http://git-wip-us.apache.org/repos/asf/flink/blob/844c874b/docs/dev/windows.md ---------------------------------------------------------------------- diff --git a/docs/dev/windows.md b/docs/dev/windows.md new file mode 100644 index 0000000..084a2ee --- /dev/null +++ b/docs/dev/windows.md @@ -0,0 +1,677 @@ +--- +title: "Windows" +nav-parent_id: dev +nav-id: windows +nav-pos: 3 +--- +<!-- +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]({{ site.baseurl }}/dev/api_concepts.html#specifying-keys)), +a *window assigner* and a *window function*. The *key* divides the infinite, non-keyed, stream +into logical keyed streams while the *window assigner* assigns elements to finite per-key windows. +Finally, the *window function* is used to process the elements of each window. + +The basic structure of a windowed transformation is thus as follows: + +<div class="codetabs" markdown="1"> +<div data-lang="java" markdown="1"> +{% highlight java %} +DataStream<T> input = ...; + +input + .keyBy(<key selector>) + .window(<window assigner>) + .<windowed transformation>(<window function>); +{% endhighlight %} +</div> + +<div data-lang="scala" markdown="1"> +{% highlight scala %} +val input: DataStream[T] = ... + +input + .keyBy(<key selector>) + .window(<window assigner>) + .<windowed transformation>(<window function>) +{% endhighlight %} +</div> +</div> + +We will cover [window assigners](#window-assigners) in a separate section below. + +The window transformation can be one of `reduce()`, `fold()` or `apply()`. Which respectively +takes a `ReduceFunction`, `FoldFunction` or `WindowFunction`. We describe each of these ways +of specifying a windowed transformation in detail below: [window functions](#window-functions). + +For more advanced use cases you can also specify a `Trigger` that determines when exactly a window +is being considered as *ready for processing*. These will be covered in more detail in +[triggers](#triggers). + +## Window Assigners + +The window assigner specifies how elements of the stream are divided into finite slices. Flink comes +with pre-implemented window assigners for the most typical use cases, namely *tumbling windows*, +*sliding windows*, *session windows* and *global windows*, but you can implement your own by +extending the `WindowAssigner` class. All the built-in window assigners, except for the global +windows one, assign elements to windows based on time, which can either be processing time or event +time. Please take a look at our section on [event time]({{ site.baseurl }}/dev/event_time.html) for more +information about how Flink deals with time. + +Let's first look at how each of these window assigners works before looking at how they can be used +in a Flink program. We will be using abstract figures to visualize the workings of each assigner: +in the following, the purple circles are elements of the stream, they are partitioned +by some key (in this case *user 1*, *user 2* and *user 3*) and the x-axis shows the progress +of time. + +### Global Windows + +Global windows are a way of specifying that we don't want to subdivide our elements into windows. +Each element is assigned to one single per-key *global window*. +This windowing scheme is only useful if you also specify a custom [trigger](#triggers). Otherwise, +no computation is ever going to be performed, as the global window does not have a natural end at +which we could process the aggregated elements. + +<img src="{{ site.baseurl }}/fig/non-windowed.svg" class="center" style="width: 80%;" /> + +### Tumbling Windows + +A *tumbling windows* assigner assigns elements to fixed length, non-overlapping windows of a +specified *window size*.. For example, if you specify a window size of 5 minutes, the window +function will get 5 minutes worth of elements in each invocation. + +<img src="{{ site.baseurl }}/fig/tumbling-windows.svg" class="center" style="width: 80%;" /> + +### Sliding Windows + +The *sliding windows* assigner assigns elements to windows of fixed length equal to *window size*, +as the tumbling windows assigner, but in this case, windows can be overlapping. The size of the +overlap is defined by the user-specified parameter *window slide*. As windows are overlapping, an +element can be assigned to multiple windows + +For example, you could have windows of size 10 minutes that slide by 5 minutes. With this you get 10 +minutes worth of elements in each invocation of the window function and it will be invoked for every +5 minutes of data. + +<img src="{{ site.baseurl }}/fig/sliding-windows.svg" class="center" style="width: 80%;" /> + +### Session Windows + +The *session windows* assigner is ideal for cases where the window boundaries need to adjust to the +incoming data. Both the *tumbling windows* and *sliding windows* assigner assign elements to windows +that start at fixed time points and have a fixed *window size*. With session windows it is possible +to have windows that start at individual points in time for each key and that end once there has +been a certain period of inactivity. The configuration parameter is the *session gap* that specifies +how long to wait for new