Github user uce commented on a diff in the pull request:
https://github.com/apache/flink/pull/2154#discussion_r69157901
--- Diff: docs/apis/streaming/windows.md ---
@@ -24,1023 +24,608 @@ 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.
+
* This will be replaced by the TOC
{:toc}
-## Windows on Keyed Data Streams
-
-Flink offers a variety of methods for defining windows on a `KeyedStream`.
All of these group elements *per key*,
-i.e., each window will contain elements with the same key value.
+## Basics
-### Basic Window Constructs
+For a windowed transformations you must at least specify a *key*
+(see [specifying keys](apis/common/index.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.
-Flink offers a general window mechanism that provides flexibility, as well
as a number of pre-defined windows
-for common use cases. See first if your use case can be served by the
pre-defined windows below before moving
-to defining your own windows.
+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 = ...;
-<br />
-
-<table class="table table-bordered">
- <thead>
- <tr>
- <th class="text-left" style="width: 25%">Transformation</th>
- <th class="text-center">Description</th>
- </tr>
- </thead>
- <tbody>
- <tr>
- <td><strong>Tumbling time window</strong><br>KeyedStream →
WindowedStream</td>
- <td>
- <p>
- Defines a window of 5 seconds, that "tumbles". This means that
elements are
- grouped according to their timestamp in groups of 5 second
duration, and every element belongs to exactly one window.
- The notion of time is specified by the selected TimeCharacteristic
(see <a href="{{ site.baseurl }}/apis/streaming/event_time.html">time</a>).
- {% highlight java %}
-keyedStream.timeWindow(Time.seconds(5));
- {% endhighlight %}
- </p>
- </td>
- </tr>
- <tr>
- <td><strong>Sliding time window</strong><br>KeyedStream →
WindowedStream</td>
- <td>
- <p>
- Defines a window of 5 seconds, that "slides" by 1 second.
This means that elements are
- grouped according to their timestamp in groups of 5 second
duration, and elements can belong to more than
- one window (since windows overlap by at most 4 seconds)
- The notion of time is specified by the selected
TimeCharacteristic (see <a href="{{ site.baseurl
}}/apis/streaming/event_time.html">time</a>).
- {% highlight java %}
-keyedStream.timeWindow(Time.seconds(5), Time.seconds(1));
- {% endhighlight %}
- </p>
- </td>
- </tr>
- <tr>
- <td><strong>Tumbling count window</strong><br>KeyedStream →
WindowedStream</td>
- <td>
- <p>
- Defines a window of 1000 elements, that "tumbles". This means
that elements are
- grouped according to their arrival time (equivalent to
processing time) in groups of 1000 elements,
- and every element belongs to exactly one window.
- {% highlight java %}
-keyedStream.countWindow(1000);
- {% endhighlight %}
- </p>
- </td>
- </tr>
- <tr>
- <td><strong>Sliding count window</strong><br>KeyedStream →
WindowedStream</td>
- <td>
- <p>
- Defines a window of 1000 elements, that "slides" every 100
elements. This means that elements are
- grouped according to their arrival time (equivalent to
processing time) in groups of 1000 elements,
- and every element can belong to more than one window (as windows
overlap by at most 900 elements).
- {% highlight java %}
-keyedStream.countWindow(1000, 100)
- {% endhighlight %}
- </p>
- </td>
- </tr>
- </tbody>
-</table>
-
+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] = ...
-<br />
-
-<table class="table table-bordered">
- <thead>
- <tr>
- <th class="text-left" style="width: 25%">Transformation</th>
- <th class="text-center">Description</th>
- </tr>
- </thead>
- <tbody>
- <tr>
- <td><strong>Tumbling time window</strong><br>KeyedStream →
WindowedStream</td>
- <td>
- <p>
- Defines a window of 5 seconds, that "tumbles". This means that
elements are
- grouped according to their timestamp in groups of 5 second
duration, and every element belongs to exactly one window.
