Project: Flink (App Definition and Development / Streaming & Messaging)
File: scala_api_extensions.md

title: "Scala API Extensions" weight: 201 type: docs aliases: /dev/scalaapiextensions.html /apis/scalaapiextensions.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. --> {{< hint warning >}} All Flink Scala APIs are deprecated and will be removed in a future Flink version. You can still build your application in Scala, but you should move to the Java version of either the DataStream and/or Table API. See <a href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-265+Deprecate+and+remove+Scala+API+support">FLIP-265 Deprecate and remove Scala API support</a> {{< /hint >}} 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 DataStream API {{< highlight scala >}} import org.apache.flink.streaming.api.scala.extensions._ {{< /highlight >}} Alternatively, you can import individual extensions a-l-carte to only use those you prefer. Normally, the DataStream API does not accept anonymous pattern matching functions to deconstruct tuples, case classes or collections, like the following: {{< highlight scala >}} val data: DataStream[(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)" } {{< /highlight >}} This extension introduces new methods in the DataStream Scala API that have a one-to-one correspondence in the extended API. These delegating methods do support anonymous pattern matching functions. <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 >}}

{ case (_, value) => value.toString } {{< /highlight >}} </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 -> ) } {{< /highlight >}} </td> </tr> <tr> <td><strong>filterWith</strong></td> <td><strong>filter (DataStream)</strong></td> <td> {{< highlight scala >}} data.filterWith { case Train(_, isOnTime) => isOnTime } {{< /highlight >}} </td> </tr> <tr> <td><strong>keyingBy</strong></td> <td><strong>keyBy (DataStream)</strong></td> <td> {{< highlight scala >}} data.keyingBy { case (id, , ) => id } {{< /highlight >}} </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 ) {{< /highlight >}} </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) ) {{< /highlight >}} </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 ) {{< /highlight >}} </td> </tr> <tr> <td><strong>reduceWith</strong></td> <td><strong>reduce (KeyedStream, WindowedStream)</strong></td> <td> {{< highlight scala >}} data.reduceWith { case ((, sum1), (, sum2) => sum1 + sum2 } {{< /highlight >}} </td> </tr> <tr> <td><strong>projecting</strong></td> <td><strong>apply (JoinedStream)</strong></td> <td> {{< highlight scala >}} data1.join(data2). whereClause(case (pk, _) => pk). isEqualTo(case (_, fk) => fk). projecting { case ((pk, tx), (products, fk)) => tx -> products } {{< /highlight >}} </td> </tr> </tbody> </table> For more information on the semantics of each method, please refer to the API documentation. To use this extension exclusively, you can add the following `import`: {{< highlight scala >}} import org.apache.flink.api.scala.extensions.acceptPartialFunctions {{< /highlight >}} for the DataSet extensions and {{< highlight scala >}} import org.apache.flink.streaming.api.scala.extensions.acceptPartialFunctions {{< /highlight >}} The following snippet shows a minimal example of how to use these extension methods together (with the DataSet API): {{< highlight scala >}} object Main { import org.apache.flink.streaming.api.scala.extensions._ case class Point(x: Double, y: Double) def main(args: Array[String]): Unit = { val env = StreamExecutionEnvironment.getExecutionEnvironment val ds = env.fromElements(Point(1, 2), Point(3, 4), Point(5, 6)) ds.filterWith { case Point(x, _) => x > 1 }.reduceWith { case (Point(x1, y1), (Point(x2, y2))) => Point(x1 + y1, x2 + y2) }.mapWith { case Point(x, y) => (x, y) }.flatMapWith { case (x, y) => Seq("x" -> x, "y" -> y) }.keyingBy { case (id, value) => id } } } {{< /highlight >}} {{< top >}}