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From gyf...@apache.org
Subject [1/3] flink git commit: [FLINK-1429] [streaming] Scala programming guide update: intro & operators, minor fixes
Date Sun, 08 Mar 2015 19:32:58 GMT
Repository: flink
Updated Branches:
  refs/heads/master fb62f6be4 -> 7582390c1


[FLINK-1429] [streaming] Scala programming guide update: intro & operators, minor fixes

This closes #463


Project: http://git-wip-us.apache.org/repos/asf/flink/repo
Commit: http://git-wip-us.apache.org/repos/asf/flink/commit/7582390c
Tree: http://git-wip-us.apache.org/repos/asf/flink/tree/7582390c
Diff: http://git-wip-us.apache.org/repos/asf/flink/diff/7582390c

Branch: refs/heads/master
Commit: 7582390c13aa1d492c20debc1c882ef56ec39d8a
Parents: bd1b916
Author: mbalassi <mbalassi@apache.org>
Authored: Sat Mar 7 15:06:41 2015 +0100
Committer: Gábor Hermann <reckoner42@gmail.com>
Committed: Sun Mar 8 20:24:41 2015 +0100

----------------------------------------------------------------------
 docs/programming_guide.md                       |   2 +-
 docs/streaming_guide.md                         | 596 +++++++++++++++----
 .../flink/streaming/api/scala/DataStream.scala  |  21 +-
 3 files changed, 473 insertions(+), 146 deletions(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/flink/blob/7582390c/docs/programming_guide.md
----------------------------------------------------------------------
diff --git a/docs/programming_guide.md b/docs/programming_guide.md
index 1f4e99e..ee1138c 100644
--- a/docs/programming_guide.md
+++ b/docs/programming_guide.md
@@ -58,7 +58,7 @@ public class WordCountExample {
     public static void main(String[] args) throws Exception {
         final ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
 
-	    DataSet<String> text = env.fromElements(
+        DataSet<String> text = env.fromElements(
             "Who's there?",
             "I think I hear them. Stand, ho! Who's there?");
 

http://git-wip-us.apache.org/repos/asf/flink/blob/7582390c/docs/streaming_guide.md
----------------------------------------------------------------------
diff --git a/docs/streaming_guide.md b/docs/streaming_guide.md
index 7a5835e..fed64f7 100644
--- a/docs/streaming_guide.md
+++ b/docs/streaming_guide.md
@@ -29,7 +29,7 @@ Introduction
 ------------
 
 
-Flink Streaming is an extension of the core Flink API for high-throughput, low-latency data stream processing. The system can connect to and process data streams from many data sources like RabbitMQ, Flume, Twitter, ZeroMQ and also from any user defined data source. Data streams can be transformed and modified using high-level functions similar to the ones provided by the batch processing API. Flink Streaming provides native support for iterative stream processing. The processed data can be pushed to different output types.
+Flink Streaming is an extension of the batch Flink API for high-throughput, low-latency data stream processing. The system can connect to and process data streams from many data sources like Apache Kafka RabbitMQ, Apache Flume, Twitter and also from any user defined data source. Data streams can be transformed and modified using high-level functions similar to the ones provided by the batch processing API. Flink Streaming provides native support for iterative stream processing. The processed data can be pushed to different output types.
 
 Flink Streaming API
 -----------
@@ -38,15 +38,38 @@ The Streaming API is currently part of the *flink-staging* Maven project. All re
 
 Add the following dependency to your `pom.xml` to use the Flink Streaming.
 
-~~~xml
+<div class="codetabs" markdown="1">
+<div data-lang="java" markdown="1">
+{% highlight xml %}
 <dependency>
-    <groupId>org.apache.flink</groupId>
-    <artifactId>flink-streaming-core</artifactId>
-    <version>{{site.FLINK_VERSION_SHORT}}</version>
+  <groupId>org.apache.flink</groupId>
+  <artifactId>flink-streaming-core</artifactId>
+  <version>{{site.FLINK_VERSION_SHORT }}</version>
 </dependency>
-~~~
+<dependency>
+  <groupId>org.apache.flink</groupId>
+  <artifactId>flink-clients</artifactId>
+  <version>{{site.FLINK_VERSION_SHORT }}</version>
+</dependency>
+{% endhighlight %}
+</div>
+<div data-lang="scala" markdown="1">
+{% highlight xml %}
+<dependency>
+  <groupId>org.apache.flink</groupId>
+  <artifactId>flink-streaming-scala</artifactId>
+  <version>{{site.FLINK_VERSION_SHORT }}</version>
+</dependency>
+<dependency>
+  <groupId>org.apache.flink</groupId>
+  <artifactId>flink-clients</artifactId>
+  <version>{{site.FLINK_VERSION_SHORT }}</version>
+</dependency>
+{% endhighlight %}
+</div>
+</div>
 
-Create a data stream flow with our Java API as described below. In order to create your own Flink Streaming program, we encourage you to start with the [skeleton](#program-skeleton) and gradually add your own [operations](#operations). The remaining sections act as references for additional operations and advanced features.
+Create a data stream flow with our Java or Scala API as described below. In order to create your own Flink Streaming program we encourage you to start with the [skeleton](#program-skeleton) and gradually add your own [transformations](#transformations). The remaining sections act as references for additional transformations and advanced features.
 
 
 Example Program
@@ -54,7 +77,10 @@ Example Program
 
 The following program is a complete, working example of streaming WordCount. You can copy &amp; paste the code to run it locally.
 
