[1/7] flink git commit: [FLINK-5529] Update Window Documentation Figures

[al...@apache.org]

Repository: flink
Updated Branches:
  refs/heads/master c89a62244 -> 4fbc6316f


http://git-wip-us.apache.org/repos/asf/flink/blob/9e5ebe40/docs/fig/tumbling-windows.svg
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diff --git a/docs/fig/tumbling-windows.svg b/docs/fig/tumbling-windows.svg
index 1857076..7da4ece 100644
--- a/docs/fig/tumbling-windows.svg
+++ b/docs/fig/tumbling-windows.svg
@@ -18,5 +18,5 @@ specific language governing permissions and limitations
 under the License.
 -->
 
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<TRUNCATED>

[2/7] flink git commit: [FLINK-5529] Update Window Documentation Figures

[al...@apache.org]

http://git-wip-us.apache.org/repos/asf/flink/blob/9e5ebe40/docs/fig/sliding-windows.svg
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diff --git a/docs/fig/sliding-windows.svg b/docs/fig/sliding-windows.svg
index 32c6bf0..19b1eb7 100644
--- a/docs/fig/sliding-windows.svg
+++ b/docs/fig/sliding-windows.svg
@@ -18,5 +18,5 @@ specific language governing permissions and limitations
 under the License.
 -->
 
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<TRUNCATED>

[7/7] flink git commit: [FLINK-5529] Fix Trailing Whitespace in Window Documentation

[al...@apache.org]

[FLINK-5529] Fix Trailing Whitespace in Window Documentation


Project: http://git-wip-us.apache.org/repos/asf/flink/repo
Commit: http://git-wip-us.apache.org/repos/asf/flink/commit/4fbc6316
Tree: http://git-wip-us.apache.org/repos/asf/flink/tree/4fbc6316
Diff: http://git-wip-us.apache.org/repos/asf/flink/diff/4fbc6316

Branch: refs/heads/master
Commit: 4fbc6316f6b0eddf2888c1162893f02339dfd6bc
Parents: 9e5ebe4
Author: Aljoscha Krettek <al...@gmail.com>
Authored: Fri Jan 27 13:16:37 2017 +0100
Committer: Aljoscha Krettek <al...@gmail.com>
Committed: Fri Jan 27 13:37:09 2017 +0100

----------------------------------------------------------------------
 docs/dev/windows.md | 290 +++++++++++++++++++++++------------------------
 1 file changed, 145 insertions(+), 145 deletions(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/flink/blob/4fbc6316/docs/dev/windows.md
----------------------------------------------------------------------
diff --git a/docs/dev/windows.md b/docs/dev/windows.md
index 056e5c9..f8be08f 100644
--- a/docs/dev/windows.md
+++ b/docs/dev/windows.md
@@ -23,17 +23,17 @@ specific language governing permissions and limitations
 under the License.
 -->
 
-Windows are at the heart of processing infinite streams. Windows split the stream into "buckets" of finite size, 
-over which we can apply computations. This document focuses on how windowing is performed in Flink and how the 
-programmer can benefit to the maximum from its offered functionality. 
+Windows are at the heart of processing infinite streams. Windows split the stream into "buckets" of finite size,
+over which we can apply computations. This document focuses on how windowing is performed in Flink and how the
+programmer can benefit to the maximum from its offered functionality.
 
 The general structure of a windowed Flink program is presented below. The first snippet refers to *keyed* streams,
 while the second to *non-keyed* ones. As one can see, the only difference is the `keyBy(...)` call for the keyed streams
-and the `window(...)` which becomes `windowAll(...)` for non-keyed streams. These is also going to serve as a roadmap 
+and the `window(...)` which becomes `windowAll(...)` for non-keyed streams. These is also going to serve as a roadmap
 for the rest of the page.
 
 **Keyed Windows**
-    
+
     stream
            .keyBy(...)          <-  keyed versus non-keyed windows
            .window(...)         <-  required: "assigner"
@@ -43,84 +43,84 @@ for the rest of the page.
            .reduce/fold/apply() <-  required: "function"
 
 **Non-Keyed Windows**
-    
+
     stream
            .windowAll(...)      <-  required: "assigner"
           [.trigger(...)]       <-  optional: "trigger" (else default trigger)
           [.evictor(...)]       <-  optional: "evictor" (else no evictor)
           [.allowedLateness()]  <-  optional, else zero
            .reduce/fold/apply() <-  required: "function"
-           
-In the above, the commands in square brackets ([...]) are optional. This reveals that Flink allows you to customize your 
-windowing logic in many different ways so that it best fits your needs. 
+
+In the above, the commands in square brackets ([...]) are optional. This reveals that Flink allows you to customize your
+windowing logic in many different ways so that it best fits your needs.
 
 * This will be replaced by the TOC
 {:toc}
 
 ## Window Lifecycle
 
-In a nutshell, a window is **created** as soon as the first element that should belong to this window arrives, and the  
-window is **completely removed** when the time (event or processing time) passes its end timestamp plus the user-specified 
-`allowed lateness` (see [Allowed Lateness](#allowed-lateness)). Flink guarantees removal only for time-based 
-windows and not for other types, *e.g.* global windows (see [Window Assigners](#window-assigners)). For example, with an 
-event-time-based windowing strategy that creates non-overlapping (or tumbling) windows every 5 minutes and has an allowed 
-lateness of 1 min, Flink will create a new window for the interval between `12:00` and `12:05` when the first element with 
+In a nutshell, a window is **created** as soon as the first element that should belong to this window arrives, and the
+window is **completely removed** when the time (event or processing time) passes its end timestamp plus the user-specified
+`allowed lateness` (see [Allowed Lateness](#allowed-lateness)). Flink guarantees removal only for time-based
+windows and not for other types, *e.g.* global windows (see [Window Assigners](#window-assigners)). For example, with an
+event-time-based windowing strategy that creates non-overlapping (or tumbling) windows every 5 minutes and has an allowed
+lateness of 1 min, Flink will create a new window for the interval between `12:00` and `12:05` when the first element with
 a timestamp that falls into this interval arrives, and it will remove it when the watermark passes the `12:06`
-timestamp. 
-
-In addition, each window will have a `Trigger` (see [Triggers](#triggers)) and a function (`WindowFunction`, `ReduceFunction` or 
-`FoldFunction`) (see [Window Functions](#window-functions)) attached to it. The function will contain the computation to 
-be applied to the contents of the window, while the `Trigger` specifies the conditions under which the window is 
-considered ready for the function to be applied. A triggering policy might be something like "when the number of elements 
-in the window is more than 4", or "when the watermark passes the end of the window". A trigger can also decide to 
-purge a window's contents any time between its creation and removal. Purging in this case only refers to the elements 
+timestamp.
+
+In addition, each window will have a `Trigger` (see [Triggers](#triggers)) and a function (`WindowFunction`, `ReduceFunction` or
+`FoldFunction`) (see [Window Functions](#window-functions)) attached to it. The function will contain the computation to
+be applied to the contents of the window, while the `Trigger` specifies the conditions under which the window is
+considered ready for the function to be applied. A triggering policy might be something like "when the number of elements
+in the window is more than 4", or "when the watermark passes the end of the window". A trigger can also decide to
+purge a window's contents any time between its creation and removal. Purging in this case only refers to the elements
 in the window, and *not* the window metadata. This means that new data can still be added to that window.
 
-Apart from the above, you can specify an `Evictor` (see [Evictors](#evictors)) which will be able to remove  
+Apart from the above, you can specify an `Evictor` (see [Evictors](#evictors)) which will be able to remove
 elements from the window after the trigger fires and before and/or after the function is applied.
 
-In the following we go into more detail for each of the components above. We start with the required parts in the above 
-snippet (see [Keyed vs Non-Keyed Windows](#keyed-vs-non-keyed-windows), [Window Assigner](#window-assigner), and 
+In the following we go into more detail for each of the components above. We start with the required parts in the above
+snippet (see [Keyed vs Non-Keyed Windows](#keyed-vs-non-keyed-windows), [Window Assigner](#window-assigner), and
 [Window Function](#window-function)) before moving to the optional ones.
 
 ## Keyed vs Non-Keyed Windows
 
-The first thing to specify is whether your stream should be keyed or not. This has to be done before defining the window. 
-Using the `keyBy(...)` will split your infinite stream into logical keyed streams. If `keyBy(...)` is not called, your 
+The first thing to specify is whether your stream should be keyed or not. This has to be done before defining the window.
+Using the `keyBy(...)` will split your infinite stream into logical keyed streams. If `keyBy(...)` is not called, your
 stream is not keyed.
 
-In the case of keyed streams, any attribute of your incoming events can be used as a key 
-(more details [here]({{ site.baseurl }}/dev/api_concepts.html#specifying-keys)). Having a keyed stream will 
-allow your windowed computation to be performed in parallel by multiple tasks, as each logical keyed stream can be processed 
-independently from the rest. All elements referring to the same key will be sent to the same parallel task. 
+In the case of keyed streams, any attribute of your incoming events can be used as a key
+(more details [here]({{ site.baseurl }}/dev/api_concepts.html#specifying-keys)). Having a keyed stream will
+allow your windowed computation to be performed in parallel by multiple tasks, as each logical keyed stream can be processed
+independently from the rest. All elements referring to the same key will be sent to the same parallel task.
 
-In case of non-keyed streams, your original stream will not be split into multiple logical streams and all the windowing logic 
+In case of non-keyed streams, your original stream will not be split into multiple logical streams and all the windowing logic
 will be performed by a single task, *i.e.* with parallelism of 1.
 
 ## Window Assigners
 
-After specifying whether your stream is keyed or not, the next step is to define a *window assigner*. 
-The window assigner defines how elements are assigned to windows. This is done by specifying the `WindowAssigner` 
-of your choice in the `window(...)` (for *keyed* streams) or the `windowAll()` (for *non-keyed* streams) call. 
+After specifying whether your stream is keyed or not, the next step is to define a *window assigner*.
+The window assigner defines how elements are assigned to windows. This is done by specifying the `WindowAssigner`
+of your choice in the `window(...)` (for *keyed* streams) or the `windowAll()` (for *non-keyed* streams) call.
 
 A `WindowAssigner` is responsible for assigning each incoming element to one or more windows. Flink comes
 with pre-defined window assigners for the most common use cases, namely *tumbling windows*,
 *sliding windows*, *session windows* and *global windows*. You can also implement a custom window assigner by
 extending the `WindowAssigner` class. All built-in window assigners (except the global
 windows) assign elements to windows based on time, which can either be processing time or event
-time. Please take a look at our section on [event time]({{ site.baseurl }}/dev/event_time.html) to learn 
+time. Please take a look at our section on [event time]({{ site.baseurl }}/dev/event_time.html) to learn
 about the difference between processing time and event time and how timestamps and watermarks are generated.
 
 In the following, we show how Flink's pre-defined window assigners work and how they are used
-in a DataStream program. The following figures visualize the workings of each assigner. The purple circles 
-represent elements of the stream, which are partitioned by some key (in this case *user 1*, *user 2* and *user 3*). 
+in a DataStream program. The following figures visualize the workings of each assigner. The purple circles
+represent elements of the stream, which are partitioned by some key (in this case *user 1*, *user 2* and *user 3*).
 The x-axis shows the progress of time.
 
 ### Tumbling Windows
 
-A *tumbling windows* assigner assigns each element to a window of a specified *window size*. 
-Tumbling windows have a fixed size and do not overlap. For example, if you specify a tumbling 
-window with a size of 5 minutes, the current window will be evaluated and a new window will be 
+A *tumbling windows* assigner assigns each element to a window of a specified *window size*.
+Tumbling windows have a fixed size and do not overlap. For example, if you specify a tumbling
+window with a size of 5 minutes, the current window will be evaluated and a new window will be
 started every five minutes as illustrated by the following figure.
 
