Github user tillrohrmann commented on a diff in the pull request:
https://github.com/apache/flink/pull/1704#discussion_r55993999
--- Diff: docs/apis/scala_api_extensions.md ---
@@ -0,0 +1,392 @@
+---
+title: "Scala API Extensions"
+# Top-level navigation
+top-nav-group: apis
+top-nav-pos: 11
+---
+<!--
+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.
+-->
+
+In order to keep a fair amount of consistency between the Scala and Java
APIs, some
+of the features that allow a high-level of expressiveness in Scala have
been left
+out from the standard APIs for both batch and streaming.
+
+If you want to _enjoy the full Scala experience_ you can choose to opt-in
to
+extensions that enhance the Scala API via implicit conversions.
+
+To use all the available extensions, you can just add a simple `import`
for the
+DataSet API
+
+{% highlight scala %}
+import org.apache.flink.api.scala.extensions._
+{% endhighlight %}
+
+or the DataStream API
+
+{% highlight scala %}
+import org.apache.flink.streaming.api.scala.extensions._
+{% endhighlight %}
+
+Alternatively, you can import individual extensions _a-là -carte_ to only
use those
+you prefer.
+
+## Accept partial functions
+
+Normally, both the DataSet and DataStream APIs don't accept anonymous
pattern
+matching functions to deconstruct tuples, case classes or collections,
like the
+following:
+
+{% highlight scala %}
+val data: DataSet[(Int, String, Double)] = // [...]
+data.map {
+ case (id, name, temperature) => // [...]
+ // The previous line causes the following compilation error:
+ // "The argument types of an anonymous function must be fully known.
(SLS 8.5)"
+}
+{% endhighlight %}
+
+This extension introduces new methods in both the DataSet and DataStream
Scala API
+that have a one-to-one correspondance in the extended API. These
delegating methods
+do support anonymous pattern matching functions.
+
+#### DataSet API
+
+<table class="table table-bordered">
+ <thead>
+ <tr>
+ <th class="text-left" style="width: 20%">Method</th>
+ <th class="text-left" style="width: 20%">Original</th>
+ <th class="text-center">Example</th>
+ </tr>
+ </thead>
+
+ <tbody>
+ <tr>
+ <td><strong>mapWith</strong></td>
+ <td><strong>map (DataSet)</strong></td>
+ <td>
+{% highlight scala %}
+data.mapWith {
+ case (_, value) => value.toString
+}
+{% endhighlight %}
+ </td>
+ </tr>
+ <tr>
+ <td><strong>mapPartitionWith</strong></td>
+ <td><strong>mapPartition (DataSet)</strong></td>
+ <td>
+{% highlight scala %}
+data.mapPartitionWith {
+ case head +: _ => head
+}
+{% endhighlight %}
+ </td>
+ </tr>
+ <tr>
+ <td><strong>flatMapWith</strong></td>
+ <td><strong>flatMap (DataSet)</strong></td>
+ <td>
+{% highlight scala %}
+data.flatMapWith {
+ case (_, name, visitTimes) => visitTimes.map(name -> _)
+}
+{% endhighlight %}
+ </td>
+ </tr>
+ <tr>
+ <td><strong>filterWith</strong></td>
+ <td><strong>filter (DataSet)</strong></td>
+ <td>
+{% highlight scala %}
+data.filterWith {
+ case Train(_, isOnTime) => isOnTime
+}
+{% endhighlight %}
+ </td>
+ </tr>
+ <tr>
+ <td><strong>reduceWith</strong></td>
+ <td><strong>reduce (DataSet, GroupedDataSet)</strong></td>
+ <td>
+{% highlight scala %}
+data.reduceWith {
+ case ((_, amount1), (_, amount2)) => amount1 + amount2
+}
+{% endhighlight %}
+ </td>
+ </tr>
+ <tr>
+ <td><strong>reduceGroupWith</strong></td>
+ <td><strong>reduceGroup (GroupedDataSet)</strong></td>
+ <td>
+{% highlight scala %}
+data.reduceGroupWith {
+ case id +: value +: _ => id -> value
+}
+{% endhighlight %}
+ </td>
+ </tr>
+ <tr>
+ <td><strong>groupingBy</strong></td>
+ <td><strong>groupBy (DataSet)</strong></td>
+ <td>
+{% highlight scala %}
+data.groupingBy {
+ case (id, _, _) => id
+}
+{% endhighlight %}
+ </td>
+ </tr>
+ <tr>
+ <td><strong>sortGroupWith</strong></td>
+ <td><strong>sortGroup (GroupedDataSet)</strong></td>
+ <td>
+{% highlight scala %}
+grouped.sortGroupWith(Order.ASCENDING) {
+ case House(_, value) => value
+}
+{% endhighlight %}
+ </td>
+ </tr>
+ <tr>
+ <td><strong>combineGroupWith</strong></td>
+ <td><strong>combineGroup (GroupedDataSet)</strong></td>
+ <td>
+{% highlight scala %}
+grouped.combineGroupWith {
+ case header +: amounts => amounts.sum
+}
+{% endhighlight %}
+ </td>
+ <tr>
+ <td><strong>projecting</strong></td>
+ <td><strong>apply (JoinDataSet, CrossDataSet)</strong></td>
+ <td>