-~~~java
+<div class="codetabs" markdown="1">
+<div data-lang="java" markdown="1">
+
+{% highlight java %}
 public class StreamingWordCount {
 
     public static void main(String[] args) {
@@ -82,7 +108,33 @@ public class StreamingWordCount {
     }
     
 }
-~~~
+{% endhighlight %}
+
+</div>
+
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+
+object WordCount {
+  def main(args: Array[String]) {
+
+    val env = StreamExecutionEnvironment.getExecutionEnvironment
+    val text = env.socketTextStream("localhost", 9999)
+
+    val counts = text.flatMap { _.toLowerCase.split("\\W+") filter { _.nonEmpty } }
+      .map { (_, 1) }
+      .groupBy(0)
+      .sum(1)
+
+    counts.print
+
+    env.execute("Scala Socket Stream WordCount")
+  }
+}
+{% endhighlight %}
+</div>
+
+</div>
 
 To run the example program start the input stream with netcat first from a terminal:
 
@@ -97,7 +149,57 @@ The lines typed to this terminal are submitted as a source for your streaming jo
 Program Skeleton
 ----------------
 
-As presented in the [example](#example-program), a Flink Streaming program looks almost identical to a regular Flink program. Each stream processing program consists of the following parts:
+<div class="codetabs" markdown="1">
+<div data-lang="java" markdown="1">
+
+As presented in the [example](#example-program) a Flink Streaming program looks almost identical to a regular Flink program. Each stream processing program consists of the following parts:
+
+1. Creating a `StreamExecutionEnvironment`,
+2. Connecting to data stream sources,
+3. Specifying transformations on the data streams,
+4. Specifying output for the processed data,
+5. Executing the program.
+
+As these steps are basically the same as in the batch API we will only note the important differences.
+For stream processing jobs, the user needs to obtain a `StreamExecutionEnvironment` in contrast with the [batch API](programming_guide.html) where one would need an `ExecutionEnvironment`. The process otherwise is essentially the same:
+
+{% highlight java %}
+StreamExecutionEnvironment.getExecutionEnvironment();
+StreamExecutionEnvironment.createLocalEnvironment(parallelism);
+StreamExecutionEnvironment.createRemoteEnvironment(…);
+{% endhighlight %}
+
+For connecting to data streams the `StreamExecutionEnvironment` has many different methods, from basic file sources to completely general user defined data sources. We will go into details in the [basics](#basics) section.
+
+{% highlight java %}
+env.socketTextStream(host, port);
+env.fromElements(elements…);
+{% endhighlight %}
+
+After defining the data stream sources the user can specify transformations on the data streams to create a new data stream. Different data streams can be also combined together for joint transformations which are being showcased in the [transformations](#transformations) section.
+
+{% highlight java %}
+dataStream.map(new Mapper()).reduce(new Reducer());
+{% endhighlight %}
+
+The processed data can be pushed to different outputs called sinks. The user can define their own sinks or use any predefined filesystem, message queue or database sink.
+
+{% highlight java %}
+dataStream.writeAsCsv(path);
+dataStream.print();
+{% endhighlight %}
+
+Once the complete program is specified `execute(programName)` is to be called on the `StreamExecutionEnvironment`. This will either execute on the local machine or submit the program for execution on a cluster, depending on the chosen execution environment.
+
+{% highlight java %}
+env.execute(programName);
+{% endhighlight %}
+
+</div>
+
+<div data-lang="scala" markdown="1">
+
+As presented in the [example](#example-program) a Flink Streaming program looks almost identical to a regular Flink program. Each stream processing program consists of the following parts:
 
 1. Creating a `StreamExecutionEnvironment`,
 2. Connecting to data stream sources,
@@ -105,40 +207,44 @@ As presented in the [example](#example-program), a Flink Streaming program looks
 4. Specifying output for the processed data,
 5. Executing the program.
 
-As these steps are basically the same as in the core API we will only note the important differences.
+As these steps are basically the same as in the batch API we will only note the important differences.
 For stream processing jobs, the user needs to obtain a `StreamExecutionEnvironment` in contrast with the batch API where one would need an `ExecutionEnvironment`. The process otherwise is essentially the same:
 
-~~~java 
-StreamExecutionEnvironment.getExecutionEnvironment()
+{% highlight scala %}
+StreamExecutionEnvironment.getExecutionEnvironment
 StreamExecutionEnvironment.createLocalEnvironment(parallelism)
 StreamExecutionEnvironment.createRemoteEnvironment(…)
-~~~
+{% endhighlight %}
 
 For connecting to data streams the `StreamExecutionEnvironment` has many different methods, from basic file sources to completely general user defined data sources. We will go into details in the [basics](#basics) section.
 
-~~~java
+{% highlight scala %}
 env.socketTextStream(host, port)
 env.fromElements(elements…)
-~~~
+{% endhighlight %}
 
-After defining the data stream sources, the user can specify transformations on the data streams to create a new data stream. Different data streams can be also combined together for joint transformations which are being showcased in the [operations](#operations) section.
+After defining the data stream sources the user can specify transformations on the data streams to create a new data stream. Different data streams can be also combined together for joint transformations which are being showcased in the [transformations](#transformations) section.
 
-~~~java
-dataStream.map(new Mapper()).reduce(new Reducer())
-~~~
+{% highlight scala %}
+dataStream.map(new Mapper).reduce(new Reducer)
+{% endhighlight %}
 
-The processed data can be pushed to different outputs called sinks. The user can define their own sinks or use any predefined filesystem or database sink.
+The processed data can be pushed to different outputs called sinks. The user can define their own sinks or use any predefined filesystem, message queue or database sink.
 
-~~~java
+{% highlight scala %}
 dataStream.writeAsCsv(path)
-dataStream.print()
-~~~
+dataStream.print
+{% endhighlight %}
 
 Once the complete program is specified `execute(programName)` is to be called on the `StreamExecutionEnvironment`. This will either execute on the local machine or submit the program for execution on a cluster, depending on the chosen execution environment.
 
-~~~java
+{% highlight scala %}
 env.execute(programName)
-~~~
+{% endhighlight %}
+
+</div>
+
+</div>
 
 [Back to top](#top)
 
@@ -147,9 +253,9 @@ Basics
 
 ### DataStream
 
-The `DataStream` is the basic abstraction provided by the Flink Streaming API. It represents a continuous stream of data of a certain type from either a data source or a transformed data stream. Operations will be applied on individual data points or windows of the `DataStream` based on the type of the operation. For example the map operator transforms each data point individually while window operations work on an interval of data points at the same time.
- 
-The operations may return different `DataStream` types allowing more elaborate transformations, for example the `groupBy(…)` method returns a `GroupedDataStream` which can be used for grouped operations such as aggregating by key.
+The `DataStream` is the basic data abstraction provided by the Flink Streaming API. It represents a continuous stream of data of a certain type from either a data source or a transformed data stream. You can apply transformations on either individual data points or windows of the `DataStream`. For example the map operator transforms each data point individually while window transformations work on intervals of data points at the same time.
+
+The transformations may return different `DataStream` types allowing more elaborate transformations, for example the `groupBy(…)` method returns a `GroupedDataStream` which can be used for grouped transformations such as aggregating by key.
 