 <img src="{{ site.baseurl }}/fig/tumbling-windows.svg" class="center" style="width: 100%;" />
@@ -180,25 +180,25 @@ input
 Time intervals can be specified by using one of `Time.milliseconds(x)`, `Time.seconds(x)`,
 `Time.minutes(x)`, and so on.
 
-As shown in the last example, tumbling window assigners also take an optional `offset` 
-parameter that can be used to change the alignment of windows. For example, without offsets 
-hourly tumbling windows are aligned with epoch, that is you will get windows such as 
-`1:00:00.000 - 1:59:59.999`, `2:00:00.000 - 2:59:59.999` and so on. If you want to change 
-that you can give an offset. With an offset of 15 minutes you would, for example, get 
-`1:15:00.000 - 2:14:59.999`, `2:15:00.000 - 3:14:59.999` etc. 
-An important use case for offsets is to adjust windows to timezones other than UTC-0. 
+As shown in the last example, tumbling window assigners also take an optional `offset`
+parameter that can be used to change the alignment of windows. For example, without offsets
+hourly tumbling windows are aligned with epoch, that is you will get windows such as
+`1:00:00.000 - 1:59:59.999`, `2:00:00.000 - 2:59:59.999` and so on. If you want to change
+that you can give an offset. With an offset of 15 minutes you would, for example, get
+`1:15:00.000 - 2:14:59.999`, `2:15:00.000 - 3:14:59.999` etc.
+An important use case for offsets is to adjust windows to timezones other than UTC-0.
 For example, in China you would have to specify an offset of `Time.hours(-8)`.
 
 ### Sliding Windows
 
-The *sliding windows* assigner assigns elements to windows of fixed length. Similar to a tumbling 
-windows assigner, the size of the windows is configured by the *window size* parameter. 
-An additional *window slide* parameter controls how frequently a sliding window is started. Hence, 
-sliding windows can be overlapping if the slide is smaller than the window size. In this case elements 
+The *sliding windows* assigner assigns elements to windows of fixed length. Similar to a tumbling
+windows assigner, the size of the windows is configured by the *window size* parameter.
+An additional *window slide* parameter controls how frequently a sliding window is started. Hence,
+sliding windows can be overlapping if the slide is smaller than the window size. In this case elements
 are assigned to multiple windows.
 
 For example, you could have windows of size 10 minutes that slides by 5 minutes. With this you get every
-5 minutes a window that contains the events that arrived during the last 10 minutes as depicted by the 
+5 minutes a window that contains the events that arrived during the last 10 minutes as depicted by the
 following figure.
 
 <img src="{{ site.baseurl }}/fig/sliding-windows.svg" class="center" style="width: 100%;" />
@@ -258,22 +258,22 @@ input
 Time intervals can be specified by using one of `Time.milliseconds(x)`, `Time.seconds(x)`,
 `Time.minutes(x)`, and so on.
 
-As shown in the last example, sliding window assigners also take an optional `offset` parameter 
-that can be used to change the alignment of windows. For example, without offsets hourly windows 
-sliding by 30 minutes are aligned with epoch, that is you will get windows such as 
-`1:00:00.000 - 1:59:59.999`, `1:30:00.000 - 2:29:59.999` and so on. If you want to change that 
-you can give an offset. With an offset of 15 minutes you would, for example, get 
-`1:15:00.000 - 2:14:59.999`, `1:45:00.000 - 2:44:59.999` etc. 
-An important use case for offsets is to adjust windows to timezones other than UTC-0. 
+As shown in the last example, sliding window assigners also take an optional `offset` parameter
+that can be used to change the alignment of windows. For example, without offsets hourly windows
+sliding by 30 minutes are aligned with epoch, that is you will get windows such as
+`1:00:00.000 - 1:59:59.999`, `1:30:00.000 - 2:29:59.999` and so on. If you want to change that
+you can give an offset. With an offset of 15 minutes you would, for example, get
+`1:15:00.000 - 2:14:59.999`, `1:45:00.000 - 2:44:59.999` etc.
+An important use case for offsets is to adjust windows to timezones other than UTC-0.
 For example, in China you would have to specify an offset of `Time.hours(-8)`.
 
 ### Session Windows
 
 The *session windows* assigner groups elements by sessions of activity. Session windows do not overlap and
-do not have a fixed start and end time, in contrast to *tumbling windows* and *sliding windows*. Instead a 
-session window closes when it does not receive elements for a certain period of time, *i.e.*, when a gap of 
+do not have a fixed start and end time, in contrast to *tumbling windows* and *sliding windows*. Instead a
+session window closes when it does not receive elements for a certain period of time, *i.e.*, when a gap of
 inactivity occurred. A session window assigner is configured with the *session gap* which
-defines how long is the required period of inactivity. When this period expires, the current session closes 
+defines how long is the required period of inactivity. When this period expires, the current session closes
 and subsequent elements are assigned to a new session window.
 
 <img src="{{ site.baseurl }}/fig/session-windows.svg" class="center" style="width: 100%;" />
@@ -321,12 +321,12 @@ input
 Time intervals can be specified by using one of `Time.milliseconds(x)`, `Time.seconds(x)`,
 `Time.minutes(x)`, and so on.
 
-<span class="label label-danger">Attention</span> Since session windows do not have a fixed start and end, 
-they are  evaluated differently than tumbling and sliding windows. Internally, a session window operator 
-creates a new window for each arriving record and merges windows together if their are closer to each other 
+<span class="label label-danger">Attention</span> Since session windows do not have a fixed start and end,
+they are  evaluated differently than tumbling and sliding windows. Internally, a session window operator
+creates a new window for each arriving record and merges windows together if their are closer to each other
 than the defined gap.
-In order to be mergeable, a session window operator requires a merging [Trigger](#triggers) and a merging 
-[Window Function](#window-functions), such as `ReduceFunction` or `WindowFunction` 
+In order to be mergeable, a session window operator requires a merging [Trigger](#triggers) and a merging
+[Window Function](#window-functions), such as `ReduceFunction` or `WindowFunction`
 (`FoldFunction` cannot merge.)
 
 ### Global Windows
@@ -366,13 +366,13 @@ input
 
 ## Window Functions
 
-After defining the window assigner, we need to specify the computation that we want 
-to perform on each of these windows. This is the responsibility of the *window function*, which is used to process the 
-elements of each (possibly keyed) window once the system determines that a window is ready for processing 
+After defining the window assigner, we need to specify the computation that we want
+to perform on each of these windows. This is the responsibility of the *window function*, which is used to process the
+elements of each (possibly keyed) window once the system determines that a window is ready for processing
 (see [triggers](#triggers) for how Flink determines when a window is ready).
 
 The window function can be one of `ReduceFunction`, `FoldFunction` or `WindowFunction`. The first
-two can be executed more efficiently (see [State Size](#state size) section) because Flink can incrementally aggregate 
+two can be executed more efficiently (see [State Size](#state size) section) because Flink can incrementally aggregate
 the elements for each window as they arrive. A `WindowFunction` gets an `Iterable` for all the elements contained in a
 window and additional meta information about the window to which the elements belong.
 
@@ -385,7 +385,7 @@ get both incremental aggregation of window elements and the additional window me
 ### ReduceFunction
 
 A `ReduceFunction` specifies how two elements from the input are combined to produce
-an output element of the same type. Flink uses a `ReduceFunction` to incrementally aggregate 
+an output element of the same type. Flink uses a `ReduceFunction` to incrementally aggregate
 the elements of a window.
 
 A `ReduceFunction` can be defined and used like this:
@@ -422,8 +422,8 @@ The above example sums up the second fields of the tuples for all elements in a
 
 ### FoldFunction
 
-A `FoldFunction` specifies how an input element of the window is combined with an element of 
-the output type. The `FoldFunction` is incrementally called for each element that is added 
+A `FoldFunction` specifies how an input element of the window is combined with an element of
+the output type. The `FoldFunction` is incrementally called for each element that is added
 to the window and the current output value. The first element is combined with a pre-defined initial value of the output type.
 
 A `FoldFunction` can be defined and used like this:
@@ -462,9 +462,9 @@ The above example appends all input `Long` values to an initially empty `String`
 
 ### WindowFunction - The Generic Case
 
-A `WindowFunction` gets an `Iterable` containing all the elements of the window and provides 
-the most flexibility of all window functions. This comes 
-at the cost of performance and resource consumption, because elements cannot be incrementally 
+A `WindowFunction` gets an `Iterable` containing all the elements of the window and provides
+the most flexibility of all window functions. This comes
+at the cost of performance and resource consumption, because elements cannot be incrementally
 aggregated but instead need to be buffered internally until the window is considered ready for processing.
 
 The signature of a `WindowFunction` looks as follows:
@@ -567,17 +567,17 @@ The example shows a `WindowFunction` to count the elements in a window. In addit
 
 ### WindowFunction with Incremental Aggregation
 
-A `WindowFunction` can be combined with either a `ReduceFunction` or a `FoldFunction` to 
-incrementally aggregate elements as they arrive in the window. 
-When the window is closed, the `WindowFunction` will be provided with the aggregated result. 
-This allows to incrementally compute windows while having access to the 
+A `WindowFunction` can be combined with either a `ReduceFunction` or a `FoldFunction` to
+incrementally aggregate elements as they arrive in the window.
+When the window is closed, the `WindowFunction` will be provided with the aggregated result.
+This allows to incrementally compute windows while having access to the
 additional window meta information of the `WindowFunction`.
 
 #### Incremental Window Aggregation with FoldFunction
 
 The following example shows how an incremental `FoldFunction` can be combined with
-a `WindowFunction` to extract the number of events in the window and return also 
-the key and end time of the window. 
+a `WindowFunction` to extract the number of events in the window and return also
+the key and end time of the window.
 
 <div class="codetabs" markdown="1">
 <div data-lang="java" markdown="1">
@@ -601,9 +601,9 @@ private static class MyFoldFunction
   }
 }
 
-private static class MyWindowFunction 
+private static class MyWindowFunction
     implements WindowFunction<Tuple3<String, Long, Integer>, Tuple3<String, Long, Integer>, String, TimeWindow> {
-  
+
   public void apply(String key,
                     TimeWindow window,
                     Iterable<Tuple3<String, Long, Integer>> counts,
@@ -624,12 +624,12 @@ input
  .keyBy(<key selector>)
  .timeWindow(<window assigner>)
  .fold (
-    ("", 0L, 0), 
+    ("", 0L, 0),
     (acc: (String, Long, Int), r: SensorReading) => { ("", 0L, acc._3 + 1) },
-    ( key: String, 
-      window: TimeWindow, 
+    ( key: String,
+      window: TimeWindow,
       counts: Iterable[(String, Long, Int)],
-      out: Collector[(String, Long, Int)] ) => 
+      out: Collector[(String, Long, Int)] ) =>
       {
         val count = counts.iterator.next()
         out.collect((key, window.getEnd, count._3))
@@ -643,8 +643,8 @@ input
 #### Incremental Window Aggregation with ReduceFunction
 
 The following example shows how an incremental `ReduceFunction` can be combined with
-a `WindowFunction` to return the smallest event in a window along 
-with the start time of the window.  
+a `WindowFunction` to return the smallest event in a window along
+with the start time of the window.
 
