changing the line lengths to make jenkins happy

Author: kmader

core/src/main/scala/org/apache/spark/SparkContext.scala

@@ -289,7 +289,7 @@ class SparkContext(config: SparkConf) extends Logging {
     value <- Option(System.getenv(envKey)).orElse(Option(System.getProperty(propKey)))} {
     executorEnvs(envKey) = value
   }
-  Option(System.getenv("SPARK_PREPEND_CLASSES")).foreach { v => 
+  Option(System.getenv("SPARK_PREPEND_CLASSES")).foreach { v =>
     executorEnvs("SPARK_PREPEND_CLASSES") = v
   }
   // The Mesos scheduler backend relies on this environment variable to set executor memory.
@@ -511,13 +511,15 @@ class SparkContext(config: SparkConf) extends Logging {
   }
 
   /**
-   * Get an RDD for a Hadoop-readable dataset as byte-streams for each file (useful for binary data)
+   * Get an RDD for a Hadoop-readable dataset as byte-streams for each file
+   * (useful for binary data)
    *
    * @param minPartitions A suggestion value of the minimal splitting number for input data.
    *
    * @note Small files are preferred, large file is also allowable, but may cause bad performance.
    */
-  def binaryFiles(path: String, minPartitions: Int = defaultMinPartitions): RDD[(String, Array[Byte])] = {
+  def binaryFiles(path: String, minPartitions: Int = defaultMinPartitions):
+  RDD[(String, Array[Byte])] = {
     val job = new NewHadoopJob(hadoopConfiguration)
     NewFileInputFormat.addInputPath(job, new Path(path))
     val updateConf = job.getConfiguration
@@ -531,15 +533,18 @@ class SparkContext(config: SparkConf) extends Logging {
   }
 
   /**
-   * Get an RDD for a Hadoop-readable dataset as DataInputStreams for each file (useful for binary data)
-   * Care must be taken to close the files afterwards
+   * Get an RDD for a Hadoop-readable dataset as DataInputStreams for each file
+   * (useful for binary data)
+   *
    *
    * @param minPartitions A suggestion value of the minimal splitting number for input data.
    *
+   * @note Care must be taken to close the files afterwards
    * @note Small files are preferred, large file is also allowable, but may cause bad performance.
    */
   @DeveloperApi
-  def dataStreamFiles(path: String, minPartitions: Int = defaultMinPartitions): RDD[(String, DataInputStream)] = {
+  def dataStreamFiles(path: String, minPartitions: Int = defaultMinPartitions):
+  RDD[(String, DataInputStream)] = {
     val job = new NewHadoopJob(hadoopConfiguration)
     NewFileInputFormat.addInputPath(job, new Path(path))
     val updateConf = job.getConfiguration
@@ -1250,7 +1255,7 @@ class SparkContext(config: SparkConf) extends Logging {
    * If <tt>checkSerializable</tt> is set, <tt>clean</tt> will also proactively 
    * check to see if <tt>f</tt> is serializable and throw a <tt>SparkException</tt> 
    * if not.
-   * 
+   *
    * @param f the closure to clean
    * @param checkSerializable whether or not to immediately check <tt>f</tt> for serializability
    * @throws <tt>SparkException<tt> if <tt>checkSerializable</tt> is set but <tt>f</tt> is not

core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala

@@ -215,9 +215,10 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork
     new JavaPairRDD(sc.wholeTextFiles(path, minPartitions))
 
