](https://flink.apache.org/news/2015/12/04/Introducing-windows.html).
-More details are in the [window docs](../dev/stream/operators/windows.html).
-
-{% top %}
-
-## Time
-
-When referring to time in a streaming program (for example to define windows), one can refer to different notions
-of time:
-
- - **Event Time** is the time when an event was created. It is usually described by a timestamp in the events,
- for example attached by the producing sensor, or the producing service. Flink accesses event timestamps
- via [timestamp assigners]({{ site.baseurl }}/dev/event_timestamps_watermarks.html).
-
- - **Ingestion time** is the time when an event enters the Flink dataflow at the source operator.
-
- - **Processing Time** is the local time at each operator that performs a time-based operation.
-
-<img src="{{ site.baseurl }}/fig/event_ingestion_processing_time.svg" alt="Event Time, Ingestion Time, and Processing Time" class="offset" width="80%" />
-
-More details on how to handle time are in the [event time docs]({{ site.baseurl }}/dev/event_time.html).
-
-{% top %}
-
 ## Next Steps
 
 Continue with the basic concepts in Flink's [Distributed Runtime](runtime.html).

docs/concepts/timely-stream-processing.md

@@ -27,6 +27,43 @@ under the License.
 * This will be replaced by the TOC
 {:toc}
 
+## Time
+
+When referring to time in a streaming program (for example to define windows), one can refer to different notions
+of time:
+
+ - **Event Time** is the time when an event was created. It is usually described by a timestamp in the events,
+ for example attached by the producing sensor, or the producing service. Flink accesses event timestamps
+ via [timestamp assigners]({{ site.baseurl }}/dev/event_timestamps_watermarks.html).
+
+ - **Ingestion time** is the time when an event enters the Flink dataflow at the source operator.
+
+ - **Processing Time** is the local time at each operator that performs a time-based operation.
+
+<img src="{{ site.baseurl }}/fig/event_ingestion_processing_time.svg" alt="Event Time, Ingestion Time, and Processing Time" class="offset" width="80%" />
+
+More details on how to handle time are in the [event time docs]({{ site.baseurl }}/dev/event_time.html).
+
+{% top %}
+
+## Windows
+
+Aggregating events (e.g., counts, sums) works differently on streams than in batch processing.
+For example, it is impossible to count all elements in a stream,
+because streams are in general infinite (unbounded). Instead, aggregates on streams (counts, sums, etc),
+are scoped by **windows**, such as *"count over the last 5 minutes"*, or *"sum of the last 100 elements"*.
+
+Windows can be *time driven* (example: every 30 seconds) or *data driven* (example: every 100 elements).
+One typically distinguishes different types of windows, such as *tumbling windows* (no overlap),
+*sliding windows* (with overlap), and *session windows* (punctuated by a gap of inactivity).
+
+<img src="{{ site.baseurl }}/fig/windows.svg" alt="Time- and Count Windows" class="offset" width="80%" />
+
+More window examples can be found in this [blog post](https://flink.apache.org/news/2015/12/04/Introducing-windows.html).
+More details are in the [window docs](../dev/stream/operators/windows.html).
+
+{% top %}
+
 ## Latency & Completeness
 
 ### Latency vs. Completeness in Batch & Stream Processing