 <div class="codetabs" markdown="1">
 <div data-lang="java" markdown="1">
@@ -665,9 +665,9 @@ private static class MyReduceFunction implements ReduceFunction<SensorReading> {
   }
 }
 
-private static class MyWindowFunction 
+private static class MyWindowFunction
     implements WindowFunction<SensorReading, Tuple2<Long, SensorReading>, String, TimeWindow> {
-  
+
   public void apply(String key,
                     TimeWindow window,
                     Iterable<SensorReading> minReadings,
@@ -689,16 +689,16 @@ input
   .timeWindow(<window assigner>)
   .reduce(
     (r1: SensorReading, r2: SensorReading) => { if (r1.value > r2.value) r2 else r1 },
-    ( key: String, 
-      window: TimeWindow, 
-      minReadings: Iterable[SensorReading], 
-      out: Collector[(Long, SensorReading)] ) => 
+    ( key: String,
+      window: TimeWindow,
+      minReadings: Iterable[SensorReading],
+      out: Collector[(Long, SensorReading)] ) =>
       {
         val min = minReadings.iterator.next()
         out.collect((window.getStart, min))
       }
   )
-  
+
 {% endhighlight %}
 </div>
 </div>
@@ -706,34 +706,34 @@ input
 ## Triggers
 
 A `Trigger` determines when a window (as formed by the *window assigner*) is ready to be
-processed by the *window function*. Each `WindowAssigner` comes with a default `Trigger`. 
+processed by the *window function*. Each `WindowAssigner` comes with a default `Trigger`.
 If the default trigger does not fit your needs, you can specify a custom trigger using `trigger(...)`.
 
 The trigger interface has five methods that allow a `Trigger` to react to different events:
 
-* The `onElement()` method is called for each element that is added to a window. 
-* The `onEventTime()` method is called when  a registered event-time timer fires. 
-* The `onProcessingTime()` method is called when a registered processing-time timer fires. 
-* The `onMerge()` method is relevant for stateful triggers and merges the states of two triggers when their corresponding windows merge, *e.g.* when using session windows. 
-* Finally the `clear()` method performs any action needed upon removal of the corresponding window. 
+* The `onElement()` method is called for each element that is added to a window.
+* The `onEventTime()` method is called when  a registered event-time timer fires.
+* The `onProcessingTime()` method is called when a registered processing-time timer fires.
+* The `onMerge()` method is relevant for stateful triggers and merges the states of two triggers when their corresponding windows merge, *e.g.* when using session windows.
+* Finally the `clear()` method performs any action needed upon removal of the corresponding window.
 
 Two things to notice about the above methods are:
 
-1) The first three decide how to act on their invocation event by returning a `TriggerResult`. The action can be one of the following: 
+1) The first three decide how to act on their invocation event by returning a `TriggerResult`. The action can be one of the following:
 
-* `CONTINUE`: do nothing, 
-* `FIRE`: trigger the computation, 
-* `PURGE`: clear the elements in the window, and 
+* `CONTINUE`: do nothing,
+* `FIRE`: trigger the computation,
+* `PURGE`: clear the elements in the window, and
 * `FIRE_AND_PURGE`: trigger the computation and clear the elements in the window afterwards.
 
-2) Any of these methods can be used to register processing- or event-time timers for future actions. 
+2) Any of these methods can be used to register processing- or event-time timers for future actions.
 
 ### Fire and Purge
 
-Once a trigger determines that a window is ready for processing, it fires, *i.e.*, it returns `FIRE` or `FIRE_AND_PURGE`. This is the signal for the window operator 
-to emit the result of the current window. Given a window with a `WindowFunction` 
-all elements are passed to the `WindowFunction` (possibly after passing them to an evictor). 
-Windows with `ReduceFunction` of `FoldFunction` simply emit their eagerly aggregated result. 
+Once a trigger determines that a window is ready for processing, it fires, *i.e.*, it returns `FIRE` or `FIRE_AND_PURGE`. This is the signal for the window operator
+to emit the result of the current window. Given a window with a `WindowFunction`
+all elements are passed to the `WindowFunction` (possibly after passing them to an evictor).
+Windows with `ReduceFunction` of `FoldFunction` simply emit their eagerly aggregated result.
 
 When a trigger fires, it can either `FIRE` or `FIRE_AND_PURGE`. While `FIRE` keeps the contents of the window, `FIRE_AND_PURGE` removes its content.
 By default, the pre-implemented triggers simply `FIRE` without purging the window state.
@@ -743,7 +743,7 @@ By default, the pre-implemented triggers simply `FIRE` without purging the windo
 ### Default Triggers of WindowAssigners
 
 The default `Trigger` of a `WindowAssigner` is appropriate for many use cases. For example, all the event-time window assigners have an `EventTimeTrigger` as
-default trigger. This trigger simply fires once the watermark passes the end of a window. 
+default trigger. This trigger simply fires once the watermark passes the end of a window.
 
 <span class="label label-danger">Attention</span> The default trigger of the `GlobalWindow` is the `NeverTrigger` which does never fire. Consequently, you always have to define a custom trigger when using a `GlobalWindow`.
 
@@ -755,23 +755,23 @@ you want to react based on both time and count.
 
 ### Built-in and Custom Triggers
 
-Flink comes with a few built-in triggers. 
+Flink comes with a few built-in triggers.
 
-* The (already mentioned) `EventTimeTrigger` fires based on the progress of event-time as measured by watermarks. 
-* The `ProcessingTimeTrigger` fires based on processing time. 
+* The (already mentioned) `EventTimeTrigger` fires based on the progress of event-time as measured by watermarks.
+* The `ProcessingTimeTrigger` fires based on processing time.
 * The `CountTrigger` fires once the number of elements in a window exceeds the given limit.
-* The `PurgingTrigger` takes as argument another trigger and transforms it into a purging one. 
+* The `PurgingTrigger` takes as argument another trigger and transforms it into a purging one.
 
-If you need to implement a custom trigger, you should check out the abstract 
-{% gh_link /flink-streaming-java/src/main/java/org/apache/flink/streaming/api/windowing/triggers/Trigger.java "Trigger" %} class. 
+If you need to implement a custom trigger, you should check out the abstract
+{% gh_link /flink-streaming-java/src/main/java/org/apache/flink/streaming/api/windowing/triggers/Trigger.java "Trigger" %} class.
 Please note that the API is still evolving and might change in future versions of Flink.
 
 ## Evictors
 
-Flink\u2019s windowing model allows specifying an optional `Evictor` in addition to the `WindowAssigner` and the `Trigger`. 
-This can be done using the `evictor(...)` method (shown in the beginning of this document). The evictor has the ability 
+Flink\u2019s windowing model allows specifying an optional `Evictor` in addition to the `WindowAssigner` and the `Trigger`.
+This can be done using the `evictor(...)` method (shown in the beginning of this document). The evictor has the ability
 to remove elements from a window *after* the trigger fires and *before and/or after* the window function is applied.
-To do so, the `Evictor` interface has two methods: 
+To do so, the `Evictor` interface has two methods:
 
     /**
      * Optionally evicts elements. Called before windowing function.
@@ -793,45 +793,45 @@ To do so, the `Evictor` interface has two methods:
      */
     void evictAfter(Iterable<TimestampedValue<T>> elements, int size, W window, EvictorContext evictorContext);
 
-The `evictBefore()` contains the eviction logic to be applied before the window function, while the `evictAfter()` 
-contains the one to be applied after the window function. Elements evicted before the application of the window 
+The `evictBefore()` contains the eviction logic to be applied before the window function, while the `evictAfter()`
+contains the one to be applied after the window function. Elements evicted before the application of the window
 function will not be processed by it.
 
 Flink comes with three pre-implemented evictors. These are:
 
-* `CountEvictor`: keeps up to a user-specified number of elements from the window and discards the remaining ones from 
+* `CountEvictor`: keeps up to a user-specified number of elements from the window and discards the remaining ones from
 the beginning of the window buffer.
-* `DeltaEvictor`: takes a `DeltaFunction` and a `threshold`, computes the delta between the last element in the 
+* `DeltaEvictor`: takes a `DeltaFunction` and a `threshold`, computes the delta between the last element in the
 window buffer and each of the remaining ones, and removes the ones with a delta greater or equal to the threshold.
-* `TimeEvictor`: takes as argument an `interval` in milliseconds and for a given window, it finds the maximum 
+* `TimeEvictor`: takes as argument an `interval` in milliseconds and for a given window, it finds the maximum
 timestamp `max_ts` among its elements and removes all the elements with timestamps smaller than `max_ts - interval`.
 
-<span class="label label-info">Default</span> By default, all the pre-implemented evictors apply their logic before the 
+<span class="label label-info">Default</span> By default, all the pre-implemented evictors apply their logic before the
 window function.
 
-<span class="label label-danger">Attention</span> Specifying an evictor prevents any pre-aggregation, as all the 
+<span class="label label-danger">Attention</span> Specifying an evictor prevents any pre-aggregation, as all the
 elements of a window have to be passed to the evictor before applying the computation.
 
 <span class="label label-danger">Attention</span> Flink provides no guarantees about the order of the elements within
-a window. This implies that although an evictor may remove elements from the beginning of the window, these are not 
+a window. This implies that although an evictor may remove elements from the beginning of the window, these are not
 necessarily the ones that arrive first or last.
 
 
 ## Allowed Lateness
 
-When working with *event-time* windowing, it can happen that elements arrive late, *i.e.* the watermark that Flink uses to 
-keep track of the progress of event-time is already past the end timestamp of a window to which an element belongs. See 
-[event time](./event_time.html) and especially [late elements](./event_time.html#late-elements) for a more thorough 
+When working with *event-time* windowing, it can happen that elements arrive late, *i.e.* the watermark that Flink uses to
+keep track of the progress of event-time is already past the end timestamp of a window to which an element belongs. See
+[event time](./event_time.html) and especially [late elements](./event_time.html#late-elements) for a more thorough
 discussion of how Flink deals with event time.
 
-By default, late elements are dropped when the watermark is past the end of the window. However, 
-Flink allows to specify a maximum *allowed lateness* for window operators. Allowed lateness 
-specifies by how much time elements can be late before they are dropped, and its default value is 0. 
-Elements that arrive after the watermark has passed the end of the window but before it passes the end of 
-the window plus the allowed lateness, are still added to the window. Depending on the trigger used, 
+By default, late elements are dropped when the watermark is past the end of the window. However,
+Flink allows to specify a maximum *allowed lateness* for window operators. Allowed lateness
+specifies by how much time elements can be late before they are dropped, and its default value is 0.
+Elements that arrive after the watermark has passed the end of the window but before it passes the end of
+the window plus the allowed lateness, are still added to the window. Depending on the trigger used,
 a late but not dropped element may cause the window to fire again. This is the case for the `EventTimeTrigger`.
 
-In order to make this work, Flink keeps the state of windows until their allowed lateness expires. Once this happens, Flink removes the window and deletes its state, as 
+In order to make this work, Flink keeps the state of windows until their allowed lateness expires. Once this happens, Flink removes the window and deletes its state, as
 also described in the [Window Lifecycle](#window-lifecycle) section.
 
 <span class="label label-info">Default</span> By default, the allowed lateness is set to
@@ -871,8 +871,8 @@ data is ever considered late because the end timestamp of the global window is `
 ### Late elements considerations
 
 When specifying an allowed lateness greater than 0, the window along with its content is kept after the watermark passes
-the end of the window. In these cases, when a late but not dropped element arrives, it could trigger another firing for the 
-window. These firings are called `late firings`, as they are triggered by late events and in contrast to the `main firing` 
+the end of the window. In these cases, when a late but not dropped element arrives, it could trigger another firing for the
+window. These firings are called `late firings`, as they are triggered by late events and in contrast to the `main firing`
 which is the first firing of the window. In case of session windows, late firings can further lead to merging of windows,
 as they may "bridge" the gap between two pre-existing, unmerged windows.
 
@@ -881,7 +881,7 @@ as they may "bridge" the gap between two pre-existing, unmerged windows.
 ## Useful state size considerations
 
 Windows can be defined over long periods of time (such as days, weeks, or months) and therefore accumulate very large state. There are a couple of rules to keep in mind when estimating the storage requirements of your windowing computation:
- 
+
 1. Flink creates one copy of each element per window to which it belongs. Given this, tumbling windows keep one copy of each element (an element belongs to exactly window unless it is dropped late). In contrast, sliding windows create several of each element, as explained in the [Window Assigners](#window-assigners) section. Hence, a sliding window of size 1 day and slide 1 second might not be a good idea.
 