- The notion of time is specified by the selected
TimeCharacteristic (see <a href="{{ site.baseurl
}}/apis/streaming/event_time.html">time</a>).
- {% highlight scala %}
-keyedStream.timeWindow(Time.seconds(5))
- {% endhighlight %}
- </p>
- </td>
- </tr>
- <tr>
- <td><strong>Sliding time window</strong><br>KeyedStream →
WindowedStream</td>
- <td>
- <p>
- Defines a window of 5 seconds, that "slides" by 1 second.
This means that elements are
- grouped according to their timestamp in groups of 5 second
duration, and elements can belong to more than
- one window (since windows overlap by at most 4 seconds)
- The notion of time is specified by the selected
TimeCharacteristic (see <a href="{{ site.baseurl
}}/apis/streaming/event_time.html">time</a>).
- {% highlight scala %}
-keyedStream.timeWindow(Time.seconds(5), Time.seconds(1))
- {% endhighlight %}
- </p>
- </td>
- </tr>
- <tr>
- <td><strong>Tumbling count window</strong><br>KeyedStream →
WindowedStream</td>
- <td>
- <p>
- Defines a window of 1000 elements, that "tumbles". This means
that elements are
- grouped according to their arrival time (equivalent to
processing time) in groups of 1000 elements,
- and every element belongs to exactly one window.
- {% highlight scala %}
-keyedStream.countWindow(1000)
- {% endhighlight %}
- </p>
- </td>
- </tr>
- <tr>
- <td><strong>Sliding count window</strong><br>KeyedStream →
WindowedStream</td>
- <td>
- <p>
- Defines a window of 1000 elements, that "slides" every 100
elements. This means that elements are
- grouped according to their arrival time (equivalent to
processing time) in groups of 1000 elements,
- and every element can belong to more than one window (as windows
overlap by at most 900 elements).
- {% highlight scala %}
-keyedStream.countWindow(1000, 100)
- {% endhighlight %}
- </p>
- </td>
- </tr>
- </tbody>
-</table>
-
+input
+ .keyBy(<key selector>)
+ .window(<window assigner>)
+ .<windowed transformation>(<window function>)
+{% endhighlight %}
</div>
</div>
-### Advanced Window Constructs
+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.
+
+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="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.
-The general mechanism can define more powerful windows at the cost of more
verbose syntax. For example,
-below is a window definition where windows hold elements of the last 5
seconds and slides every 1 second,
-but the execution of the window function is triggered when 100 elements
have been added to the
-window, and every time execution is triggered, 10 elements are retained in
the window:
+<img src="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="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="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](/apis/streaming/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 %}
-keyedStream
- .window(SlidingEventTimeWindows.of(Time.seconds(5), Time.seconds(1))
- .trigger(CountTrigger.of(100))
- .evictor(CountEvictor.of(10));
+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 %}
-keyedStream
- .window(SlidingEventTimeWindows.of(Time.seconds(5), Time.seconds(1))
- .trigger(CountTrigger.of(100))
- .evictor(CountEvictor.of(10))
+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>
-The general recipe for building a custom window is to specify (1) a
`WindowAssigner`, (2) a `Trigger` (optionally),
-and (3) an `Evictor` (optionally).
+## Window Functions
-The `WindowAssigner` defines how incoming elements are assigned to
windows. A window is a logical group of elements
-that has a begin-value, and an end-value corresponding to a begin-time and
end-time. Elements with timestamp (according
-to some notion of time described above within these values are part of the
window).
+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).
-For example, the `SlidingEventTimeWindows`
-assigner in the code above defines a window of size 5 seconds, and a slide
of 1 second. Assume that
-time starts from 0 and is measured in milliseconds. Then, we have 6 windows
-that overlap: [0,5000], [1000,6000], [2000,7000], [3000, 8000], [4000,
9000], and [5000, 10000]. Each incoming
-element is assigned to the windows according to its timestamp. For
example, an element with timestamp 2000 will be
-assigned to the first three windows. Flink comes bundled with window
assigners that cover the most common use cases. You can write your
-own window types by extending the `WindowAssigner` class.