 ### Partitioning
 
@@ -161,8 +267,7 @@ Usage: `dataStream.forward()`
 Usage: `dataStream.shuffle()`
  * *Distribute*: Distribute partitioning directs the output data stream to the next operator in a round-robin fashion, achieving a balanced distribution.
 Usage: `dataStream.distribute()`
- * *Field/Key*: Field/Key partitioning partitions the output data stream based on the hash code of a selected key of the tuples. Data points with the same key are directed to the same operator instance. The user can define keys by field positions (for tuple and array types), field expressions (for Pojo types) and custom keys using the `KeySelector` interface. 
-Usage: `dataStream.partitionBy(keys)`
+ * *Field/Key*: Field/Key partitioning partitions the output data stream based on the hash code of a selected key of the tuples. Data points with the same key are directed to the same operator instance. This partition is applied when using the `groupBy` operator.
  * *Broadcast*: Broadcast partitioning sends the output data stream to all parallel instances of the next operator.
 Usage: `dataStream.broadcast()`
  * *Global*: All data points end up at the same operator instance. To achieve this use the parallelism setting of the corresponding operator.
@@ -174,9 +279,9 @@ The user is expected to connect to the outside world through the source and the
 
 #### Sources
 
-The user can connect to data streams by the different implementations of `SourceFunction` using `StreamExecutionEnvironment.addSource(SourceFunction)`. In contrast with other operators, DataStreamSources have a default operator parallelism of 1.
+The user can connect to data streams by the different implementations of `SourceFunction` using `StreamExecutionEnvironment.addSource(sourceFunction)`. In contrast with other operators, DataStreamSources have a default operator parallelism of 1.
 
-To create parallel sources the users source function needs to implement `ParallelSourceFunction` or extend `RichParallelSourceFunction` in which cases the source will have the parallelism of the environment. The degree of parallelism for ParallelSourceFunctions can be changed afterwards using `source.setParallelism(int dop)`.
+To create parallel sources the users source function needs to implement `ParallelSourceFunction` or extend `RichParallelSourceFunction` in which cases the source will have the parallelism of the environment. The degree of parallelism for ParallelSourceFunctions can be changed afterwards using `source.setParallelism(dop)`.
 
 There are several predefined ones similar to the ones of the batch API and some streaming specific ones like:
 
@@ -202,91 +307,247 @@ The user can also implement arbitrary sink functionality by implementing the `Si
 
 [Back to top](#top)
 
-Operations
+Transformations
 ----------------
 
-Operations represent transformations on the `DataStream`. The user can chain and combine multiple operators on the data stream to produce the desired processing steps. Most of the operators work very similar to the core Flink API allowing developers to reason about `DataStream` the same way as they would about `DataSet`. At the same time there are operators that exploit the streaming nature of the data to allow advanced functionality.
+Transformations represent the users' business logic on the data stream. The user can chain and combine multiple operators on the data stream to produce the desired processing steps. Most of the operators work very similar to the batch Flink API allowing developers to reason about `DataStream` the same way as they would about `DataSet`. At the same time there are operators that exploit the streaming nature of the data to allow advanced functionality.
 
-### Basic operators
+### Basic transformations
 
-Basic operators can be seen as functions that transform each data element in the data stream.
- 
-#### Map
-The Map transformation applies a user-defined `MapFunction` on each element of a `DataStream`. It implements a one-to-one mapping, that is, exactly one element must be returned by the function.
-A map operator that doubles the values of the input stream:
+Basic transformations can be seen as functions that operate on records of the data stream.
 
-~~~java
+<div class="codetabs" markdown="1">
+<div data-lang="java" markdown="1">
+
+<br />
+
+<table class="table table-bordered">
+  <thead>
+    <tr>
+      <th class="text-left" style="width: 20%">Transformation</th>
+      <th class="text-center">Description</th>
+    </tr>
+  </thead>
+
+  <tbody>
+    <tr>
+      <td><strong>Map</strong></td>
+      <td>
+        <p>Takes one element and produces one element. A map that doubles the values of the input stream:</p>
+{% highlight java %}
 dataStream.map(new MapFunction<Integer, Integer>() {
             @Override
             public Integer map(Integer value) throws Exception {
                 return 2 * value;
             }
-        })
-~~~
-
-#### FlatMap
-The FlatMap transformation applies a user-defined `FlatMapFunction` on each element of a `DataStream`. This variant of a map function can return arbitrary many result elements (including none) for each input element.
-A flatmap operator that splits sentences to words:
+        });
+{% endhighlight %}
+      </td>
+    </tr>
 
-~~~java
+    <tr>
+      <td><strong>FlatMap</strong></td>
+      <td>
+        <p>Takes one element and produces zero, one, or more elements. A flatmap that splits sentences to words:</p>
+{% highlight java %}
 dataStream.flatMap(new FlatMapFunction<String, String>() {
             @Override
-            public void flatMap(String value, Collector<String> out) throws Exception {
+            public void flatMap(String value, Collector<String> out) 
+                throws Exception {
                 for(String word: value.split(" ")){
                     out.collect(word);
                 }
             }
-        })
-~~~
-
-#### Filter
-The Filter transformation applies a user-defined `FilterFunction` on each element of a `DataStream` and retains only those elements for which the function returns true.
-A filter that filters out zero values:
+        });
+{% endhighlight %}
+      </td>
+    </tr>
+
+    <tr>
+      <td><strong>Filter</strong></td>
+      <td>
+        <p>Evaluates a boolean function for each element and retains those for which the function returns true.
+        <br/>
+        
+        <strong>IMPORTANT:</strong> The system assumes that the function does not modify the elements on which the predicate is applied. Violating this assumption
+        can lead to incorrect results.
 