 2. `FoldFunction` and `ReduceFunction` can significantly reduce the storage requirements, as they eagerly aggregate elements and store only one value per window. In contrast, just using a `WindowFunction` requires accumulating all elements.

[4/7] flink git commit: [FLINK-5529] Update Window Documentation Figures

[al...@apache.org]

[FLINK-5529] Update Window Documentation Figures


Project: http://git-wip-us.apache.org/repos/asf/flink/repo
Commit: http://git-wip-us.apache.org/repos/asf/flink/commit/9e5ebe40
Tree: http://git-wip-us.apache.org/repos/asf/flink/tree/9e5ebe40
Diff: http://git-wip-us.apache.org/repos/asf/flink/diff/9e5ebe40

Branch: refs/heads/master
Commit: 9e5ebe408221313d36262917a5534cfe7b160785
Parents: d398f34
Author: Aljoscha Krettek <al...@gmail.com>
Authored: Thu Jan 26 19:33:25 2017 +0100
Committer: Aljoscha Krettek <al...@gmail.com>
Committed: Fri Jan 27 13:30:24 2017 +0100

----------------------------------------------------------------------
 docs/dev/windows.md           | 8 ++++----
 docs/fig/non-windowed.svg     | 2 +-
 docs/fig/session-windows.svg  | 2 +-
 docs/fig/sliding-windows.svg  | 2 +-
 docs/fig/tumbling-windows.svg | 2 +-
 5 files changed, 8 insertions(+), 8 deletions(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/flink/blob/9e5ebe40/docs/dev/windows.md
----------------------------------------------------------------------
diff --git a/docs/dev/windows.md b/docs/dev/windows.md
index 82906a0..056e5c9 100644
--- a/docs/dev/windows.md
+++ b/docs/dev/windows.md
@@ -123,7 +123,7 @@ Tumbling windows have a fixed size and do not overlap. For example, if you speci
 window with a size of 5 minutes, the current window will be evaluated and a new window will be 
 started every five minutes as illustrated by the following figure.
 
-<img src="{{ site.baseurl }}/fig/tumbling-windows.svg" class="center" style="width: 80%;" />
+<img src="{{ site.baseurl }}/fig/tumbling-windows.svg" class="center" style="width: 100%;" />
 
 The following code snippets show how to use tumbling windows.
 
@@ -201,7 +201,7 @@ For example, you could have windows of size 10 minutes that slides by 5 minutes.
 5 minutes a window that contains the events that arrived during the last 10 minutes as depicted by the 
 following figure.
 
-<img src="{{ site.baseurl }}/fig/sliding-windows.svg" class="center" style="width: 80%;" />
+<img src="{{ site.baseurl }}/fig/sliding-windows.svg" class="center" style="width: 100%;" />
 
 The following code snippets show how to use sliding windows.
 
@@ -276,7 +276,7 @@ inactivity occurred. A session window assigner is configured with the *session g
 defines how long is the required period of inactivity. When this period expires, the current session closes 
 and subsequent elements are assigned to a new session window.
 
-<img src="{{ site.baseurl }}/fig/session-windows.svg" class="center" style="width: 80%;" />
+<img src="{{ site.baseurl }}/fig/session-windows.svg" class="center" style="width: 100%;" />
 
 The following code snippets show how to use session windows.
 
@@ -336,7 +336,7 @@ This windowing scheme is only useful if you also specify a custom [trigger](#tri
 no computation will be performed, as the global window does not have a natural end at
 which we could process the aggregated elements.
 
-<img src="{{ site.baseurl }}/fig/non-windowed.svg" class="center" style="width: 80%;" />
+<img src="{{ site.baseurl }}/fig/non-windowed.svg" class="center" style="width: 100%;" />
 
 The following code snippets show how to use a global window.
 

http://git-wip-us.apache.org/repos/asf/flink/blob/9e5ebe40/docs/fig/non-windowed.svg
----------------------------------------------------------------------
diff --git a/docs/fig/non-windowed.svg b/docs/fig/non-windowed.svg
index 3c1cdaa..4064fec 100644
--- a/docs/fig/non-windowed.svg
+++ b/docs/fig/non-windowed.svg
@@ -18,5 +18,5 @@ specific language governing permissions and limitations
 under the License.
 -->
 
-<svg version="1.1" viewBox="0.0 0.0 800.0 600.0" fill="none" stroke="none" stroke-linecap="square" stroke-miterlimit="10" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><clipPath id="p.0"><path d="m0 0l800.0 0l0 600.0l-800.0 0l0 -600.0z" clip-rule="nonzero"></path></clipPath><g clip-path="url(#p.0)"><path fill="#000000" fill-opacity="0.0" d="m0 0l800.0 0l0 600.0l-800.0 0z" fill-rule="nonzero"></path><path fill="#000000" fill-opacity="0.0" d="m145.49606 485.0l509.0079 0" fill-rule="nonzero"></path><path stroke="#000000" stroke-width="1.0" stroke-linejoin="round" stroke-linecap="butt" d="m145.49606 485.0l503.0079 0" fill-rule="evenodd"></path><path fill="#000000" stroke="#000000" stroke-width="1.0" stroke-linecap="butt" d="m648.50397 486.65173l4.538086 -1.6517334l-4.538086 -1.6517334z" fill-rule="evenodd"></path><path fill="#000000" fill-opacity="0.0" d="m145.49606 485.0l0 -394.99213" fill-rule="nonzero"></path><path stroke="#000000" stroke-width="1.0" s
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[5/7] flink git commit: [FLINK-4752] [docs] Improve window assigner documentation.

[al...@apache.org]

[FLINK-4752] [docs] Improve window assigner documentation.


Project: http://git-wip-us.apache.org/repos/asf/flink/repo
Commit: http://git-wip-us.apache.org/repos/asf/flink/commit/d398f345
Tree: http://git-wip-us.apache.org/repos/asf/flink/tree/d398f345
Diff: http://git-wip-us.apache.org/repos/asf/flink/diff/d398f345

Branch: refs/heads/master
Commit: d398f3451d241920c74164f859ed53055bce4035
Parents: c30c86b
Author: Fabian Hueske <fh...@apache.org>
Authored: Fri Jan 27 11:48:26 2017 +0100
Committer: Aljoscha Krettek <al...@gmail.com>
Committed: Fri Jan 27 13:30:24 2017 +0100

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 docs/dev/windows.md | 299 +++++++++++++++++++++++++++++++++++++++++------
 1 file changed, 266 insertions(+), 33 deletions(-)
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http://git-wip-us.apache.org/repos/asf/flink/blob/d398f345/docs/dev/windows.md
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diff --git a/docs/dev/windows.md b/docs/dev/windows.md
index 5752ef2..82906a0 100644
--- a/docs/dev/windows.md
+++ b/docs/dev/windows.md
@@ -97,39 +97,272 @@ independently from the rest. All elements referring to the same key will be sent
 In case of non-keyed streams, your original stream will not be split into multiple logical streams and all the windowing logic 
 will be performed by a single task, *i.e.* with parallelism of 1.
 