+The window function can be one of `ReduceFunction`, `FoldFunction` or
`WindowFunction`. The former
+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
+window and additional meta information about the window to which the
elements belong.
-<div class="codetabs" markdown="1">
+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
+`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` can be used in a program like this:
+
+<div class="codetabs" markdown="1">
<div data-lang="java" markdown="1">
-<table class="table table-bordered">
- <thead>
- <tr>
- <th class="text-left" style="width: 25%">Transformation</th>
- <th class="text-center">Description</th>
- </tr>
- </thead>
- <tbody>
- <tr>
- <td><strong>Global window</strong><br>KeyedStream →
WindowedStream</td>
- <td>
- <p>
- All incoming elements of a given key are assigned to the same
window.
- The window does not contain a default trigger, hence it will never
be triggered
- if a trigger is not explicitly specified.
- </p>
- {% highlight java %}
-stream.window(GlobalWindows.create());
- {% endhighlight %}
- </td>
- </tr>
- <tr>
- <td><strong>Tumbling event-time windows</strong><br>KeyedStream
→ WindowedStream</td>
- <td>
- <p>
- Incoming elements are assigned to a window of a certain size
(1 second below) based on
- their timestamp. Windows do not overlap, i.e., each element is
assigned to exactly one window.
- This assigner comes with a default trigger that fires for a
window when a
- watermark with value higher than its end-value is received.
- </p>
- {% highlight java %}
-stream.window(TumblingEventTimeWindows.of(Time.seconds(1)));
- {% endhighlight %}
- </td>
- </tr>
- <tr>
- <td><strong>Sliding event-time windows</strong><br>KeyedStream
→ WindowedStream</td>
- <td>
- <p>
- Incoming elements are assigned to a window of a certain size
(5 seconds below) based on
- their timestamp. Windows "slide" by the provided value (1
second in the example), and hence
- overlap. This assigner comes with a default trigger that fires
for a window when a
- watermark with value higher than its end-value is received.
- </p>
- {% highlight java %}
-stream.window(SlidingEventTimeWindows.of(Time.seconds(5),
Time.seconds(1)));
- {% endhighlight %}
- </td>
- </tr>
- <tr>
- <td><strong>Tumbling processing time
windows</strong><br>KeyedStream → WindowedStream</td>
- <td>
- <p>
- Incoming elements are assigned to a window of a certain size
(1 second below) based on
- the current processing time. Windows do not overlap, i.e.,
each element is assigned to exactly one window.
- This assigner comes with a default trigger that fires for a
window a window when the current
- processing time exceeds its end-value.
- </p>
- {% highlight java %}
-stream.window(TumblingProcessingTimeWindows.of(Time.seconds(1)));
- {% endhighlight %}
- </td>
- </tr>
- <tr>
- <td><strong>Sliding processing time
windows</strong><br>KeyedStream → WindowedStream</td>
- <td>
- <p>
- Incoming elements are assigned to a window of a certain size
(5 seconds below) based on
- their timestamp. Windows "slide" by the provided value (1
second in the example), and hence
- overlap. This assigner comes with a default trigger that fires
for a window a window when the current
- processing time exceeds its end-value.
- </p>
- {% highlight java %}
-stream.window(SlidingProcessingTimeWindows.of(Time.seconds(5),
Time.seconds(1)));
- {% endhighlight %}
- </td>
- </tr>
- <tr>
- <td><strong>Event-time Session windows</strong><br>KeyedStream
→ WindowedStream</td>
- <td>
- <p>
- Incoming elements are assigned to sessions based on a session
gap interval (5 seconds in the example below).
- Elements whose timestamp differs by more than the session gap
are assigned to different sessions. If there are
- consecutive elements which are less than the session gap apart
then these will also be put into the same session, i.e. elements
- can be connected into a session by intermediate elements.