-~~~java
+        <br/>
+        A filter that filters out zero values:
+        </p>
+{% highlight java %}
 dataStream.filter(new FilterFunction<Integer>() { 
             @Override
             public boolean filter(Integer value) throws Exception {
                 return value != 0;
             }
-        })
-~~~
-
-#### Reduce
-The Reduce transformation applies a user-defined `ReduceFunction` to all elements of a `DataStream`. The `ReduceFunction` subsequently combines pairs of elements into one element and outputs the current reduced value as a `DataStream`.
-A reducer that sums up the incoming stream:
+        });
+{% endhighlight %}
+      </td>
+    </tr>
+
+    <tr>
+      <td><strong>Reduce</strong></td>
+      <td>
+        <p>Combines a group of elements into a single element by repeatedly combining two elements
+        into one and emits the current state after every reduction. Reduce may be applied on a full, windowed or grouped data stream.
+        <br/>
+        
+        <strong>IMPORTANT:</strong> The streaming and the batch reduce functions have different semantics. A streaming reduce on a full or grouped data stream emits the current reduced value for every new element on a data stream. On a windowed data stream it works as a batch reduce: it produces at most one value.
+        <br/>
 
-~~~java
+         A reducer that sums up the incoming stream:</p>
+{% highlight java %}
 dataStream.reduce(new ReduceFunction<Integer>() {
             @Override
-            public Integer reduce(Integer value1, Integer value2) throws Exception {
-                return value1+value2;
+            public Integer reduce(Integer value1, Integer value2) 
+            throws Exception {
+                return value1 + value2;
             }
-        })
-~~~
+        });
+{% endhighlight %}
+      </td>
+    </tr>
 
-#### Merge
-Merges two or more `DataStream` outputs, creating a new DataStream containing all the elements from all the streams.
+    <tr>
+      <td><strong>Merge</strong></td>
+      <td>
+        <p>Merges two or more datastreams creating a new stream containing all the elements from all the streams.</p>
+{% highlight java %}
+dataStream.merge(otherStream1, otherStream2, …)
+{% endhighlight %}
+      </td>
+    </tr>
+  </tbody>
+</table>
+
+----------
+
+The following transformations are available on data sets of Tuples:
+
+<table class="table table-bordered">
+  <thead>
+    <tr>
+      <th class="text-left" style="width: 20%">Transformation</th>
+      <th class="text-center">Description</th>
+    </tr>
+  </thead>
+  <tbody>
+   <tr>
+      <td><strong>Project</strong></td>
+      <td>
+        <p>Selects a subset of fields from the tuples</p>
+{% highlight java %}
+DataSet<Tuple3<Integer, Double, String>> in = // [...]
+DataSet<Tuple2<String, Integer>> out = in.project(2,0);
+{% endhighlight %}
+      </td>
+    </tr>
+  </tbody>
+</table>
 
-~~~java
-dataStream.merge(otherStream1, otherStream2…)
-~~~
+</div>
+
+<div data-lang="scala" markdown="1">
+
+<br />
+
+
+
+<table class="table table-bordered">
+  <thead>
+    <tr>
+      <th class="text-left" style="width: 20%">Transformation</th>
+      <th class="text-center">Description</th>
+    </tr>
+  </thead>
+
+  <tbody>
+
+    <tr>
+      <td><strong>Map</strong></td>
+      <td>
+        <p>Takes one element and produces one element. A map that doubles the values of the input stream:</p>
+{% highlight scala %}
+dataStream.map{ x => x * 2 }
+{% endhighlight %}
+      </td>
+    </tr>
+
+    <tr>
+      <td><strong>FlatMap</strong></td>
+      <td>
+        <p>Takes one element and produces zero, one, or more elements. A flatmap that splits sentences to words:</p>
+{% highlight scala %}
+data.flatMap { str => str.split(" ") }
+{% endhighlight %}
+      </td>
+    </tr>
+
+    <tr>
+      <td><strong>Filter</strong></td>
+      <td>
+        <p>Evaluates a boolean function for each element and retains those for which the function returns true.
+        <br/>
+        
+        <strong>IMPORTANT:</strong> The system assumes that the function does not modify the elements on which the predicate is applied. Violating this assumption
+        can lead to incorrect results.
+        <br/>
+        
+        A filter that filters out zero values:
+        </p>
+{% highlight scala %}
+dataStream.filter{ _ != 0 }
+{% endhighlight %}
+      </td>
+    </tr>
+
+    <tr>
+      <td><strong>Reduce</strong></td>
+        <td>
+        <p>Combines a group of elements into a single element by repeatedly combining two elements
+        into one and emits the current state after every reduction. Reduce may be applied on a full, windowed or grouped data stream.
+        <br/>
+        
+        <strong>IMPORTANT:</strong> The streaming and the batch reduce functions have different semantics. A streaming reduce on a full or grouped data stream emits the current reduced value for every new element on a data stream. On a windowed data stream it works as a batch reduce: it produces at most one value.
+        <br/>
+
+         A reducer that sums up the incoming stream:</p>
+{% highlight scala %}
+dataStream.reduce{ _ + _ }
+{% endhighlight %}
+      </td>
+    </tr>
+
+    <tr>
+      <td><strong>Merge</strong></td>
+      <td>
+        <p>Merges two or more datastreams creating a new stream containing all the elements from all the streams.</p>
+{% highlight scala %}
+dataStream.merge(otherStream1, otherStream2, …)
+{% endhighlight %}
+      </td>
+    </tr>
+
+  </tbody>
+
+
+</table>
+
+</div>
+
+</div>
 
 ### Grouped operators
 
 Some transformations require that the elements of a `DataStream` are grouped on some key. The user can create a `GroupedDataStream` by calling the `groupBy(key)` method of a non-grouped `DataStream`. 
 Keys can be of three types: fields positions (applicable for tuple/array types), field expressions (applicable for pojo types), KeySelector instances. 
 
-The user can apply different reduce transformations on the obtained `GroupedDataStream`:
-
-#### Reduce on GroupedDataStream
-When the reduce operator is applied on a grouped data stream, the user-defined `ReduceFunction` will combine subsequent pairs of elements having the same key value. The combined results are sent to the output stream.
-
 ### Aggregations
 
-The Flink Streaming API supports different types of pre-defined aggregation operators similarly to the core API.
+The Flink Streaming API supports different types of pre-defined aggregation operators similarly to the batch API.
 
