[FLINK-7567] [scala] Remove keepPartitioning parameter from DataStrea…

…m.iterate()

flink-streaming-java/src/main/java/org/apache/flink/streaming/api/transformations/FeedbackTransformation.java

@@ -87,7 +87,8 @@ public void addFeedbackEdge(StreamTransformation<T> transform) {
  throw new UnsupportedOperationException(
  "Parallelism of the feedback stream must match the parallelism of the original" +
  " stream. Parallelism of original stream: " + this.getParallelism() +
- "; parallelism of feedback stream: " + transform.getParallelism());
+ "; parallelism of feedback stream: " + transform.getParallelism() +
+ ". Parallelism can be modified using DataStream#setParallelism() method");
  }
 
  feedbackEdges.add(transform);

flink-streaming-scala/pom.xml

@@ -228,6 +228,10 @@ under the License.
  <excludes combine.children="append">
  <!-- Exclude generated classes from api compatibility checks -->
  <exclude>*\$\$anon\$*</exclude>
+
+ <!-- Ignore method which was created automatically by Scala for default value calculation.
+ Can be removed once https://github.com/siom79/japicmp/issues/176 will be fixed -->
+ <exclude>org.apache.flink.streaming.api.scala.DataStream#iterate\$default\$3()</exclude>
  </excludes>
  </parameter>
  </configuration>

flink-streaming-scala/src/main/scala/org/apache/flink/streaming/api/scala/DataStream.scala

@@ -499,23 +499,22 @@ class DataStream[T](stream: JavaStream[T]) {
  * stepfunction: initialStream => (feedback, output)
  *
  * A common pattern is to use output splitting to create feedback and output DataStream.
- * Please refer to the .split(...) method of the DataStream
+ * Please refer to the [[split]] method of the DataStream
  *
  * By default a DataStream with iteration will never terminate, but the user
  * can use the maxWaitTime parameter to set a max waiting time for the iteration head.
  * If no data received in the set time the stream terminates.
  *
- * By default the feedback partitioning is set to match the input, to override this set
- * the keepPartitioning flag to true
- *
+ * Parallelism of the feedback stream must match the parallelism of the original stream.
+ * Please refer to the [[setParallelism]] method for parallelism modification
  */
  @PublicEvolving
  def iterate[R](stepFunction: DataStream[T] => (DataStream[T], DataStream[R]),
- maxWaitTimeMillis:Long = 0,
- keepPartitioning: Boolean = false) : DataStream[R] = {
+ maxWaitTimeMillis:Long = 0) : DataStream[R] = {
  val iterativeStream = stream.iterate(maxWaitTimeMillis)
 
  val (feedback, output) = stepFunction(new DataStream[T](iterativeStream))
+
  iterativeStream.closeWith(feedback.javaStream)
  output
  }