- </p>
- {% highlight scala %}
-keyedStream.window(EventTimeSessionWindows.withGap(Time.seconds(5)));
- {% endhighlight %}
- </td>
- </tr>
- <tr>
- <td><strong>Processing time Session
windows</strong><br>KeyedStream → WindowedStream</td>
- <td>
- <p>
- This is similar to event-time session windows but works on the
current processing
- time instead of the timestamp of elements
- </p>
- {% highlight scala %}
-keyedStream.window(ProcessingTimeSessionWindows.withGap(Time.seconds(5)));
- {% endhighlight %}
- </td>
- </tr>
- </tbody>
-</table>
+{% highlight java %}
+DataStream<Tuple2<String, Long>> input = ...;
+
+input
+ .keyBy(<key selector>)
+ .window(<window assigner>)
+ .reduce(new ReduceFunction<Tuple2<String, Long>> {
+ public Tuple2<String, Long> reduce(Tuple2<String, Long> v1,
Tuple2<String, Long> v2) {
+ return new Tuple2<>(v1.f0, v1.f1 + v2.f1);
+ }
+ });
+{% endhighlight %}
</div>
<div data-lang="scala" markdown="1">
-<table class="table table-bordered">
- <thead>
- <tr>
- <th class="text-left" style="width: 25%">Transformation</th>
- <th class="text-center">Description</th>
- </tr>
- </thead>
- <tbody>
- <tr>
- <td><strong>Global window</strong><br>KeyedStream →
WindowedStream</td>
- <td>
- <p>
- All incoming elements of a given key are assigned to the same
window.
- The window does not contain a default trigger, hence it will never
be triggered
- if a trigger is not explicitly specified.
- </p>
- {% highlight scala %}
-stream.window(GlobalWindows.create)
- {% endhighlight %}
- </td>
- </tr>
- <tr>
- <td><strong>Tumbling event-time windows</strong><br>KeyedStream
→ WindowedStream</td>
- <td>
- <p>
- Incoming elements are assigned to a window of a certain size
(1 second below) based on
- their timestamp. Windows do not overlap, i.e., each element is
assigned to exactly one window.
- This assigner comes with a default trigger that fires for a
window when a
- watermark with value higher than its end-value is received.
- </p>
- {% highlight scala %}
-stream.window(TumblingEventTimeWindows.of(Time.seconds(1)))
- {% endhighlight %}
- </td>
- </tr>
- <tr>
- <td><strong>Sliding event-time windows</strong><br>KeyedStream
→ WindowedStream</td>
- <td>
- <p>
- Incoming elements are assigned to a window of a certain size
(5 seconds below) based on
- their timestamp. Windows "slide" by the provided value (1
second in the example), and hence
- overlap. This assigner comes with a default trigger that fires
for a window when a
- watermark with value higher than its end-value is received.
- </p>
- {% highlight scala %}
-stream.window(SlidingEventTimeWindows.of(Time.seconds(5), Time.seconds(1)))
- {% endhighlight %}
- </td>
- </tr>
- <tr>
- <td><strong>Tumbling processing time
windows</strong><br>KeyedStream → WindowedStream</td>
- <td>
- <p>
- Incoming elements are assigned to a window of a certain size
(1 second below) based on
- the current processing time. Windows do not overlap, i.e.,
each element is assigned to exactly one window.
- This assigner comes with a default trigger that fires for a
window a window when the current
- processing time exceeds its end-value.
-
- </p>
- {% highlight scala %}
-stream.window(TumblingProcessingTimeWindows.of(Time.seconds(1)))
- {% endhighlight %}
- </td>
- </tr>
- <tr>
- <td><strong>Sliding processing time
windows</strong><br>KeyedStream → WindowedStream</td>
- <td>
- <p>
- Incoming elements are assigned to a window of a certain size
(5 seconds below) based on
- their timestamp. Windows "slide" by the provided value (1
second in the example), and hence
- overlap. This assigner comes with a default trigger that fires
for a window a window when the current
- processing time exceeds its end-value.