data before considering a session as closed. + +<img src="{{ site.baseurl }}/fig/session-windows.svg" class="center" style="width: 80%;" /> + +### Specifying a Window Assigner + +The built-in window assigners (except `GlobalWindows`) come in two versions. One for processing-time +windowing and one for event-time windowing. The processing-time assigners assign elements to +windows based on the current clock of the worker machines while the event-time assigners assign +windows based on the timestamps of elements. Please have a look at +[event time]({{ site.baseurl }}/dev/event_time.html) to learn about the difference between processing time +and event time and about how timestamps can be assigned to elements. + +The following code snippets show how each of the window assigners can be used in a program: + +<div class="codetabs" markdown="1"> +<div data-lang="java" markdown="1"> +{% highlight java %} +DataStream<T> input = ...; + +// tumbling event-time windows +input + .keyBy(<key selector>) + .window(TumblingEventTimeWindows.of(Time.seconds(5))) + .<windowed transformation>(<window function>); + +// sliding event-time windows +input + .keyBy(<key selector>) + .window(SlidingEventTimeWindows.of(Time.seconds(10), Time.seconds(5))) + .<windowed transformation>(<window function>); + +// event-time session windows +input + .keyBy(<key selector>) + .window(EventTimeSessionWindows.withGap(Time.minutes(10))) + .<windowed transformation>(<window function>); + +// tumbling processing-time windows +input + .keyBy(<key selector>) + .window(TumblingProcessingTimeWindows.of(Time.seconds(5))) + .<windowed transformation>(<window function>); + +// sliding processing-time windows +input + .keyBy(<key selector>) + .window(SlidingProcessingTimeWindows.of(Time.seconds(10), Time.seconds(5))) + .<windowed transformation>(<window function>); + +// processing-time session windows +input + .keyBy(<key selector>) + .window(ProcessingTimeSessionWindows.withGap(Time.minutes(10))) + .<windowed transformation>(<window function>); + +// global windows +input + .keyBy(<key selector>) + .window(GlobalWindows.create()) + .<windowed transformation>(<window function>); +{% endhighlight %} +</div> + +<div data-lang="scala" markdown="1"> +{% highlight scala %} +val input: DataStream[T] = ... + +// tumbling event-time windows +input + .keyBy(<key selector>) + .window(TumblingEventTimeWindows.of(Time.seconds(5))) + .<windowed transformation>(<window function>) + +// sliding event-time windows +input + .keyBy(<key selector>) + .window(SlidingEventTimeWindows.of(Time.seconds(10), Time.seconds(5))) + .<windowed transformation>(<window function>) + +// event-time session windows +input + .keyBy(<key selector>) + .window(EventTimeSessionWindows.withGap(Time.minutes(10))) + .<windowed transformation>(<window function>) + +// tumbling processing-time windows +input + .keyBy(<key selector>) + .window(TumblingProcessingTimeWindows.of(Time.seconds(5))) + .<windowed transformation>(<window function>) + +// sliding processing-time windows +input + .keyBy(<key selector>) + .window(SlidingProcessingTimeWindows.of(Time.seconds(10), Time.seconds(5))) + .<windowed transformation>(<window function>) + +// processing-time session windows +input + .keyBy(<key selector>) + .window(ProcessingTimeSessionWindows.withGap(Time.minutes(10))) + .<windowed transformation>(<window function>) + +// global windows +input + .keyBy(<key selector>) + .window(GlobalWindows.create()) +{% endhighlight %} +</div> +</div> + +Note, how we can specify a time interval by using one of `Time.milliseconds(x)`, `Time.seconds(x)`, +`Time.minutes(x)`, and so on. + +The time-based window assigners also take an optional `offset` parameter that can be used to +change the alignment of windows. For example, without offsets hourly windows are aligned +with epoch, that is you will get windows such as `1:00 - 1:59`, `2:00 - 2:59` and so on. If you +want to change that you can give an offset. With an offset of 15 minutes you would, for example, +get `1:15 - 2:14`, `2:15 - 3:14` etc. Another important use case for offsets is when you +want to have daily windows and live in a timezone other than UTC-0. For example, in China +you would have to specify an offset of `Time.hours(-8)`. + +This example shows how an offset can be specified for tumbling event time windows (the other +windows work accordingly): +<div class="codetabs" markdown="1"> +<div data-lang="java" markdown="1"> +{% highlight java %} +DataStream<T> input = ...; + +// tumbling event-time windows +input + .keyBy(<key selector>) + .window(TumblingEventTimeWindows.of(Time.days(1), Time.hours(-8))) + .<windowed transformation>(<window function>); +{% endhighlight %} +</div> + +<div data-lang="scala" markdown="1"> +{% highlight scala %} +val input: DataStream[T] = ... + +// tumbling event-time windows +input + .keyBy(<key selector>) + .window(TumblingEventTimeWindows.of(Time.days(1), Time.hours(-8))) + .<windowed transformation>(<window function>) +{% endhighlight %} +</div> +</div> + +## Window 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>
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