-Types of aggregations: `sum(field)`, `min(field)`, `max(field)`, `minBy(field, first)`, `maxBy(field, first)`
+Types of aggregations: `sum(field)`, `min(field)`, `max(field)`, `minBy(field, first)`, `maxBy(field, first)`.
 
 With `sum`, `min`, and `max` for every incoming tuple the selected field is replaced with the current aggregated value. Fields can be selected using either field positions or field expressions (similarly to grouping).
 
@@ -296,7 +557,7 @@ There is also an option to apply user defined aggregations with the usage of the
 
 ### Window operators
 
-Flink streaming provides very flexible windowing semantics to create arbitrary windows (also referred to as discretizations or slices) of the data streams and apply reduce, map or aggregation operations on the windows acquired. Windowing can be used for instance to create rolling aggregations of the most recent N elements, where N could be defined by Time, Count or any arbitrary user defined measure.
+Flink streaming provides very flexible windowing semantics to create arbitrary windows (also referred to as discretizations or slices) of the data streams and apply reduce, map or aggregation transformations on the windows acquired. Windowing can be used for instance to create rolling aggregations of the most recent N elements, where N could be defined by Time, Count or any arbitrary user defined measure.
 
 The user can control the size (eviction) of the windows and the frequency of reduction or aggregation calls (trigger) on them in an intuitive API (some examples):
 
@@ -304,7 +565,7 @@ The user can control the size (eviction) of the windows and the frequency of red
  * `dataStream.window(…).every(…).mapWindow(…).flatten()`
  * `dataStream.window(…).every(…).groupBy(…).aggregate(…).getDiscretizedStream()`
 
-The core abstraction of the Windowing semantics is the `WindowedDataStream` and the `StreamWindow`. The `WindowedDataStream` is created when we call the `.window(…)` method of the DataStream and represents the windowed discretisation of the underlying stream. The user can think about it simply as a `DataStream<StreamWindow<T>>` where additional API functions are supplied to provide efficient transformations of individual windows. 
+The core abstraction of the Windowing semantics is the `WindowedDataStream` and the `StreamWindow`. The `WindowedDataStream` is created when we call the `window(…)` method of the DataStream and represents the windowed discretisation of the underlying stream. The user can think about it simply as a `DataStream<StreamWindow<T>>` where additional API functions are supplied to provide efficient transformations of individual windows. 
 
 The result of a window transformation is again a `WindowedDataStream` which can also be used to further transform the resulting windows. In this sense, window transformations define mapping from stream windows to stream windows.
 
@@ -316,17 +577,35 @@ The user has different ways of using the a result of a window operation:
 
 The next example would create windows that hold elements of the last 5 seconds, and the user defined transformation would be executed on the windows every second (sliding the window by 1 second):
 
-~~~java
+<div class="codetabs" markdown="1">
+<div data-lang="java" markdown="1">
+{% highlight java %}
+dataStream.window(Time.of(5, TimeUnit.SECONDS)).every(Time.of(1, TimeUnit.SECONDS));
+{% endhighlight %}
+</div>
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
 dataStream.window(Time.of(5, TimeUnit.SECONDS)).every(Time.of(1, TimeUnit.SECONDS))
-~~~
+{% endhighlight %}
+</div>
+</div>
 
 This approach is often referred to as policy based windowing. Different policies (count, time, etc.) can be mixed as well, for example to downsample our stream, a window that takes the latest 100 elements of the stream every minute is created as follows:
 
-~~~java
+<div class="codetabs" markdown="1">
+<div data-lang="java" markdown="1">
+{% highlight java %}
+dataStream.window(Count.of(100)).every(Time.of(1, TimeUnit.MINUTES));
+{% endhighlight %}
+</div>
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
 dataStream.window(Count.of(100)).every(Time.of(1, TimeUnit.MINUTES))
-~~~
+{% endhighlight %}
+</div>
+</div>
 
-The user can also omit the `.every(…)` call which results in a tumbling window emptying the window after every transformation call.
+The user can also omit the `every(…)` call which results in a tumbling window emptying the window after every transformation call.
 
 Several predefined policies are provided in the API, including delta-based, count-based and time-based policies. These can be accessed through the static methods provided by the `PolicyHelper` classes:
 
@@ -339,17 +618,28 @@ For detailed description of these policies please refer to the [Javadocs](http:/
 #### Policy based windowing
 The policy based windowing is a highly flexible way to specify stream discretisation also called windowing semantics. Two types of policies are used for such a specification:
 
- * `TriggerPolicy` defines when to trigger the reduce UDF on the current window and emit the result. In the API it completes a window statement such as: `.window(…).every(…)`, while the triggering policy is passed within `every`. 
+ * `TriggerPolicy` defines when to trigger the reduce UDF on the current window and emit the result. In the API it completes a window statement such as: `window(…).every(…)`, while the triggering policy is passed within `every`. 
 
 Several predefined policies are provided in the API, including delta-based, punctuation based, count-based and time-based policies. Policies are in general UDFs and can implement any custom behaviour.
 
- * `EvictionPolicy` defines the length of a window as a means of a predicate for evicting tuples when they are no longer needed. In the API this can be defined by the `.window(…)` operation on a stream. There are mostly the same predefined policy types provided as for trigger policies.
+ * `EvictionPolicy` defines the length of a window as a means of a predicate for evicting tuples when they are no longer needed. In the API this can be defined by the `window(…)` operation on a stream. There are mostly the same predefined policy types provided as for trigger policies.
 
 In addition to the `dataStream.window(…).every(…)` style users can specifically pass the trigger and eviction policies during the window call:
 
-~~~java
-dataStream.window(TriggerPolicy, EvictionPolicy)
-~~~
+<div class="codetabs" markdown="1">
+<div data-lang="java" markdown="1">
+{% highlight java %}
+dataStream.window(triggerPolicy, evictionPolicy);
+{% endhighlight %}
+</div>
+
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+dataStream.window(triggerPolicy, evictionPolicy)
+{% endhighlight %}
+</div>
+
+</div>
 
 By default most triggers can only trigger when a new element arrives. This might not be suitable for all the use-cases, especially when time based windowing is applied. To also provide triggering between elements so called active policies can be used. The predefined time-based policies are already implemented in such a way and can hold as an example for user defined active policy implementations. 
 