- </p>
- {% highlight scala %}
-stream.window(SlidingProcessingTimeWindows.of(Time.seconds(5),
Time.seconds(1)))
- {% endhighlight %}
- </td>
- </tr>
- <tr>
- <td><strong>Event-time Session windows</strong><br>KeyedStream
→ WindowedStream</td>
- <td>
- <p>
- Incoming elements are assigned to sessions based on a session
gap interval (5 seconds in the example below).
- Elements whose timestamp differs by more than the session gap
are assigned to different sessions. If there are
- consecutive elements which are less than the session gap apart
then these will also be put into the same session, i.e. elements
- can be connected into a session by intermediate elements.
- </p>
- {% highlight scala %}
-keyedStream.window(EventTimeSessionWindows.withGap(Time.seconds(5)))
- {% endhighlight %}
- </td>
- </tr>
- <tr>
- <td><strong>Processing time Session
windows</strong><br>KeyedStream → WindowedStream</td>
- <td>
- <p>
- This is similar to event-time session windows but works on the
current processing
- time instead of the timestamp of elements
- </p>
- {% highlight scala %}
-keyedStream.window(ProcessingTimeSessionWindows.withGap(Time.seconds(5)))
- {% endhighlight %}
- </td>
- </tr>
- </tbody>
-</table>
-</div>
+{% highlight scala %}
+val input: DataStream[(String, Long)] = ...
+input
+ .keyBy(<key selector>)
+ .window(<window assigner>)
+ .reduce { (v1, v2) => (v1._1, v1._2 + v2._2) }
+{% endhighlight %}
+</div>
</div>
-The `Trigger` specifies when the function that comes after the window
clause (e.g., `sum`, `count`) is evaluated ("fires")
-for each window. If a trigger is not specified, a default trigger for each
window type is used (that is part of the
-definition of the `WindowAssigner`). Flink comes bundled with a set of
triggers if the ones that windows use by
-default do not fit the application. You can write your own trigger by
implementing the `Trigger` interface. Note that
-specifying a trigger will override the default trigger of the window
assigner.
+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.
-<div class="codetabs" markdown="1">
+### FoldFunction
+A fold function can be specified like this:
+
+<div class="codetabs" markdown="1">
<div data-lang="java" markdown="1">
-<table class="table table-bordered">
- <thead>
- <tr>
- <th class="text-left" style="width: 25%">Transformation</th>
- <th class="text-center">Description</th>
- </tr>
- </thead>
- <tbody>
- <tr>
- <td><strong>Processing time trigger</strong></td>
- <td>
- <p>
- A window is fired when the current processing time exceeds its
end-value.
- The elements on the triggered window are henceforth discarded.
- </p>
-{% highlight java %}
-windowedStream.trigger(ProcessingTimeTrigger.create());
-{% endhighlight %}
- </td>
- </tr>
- <tr>
- <td><strong>Watermark trigger</strong></td>
- <td>
- <p>
- A window is fired when a watermark with value that exceeds the
window's end-value has been received.
- The elements on the triggered window are henceforth discarded.
- </p>
{% highlight java %}
-windowedStream.trigger(EventTimeTrigger.create());
+DataStream<Tuple2<String, Long>> input = ...;
+
+input
+ .keyBy(<key selector>)
+ .window(<window assigner>)
+ .fold("", new FoldFunction<Tuple2<String, Long>, String>> {
+ public String fold(String acc, Tuple2<String, Long> value) {
+ return acc + value.f1;
+ }
+ });
{% endhighlight %}
- </td>
- </tr>
- <tr>
- <td><strong>Continuous processing time trigger</strong></td>
- <td>
- <p>
- A window is periodically considered for being fired (every 5
seconds in the example).
- The window is actually fired only when the current processing time
exceeds its end-value.
- The elements on the triggered window are retained.
