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Posted to commits@flink.apache.org by uc...@apache.org on 2016/08/24 09:26:54 UTC
[34/51] [partial] flink git commit: [FLINK-4317,
FLIP-3] [docs] Restructure docs
http://git-wip-us.apache.org/repos/asf/flink/blob/844c874b/docs/apis/streaming/tumbling-windows.svg
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diff --git a/docs/apis/streaming/tumbling-windows.svg b/docs/apis/streaming/tumbling-windows.svg
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-
http://git-wip-us.apache.org/repos/asf/flink/blob/844c874b/docs/apis/streaming/windows.md
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diff --git a/docs/apis/streaming/windows.md b/docs/apis/streaming/windows.md
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--- a/docs/apis/streaming/windows.md
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@@ -1,678 +0,0 @@
----
-title: "Windows"
-
-sub-nav-id: windows
-sub-nav-group: streaming
-sub-nav-pos: 4
----
-<!--
-Licensed to the Apache Software Foundation (ASF) under one
-or more contributor license agreements. See the NOTICE file
-distributed with this work for additional information
-regarding copyright ownership. The ASF licenses this file
-to you under the Apache License, Version 2.0 (the
-"License"); you may not use this file except in compliance
-with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an
-"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-KIND, either express or implied. See the License for the
-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}
-
-## Basics
-
-For a windowed transformation 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.
-
-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](/apis/streaming/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="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="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 %}
-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 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).
-
-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
-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
-`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">
-{% 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">
-{% 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>
-
-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.
-
-### FoldFunction
-
-A fold function can be specified like this:
-
-<div class="codetabs" markdown="1">
-<div data-lang="java" markdown="1">
-{% highlight java %}
-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 %}
-</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 %}
-</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 %}
-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 %}
-</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 %}
-</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 %}
-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 %}
-</div>
-
-<div data-lang="scala" markdown="1">
-{% highlight scala %}
-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 %}
-</div>
-</div>
-
-### WindowFunction with Incremental Aggregation
-
-A `WindowFunction` can be combined with either a `ReduceFunction` or a `FoldFunction`. When doing
-this, the `ReduceFunction`/`FoldFunction` will be used to incrementally aggregate elements as they
-arrive while the `WindowFunction` will be provided with the aggregated result when the window is
-ready for processing. 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 example that shows how incremental aggregation functions can be combined with
-a `WindowFunction`.
-
-<div class="codetabs" markdown="1">
-<div data-lang="java" markdown="1">
-{% highlight java %}
-DataStream<Tuple2<String, Long>> input = ...;
-
-// for folding incremental computation
-input
- .keyBy(<key selector>)
- .window(<window assigner>)
- .apply(<initial value>, new MyFoldFunction(), new MyWindowFunction());
-
-// for reducing incremental computation
-input
- .keyBy(<key selector>)
- .window(<window assigner>)
- .apply(new MyReduceFunction(), new MyWindowFunction());
-{% endhighlight %}
-</div>
-
-<div data-lang="scala" markdown="1">
-{% highlight scala %}
-val input: DataStream[(String, Long)] = ...
-
-// for folding incremental computation
-input
- .keyBy(<key selector>)
- .window(<window assigner>)
- .apply(<initial value>, new MyFoldFunction(), new MyWindowFunction())
-
-// for reducing incremental computation
-input
- .keyBy(<key selector>)
- .window(<window assigner>)
- .apply(new MyReduceFunction(), new MyWindowFunction())
-{% endhighlight %}
-</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](/apis/streaming/event_time.html) and especially
-[late elements](/apis/streaming/event_time.html#late-elements) for a more thorough discussion of
-how Flink deals with event time.
-
-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.
-
-<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">
-{% 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>
-
-<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`.
-
-## Triggers
-
-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.
-
-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.
-
-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:
-
-<div class="codetabs" markdown="1">
-<div data-lang="java" markdown="1">
-{% highlight java %}
-DataStream<T> input = ...;
-
-input
- .keyBy(<key selector>)
- .window(<window assigner>)
- .trigger(<trigger>)
- .<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>)
- .trigger(<trigger>)
- .<windowed transformation>(<window function>)
-{% endhighlight %}
-</div>
-</div>
-
-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> By specifying a trigger using `trigger()` you
-are overwriting the default trigger of a `WindowAssigner`. For example, if you specify a
-`CountTrigger` for `TumblingEventTimeWindows` you will no longer get window firings based on the
-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" %}.
-
-## Non-keyed Windowing
-
-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.
-
-<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 basic structure of a non-keyed windowed transformation is as follows:
-
-<div class="codetabs" markdown="1">
-<div data-lang="java" markdown="1">
-{% highlight java %}
-DataStream<T> input = ...;
-
-input
- .windowAll(<window assigner>)
- .<windowed transformation>(<window function>);
-{% endhighlight %}
-</div>
-
-<div data-lang="scala" markdown="1">
-{% highlight scala %}
-val input: DataStream[T] = ...
-
-input
- .windowAll(<window assigner>)
- .<windowed transformation>(<window function>)
-{% endhighlight %}
-</div>
-</div>
http://git-wip-us.apache.org/repos/asf/flink/blob/844c874b/docs/apis/streaming_guide.md
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----
-title: DataStream API
----
-
-<meta http-equiv="refresh" content="1; url={{ site.baseurl }}/apis/streaming/index.html" />
-
-<!--
-Licensed to the Apache Software Foundation (ASF) under one
-or more contributor license agreements. See the NOTICE file
-distributed with this work for additional information
-regarding copyright ownership. The ASF licenses this file
-to you under the Apache License, Version 2.0 (the
-"License"); you may not use this file except in compliance
-with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an
-"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-KIND, either express or implied. See the License for the
-specific language governing permissions and limitations
-under the License.
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-
-The *DataStream API guide* has been moved. Redirecting to [{{ site.baseurl }}/apis/streaming/index.html]({{ site.baseurl }}/apis/streaming/index.html) in 1 second.