@@ -360,49 +650,105 @@ The `WindowedDataStream<T>.reduceWindow(ReduceFunction<T>)` transformation calls
 
 The following is an example for a window reduce that sums the elements in the last minute with 10 seconds slide interval:
 
-~~~java
+<div class="codetabs" markdown="1">
+<div data-lang="java" markdown="1">
+{% highlight java %}
 dataStream.window(Time.of(1, TimeUnit.MINUTES)).every(Time.of(10,TimeUnit.SECONDS)).sum(field);
-~~~
+{% endhighlight %}
+</div>
+
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+dataStream.window(Time.of(1, TimeUnit.MINUTES)).every(Time.of(10,TimeUnit.SECONDS)).sum(field)
+{% endhighlight %}
+</div>
+
+</div>
+
 
 #### Map on windowed data streams
 The `WindowedDataStream<T>.mapWindow(WindowMapFunction<T,O>)` transformation calls  `mapWindow(…)` for each `StreamWindow` in the discretised stream providing access to all elements in the window through the iterable interface. At each function call the output `StreamWindow<O>` will consist of all the elements collected to the collector. This allows a straightforward way of mapping one stream window to another.
 
-~~~java
+<div class="codetabs" markdown="1">
+<div data-lang="java" markdown="1">
+{% highlight java %}
+windowedDataStream.mapWindow(windowMapFunction);
+{% endhighlight %}
+</div>
+
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
 windowedDataStream.mapWindow(windowMapFunction)
-~~~
+{% endhighlight %}
+</div>
+
+</div>
 
-#### Grouped operations on windowed data streams
-Calling the `.groupBy(…)` method on a windowed stream groups the elements by the given fields inside the stream windows. The window sizes (evictions) and slide sizes (triggers) will be calculated on the whole stream (in a global fashion), but the user defined functions will be applied on a per group basis. This means that for a call `windowedStream.groupBy(…).reduceWindow(…)` will transform each window into another window consisting of as many elements as groups, with the reduced values per key. Similarly the `mapWindow` transformation is applied per group as well.
+#### Grouped transformations on windowed data streams
+Calling the `groupBy(…)` method on a windowed stream groups the elements by the given fields inside the stream windows. The window sizes (evictions) and slide sizes (triggers) will be calculated on the whole stream (in a global fashion), but the user defined functions will be applied on a per group basis. This means that for a call `windowedStream.groupBy(…).reduceWindow(…)` will transform each window into another window consisting of as many elements as groups, with the reduced values per key. Similarly the `mapWindow` transformation is applied per group as well.
 
-The user can also create discretisation on a per group basis calling `.window(…).every(…)` on an already grouped data stream. This will apply the discretisation logic independently for each key.
+The user can also create discretisation on a per group basis calling `window(…).every(…)` on an already grouped data stream. This will apply the discretisation logic independently for each key.
 
 To highlight the differences let us look at two examples.
 
 To get the maximal value for each key on the last 100 elements (global) we use the first approach:
 
-~~~java
+<div class="codetabs" markdown="1">
+<div data-lang="java" markdown="1">
+{% highlight java %}
 dataStream.window(Count.of(100)).every(…).groupBy(groupingField).max(field);
-~~~
+{% endhighlight %}
+</div>
+
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+dataStream.window(Count.of(100)).every(…).groupBy(groupingField).max(field)
+{% endhighlight %}
+</div>
+
+</div>
 
 Using this approach we took the last 100 elements, divided it into groups by key then applied the aggregation. To create fixed size windows for every key we need to reverse the order of the groupBy call. So to take the max for the last 100 elements in Each group:
 
-~~~java
+<div class="codetabs" markdown="1">
+<div data-lang="java" markdown="1">
+{% highlight java %}
 dataStream.groupBy(groupingField).window(Count.of(100)).every(…).max(field);
-~~~
+{% endhighlight %}
+</div>
+
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+dataStream.groupBy(groupingField).window(Count.of(100)).every(…).max(field)
+{% endhighlight %}
+</div>
+
+</div>
 
 This will create separate windows for different keys and apply the trigger and eviction policies on a per group basis.
 
 #### Applying multiple transformations on a window
 Using the `WindowedDataStream` abstraction we can apply several transformations one after another on the discretised streams without having to re-discretise it:
 
-~~~java
+<div class="codetabs" markdown="1">
+<div data-lang="java" markdown="1">
+{% highlight java %}
+dataStream.window(Count.of(1000)).groupBy(firstKey).mapWindow(…)
+    .groupBy(secondKey).reduceWindow(…).flatten();
+{% endhighlight %}
+</div>
+
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
 dataStream.window(Count.of(1000)).groupBy(firstKey).mapWindow(…)
     .groupBy(secondKey).reduceWindow(…).flatten()
-~~~
+{% endhighlight %}
+</div>
+</div>
 
 The above call would create global windows of 1000 elements group it by the first key and then apply a mapWindow transformation. The resulting windowed stream will then be grouped by the second key and further reduced. The results of the reduce transformation are then flattened.
 
-Notice here that we only defined the window size once at the beginning of the transformation. This means that anything that happens afterwards (`.groupBy(firstKey).mapWindow(…).groupBy(secondKey).reduceWindow(…)`) happens inside the 1000 element windows. Of course the mapWindow might reduce the number of elements but the key idea is that each transformation still corresponds to the same 1000 elements in the original stream.
+Notice that here we only defined the window size once at the beginning of the transformation. This means that anything that happens afterwards (`groupBy(firstKey).mapWindow(…).groupBy(secondKey).reduceWindow(…)`) happens inside the 1000 element windows. Of course the mapWindow might reduce the number of elements but the key idea is that each transformation still corresponds to the same 1000 elements in the original stream.
 
