[SPARK-3850] Trim trailing spaces for examples/streaming/yarn.

Author: Reynold Xin <rxin@databricks.com>

Closes #6530 from rxin/trim-whitespace-1 and squashes the following commits:

7b7b3a0 [Reynold Xin] Reset again.
dc14597 [Reynold Xin] Reset scalastyle.
cd556c4 [Reynold Xin] YARN, Kinesis, Flume.
4223fe1 [Reynold Xin] [SPARK-3850] Trim trailing spaces for examples/streaming.

5 files changed, 12 insertions, 12 deletions

diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala
index 60e2994431..1e32a365a1 100644
--- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala
@@ -152,9 +152,9 @@ class FlumeReceiver(
       val channelFactory = new NioServerSocketChannelFactory(Executors.newCachedThreadPool(),
                                                              Executors.newCachedThreadPool())
       val channelPipelineFactory = new CompressionChannelPipelineFactory()
-      
+
       new NettyServer(
-        responder, 
+        responder,
         new InetSocketAddress(host, port),
         channelFactory,
         channelPipelineFactory,
@@ -188,12 +188,12 @@ class FlumeReceiver(
 
   override def preferredLocation: Option[String] = Option(host)
 
-  /** A Netty Pipeline factory that will decompress incoming data from 
+  /** A Netty Pipeline factory that will decompress incoming data from
     * and the Netty client and compress data going back to the client.
     *
     * The compression on the return is required because Flume requires
-    * a successful response to indicate it can remove the event/batch 
-    * from the configured channel 
+    * a successful response to indicate it can remove the event/batch
+    * from the configured channel
     */
   private[streaming]
   class CompressionChannelPipelineFactory extends ChannelPipelineFactory {
diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumePollingInputDStream.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumePollingInputDStream.scala
index 92fa5b41be..583e7dca31 100644
--- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumePollingInputDStream.scala
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumePollingInputDStream.scala
@@ -110,7 +110,7 @@ private[streaming] class FlumePollingReceiver(
 }
 
 /**
- * A wrapper around the transceiver and the Avro IPC API. 
+ * A wrapper around the transceiver and the Avro IPC API.
  * @param transceiver The transceiver to use for communication with Flume
  * @param client The client that the callbacks are received on.
  */
diff --git a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala
index 3d9daeb6e4..c926359987 100644
--- a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala
+++ b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala
@@ -138,7 +138,7 @@ class FlumeStreamSuite extends SparkFunSuite with BeforeAndAfter with Matchers w
       val status = client.appendBatch(inputEvents.toList)
       status should be (avro.Status.OK)
     }
-    
+
     eventually(timeout(10 seconds), interval(100 milliseconds)) {
       val outputEvents = outputBuffer.flatten.map { _.event }
       outputEvents.foreach {
diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaCluster.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaCluster.scala
index 6cf254a7b6..65d51d87f8 100644
--- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaCluster.scala
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaCluster.scala
@@ -113,7 +113,7 @@ class KafkaCluster(val kafkaParams: Map[String, String]) extends Serializable {
       r.flatMap { tm: TopicMetadata =>
         tm.partitionsMetadata.map { pm: PartitionMetadata =>
           TopicAndPartition(tm.topic, pm.partitionId)
-        }    
+        }
       }
     }
   }
diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala
index 8be2707528..0b8a391a2c 100644
--- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala
@@ -315,7 +315,7 @@ object KafkaUtils {
    * Points to note:
    *  - No receivers: This stream does not use any receiver. It directly queries Kafka
    *  - Offsets: This does not use Zookeeper to store offsets. The consumed offsets are tracked
-   *    by the stream itself. For interoperability with Kafka monitoring tools that depend on 
+   *    by the stream itself. For interoperability with Kafka monitoring tools that depend on
    *    Zookeeper, you have to update Kafka/Zookeeper yourself from the streaming application.
    *    You can access the offsets used in each batch from the generated RDDs (see
    *    [[org.apache.spark.streaming.kafka.HasOffsetRanges]]).
@@ -363,7 +363,7 @@ object KafkaUtils {
    * Points to note:
    *  - No receivers: This stream does not use any receiver. It directly queries Kafka
    *  - Offsets: This does not use Zookeeper to store offsets. The consumed offsets are tracked
-   *    by the stream itself. For interoperability with Kafka monitoring tools that depend on 
+   *    by the stream itself. For interoperability with Kafka monitoring tools that depend on
    *    Zookeeper, you have to update Kafka/Zookeeper yourself from the streaming application.
    *    You can access the offsets used in each batch from the generated RDDs (see
    *    [[org.apache.spark.streaming.kafka.HasOffsetRanges]]).
@@ -427,7 +427,7 @@ object KafkaUtils {
    * Points to note:
    *  - No receivers: This stream does not use any receiver. It directly queries Kafka
    *  - Offsets: This does not use Zookeeper to store offsets. The consumed offsets are tracked
-   *    by the stream itself. For interoperability with Kafka monitoring tools that depend on 
+   *    by the stream itself. For interoperability with Kafka monitoring tools that depend on
    *    Zookeeper, you have to update Kafka/Zookeeper yourself from the streaming application.
    *    You can access the offsets used in each batch from the generated RDDs (see
    *    [[org.apache.spark.streaming.kafka.HasOffsetRanges]]).
@@ -489,7 +489,7 @@ object KafkaUtils {
    * Points to note:
    *  - No receivers: This stream does not use any receiver. It directly queries Kafka
    *  - Offsets: This does not use Zookeeper to store offsets. The consumed offsets are tracked
-   *    by the stream itself. For interoperability with Kafka monitoring tools that depend on 
+   *    by the stream itself. For interoperability with Kafka monitoring tools that depend on
    *    Zookeeper, you have to update Kafka/Zookeeper yourself from the streaming application.
    *    You can access the offsets used in each batch from the generated RDDs (see
    *    [[org.apache.spark.streaming.kafka.HasOffsetRanges]]).