-## Window Assigner
-
-After specifying whether your stream is keyed or not, the next step is to specify a *windowing strategy*. 
-This will dictate how your elements will be assigned into windows and the general way to do so, is by specifying the 
-`WindowAssigner` that corresponds to the windowing strategy of your choice in the `window(...)` call. 
-
-The `WindowAssigner` is responsible for assigning each incoming element to one or more windows. Flink comes with 
-pre-implemented window assigners for the most typical use cases, namely *tumbling windows*, *sliding windows*, 
-*session windows* and *global windows*, but you can implement your own by extending the `WindowAssigner` class. All the 
-built-in window assigners, except for the *global windows* one, assign elements to windows based on time, which can 
-either be *processing* time or *event* time. A list of the pre-implemented `WindowAssigners` follows:
-
-* **TumblingTimeWindows**: non-overlapping windows of a user-specified *duration* `d`. Each element belongs to a single 
-            window based on its timestamp. As an example, an element with timestamp 12.01 and a tumbling windowing 
-            strategy with `d = 5 min` will be assigned to the window `[start=12.00, end=12.05]`.
-
-* **SlidingTimeWindows**: overlapping windows of a user-specified *duration* `d` and a *slide* `s`. Each element belongs to 
-            a `d/s` windows based on its timestamp. Our element with timestamp 12.01 and a sliding windowing 
-            strategy with `d = 5 min` and `s = 1 min` will be assigned to the windows `[start=12.00, end=12.05]`, 
-            `[start=12.01, end=12.06]`, `[start=12.02, end=12.07]`, `[start=12.03, end=12.08]`, `[start=12.04, end=12.09]`.
-
-* **TimeSessionWindows**: contrary to the previous assigners, here the window boundaries depend on the incoming data. The user 
-            specifies a gap `g` and if there is a period of inactivity for the stream of more than `g` time units, then the 
-            open window closes and a new, clean one opens to receive new events. In case we are operating in `event time`, two 
-            consecutive session windows can be *merged* if an element arrives and makes the gap between the two windows less than 
-            `g`. This is the reason why session windows belong to the category of `MergingWindowAssigners`. To illustrate
-            the latter, if `g = 5` and we have two windows `w1=[start=12.00, end=12.05]` and `w2=[start=12.10, end=12.15]`,
-            then if element `e` arrives with timestamp `t=12.03`, the two previous windows will be merged into one, 
-            `w3=[start=12.00, end=12.15]`.
-
-* **GlobalWindows**: this is a way of specifying that we don\u2019t want to subdivide our elements into windows. All elements are 
-            assigned to the same per-key global window. This implies that this strategy is only meaningful when combined 
-            with a custom trigger, *e.g.* a `CountTrigger`, which will tell the window when to fire.
+## Window Assigners
+
+After specifying whether your stream is keyed or not, the next step is to define a *window assigner*. 
+The window assigner defines how elements are assigned to windows. This is done by specifying the `WindowAssigner` 
+of your choice in the `window(...)` (for *keyed* streams) or the `windowAll()` (for *non-keyed* streams) call. 
+
+A `WindowAssigner` is responsible for assigning each incoming element to one or more windows. Flink comes
+with pre-defined window assigners for the most common use cases, namely *tumbling windows*,
+*sliding windows*, *session windows* and *global windows*. You can also implement a custom window assigner by
+extending the `WindowAssigner` class. All built-in window assigners (except the global
+windows) assign elements to windows based on time, which can either be processing time or event
+time. Please take a look at our section on [event time]({{ site.baseurl }}/dev/event_time.html) to learn 
+about the difference between processing time and event time and how timestamps and watermarks are generated.
+
+In the following, we show how Flink's pre-defined window assigners work and how they are used
+in a DataStream program. The following figures visualize the workings of each assigner. The purple circles 
+represent elements of the stream, which are partitioned by some key (in this case *user 1*, *user 2* and *user 3*). 
+The x-axis shows the progress of time.
+
+### Tumbling Windows
+
+A *tumbling windows* assigner assigns each element to a window of a specified *window size*. 
+Tumbling windows have a fixed size and do not overlap. For example, if you specify a tumbling 
+window with a size of 5 minutes, the current window will be evaluated and a new window will be 
+started every five minutes as illustrated by the following figure.
+
+<img src="{{ site.baseurl }}/fig/tumbling-windows.svg" class="center" style="width: 80%;" />
+
+The following code snippets show how to use tumbling windows.
+
+<div class="codetabs" markdown="1">
+<div data-lang="java" markdown="1">
+{% highlight java %}
+DataStream<T> input = ...;
+
+// tumbling event-time windows
+input
+    .keyBy(<key selector>)
+    .window(TumblingEventTimeWindows.of(Time.seconds(5)))
+    .<windowed transformation>(<window function>);
+
+// tumbling processing-time windows
+input
+    .keyBy(<key selector>)
+    .window(TumblingProcessingTimeWindows.of(Time.seconds(5)))
+    .<windowed transformation>(<window function>);
+
+// daily tumbling event-time windows offset by -8 hours.
+input
+    .keyBy(<key selector>)
+    .window(TumblingEventTimeWindows.of(Time.days(1), Time.hours(-8)))
+    .<windowed transformation>(<window function>);
+{% endhighlight %}
+</div>
+
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+val input: DataStream[T] = ...
+
+// tumbling event-time windows
+input
+    .keyBy(<key selector>)
+    .window(TumblingEventTimeWindows.of(Time.seconds(5)))
+    .<windowed transformation>(<window function>)
+
+// tumbling processing-time windows
+input
+    .keyBy(<key selector>)
+    .window(TumblingProcessingTimeWindows.of(Time.seconds(5)))
+    .<windowed transformation>(<window function>)
+
+// daily tumbling event-time windows offset by -8 hours.
+input
+    .keyBy(<key selector>)
+    .window(TumblingEventTimeWindows.of(Time.days(1), Time.hours(-8)))
+    .<windowed transformation>(<window function>)
+{% endhighlight %}
+</div>
+</div>
+
+Time intervals can be specified by using one of `Time.milliseconds(x)`, `Time.seconds(x)`,
+`Time.minutes(x)`, and so on.
+
+As shown in the last example, tumbling window assigners also take an optional `offset` 
+parameter that can be used to change the alignment of windows. For example, without offsets 
+hourly tumbling windows are aligned with epoch, that is you will get windows such as 
+`1:00:00.000 - 1:59:59.999`, `2:00:00.000 - 2:59:59.999` and so on. If you want to change 
+that you can give an offset. With an offset of 15 minutes you would, for example, get 
+`1:15:00.000 - 2:14:59.999`, `2:15:00.000 - 3:14:59.999` etc. 
+An important use case for offsets is to adjust windows to timezones other than UTC-0. 
+For example, in China you would have to specify an offset of `Time.hours(-8)`.
+
+### Sliding Windows
+
+The *sliding windows* assigner assigns elements to windows of fixed length. Similar to a tumbling 
+windows assigner, the size of the windows is configured by the *window size* parameter. 
+An additional *window slide* parameter controls how frequently a sliding window is started. Hence, 
+sliding windows can be overlapping if the slide is smaller than the window size. In this case elements 
+are assigned to multiple windows.
+
+For example, you could have windows of size 10 minutes that slides by 5 minutes. With this you get every
+5 minutes a window that contains the events that arrived during the last 10 minutes as depicted by the 
+following figure.
+
+<img src="{{ site.baseurl }}/fig/sliding-windows.svg" class="center" style="width: 80%;" />
+
+The following code snippets show how to use sliding windows.
+
+<div class="codetabs" markdown="1">
+<div data-lang="java" markdown="1">
+{% highlight java %}
+DataStream<T> input = ...;
+
+// sliding event-time windows
+input
+    .keyBy(<key selector>)
+    .window(SlidingEventTimeWindows.of(Time.seconds(10), Time.seconds(5)))
+    .<windowed transformation>(<window function>);
+
+// sliding processing-time windows
+input
+    .keyBy(<key selector>)
+    .window(SlidingProcessingTimeWindows.of(Time.seconds(10), Time.seconds(5)))
+    .<windowed transformation>(<window function>);
+
+// sliding processing-time windows offset by -8 hours
+input
+    .keyBy(<key selector>)
+    .window(SlidingProcessingTimeWindows.of(Time.hours(12), Time.hours(1), Time.hours(-8)))
+    .<windowed transformation>(<window function>);
+{% endhighlight %}
+</div>
+
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+val input: DataStream[T] = ...
+
+// sliding event-time windows
+input
+    .keyBy(<key selector>)
+    .window(SlidingEventTimeWindows.of(Time.seconds(10), Time.seconds(5)))
+    .<windowed transformation>(<window function>)
+
+// sliding processing-time windows
+input
+    .keyBy(<key selector>)
+    .window(SlidingProcessingTimeWindows.of(Time.seconds(10), Time.seconds(5)))
+    .<windowed transformation>(<window function>)
+
+// sliding processing-time windows offset by -8 hours
+input
+    .keyBy(<key selector>)
+    .window(SlidingProcessingTimeWindows.of(Time.hours(12), Time.hours(1), Time.hours(-8)))
+    .<windowed transformation>(<window function>)
+{% endhighlight %}
+</div>
+</div>
+
+Time intervals can be specified by using one of `Time.milliseconds(x)`, `Time.seconds(x)`,
+`Time.minutes(x)`, and so on.
+
+As shown in the last example, sliding window assigners also take an optional `offset` parameter 
+that can be used to change the alignment of windows. For example, without offsets hourly windows 
+sliding by 30 minutes are aligned with epoch, that is you will get windows such as 
+`1:00:00.000 - 1:59:59.999`, `1:30:00.000 - 2:29:59.999` and so on. If you want to change that 
+you can give an offset. With an offset of 15 minutes you would, for example, get 
+`1:15:00.000 - 2:14:59.999`, `1:45:00.000 - 2:44:59.999` etc. 
+An important use case for offsets is to adjust windows to timezones other than UTC-0. 
+For example, in China you would have to specify an offset of `Time.hours(-8)`.
+
+### Session Windows
+
+The *session windows* assigner groups elements by sessions of activity. Session windows do not overlap and
+do not have a fixed start and end time, in contrast to *tumbling windows* and *sliding windows*. Instead a 
+session window closes when it does not receive elements for a certain period of time, *i.e.*, when a gap of 
+inactivity occurred. A session window assigner is configured with the *session gap* which
+defines how long is the required period of inactivity. When this period expires, the current session closes 
+and subsequent elements are assigned to a new session window.
+
+<img src="{{ site.baseurl }}/fig/session-windows.svg" class="center" style="width: 80%;" />
+
+The following code snippets show how to use session windows.
+
+<div class="codetabs" markdown="1">
+<div data-lang="java" markdown="1">
+{% highlight java %}
+DataStream<T> input = ...;
+
+// event-time session windows
+input
+    .keyBy(<key selector>)
+    .window(EventTimeSessionWindows.withGap(Time.minutes(10)))
+    .<windowed transformation>(<window function>);
+
+// processing-time session windows
+input
+    .keyBy(<key selector>)
+    .window(ProcessingTimeSessionWindows.withGap(Time.minutes(10)))
+    .<windowed transformation>(<window function>);
+{% endhighlight %}
+</div>
+
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+val input: DataStream[T] = ...
+
+// event-time session windows
+input
+    .keyBy(<key selector>)
+    .window(EventTimeSessionWindows.withGap(Time.minutes(10)))
+    .<windowed transformation>(<window function>)
+
+// processing-time session windows
+input
+    .keyBy(<key selector>)
+    .window(ProcessingTimeSessionWindows.withGap(Time.minutes(10)))
+    .<windowed transformation>(<window function>)
+{% endhighlight %}
+</div>
+</div>
+
+Time intervals can be specified by using one of `Time.milliseconds(x)`, `Time.seconds(x)`,
+`Time.minutes(x)`, and so on.
+
+<span class="label label-danger">Attention</span> Since session windows do not have a fixed start and end, 
+they are  evaluated differently than tumbling and sliding windows. Internally, a session window operator 
+creates a new window for each arriving record and merges windows together if their are closer to each other 
+than the defined gap.
+In order to be mergeable, a session window operator requires a merging [Trigger](#triggers) and a merging 
+[Window Function](#window-functions), such as `ReduceFunction` or `WindowFunction` 
+(`FoldFunction` cannot merge.)
+
+### Global Windows
+
+A *global windows* assigner assigns all elements with the same key to the same single *global window*.
+This windowing scheme is only useful if you also specify a custom [trigger](#triggers). Otherwise,
+no computation will be performed, as the global window does not have a natural end at
+which we could process the aggregated elements.
+
+<img src="{{ site.baseurl }}/fig/non-windowed.svg" class="center" style="width: 80%;" />
+
+The following code snippets show how to use a global window.
+
+<div class="codetabs" markdown="1">
+<div data-lang="java" markdown="1">
+{% highlight java %}
+DataStream<T> input = ...;
+
+input
+    .keyBy(<key selector>)
+    .window(GlobalWindows.create())
+    .<windowed transformation>(<window function>);
+{% endhighlight %}
+</div>
+
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+val input: DataStream[T] = ...
+
+input
+    .keyBy(<key selector>)
+    .window(GlobalWindows.create())
+    .<windowed transformation>(<window function>)
+{% endhighlight %}
+</div>
+</div>
 
 ## Window Functions
 

[6/7] flink git commit: [FLINK-5529] [docs] Improve / extends windowing documentation

[al...@apache.org]

[FLINK-5529] [docs] Improve / extends windowing documentation


Project: http://git-wip-us.apache.org/repos/asf/flink/repo
Commit: http://git-wip-us.apache.org/repos/asf/flink/commit/c30c86b8
Tree: http://git-wip-us.apache.org/repos/asf/flink/tree/c30c86b8
Diff: http://git-wip-us.apache.org/repos/asf/flink/diff/c30c86b8

Branch: refs/heads/master
Commit: c30c86b8935caa3c43a2e8c83c863bce70d98724
Parents: c89a622
Author: kl0u <kk...@gmail.com>
Authored: Tue Jan 17 16:51:09 2017 +0100
Committer: Aljoscha Krettek <al...@gmail.com>
Committed: Fri Jan 27 13:30:24 2017 +0100

----------------------------------------------------------------------
 docs/dev/windows.md | 653 ++++++++++++++++++++---------------------------
 1 file changed, 272 insertions(+), 381 deletions(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/flink/blob/c30c86b8/docs/dev/windows.md
----------------------------------------------------------------------
diff --git a/docs/dev/windows.md b/docs/dev/windows.md
index 1170d0d..5752ef2 100644
--- a/docs/dev/windows.md
+++ b/docs/dev/windows.md
@@ -23,293 +23,139 @@ specific language governing permissions and limitations
 under the License.
 -->
 
-Flink uses a concept called *windows* to divide a (potentially) infinite `DataStream` into finite
-slices based on the timestamps of elements or other criteria. This division is required when working
-with infinite streams of data and performing transformations that aggregate elements.
-
-<span class="label label-info">Info</span> We will mostly talk about *keyed windowing* here, i.e.
-windows that are applied on a `KeyedStream`. Keyed windows have the advantage that elements are
-subdivided based on both window and key before being given to
-a user function. The work can thus be distributed across the cluster
-because the elements for different keys can be processed independently. If you absolutely have to,
-you can check out [non-keyed windowing](#non-keyed-windowing) where we describe how non-keyed
-windows work.
+Windows are at the heart of processing infinite streams. Windows split the stream into "buckets" of finite size, 
+over which we can apply computations. This document focuses on how windowing is performed in Flink and how the 
+programmer can benefit to the maximum from its offered functionality. 
+
+The general structure of a windowed Flink program is presented below. The first snippet refers to *keyed* streams,
+while the second to *non-keyed* ones. As one can see, the only difference is the `keyBy(...)` call for the keyed streams
+and the `window(...)` which becomes `windowAll(...)` for non-keyed streams. These is also going to serve as a roadmap 
+for the rest of the page.
+
+**Keyed Windows**
+    
+    stream
+           .keyBy(...)          <-  keyed versus non-keyed windows
+           .window(...)         <-  required: "assigner"
+          [.trigger(...)]       <-  optional: "trigger" (else default trigger)
+          [.evictor(...)]       <-  optional: "evictor" (else no evictor)
+          [.allowedLateness()]  <-  optional, else zero
+           .reduce/fold/apply() <-  required: "function"
+
+**Non-Keyed Windows**
+    
+    stream
+           .windowAll(...)      <-  required: "assigner"
+          [.trigger(...)]       <-  optional: "trigger" (else default trigger)
+          [.evictor(...)]       <-  optional: "evictor" (else no evictor)
+          [.allowedLateness()]  <-  optional, else zero
+           .reduce/fold/apply() <-  required: "function"
+           
+In the above, the commands in square brackets ([...]) are optional. This reveals that Flink allows you to customize your 
+windowing logic in many different ways so that it best fits your needs. 
 