+{% highlight scala %}
+data1.join(data2).where(0).equalTo(1).projecting {
+ case ((pk, tx), (products, fk)) => tx -> products
+}
+
+data1.cross(data2).projecting {
+ case ((a, _), (_, b) => a -> b
+}
+{% endhighlight %}
+ </td>
+ </tr>
+ <tr>
+ <td><strong>projecting</strong></td>
+ <td><strong>apply (CoGroupDataSet)</strong></td>
+ <td>
+{% highlight scala %}
+data1.coGroup(data2).where(0).equalTo(1).projecting {
+ case (head1 +: _, head2 +: _) => head1 -> head2
+}
+{% endhighlight %}
+ </td>
+ </tr>
+ </tr>
+ </tbody>
+</table>
+
+#### DataStream API
+
+<table class="table table-bordered">
+ <thead>
+ <tr>
+ <th class="text-left" style="width: 20%">Method</th>
+ <th class="text-left" style="width: 20%">Original</th>
+ <th class="text-center">Example</th>
+ </tr>
+ </thead>
+
+ <tbody>
+ <tr>
+ <td><strong>mapWith</strong></td>
+ <td><strong>map (DataStream)</strong></td>
+ <td>
+{% highlight scala %}
+data.mapWith {
+ case (_, value) => value.toString
+}
+{% endhighlight %}
+ </td>
+ </tr>
+ <tr>
+ <td><strong>mapPartitionWith</strong></td>
+ <td><strong>mapPartition (DataStream)</strong></td>
+ <td>
+{% highlight scala %}
+data.mapPartitionWith {
+ case head +: _ => head
+}
+{% endhighlight %}
+ </td>
+ </tr>
+ <tr>
+ <td><strong>flatMapWith</strong></td>
+ <td><strong>flatMap (DataStream)</strong></td>
+ <td>
+{% highlight scala %}
+data.flatMapWith {
+ case (_, name, visits) => visits.map(name -> _)
+}
+{% endhighlight %}
+ </td>
+ </tr>
+ <tr>
+ <td><strong>filterWith</strong></td>
+ <td><strong>filter (DataStream)</strong></td>
+ <td>
+{% highlight scala %}
+data.filterWith {
+ case Train(_, isOnTime) => isOnTime
+}
+{% endhighlight %}
+ </td>
+ </tr>
+ <tr>
+ <td><strong>keyingBy</strong></td>
+ <td><strong>keyBy (DataStream)</strong></td>
+ <td>
+{% highlight scala %}
+data.keyingBy {
+ case (id, _, _) => id
+}
+{% endhighlight %}
+ </td>
+ </tr>
+ <tr>
+ <td><strong>mapWith</strong></td>
+ <td><strong>map (ConnectedDataStream)</strong></td>
+ <td>
+{% highlight scala %}
+data.mapWith(
+ map1 = case (_, value) => value.toString,
+ map2 = case (_, _, value, _) => value + 1
+)
+{% endhighlight %}
+ </td>
+ </tr>
+ <tr>
+ <td><strong>flatMapWith</strong></td>
+ <td><strong>flatMap (ConnectedDataStream)</strong></td>
+ <td>
+{% highlight scala %}
+data.flatMapWith(
+ flatMap1 = case (_, json) => parse(json),
+ flatMap2 = case (_, _, json, _) => parse(json)
+)
+{% endhighlight %}
+ </td>
+ </tr>
+ <tr>
+ <td><strong>keyingBy</strong></td>
+ <td><strong>keyBy (ConnectedDataStream)</strong></td>
+ <td>
+{% highlight scala %}
+data.keyingBy(
+ key1 = case (_, timestamp) => timestamp,
+ key2 = case (id, _, _) => id
+)
+{% endhighlight %}
+ </td>
+ </tr>
+ <tr>
+ <td><strong>reduceWith</strong></td>
+ <td><strong>reduce (KeyedDataStream,
WindowedDataStream)</strong></td>
+ <td>
+{% highlight scala %}
+data.reduceWith {
+ case ((_, sum1), (_, sum2) => sum1 + sum2
+}
+{% endhighlight %}
+ </td>
+ </tr>
+ <tr>
+ <td><strong>foldWith</strong></td>
+ <td><strong>fold (KeyedDataStream, WindowedDataStream)</strong></td>
+ <td>
+{% highlight scala %}
+data.foldWith(User(bought = 0)) {
+ case (User(b), (_, items)) => User(b + items.size)
+}
+{% endhighlight %}
+ </td>
+ </tr>
+ <tr>
+ <td><strong>applyWith</strong></td>
+ <td><strong>apply (WindowedDataStream)</strong></td>
+ <td>
+{% highlight scala %}
+data.applyWith(0)(
+ foldFunction = case (sum, amount) => sum + amount
+ windowFunction = case (k, w, sum) => // [...]
+)
+{% endhighlight %}
+ </td>
+ </tr>
+ <tr>
+ <td><strong>projecting</strong></td>
+ <td><strong>apply (JoinedDataStream)</strong></td>
+ <td>
+{% highlight scala %}
+data1.join(data2).where(0).equalTo(1).projecting {
+ case ((pk, tx), (products, fk)) => tx -> products
+}
+{% endhighlight %}
+ </td>
+ </tr>
+ </tbody>
+</table>
+
+
+
+For more information on the semantics of each method, please refer to the
+[DataStream](batch/index.html) and [DataSet](streaming/index.html) API
documentation.
+
+To use this extension exclusively, you can add the following `import`:
+
+{% highlight scala %}
+import org.apache.flink.api.scala.extensions.acceptPartialFunctions
--- End diff --
Does this really work? Don't you have to import
`o.a.f.api.scala.extensions.acceptPartialFunctionsOnDataSet` etc.?
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