- </p>
-{% highlight java %}
-windowedStream.trigger(ContinuousProcessingTimeTrigger.of(Time.seconds(5)));
-{% endhighlight %}
- </td>
- </tr>
- <tr>
- <td><strong>Continuous watermark time trigger</strong></td>
- <td>
- <p>
- A window is periodically considered for being fired (every 5
seconds in the example).
- A window is actually fired when a watermark with value that
exceeds the window's end-value has been received.
- The elements on the triggered window are retained.
- </p>
-{% highlight java %}
-windowedStream.trigger(ContinuousEventTimeTrigger.of(Time.seconds(5)));
+</div>
+
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+val input: DataStream[(String, Long)] = ...
+
+input
+ .keyBy(<key selector>)
+ .window(<window assigner>)
+ .fold("") { (acc, v) => acc + v._2 }
{% endhighlight %}
- </td>
- </tr>
- <tr>
- <td><strong>Count trigger</strong></td>
- <td>
- <p>
- A window is fired when it has more than a certain number of
elements (1000 below).
- The elements of the triggered window are retained.
- </p>
+</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.
+
+### WindowFunction - The Generic Case
+
+Using a `WindowFunction` provides 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:
+
+<div class="codetabs" markdown="1">
+<div data-lang="java" markdown="1">
{% highlight java %}
-windowedStream.trigger(CountTrigger.of(1000));
+public interface WindowFunction<IN, OUT, KEY, W extends Window> extends
Function, Serializable {
+
+ /**
+ * Evaluates the window and outputs none or several elements.
+ *
+ * @param key The key for which this window is evaluated.
+ * @param window The window that is being evaluated.
+ * @param input The elements in the window being evaluated.
+ * @param out A collector for emitting elements.
+ *
+ * @throws Exception The function may throw exceptions to fail the
program and trigger recovery.
+ */
+ void apply(KEY key, W window, Iterable<IN> input, Collector<OUT> out)
throws Exception;
+}
{% endhighlight %}
- </td>
- </tr>
- <tr>
- <td><strong>Purging trigger</strong></td>
- <td>
- <p>
- Takes any trigger as an argument and forces the triggered window
elements to be
- "purged" (discarded) after triggering.
- </p>
-{% highlight java %}
-windowedStream.trigger(PurgingTrigger.of(CountTrigger.of(1000)));
+</div>
+
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+public interface WindowFunction<IN, OUT, KEY, W extends Window> extends
Function, Serializable {
+
+ /**
+ * Evaluates the window and outputs none or several elements.
+ *
+ * @param key The key for which this window is evaluated.
+ * @param window The window that is being evaluated.
+ * @param input The elements in the window being evaluated.
+ * @param out A collector for emitting elements.
+ *
+ * @throws Exception The function may throw exceptions to fail the
program and trigger recovery.
+ */
+ void apply(KEY key, W window, Iterable<IN> input, Collector<OUT> out)
throws Exception;
+}
{% endhighlight %}
- </td>
- </tr>
- <tr>
- <td><strong>Delta trigger</strong></td>
- <td>
- <p>
- A window is periodically considered for being fired (every 5000
milliseconds in the example).
- A window is actually fired when the value of the last added
element exceeds the value of
- the first element inserted in the window according to a
`DeltaFunction`.
- </p>
+</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`.