 #### Global vs local discretisation
 By default all window discretisation calls (`dataStream.window(…)`) define global windows meaning that a global window of count 100 will contain the last 100 elements arrived at the discretisation operator in order. In most cases (except for Time) this means that the operator doing the actual discretisation needs to have a degree of parallelism of 1 to be able to correctly execute the discretisation logic.
@@ -532,11 +878,9 @@ dataStream1.connect(dataStream2)
 val dataStream1 : DataStream[Int] = ...
 val dataStream2 : DataStream[String] = ...
 
-dataStream2.flatMap((str : String) => str.split(" "))
-
 (dataStream1 connect dataStream2)
   .flatMap(
-    (num : Int) => num.toString,
+    (num : Int) => List(num.toString),
     (str : String) => str.split(" ")
   )
 {% endhighlight %}
@@ -549,8 +893,8 @@ The windowReduce operator applies a user defined `CoWindowFunction` to time alig
 #### Reduce on ConnectedDataStream
 The Reduce operator for the `ConnectedDataStream` applies a simple reduce transformation on the joined data streams and then maps the reduced elements to a common output type.
 
-<div class="codetabs" markdown="1">
 ### Output splitting
+<div class="codetabs" markdown="1">
 <div data-lang="java" markdown="1">
 
 Most data stream operators support directed outputs (output splitting), meaning that different output elements are sent only to specific outputs. The outputs are referenced by their name given at the point of receiving:
@@ -600,8 +944,8 @@ Most data stream operators support directed outputs (output splitting), meaning
 val split = someDataStream.split(
   (num: Int) =>
     (num % 2) match {
-      case 0 => "even"
-      case 1 => "odd"
+      case 0 => List("even")
+      case 1 => List("odd")
     }
 )
 
@@ -620,11 +964,11 @@ Every output will be emitted to the selected outputs exactly once, even if you a
 
 </div>
 
-<div class="codetabs" markdown="1">
 ### Iterations
+<div class="codetabs" markdown="1">
 <div data-lang="java" markdown="1">
-The Flink Streaming API supports implementing iterative stream processing dataflows similarly to the core Flink API. Iterative streaming programs also implement a step function and embed it into an `IterativeDataStream`.
-Unlike in the core API the user does not define the maximum number of iterations, but at the tail of each iteration part of the output is streamed forward to the next operator and part is streamed back to the iteration head. The user controls the output of the iteration tail using [output splitting](#output-splitting) or [filters](#filter).
+The Flink Streaming API supports implementing iterative stream processing dataflows similarly to the batch Flink API. Iterative streaming programs also implement a step function and embed it into an `IterativeDataStream`.
+Unlike in the batch API the user does not define the maximum number of iterations, but at the tail of each iteration part of the output is streamed forward to the next operator and part is streamed back to the iteration head. The user controls the output of the iteration tail using [output splitting](#output-splitting) or [filters](#filter).
 To start an iterative part of the program the user defines the iteration starting point:
 
 {% highlight java %}
@@ -637,7 +981,7 @@ The operator applied on the iteration starting point is the head of the iteratio
 DataStream<Integer> head = iteration.map(new IterationHead());
 {% endhighlight %}
 
-To close an iteration and define the iteration tail, the user calls `.closeWith(iterationTail)` method of the `IterativeDataStream`. This iteration tail (the DataStream given to the `closeWith` function) will be fed back to the iteration head. A common pattern is to use [filters](#filter) to separate the output of the iteration from the feedback-stream.
+To close an iteration and define the iteration tail, the user calls `closeWith(iterationTail)` method of the `IterativeDataStream`. This iteration tail (the DataStream given to the `closeWith` function) will be fed back to the iteration head. A common pattern is to use [filters](#filter) to separate the output of the iteration from the feedback-stream.
 
 {% highlight java %}
 DataStream<Integer> tail = head.map(new IterationTail());
@@ -646,17 +990,17 @@ iteration.closeWith(tail.filter(isFeedback));
 
 DataStream<Integer> output = tail.filter(isOutput);
 
-output.map(…).project(…)…
+output.map(…).project(…);
 {% endhighlight %}
 
 In this case all values passing the `isFeedback` filter will be fed back to the iteration head, and the values passing the `isOutput` filter will produce the output of the iteration that can be transformed further (here with a `map` and a `projection`) outside the iteration.
 
-Because iterative streaming programs do not have a set number of iterations for each data element, the streaming program has no information on the end of its input. From this it follows that iterative streaming programs run until the user manually stops the program. While this is acceptable under normal circumstances a method is provided to allow iterative programs to shut down automatically if no input received by the iteration head for a predefined number of milliseconds.
+Because iterative streaming programs do not have a set number of iterations for each data element, the streaming program has no information on the end of its input. As a consequence iterative streaming programs run until the user manually stops the program. While this is acceptable under normal circumstances a method is provided to allow iterative programs to shut down automatically if no input received by the iteration head for a predefined number of milliseconds.
 To use this functionality the user needs to add the maxWaitTimeMillis parameter to the `dataStream.iterate(…)` call to control the max wait time. 
 </div>
 <div data-lang="scala" markdown="1">
-The Flink Streaming API supports implementing iterative stream processing dataflows similarly to the core Flink API. Iterative streaming programs also implement a step function and embed it into an `IterativeDataStream`.
-Unlike in the core API the user does not define the maximum number of iterations, but at the tail of each iteration part of the output is streamed forward to the next operator and part is streamed back to the iteration head. The user controls the output of the iteration tail by defining a step function that return two DataStreams: a feedback and an output. The first one is the output that will be fed back to the start of the iteration and the second is the output stream of the iterative part.
+The Flink Streaming API supports implementing iterative stream processing dataflows similarly to the batch Flink API. Iterative streaming programs also implement a step function and embed it into an `IterativeDataStream`.
+Unlike in the batch API the user does not define the maximum number of iterations, but at the tail of each iteration part of the output is streamed forward to the next operator and part is streamed back to the iteration head. The user controls the output of the iteration tail by defining a step function that return two DataStreams: a feedback and an output. The first one is the output that will be fed back to the start of the iteration and the second is the output stream of the iterative part.
 