 * This will be replaced by the TOC
 {:toc}
 
-## Basics
-
-For a windowed transformation you must at least specify a *key*
-(see [specifying keys]({{ site.baseurl }}/dev/api_concepts.html#specifying-keys)),
-a *window assigner* and a *window function*. The *key* divides the infinite, non-keyed, stream
-into logical keyed streams while the *window assigner* assigns elements to finite per-key windows.
-Finally, the *window function* is used to process the elements of each window.
-
-The basic structure of a windowed transformation is thus as follows:
-
-<div class="codetabs" markdown="1">
-<div data-lang="java" markdown="1">
-{% highlight java %}
-DataStream<T> input = ...;
-
-input
-    .keyBy(<key selector>)
-    .window(<window assigner>)
-    .<windowed transformation>(<window function>);
-{% endhighlight %}
-</div>
-
-<div data-lang="scala" markdown="1">
-{% highlight scala %}
-val input: DataStream[T] = ...
-
-input
-    .keyBy(<key selector>)
-    .window(<window assigner>)
-    .<windowed transformation>(<window function>)
-{% endhighlight %}
-</div>
-</div>
-
-We will cover [window assigners](#window-assigners) in a separate section below.
-
-The window transformation can be one of `reduce()`, `fold()` or `apply()`. Which respectively
-takes a `ReduceFunction`, `FoldFunction` or `WindowFunction`. We describe each of these ways
-of specifying a windowed transformation in detail below: [window functions](#window-functions).
-
-For more advanced use cases you can also specify a `Trigger` that determines when exactly a window
-is being considered as *ready for processing*. These will be covered in more detail in
-[triggers](#triggers).
-
-## Window Assigners
-
-The window assigner specifies how elements of the stream are divided into finite slices. Flink comes
-with pre-implemented window assigners for the most typical use cases, namely *tumbling windows*,
-*sliding windows*, *session windows* and *global windows*, but you can implement your own by
-extending the `WindowAssigner` class. All the built-in window assigners, except for the global
-windows one, assign elements to windows based on time, which can either be processing time or event
-time. Please take a look at our section on [event time]({{ site.baseurl }}/dev/event_time.html) for more
-information about how Flink deals with time.
-
-Let's first look at how each of these window assigners works before looking at how they can be used
-in a Flink program. We will be using abstract figures to visualize the workings of each assigner:
-in the following, the purple circles are elements of the stream, they are partitioned
-by some key (in this case *user 1*, *user 2* and *user 3*) and the x-axis shows the progress
-of time.
-
-### Global Windows
-
-Global windows are a way of specifying that we don't want to subdivide our elements into windows.
-Each element is assigned to one single per-key *global window*.
-This windowing scheme is only useful if you also specify a custom [trigger](#triggers). Otherwise,
-no computation is ever going to be performed, as the global window does not have a natural end at
-which we could process the aggregated elements.
-
-<img src="{{ site.baseurl }}/fig/non-windowed.svg" class="center" style="width: 80%;" />
-
-### Tumbling Windows
-
-A *tumbling windows* assigner assigns elements to fixed length, non-overlapping windows of a
-specified *window size*. For example, if you specify a window size of 5 minutes, the window
-function will get 5 minutes worth of elements in each invocation.
-
-<img src="{{ site.baseurl }}/fig/tumbling-windows.svg" class="center" style="width: 80%;" />
-
-### Sliding Windows
-
-The *sliding windows* assigner assigns elements to windows of fixed length equal to *window size*,
-as the tumbling windows assigner, but in this case, windows can be overlapping. The size of the
-overlap is defined by the user-specified parameter *window slide*. As windows are overlapping, an
-element can be assigned to multiple windows
-
-For example, you could have windows of size 10 minutes that slide by 5 minutes. With this you get 10
-minutes worth of elements in each invocation of the window function and it will be invoked for every
-5 minutes of data.
-
-<img src="{{ site.baseurl }}/fig/sliding-windows.svg" class="center" style="width: 80%;" />
-
-### Session Windows
-
-The *session windows* assigner is ideal for cases where the window boundaries need to adjust to the
-incoming data. Both the *tumbling windows* and *sliding windows* assigner assign elements to windows
-that start at fixed time points and have a fixed *window size*. With session windows it is possible
-to have windows that start at individual points in time for each key and that end once there has
-been a certain period of inactivity. The configuration parameter is the *session gap* that specifies
-how long to wait for new data before considering a session as closed.
-
-<img src="{{ site.baseurl }}/fig/session-windows.svg" class="center" style="width: 80%;" />
-
-### Specifying a Window Assigner
-
-The built-in window assigners (except `GlobalWindows`) come in two versions. One for processing-time
-windowing and one for event-time windowing. The processing-time assigners assign elements to
-windows based on the current clock of the worker machines while the event-time assigners assign
-windows based on the timestamps of elements. Please have a look at
-[event time]({{ site.baseurl }}/dev/event_time.html) to learn about the difference between processing time
-and event time and about how timestamps can be assigned to elements.
-
-The following code snippets show how each of the window assigners can be used in a program:
-
-<div class="codetabs" markdown="1">
-<div data-lang="java" markdown="1">
-{% highlight java %}
-DataStream<T> input = ...;
-
-// tumbling event-time windows
-input
-    .keyBy(<key selector>)
-    .window(TumblingEventTimeWindows.of(Time.seconds(5)))
-    .<windowed transformation>(<window function>);
-
-// sliding event-time windows
-input
-    .keyBy(<key selector>)
-    .window(SlidingEventTimeWindows.of(Time.seconds(10), Time.seconds(5)))
-    .<windowed transformation>(<window function>);
-
-// event-time session windows
-input
-    .keyBy(<key selector>)
-    .window(EventTimeSessionWindows.withGap(Time.minutes(10)))
-    .<windowed transformation>(<window function>);
-
-// tumbling processing-time windows
-input
-    .keyBy(<key selector>)
-    .window(TumblingProcessingTimeWindows.of(Time.seconds(5)))
-    .<windowed transformation>(<window function>);
-
-// sliding processing-time windows
-input
-    .keyBy(<key selector>)
-    .window(SlidingProcessingTimeWindows.of(Time.seconds(10), Time.seconds(5)))
-    .<windowed transformation>(<window function>);
-
-// processing-time session windows
-input
-    .keyBy(<key selector>)
-    .window(ProcessingTimeSessionWindows.withGap(Time.minutes(10)))
-    .<windowed transformation>(<window function>);
-
-// global windows
-input
-    .keyBy(<key selector>)
-    .window(GlobalWindows.create())
-    .<windowed transformation>(<window function>);
-{% endhighlight %}
-</div>
-
-<div data-lang="scala" markdown="1">
-{% highlight scala %}
-val input: DataStream[T] = ...
-
-// tumbling event-time windows
-input
-    .keyBy(<key selector>)
-    .window(TumblingEventTimeWindows.of(Time.seconds(5)))
-    .<windowed transformation>(<window function>)
-
-// sliding event-time windows
-input
-    .keyBy(<key selector>)
-    .window(SlidingEventTimeWindows.of(Time.seconds(10), Time.seconds(5)))
-    .<windowed transformation>(<window function>)
-
-// event-time session windows
-input
-    .keyBy(<key selector>)
-    .window(EventTimeSessionWindows.withGap(Time.minutes(10)))
-    .<windowed transformation>(<window function>)
-
-// tumbling processing-time windows
-input
-    .keyBy(<key selector>)
-    .window(TumblingProcessingTimeWindows.of(Time.seconds(5)))
-    .<windowed transformation>(<window function>)
-
-// sliding processing-time windows
-input
-    .keyBy(<key selector>)
-    .window(SlidingProcessingTimeWindows.of(Time.seconds(10), Time.seconds(5)))
-    .<windowed transformation>(<window function>)
-
-// processing-time session windows
-input
-    .keyBy(<key selector>)
-    .window(ProcessingTimeSessionWindows.withGap(Time.minutes(10)))
-    .<windowed transformation>(<window function>)
-
-// global windows
-input
-    .keyBy(<key selector>)
-    .window(GlobalWindows.create())
-{% endhighlight %}
-</div>
-</div>
-
-Note, how we can specify a time interval by using one of `Time.milliseconds(x)`, `Time.seconds(x)`,
-`Time.minutes(x)`, and so on.
-
-The time-based window assigners also take an optional `offset` parameter that can be used to
-change the alignment of windows. For example, without offsets hourly windows are aligned
-with epoch, that is you will get windows such as `1:00 - 1:59`, `2:00 - 2:59` and so on. If you
-want to change that you can give an offset. With an offset of 15 minutes you would, for example,
-get `1:15 - 2:14`, `2:15 - 3:14` etc. Another important use case for offsets is when you
-want to have daily windows and live in a timezone other than UTC-0. For example, in China
-you would have to specify an offset of `Time.hours(-8)`.
-
-This example shows how an offset can be specified for tumbling event time windows (the other
-windows work accordingly):
-<div class="codetabs" markdown="1">
-<div data-lang="java" markdown="1">
-{% highlight java %}
-DataStream<T> input = ...;
-
-// tumbling event-time windows
-input
-    .keyBy(<key selector>)
-    .window(TumblingEventTimeWindows.of(Time.days(1), Time.hours(-8)))
-    .<windowed transformation>(<window function>);
-{% endhighlight %}
-</div>
-
-<div data-lang="scala" markdown="1">
-{% highlight scala %}
-val input: DataStream[T] = ...
-
-// tumbling event-time windows
-input
-    .keyBy(<key selector>)
-    .window(TumblingEventTimeWindows.of(Time.days(1), Time.hours(-8)))
-    .<windowed transformation>(<window function>)
-{% endhighlight %}
-</div>
-</div>
+## Window Lifecycle
+
+In a nutshell, a window is **created** as soon as the first element that should belong to this window arrives, and the  
+window is **completely removed** when the time (event or processing time) passes its end timestamp plus the user-specified 
+`allowed lateness` (see [Allowed Lateness](#allowed-lateness)). Flink guarantees removal only for time-based 
+windows and not for other types, *e.g.* global windows (see [Window Assigners](#window-assigners)). For example, with an 
+event-time-based windowing strategy that creates non-overlapping (or tumbling) windows every 5 minutes and has an allowed 
+lateness of 1 min, Flink will create a new window for the interval between `12:00` and `12:05` when the first element with 
+a timestamp that falls into this interval arrives, and it will remove it when the watermark passes the `12:06`
+timestamp. 
+
+In addition, each window will have a `Trigger` (see [Triggers](#triggers)) and a function (`WindowFunction`, `ReduceFunction` or 
+`FoldFunction`) (see [Window Functions](#window-functions)) attached to it. The function will contain the computation to 
+be applied to the contents of the window, while the `Trigger` specifies the conditions under which the window is 
+considered ready for the function to be applied. A triggering policy might be something like "when the number of elements 
+in the window is more than 4", or "when the watermark passes the end of the window". A trigger can also decide to 
+purge a window's contents any time between its creation and removal. Purging in this case only refers to the elements 
+in the window, and *not* the window metadata. This means that new data can still be added to that window.
+
+Apart from the above, you can specify an `Evictor` (see [Evictors](#evictors)) which will be able to remove  
+elements from the window after the trigger fires and before and/or after the function is applied.
+
+In the following we go into more detail for each of the components above. We start with the required parts in the above 
+snippet (see [Keyed vs Non-Keyed Windows](#keyed-vs-non-keyed-windows), [Window Assigner](#window-assigner), and 
+[Window Function](#window-function)) before moving to the optional ones.
+
+## Keyed vs Non-Keyed Windows
+
+The first thing to specify is whether your stream should be keyed or not. This has to be done before defining the window. 
+Using the `keyBy(...)` will split your infinite stream into logical keyed streams. If `keyBy(...)` is not called, your 
+stream is not keyed.
+
+In the case of keyed streams, any attribute of your incoming events can be used as a key 
+(more details [here]({{ site.baseurl }}/dev/api_concepts.html#specifying-keys)). Having a keyed stream will 
+allow your windowed computation to be performed in parallel by multiple tasks, as each logical keyed stream can be processed 
+independently from the rest. All elements referring to the same key will be sent to the same parallel task. 
+
+In case of non-keyed streams, your original stream will not be split into multiple logical streams and all the windowing logic 
+will be performed by a single task, *i.e.* with parallelism of 1.
+
+## Window Assigner
+
+After specifying whether your stream is keyed or not, the next step is to specify a *windowing strategy*. 
+This will dictate how your elements will be assigned into windows and the general way to do so, is by specifying the 
+`WindowAssigner` that corresponds to the windowing strategy of your choice in the `window(...)` call. 
+
+The `WindowAssigner` is responsible for assigning each incoming element to one or more windows. Flink comes with 
+pre-implemented window assigners for the most typical use cases, namely *tumbling windows*, *sliding windows*, 
+*session windows* and *global windows*, but you can implement your own by extending the `WindowAssigner` class. All the 
+built-in window assigners, except for the *global windows* one, assign elements to windows based on time, which can 
+either be *processing* time or *event* time. A list of the pre-implemented `WindowAssigners` follows:
+
+* **TumblingTimeWindows**: non-overlapping windows of a user-specified *duration* `d`. Each element belongs to a single 
+            window based on its timestamp. As an example, an element with timestamp 12.01 and a tumbling windowing 
+            strategy with `d = 5 min` will be assigned to the window `[start=12.00, end=12.05]`.
+
+* **SlidingTimeWindows**: overlapping windows of a user-specified *duration* `d` and a *slide* `s`. Each element belongs to 
+            a `d/s` windows based on its timestamp. Our element with timestamp 12.01 and a sliding windowing 
+            strategy with `d = 5 min` and `s = 1 min` will be assigned to the windows `[start=12.00, end=12.05]`, 
+            `[start=12.01, end=12.06]`, `[start=12.02, end=12.07]`, `[start=12.03, end=12.08]`, `[start=12.04, end=12.09]`.
+
+* **TimeSessionWindows**: contrary to the previous assigners, here the window boundaries depend on the incoming data. The user 
+            specifies a gap `g` and if there is a period of inactivity for the stream of more than `g` time units, then the 
+            open window closes and a new, clean one opens to receive new events. In case we are operating in `event time`, two 
+            consecutive session windows can be *merged* if an element arrives and makes the gap between the two windows less than 
+            `g`. This is the reason why session windows belong to the category of `MergingWindowAssigners`. To illustrate
+            the latter, if `g = 5` and we have two windows `w1=[start=12.00, end=12.05]` and `w2=[start=12.10, end=12.15]`,
+            then if element `e` arrives with timestamp `t=12.03`, the two previous windows will be merged into one, 
+            `w3=[start=12.00, end=12.15]`.
+
+* **GlobalWindows**: this is a way of specifying that we don\u2019t want to subdivide our elements into windows. All elements are 
+            assigned to the same per-key global window. This implies that this strategy is only meaningful when combined 
+            with a custom trigger, *e.g.* a `CountTrigger`, which will tell the window when to fire.
 