+
+<div class="codetabs" markdown="1">
+<div data-lang="java" markdown="1">
{% highlight java %}
-windowedStream.trigger(new DeltaTrigger.of(5000.0, new
DeltaFunction<Double>() {
- @Override
- public double getDelta (Double old, Double new) {
- return (new - old > 0.01);
+DataStream<Tuple2<String, Long>> input = ...;
+
+input
+ .keyBy(<key selector>)
+ .window(<window assigner>)
+ .apply(new MyWindowFunction());
+
+/* ... */
+
+public class MyWindowFunction implements WindowFunction<Tuple<String,
Long>, String, String, TimeWindow> {
+
+ void apply(String key, TimeWindow window, Iterable<Tuple<String, Long>>
input, Collector<String> out) {
+ long count = 0;
+ for (Tuple<String, Long> in: input) {
+ count++;
}
-}));
+ out.collect("Window: " + window + "count: " + count);
+ }
+}
+
{% endhighlight %}
- </td>
- </tr>
- </tbody>
-</table>
</div>
-
<div data-lang="scala" markdown="1">
-<table class="table table-bordered">
- <thead>
- <tr>
- <th class="text-left" style="width: 25%">Transformation</th>
- <th class="text-center">Description</th>
- </tr>
- </thead>
- <tbody>
- <tr>
- <td><strong>Processing time trigger</strong></td>
- <td>
- <p>
- A window is fired when the current processing time exceeds its
end-value.
- The elements on the triggered window are henceforth discarded.
- </p>
-{% highlight scala %}
-windowedStream.trigger(ProcessingTimeTrigger.create);
-{% endhighlight %}
- </td>
- </tr>
- <tr>
- <td><strong>Watermark trigger</strong></td>
- <td>
- <p>
- A window is fired when a watermark with value that exceeds the
window's end-value has been received.
- The elements on the triggered window are henceforth discarded.
- </p>
-{% highlight scala %}
-windowedStream.trigger(EventTimeTrigger.create);
-{% endhighlight %}
- </td>
- </tr>
- <tr>
- <td><strong>Continuous processing time trigger</strong></td>
- <td>
- <p>
- A window is periodically considered for being fired (every 5
seconds in the example).
- The window is actually fired only when the current processing time
exceeds its end-value.
- The elements on the triggered window are retained.
- </p>
-{% highlight scala %}
-windowedStream.trigger(ContinuousProcessingTimeTrigger.of(Time.seconds(5)));
-{% endhighlight %}
- </td>
- </tr>
- <tr>
- <td><strong>Continuous watermark time trigger</strong></td>
- <td>
- <p>
- A window is periodically considered for being fired (every 5
seconds in the example).
- A window is actually fired when a watermark with value that
exceeds the window's end-value has been received.
- The elements on the triggered window are retained.
- </p>
-{% highlight scala %}
-windowedStream.trigger(ContinuousEventTimeTrigger.of(Time.seconds(5)));
-{% endhighlight %}
- </td>
- </tr>
- <tr>
- <td><strong>Count trigger</strong></td>
- <td>
- <p>
- A window is fired when it has more than a certain number of
elements (1000 below).
- The elements of the triggered window are retained.
- </p>
{% highlight scala %}
-windowedStream.trigger(CountTrigger.of(1000));
+val input: DataStream[(String, Long)] = ...
+
+input
+ .keyBy(<key selector>)
+ .window(<window assigner>)
+ .apply(new MyWindowFunction())
+
+/* ... */
+
+class MyWindowFunction extends WindowFunction[(String, Long), String,
String, TimeWindow] {
+
+ def apply(key: String, window: TimeWindow, input: Iterable[(String,
Long)], out: Collector[String]): () = {
+ var count = 0L
+ for (in <- input) {
+ count = count + 1
+ }
+ out.collect(s"Window $window count: $count")
+ }
+}
{% endhighlight %}
- </td>
- </tr>
- <tr>
- <td><strong>Purging trigger</strong></td>
- <td>
- <p>
- Takes any trigger as an argument and forces the triggered window
elements to be
- "purged" (discarded) after triggering.
- </p>
-{% highlight scala %}
-windowedStream.trigger(PurgingTrigger.of(CountTrigger.of(1000)));
+</div>
+</div>
+
+### WindowFunction with Incremental Aggregation
+
+A `WindowFunction` can be combined with either a `ReduceFunction` or a
`FoldFunction`. This allows
+to get the benefit of incremental window computation and also have the
additional meta information
+that writing a `WindowFunction` provides.
+
+This is an exampel that shows how incremental aggregation functions can be
combined with
--- End diff --
typo: exampel
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