 A common pattern is to use [filters](#filter) to separate the output from the feedback-stream. In this case all values passing the `isFeedback` filter will be fed back to the iteration head, and the values passing the `isOutput` filter will produce the output of the iteration that can be transformed further (here with a `map` and a `projection`) outside the iteration.
 
@@ -667,17 +1011,17 @@ val iteratedStream = someDataStream.iterate(maxWaitTime) {
     val tail = head.map(iterationTail)
     (tail.filter(isFeedback), tail.filter(isOutput))
   }
-}.map(…).project(…)…
+}.map(…).project(…)
 {% endhighlight %}
 
-Because iterative streaming programs do not have a set number of iterations for each data element, the streaming program has no information on the end of its input. From this it follows that iterative streaming programs run until the user manually stops the program. While this is acceptable under normal circumstances a method is provided to allow iterative programs to shut down automatically if no input received by the iteration head for a predefined number of milliseconds.
+Because iterative streaming programs do not have a set number of iterations for each data element, the streaming program has no information on the end of its input. As a consequence iterative streaming programs run until the user manually stops the program. While this is acceptable under normal circumstances a method is provided to allow iterative programs to shut down automatically if no input received by the iteration head for a predefined number of milliseconds.
 To use this functionality the user needs to add the maxWaitTimeMillis parameter to the `dataStream.iterate(…)` call to control the max wait time. 
 </div>
 
 </div>
 
 ### Rich functions
-The usage of rich functions are essentially the same as in the core Flink API. All transformations that take as argument a user-defined function can instead take a rich function as argument:
+The [usage](programming_guide.html#rich-functions) of rich functions are essentially the same as in the batch Flink API. All transformations that take as argument a user-defined function can instead take a rich function as argument:
 
 <div class="codetabs" markdown="1">
 <div data-lang="java" markdown="1">
@@ -765,12 +1109,12 @@ Operator Settings
 
 ### Parallelism
 
-Setting parallelism for operators works exactly the same way as in the core Flink API. The user can control the number of parallel instances created for each operator by calling the `operator.setParallelism(dop)` method.
+Setting parallelism for operators works exactly the same way as in the batch Flink API. The user can control the number of parallel instances created for each operator by calling the `operator.setParallelism(dop)` method.
 
 ### Buffer timeout
 
 By default data points are not transferred on the network one-by-one, which would cause unnecessary network traffic, but are buffered in the output buffers. The size of the output buffers can be set in the Flink config files. While this method is good for optimizing throughput, it can cause latency issues when the incoming stream is not fast enough.
-To tackle this issue the user can call `env.setBufferTimeout(timeoutMillis)` on the execution environment (or on individual operators) to set a maximum wait time for the buffers to fill up. After this time the buffers are flushed automatically even if they are not full. The default value for this timeout is 100ms which should be appropriate for most use-cases. 
+To tackle this issue the user can call `env.setBufferTimeout(timeoutMillis)` on the execution environment (or on individual operators) to set a maximum wait time for the buffers to fill up. After this time the buffers are flushed automatically even if they are not full. The default value for this timeout is 100 ms which should be appropriate for most use-cases. 
 
 Usage:
 
@@ -794,7 +1138,7 @@ env.genereateSequence(1,10).map(myMap).setBufferTimeout(timeoutMillis)
 </div>
 
 To maximise the throughput the user can call `setBufferTimeout(-1)` which will remove the timeout and buffers will only be flushed when they are full.
-To minimise latency, set the timeout to a value close to 0 (fro example 5 or 10 ms). Theoretically a buffer timeout of 0 will cause all outputs to be flushed when produced, but this setting should be avoided because it can cause severe performance degradation.
+To minimise latency, set the timeout to a value close to 0 (for example 5 or 10 ms). Theoretically a buffer timeout of 0 will cause all outputs to be flushed when produced, but this setting should be avoided because it can cause severe performance degradation.
 
 
 [Back to top](#top)

http://git-wip-us.apache.org/repos/asf/flink/blob/7582390c/flink-staging/flink-streaming/flink-streaming-scala/src/main/scala/org/apache/flink/streaming/api/scala/DataStream.scala
----------------------------------------------------------------------
diff --git a/flink-staging/flink-streaming/flink-streaming-scala/src/main/scala/org/apache/flink/streaming/api/scala/DataStream.scala b/flink-staging/flink-streaming/flink-streaming-scala/src/main/scala/org/apache/flink/streaming/api/scala/DataStream.scala
index b673f25..15467bb 100644
--- a/flink-staging/flink-streaming/flink-streaming-scala/src/main/scala/org/apache/flink/streaming/api/scala/DataStream.scala
+++ b/flink-staging/flink-streaming/flink-streaming-scala/src/main/scala/org/apache/flink/streaming/api/scala/DataStream.scala
@@ -221,8 +221,8 @@ class DataStream[T](javaStream: JavaStream[T]) {
    *
    *
    */
-  def iterate[R](maxWaitTimeMillis:Long = 0)(stepFunction: DataStream[T] => (DataStream[T], DataStream[R]))
-        : DataStream[R] = {
+  def iterate[R](maxWaitTimeMillis:Long = 0)
+                (stepFunction: DataStream[T] => (DataStream[T], DataStream[R])) : DataStream[R] = {
     val iterativeStream = javaStream.iterate(maxWaitTimeMillis)
 
     val (feedback, output) = stepFunction(new DataStream[T](iterativeStream))
@@ -495,23 +495,6 @@ class DataStream[T](javaStream: JavaStream[T]) {
    */
   def split(selector: OutputSelector[T]): SplitDataStream[T] = javaStream.split(selector)
 
-//  /**
-//   * Creates a new SplitDataStream that contains only the elements satisfying the
-//   *  given output selector predicate.
-//   */
-//  def split(fun: T => String): SplitDataStream[T] = {
-//    if (fun == null) {
-//      throw new NullPointerException("OutputSelector must not be null.")
-//    }
-//    val selector = new OutputSelector[T] {
-//      val cleanFun = clean(fun)
-//      def select(in: T): java.lang.Iterable[String] = {
-//        List(cleanFun(in))
-//      }
-//    }
-//    split(selector)
-//  }
-
   /**
    * Creates a new SplitDataStream that contains only the elements satisfying the
    *  given output selector predicate.


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