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http://git-wip-us.apache.org/repos/asf/flink/blob/844c874b/docs/apis/streaming/windows.md ---------------------------------------------------------------------- diff --git a/docs/apis/streaming/windows.md b/docs/apis/streaming/windows.md deleted file mode 100644 index b9847a5..0000000 --- a/docs/apis/streaming/windows.md +++ /dev/null @@ -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 ---------------------------------------------------------------------- diff --git a/docs/apis/streaming_guide.md b/docs/apis/streaming_guide.md deleted file mode 100644 index a09cf64..0000000 --- a/docs/apis/streaming_guide.md +++ /dev/null @@ -1,26 +0,0 @@ ---- -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. ---> - -The *DataStream API guide* has been moved. Redirecting to [{{ site.baseurl }}/apis/streaming/index.html]({{ site.baseurl }}/apis/streaming/index.html) in 1 second.
