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https://issues.apache.org/jira/browse/FLINK-1159?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15193226#comment-15193226
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ASF GitHub Bot commented on FLINK-1159:
---------------------------------------
Github user tillrohrmann commented on a diff in the pull request:
https://github.com/apache/flink/pull/1704#discussion_r55995289
--- Diff:
flink-streaming-scala/src/main/scala/org/apache/flink/streaming/api/scala/extensions/package.scala
---
@@ -0,0 +1,202 @@
+/*
+ * 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.
+ */
+package org.apache.flink.streaming.api.scala
+
+import org.apache.flink.api.common.typeinfo.TypeInformation
+import
org.apache.flink.streaming.api.scala.extensions.acceptPartialFunctions._
+import org.apache.flink.streaming.api.windowing.windows.Window
+
+package object extensions {
+
+ /**
+ * acceptPartialFunctions extends the original DataStream with methods
with unique names
+ * that delegate to core higher-order functions (e.g. `map`) so that we
can work around
+ * the fact that overloaded methods taking functions as parameters
can't accept partial
+ * functions as well. This enables the possibility to directly apply
pattern matching
+ * to decompose inputs such as tuples, case classes and collections.
+ *
+ * e.g.
+ * {{{
+ * object Main {
+ * import org.apache.flink.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
+ * }
+ * }
+ * }
+ * }}}
+ *
+ */
+ implicit def acceptPartialFunctionsOnDataStream[T: TypeInformation](ds:
DataStream[T]):
+ OnDataStream[T] =
+ new OnDataStream[T](ds)
+
+ /**
+ * acceptPartialFunctions extends the original DataStream with methods
with unique names
+ * that delegate to core higher-order functions (e.g. `map`) so that we
can work around
+ * the fact that overloaded methods taking functions as parameters
can't accept partial
+ * functions as well. This enables the possibility to directly apply
pattern matching
+ * to decompose inputs such as tuples, case classes and collections.
+ *
+ * e.g.
+ * {{{
+ * object Main {
+ * import org.apache.flink.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
+ * }
+ * }
+ * }
+ * }}}
+ *
+ */
+ implicit def acceptPartialFunctionsOnKeyedStream[T: TypeInformation,
K](ds: KeyedStream[T, K]):
+ OnKeyedStream[T, K] =
+ new OnKeyedStream[T, K](ds)
+
+ /**
+ * acceptPartialFunctions extends the original DataStream with methods
with unique names
+ * that delegate to core higher-order functions (e.g. `map`) so that we
can work around
+ * the fact that overloaded methods taking functions as parameters
can't accept partial
+ * functions as well. This enables the possibility to directly apply
pattern matching
+ * to decompose inputs such as tuples, case classes and collections.
+ *
+ * e.g.
+ * {{{
+ * object Main {
+ * import org.apache.flink.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
+ * }
+ * }
+ * }
+ * }}}
+ *
+ */
+ implicit def acceptPartialFunctionsOnJoinedStream
+ [L: TypeInformation, R: TypeInformation, K, W <: Window](
+ ds: JoinedStreams[L, R]#Where[K]#EqualTo#WithWindow[W]) =
+ new OnJoinedStream[L, R, K, W](ds)
+
+ /**
+ * acceptPartialFunctions extends the original DataStream with methods
with unique names
+ * that delegate to core higher-order functions (e.g. `map`) so that we
can work around
+ * the fact that overloaded methods taking functions as parameters
can't accept partial
+ * functions as well. This enables the possibility to directly apply
pattern matching
+ * to decompose inputs such as tuples, case classes and collections.
+ *
+ * e.g.
+ * {{{
+ * object Main {
+ * import org.apache.flink.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
+ * }
+ * }
+ * }
+ * }}}
+ *
+ */
+ implicit def acceptPartialFunctionsOnConnectedStream[IN1:
TypeInformation, IN2: TypeInformation](
+ ds: ConnectedStreams[IN1, IN2]) =
+ new OnConnectedStream[IN1, IN2](ds)
+
+ /**
+ * acceptPartialFunctions extends the original DataStream with methods
with unique names
+ * that delegate to core higher-order functions (e.g. `map`) so that we
can work around
+ * the fact that overloaded methods taking functions as parameters
can't accept partial
+ * functions as well. This enables the possibility to directly apply
pattern matching
+ * to decompose inputs such as tuples, case classes and collections.
+ *
+ * e.g.
+ * {{{
+ * object Main {
+ * import org.apache.flink.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
+ * }
+ * }
+ * }
+ * }}}
+ *
+ */
+ implicit def acceptPartialFunctionsOnWindowedStream[T, K, W <: Window](
+ ds: WindowedStream[T, K, W]) =
+ new OnWindowedStream[T, K, W](ds)
+
--- End diff --
The same comments as for the extensions for the `DataSet` apply here.
> Case style anonymous functions not supported by Scala API
> ---------------------------------------------------------
>
> Key: FLINK-1159
> URL: https://issues.apache.org/jira/browse/FLINK-1159
> Project: Flink
> Issue Type: Bug
> Components: Scala API
> Reporter: Till Rohrmann
> Assignee: Stefano Baghino
>
> In Scala it is very common to define anonymous functions of the following form
> {code}
> {
> case foo: Bar => foobar(foo)
> case _ => throw new RuntimeException()
> }
> {code}
> These case style anonymous functions are not supported yet by the Scala API.
> Thus, one has to write redundant code to name the function parameter.
> What works is the following pattern, but it is not intuitive for someone
> coming from Scala:
> {code}
> dataset.map{
> _ match{
> case foo:Bar => ...
> }
> }
> {code}
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