 ## Window Functions
 
-The *window function* is used to process the elements of each window (and key) once the system
-determines that a window is ready for processing (see [triggers](#triggers) for how the system
-determines when a window is ready).
+After defining the window assigner, we need to specify the computation that we want 
+to perform on each of these windows. This is the responsibility of the *window function*, which is used to process the 
+elements of each (possibly keyed) window once the system determines that a window is ready for processing 
+(see [triggers](#triggers) for how Flink determines when a window is ready).
 
 The window function can be one of `ReduceFunction`, `FoldFunction` or `WindowFunction`. The first
-two can be executed more efficiently because Flink can incrementally aggregate the elements for each
-window as they arrive. A `WindowFunction` gets an `Iterable` for all the elements contained in a
+two can be executed more efficiently (see [State Size](#state size) section) because Flink can incrementally aggregate 
+the elements for each window as they arrive. A `WindowFunction` gets an `Iterable` for all the elements contained in a
 window and additional meta information about the window to which the elements belong.
 
 A windowed transformation with a `WindowFunction` cannot be executed as efficiently as the other
 cases because Flink has to buffer *all* elements for a window internally before invoking the function.
 This can be mitigated by combining a `WindowFunction` with a `ReduceFunction` or `FoldFunction` to
-get both incremental aggregation of window elements and the additional information that the
+get both incremental aggregation of window elements and the additional window metadata that the
 `WindowFunction` receives. We will look at examples for each of these variants.
 
 ### ReduceFunction
 
-A reduce function specifies how two values can be combined to form one element. Flink can use this
-to incrementally aggregate the elements in a window.
+A `ReduceFunction` specifies how two elements from the input are combined to produce
+an output element of the same type. Flink uses a `ReduceFunction` to incrementally aggregate 
+the elements of a window.
 
-A `ReduceFunction` can be used in a program like this:
+A `ReduceFunction` can be defined and used like this:
 
 <div class="codetabs" markdown="1">
 <div data-lang="java" markdown="1">
@@ -339,13 +185,15 @@ input
 </div>
 </div>
 
-A `ReduceFunction` specifies how two elements from the input can be combined to produce
-an output element. This example will sum up the second field of the tuple for all elements
-in a window.
+The above example sums up the second fields of the tuples for all elements in a window.
 
 ### FoldFunction
 
-A fold function can be specified like this:
+A `FoldFunction` specifies how an input element of the window is combined with an element of 
+the output type. The `FoldFunction` is incrementally called for each element that is added 
+to the window and the current output value. The first element is combined with a pre-defined initial value of the output type.
+
+A `FoldFunction` can be defined and used like this:
 
 <div class="codetabs" markdown="1">
 <div data-lang="java" markdown="1">
@@ -375,17 +223,18 @@ input
 </div>
 </div>
 
-A `FoldFunction` specifies how elements from the input will be added to an initial
-accumulator value (`""`, the empty string, in our example). This example will compute
-a concatenation of all the `Long` fields of the input.
+The above example appends all input `Long` values to an initially empty `String`.
+
+<span class="label label-danger">Attention</span> `fold()` cannot be used with session windows or other mergeable windows.
 
 ### WindowFunction - The Generic Case
 
-Using a `WindowFunction` provides the most flexibility, at the cost of performance. The reason for this
-is that elements cannot be incrementally aggregated for a window and instead need to be buffered
-internally until the window is considered ready for processing. A `WindowFunction` gets an
-`Iterable` containing all the elements of the window being processed. The signature of
-`WindowFunction` is this:
+A `WindowFunction` gets an `Iterable` containing all the elements of the window and provides 
+the most flexibility of all window functions. This comes 
+at the cost of performance and resource consumption, because elements cannot be incrementally 
+aggregated but instead need to be buffered internally until the window is considered ready for processing.
+
+The signature of a `WindowFunction` looks as follows:
 
 <div class="codetabs" markdown="1">
 <div data-lang="java" markdown="1">
@@ -426,12 +275,7 @@ trait WindowFunction[IN, OUT, KEY, W <: Window] extends Function with Serializab
 </div>
 </div>
 
-Here we show an example that uses a `WindowFunction` to count the elements in a window. We do this
-because we want to access information about the window itself to emit it along with the count.
-This is very inefficient, however, and should be implemented with a
-`ReduceFunction` in practice. Below, we will see an example of how a `ReduceFunction` can
-be combined with a `WindowFunction` to get both incremental aggregation and the added
-information of a `WindowFunction`.
+A `WindowFunction` can be defined and used like this:
 
 <div class="codetabs" markdown="1">
 <div data-lang="java" markdown="1">
@@ -484,6 +328,10 @@ class MyWindowFunction extends WindowFunction[(String, Long), String, String, Ti
 </div>
 </div>
 
+The example shows a `WindowFunction` to count the elements in a window. In addition, the window function adds information about the window to the output.
+
+<span class="label label-danger">Attention</span> Note that using `WindowFunction` for simple aggregates such as count is quite inefficient. The next section shows how a `ReduceFunction` can be combined with a `WindowFunction` to get both incremental aggregation and the added information of a `WindowFunction`.
+
 ### WindowFunction with Incremental Aggregation
 
 A `WindowFunction` can be combined with either a `ReduceFunction` or a `FoldFunction` to 
@@ -622,102 +470,49 @@ input
 </div>
 </div>
 
-## Dealing with Late Data
-
-When working with event-time windowing it can happen that elements arrive late, i.e the
-watermark that Flink uses to keep track of the progress of event-time is already past the
-end timestamp of a window to which an element belongs. Please
-see [event time](./event_time.html) and especially
-[late elements](./event_time.html#late-elements) for a more thorough discussion of
-how Flink deals with event time.
+## Triggers
 
-You can specify how a windowed transformation should deal with late elements and how much lateness
-is allowed. The parameter for this is called *allowed lateness*. This specifies by how much time
-elements can be late. Elements that arrive within the allowed lateness are still put into windows
-and are considered when computing window results. If elements arrive after the allowed lateness they
-will be dropped. Flink will also make sure that any state held by the windowing operation is garbage
-collected once the watermark passes the end of a window plus the allowed lateness.
+A `Trigger` determines when a window (as formed by the *window assigner*) is ready to be
+processed by the *window function*. Each `WindowAssigner` comes with a default `Trigger`. 
+If the default trigger does not fit your needs, you can specify a custom trigger using `trigger(...)`.
 
-<span class="label label-info">Default</span> By default, the allowed lateness is set to
-`0`. That is, elements that arrive behind the watermark will be dropped.
+The trigger interface has five methods that allow a `Trigger` to react to different events:
 
-You can specify an allowed lateness like this:
+* The `onElement()` method is called for each element that is added to a window. 
+* The `onEventTime()` method is called when  a registered event-time timer fires. 
+* The `onProcessingTime()` method is called when a registered processing-time timer fires. 
+* The `onMerge()` method is relevant for stateful triggers and merges the states of two triggers when their corresponding windows merge, *e.g.* when using session windows. 
+* Finally the `clear()` method performs any action needed upon removal of the corresponding window. 
 
-<div class="codetabs" markdown="1">
-<div data-lang="java" markdown="1">
-{% highlight java %}
-DataStream<T> input = ...;
-
-input
-    .keyBy(<key selector>)
-    .window(<window assigner>)
-    .allowedLateness(<time>)
-    .<windowed transformation>(<window function>);
-{% endhighlight %}
-</div>
-
-<div data-lang="scala" markdown="1">
-{% highlight scala %}
-val input: DataStream[T] = ...
-
-input
-    .keyBy(<key selector>)
-    .window(<window assigner>)
-    .allowedLateness(<time>)
-    .<windowed transformation>(<window function>)
-{% endhighlight %}
-</div>
-</div>
+Two things to notice about the above methods are:
 
-<span class="label label-info">Note</span> When using the `GlobalWindows` window assigner no
-data is ever considered late because the end timestamp of the global window is `Long.MAX_VALUE`.
+1) The first three decide how to act on their invocation event by returning a `TriggerResult`. The action can be one of the following: 
 
-## Triggers
+* `CONTINUE`: do nothing, 
+* `FIRE`: trigger the computation, 
+* `PURGE`: clear the elements in the window, and 
+* `FIRE_AND_PURGE`: trigger the computation and clear the elements in the window afterwards.
 
