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.
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