-A `Trigger` determines when a window (as assigned by the `WindowAssigner`) is ready for being
-processed by the *window function*. The trigger observes how elements are added to windows
-and can also keep track of the progress of processing time and event time. Once a trigger
-determines that a window is ready for processing, it fires. This is the signal for the
-window operation to take the elements that are currently in the window and pass them along to
-the window function to produce output for the firing window.
+2) Any of these methods can be used to register processing- or event-time timers for future actions. 
 
-Each `WindowAssigner` (except `GlobalWindows`) comes with a default trigger that should be
-appropriate for most use cases. For example, `TumblingEventTimeWindows` has an `EventTimeTrigger` as
-default trigger. This trigger simply fires once the watermark passes the end of a window.
+### Fire and Purge
 
-You can specify the trigger to be used by calling `trigger()` with a given `Trigger`. The
-whole specification of the windowed transformation would then look like this:
+Once a trigger determines that a window is ready for processing, it fires, *i.e.*, it returns `FIRE` or `FIRE_AND_PURGE`. This is the signal for the window operator 
+to emit the result of the current window. Given a window with a `WindowFunction` 
+all elements are passed to the `WindowFunction` (possibly after passing them to an evictor). 
+Windows with `ReduceFunction` of `FoldFunction` simply emit their eagerly aggregated result. 
 
-<div class="codetabs" markdown="1">
-<div data-lang="java" markdown="1">
-{% highlight java %}
-DataStream<T> input = ...;
+When a trigger fires, it can either `FIRE` or `FIRE_AND_PURGE`. While `FIRE` keeps the contents of the window, `FIRE_AND_PURGE` removes its content.
+By default, the pre-implemented triggers simply `FIRE` without purging the window state.
 
-input
-    .keyBy(<key selector>)
-    .window(<window assigner>)
-    .trigger(<trigger>)
-    .<windowed transformation>(<window function>);
-{% endhighlight %}
-</div>
+<span class="label label-danger">Attention</span> Purging will simply remove the contents of the window and will leave any potential meta-information about the window and any trigger state intact.
 
-<div data-lang="scala" markdown="1">
-{% highlight scala %}
-val input: DataStream[T] = ...
+### Default Triggers of WindowAssigners
 
-input
-    .keyBy(<key selector>)
-    .window(<window assigner>)
-    .trigger(<trigger>)
-    .<windowed transformation>(<window function>)
-{% endhighlight %}
-</div>
-</div>
+The default `Trigger` of a `WindowAssigner` is appropriate for many use cases. For example, all the event-time window assigners have an `EventTimeTrigger` as
+default trigger. This trigger simply fires once the watermark passes the end of a window. 
 
-Flink comes with a few triggers out-of-box: there is the already mentioned `EventTimeTrigger` that
-fires based on the progress of event-time as measured by the watermark, the `ProcessingTimeTrigger`
-does the same but based on processing time and the `CountTrigger` fires once the number of elements
-in a window exceeds the given limit.
+<span class="label label-danger">Attention</span> The default trigger of the `GlobalWindow` is the `NeverTrigger` which does never fire. Consequently, you always have to define a custom trigger when using a `GlobalWindow`.
 
 <span class="label label-danger">Attention</span> By specifying a trigger using `trigger()` you
 are overwriting the default trigger of a `WindowAssigner`. For example, if you specify a
@@ -725,22 +520,91 @@ are overwriting the default trigger of a `WindowAssigner`. For example, if you s
 progress of time but only by count. Right now, you have to write your own custom trigger if
 you want to react based on both time and count.
 
-The internal `Trigger` API is still considered experimental but you can check out the code
-if you want to write your own custom trigger:
-{% gh_link /flink-streaming-java/src/main/java/org/apache/flink/streaming/api/windowing/triggers/Trigger.java "Trigger.java" %}.
+### Built-in and Custom Triggers
+
+Flink comes with a few built-in triggers. 
+
+* The (already mentioned) `EventTimeTrigger` fires based on the progress of event-time as measured by watermarks. 
+* The `ProcessingTimeTrigger` fires based on processing time. 
+* The `CountTrigger` fires once the number of elements in a window exceeds the given limit.
+* The `PurgingTrigger` takes as argument another trigger and transforms it into a purging one. 
+
+If you need to implement a custom trigger, you should check out the abstract 
+{% gh_link /flink-streaming-java/src/main/java/org/apache/flink/streaming/api/windowing/triggers/Trigger.java "Trigger" %} class. 
+Please note that the API is still evolving and might change in future versions of Flink.
+
+## Evictors
+
+Flink\u2019s windowing model allows specifying an optional `Evictor` in addition to the `WindowAssigner` and the `Trigger`. 
+This can be done using the `evictor(...)` method (shown in the beginning of this document). The evictor has the ability 
+to remove elements from a window *after* the trigger fires and *before and/or after* the window function is applied.
+To do so, the `Evictor` interface has two methods: 
 
-## Non-keyed Windowing
+    /**
+     * Optionally evicts elements. Called before windowing function.
+     *
+     * @param elements The elements currently in the pane.
+     * @param size The current number of elements in the pane.
+     * @param window The {@link Window}
+     * @param evictorContext The context for the Evictor
+     */
+    void evictBefore(Iterable<TimestampedValue<T>> elements, int size, W window, EvictorContext evictorContext);
 
-You can also leave out the `keyBy()` when specifying a windowed transformation. This means, however,
-that Flink cannot process windows for different keys in parallel, essentially turning the
-transformation into a non-parallel operation.
+    /**
+     * Optionally evicts elements. Called after windowing function.
+     *
+     * @param elements The elements currently in the pane.
+     * @param size The current number of elements in the pane.
+     * @param window The {@link Window}
+     * @param evictorContext The context for the Evictor
+     */
+    void evictAfter(Iterable<TimestampedValue<T>> elements, int size, W window, EvictorContext evictorContext);
 
-<span class="label label-danger">Warning</span> As mentioned in the introduction, non-keyed
-windows have the disadvantage that work cannot be distributed in the cluster because
-windows cannot be computed independently per key. This can have severe performance implications.
+The `evictBefore()` contains the eviction logic to be applied before the window function, while the `evictAfter()` 
+contains the one to be applied after the window function. Elements evicted before the application of the window 
+function will not be processed by it.
 
+Flink comes with three pre-implemented evictors. These are:
 
-The basic structure of a non-keyed windowed transformation is as follows:
+* `CountEvictor`: keeps up to a user-specified number of elements from the window and discards the remaining ones from 
+the beginning of the window buffer.
+* `DeltaEvictor`: takes a `DeltaFunction` and a `threshold`, computes the delta between the last element in the 
+window buffer and each of the remaining ones, and removes the ones with a delta greater or equal to the threshold.
+* `TimeEvictor`: takes as argument an `interval` in milliseconds and for a given window, it finds the maximum 
+timestamp `max_ts` among its elements and removes all the elements with timestamps smaller than `max_ts - interval`.
+
+<span class="label label-info">Default</span> By default, all the pre-implemented evictors apply their logic before the 
+window function.
+
+<span class="label label-danger">Attention</span> Specifying an evictor prevents any pre-aggregation, as all the 
+elements of a window have to be passed to the evictor before applying the computation.
+
+<span class="label label-danger">Attention</span> Flink provides no guarantees about the order of the elements within
+a window. This implies that although an evictor may remove elements from the beginning of the window, these are not 
+necessarily the ones that arrive first or last.
+
+
+## Allowed Lateness
+
+When working with *event-time* windowing, it can happen that elements arrive late, *i.e.* the watermark that Flink uses to 
+keep track of the progress of event-time is already past the end timestamp of a window to which an element belongs. See 
+[event time](./event_time.html) and especially [late elements](./event_time.html#late-elements) for a more thorough 
+discussion of how Flink deals with event time.
+
+By default, late elements are dropped when the watermark is past the end of the window. However, 
+Flink allows to specify a maximum *allowed lateness* for window operators. Allowed lateness 
+specifies by how much time elements can be late before they are dropped, and its default value is 0. 
+Elements that arrive after the watermark has passed the end of the window but before it passes the end of 
+the window plus the allowed lateness, are still added to the window. Depending on the trigger used, 
+a late but not dropped element may cause the window to fire again. This is the case for the `EventTimeTrigger`.
+
+In order to make this work, Flink keeps the state of windows until their allowed lateness expires. Once this happens, Flink removes the window and deletes its state, as 
+also described in the [Window Lifecycle](#window-lifecycle) section.
+
+<span class="label label-info">Default</span> By default, the allowed lateness is set to
+`0`. That is, elements that arrive behind the watermark will be dropped.
+
+You can specify an allowed lateness like this:
 
 <div class="codetabs" markdown="1">
 <div data-lang="java" markdown="1">
@@ -748,7 +612,9 @@ The basic structure of a non-keyed windowed transformation is as follows:
 DataStream<T> input = ...;
 
 input
-    .windowAll(<window assigner>)
+    .keyBy(<key selector>)
+    .window(<window assigner>)
+    .allowedLateness(<time>)
     .<windowed transformation>(<window function>);
 {% endhighlight %}
 </div>
@@ -758,8 +624,33 @@ input
 val input: DataStream[T] = ...
 
 input
-    .windowAll(<window assigner>)
+    .keyBy(<key selector>)
+    .window(<window assigner>)
+    .allowedLateness(<time>)
     .<windowed transformation>(<window function>)
 {% endhighlight %}
 </div>
 </div>
+
+<span class="label label-info">Note</span> When using the `GlobalWindows` window assigner no
+data is ever considered late because the end timestamp of the global window is `Long.MAX_VALUE`.
+
+### Late elements considerations
+
+When specifying an allowed lateness greater than 0, the window along with its content is kept after the watermark passes
+the end of the window. In these cases, when a late but not dropped element arrives, it could trigger another firing for the 
+window. These firings are called `late firings`, as they are triggered by late events and in contrast to the `main firing` 
+which is the first firing of the window. In case of session windows, late firings can further lead to merging of windows,
+as they may "bridge" the gap between two pre-existing, unmerged windows.
+
+<span class="label label-info">Attention</span> You should be aware that the elements emitted by a late firing should be treated as updated results of a previous computation, i.e., your data stream will contain multiple results for the same computation. Depending on your application, you need to take these duplicated results into account or deduplicate them.
+
+## Useful state size considerations
+
+Windows can be defined over long periods of time (such as days, weeks, or months) and therefore accumulate very large state. There are a couple of rules to keep in mind when estimating the storage requirements of your windowing computation:
+ 
+1. Flink creates one copy of each element per window to which it belongs. Given this, tumbling windows keep one copy of each element (an element belongs to exactly window unless it is dropped late). In contrast, sliding windows create several of each element, as explained in the [Window Assigners](#window-assigners) section. Hence, a sliding window of size 1 day and slide 1 second might not be a good idea.
+
+2. `FoldFunction` and `ReduceFunction` can significantly reduce the storage requirements, as they eagerly aggregate elements and store only one value per window. In contrast, just using a `WindowFunction` requires accumulating all elements.
+
+3. Using an `Evictor` prevents any pre-aggregation, as all the elements of a window have to be passed through the evictor before applying the computation (see [Evictors](#evictors)).

[3/7] flink git commit: [FLINK-5529] Update Window Documentation Figures

[al...@apache.org]

http://git-wip-us.apache.org/repos/asf/flink/blob/9e5ebe40/docs/fig/session-windows.svg
----------------------------------------------------------------------
diff --git a/docs/fig/session-windows.svg b/docs/fig/session-windows.svg
index 92785c7..6904221 100644
--- a/docs/fig/session-windows.svg
+++ b/docs/fig/session-windows.svg
@@ -18,5 +18,5 @@ specific language governing permissions and limitations
 under the License.
 -->
 
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