   /**
-   * Read a directory of binary files from HDFS, a local file system (available on all nodes), or any
-   * Hadoop-supported file system URI as a byte array. Each file is read as a single record and returned in a
-   * key-value pair, where the key is the path of each file, the value is the content of each file.
+   * Read a directory of binary files from HDFS, a local file system (available on all nodes),
+   * or any Hadoop-supported file system URI as a byte array. Each file is read as a single
+   * record and returned in a key-value pair, where the key is the path of each file,
+   * the value is the content of each file.
    *
    * <p> For example, if you have the following files:
    * {{{
@@ -227,7 +228,8 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork
    *   hdfs://a-hdfs-path/part-nnnnn
    * }}}
    *
-   * Do `JavaPairRDD<String, byte[]> rdd = sparkContext.dataStreamFiles("hdfs://a-hdfs-path")`,
+   * Do
+   * `JavaPairRDD<String, byte[]> rdd = sparkContext.dataStreamFiles("hdfs://a-hdfs-path")`,
    *
    * <p> then `rdd` contains
    * {{{
@@ -241,13 +243,14 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork
    *
    * @param minPartitions A suggestion value of the minimal splitting number for input data.
    */
-  def dataStreamFiles(path: String, minPartitions: Int = defaultMinPartitions): JavaPairRDD[String,DataInputStream] =
-    new JavaPairRDD(sc.dataStreamFiles(path,minPartitions))
+  def dataStreamFiles(path: String, minPartitions: Int = defaultMinPartitions):
+  JavaPairRDD[String,DataInputStream] = new JavaPairRDD(sc.dataStreamFiles(path,minPartitions))
 
   /**
-   * Read a directory of files as DataInputStreams from HDFS, a local file system (available on all nodes), or any
-   * Hadoop-supported file system URI as a byte array. Each file is read as a single record and returned in a
-   * key-value pair, where the key is the path of each file, the value is the content of each file.
+   * Read a directory of files as DataInputStream from HDFS,
+   * a local file system (available on all nodes), or any Hadoop-supported file system URI
+   * as a byte array. Each file is read as a single record and returned in a
+   * key-value pair, where the key is the path of each file, the value is the content of each.
    *
    * <p> For example, if you have the following files:
    * {{{
@@ -257,7 +260,8 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork
    *   hdfs://a-hdfs-path/part-nnnnn
    * }}}
    *
-   * Do `JavaPairRDD<String, DataInputStream> rdd = sparkContext.binaryFiles("hdfs://a-hdfs-path")`,
+   * Do
+   * `JavaPairRDD<String,DataInputStream> rdd = sparkContext.binaryFiles("hdfs://a-hdfs-path")`,
    *
    * <p> then `rdd` contains
    * {{{
@@ -271,8 +275,8 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork
    *
    * @param minPartitions A suggestion value of the minimal splitting number for input data.
    */
-  def binaryFiles(path: String, minPartitions: Int = defaultMinPartitions): JavaPairRDD[String,Array[Byte]] =
-    new JavaPairRDD(sc.binaryFiles(path,minPartitions))
+  def binaryFiles(path: String, minPartitions: Int = defaultMinPartitions):
+  JavaPairRDD[String,Array[Byte]] = new JavaPairRDD(sc.binaryFiles(path,minPartitions))
 
   /**
    * Read a directory of text files from HDFS, a local file system (available on all nodes), or any

core/src/main/scala/org/apache/spark/input/RawFileInput.scala

@@ -31,7 +31,8 @@ import java.io.DataInputStream
 
 
 /**
- *  A general format for reading whole files in as streams, byte arrays, or other functions to be added
+ *  A general format for reading whole files in as streams, byte arrays,
+ *  or other functions to be added
  */
 abstract class StreamFileInputFormat[T]
   extends CombineFileInputFormat[String,T]  {
@@ -49,12 +50,14 @@ abstract class StreamFileInputFormat[T]
     super.setMaxSplitSize(maxSplitSize)
   }
 
-  def createRecordReader(split: InputSplit, taContext: TaskAttemptContext): RecordReader[String,T]
+  def createRecordReader(split: InputSplit, taContext: TaskAttemptContext):
+    RecordReader[String,T]
 
 }
 
 /**
- * An abstract class of [[org.apache.hadoop.mapreduce.RecordReader RecordReader]] to reading files out as streams
+ * An abstract class of [[org.apache.hadoop.mapreduce.RecordReader RecordReader]]
+ * to reading files out as streams
  */
 abstract class StreamBasedRecordReader[T](
                                       split: CombineFileSplit,
@@ -111,17 +114,20 @@ class StreamRecordReader(
 }
 
 /**
- * A class for extracting the information from the file using the BinaryRecordReader (as Byte array)
+ * A class for extracting the information from the file using the
+ * BinaryRecordReader (as Byte array)
  */
 class StreamInputFormat extends StreamFileInputFormat[DataInputStream] {
   override def createRecordReader(split: InputSplit, taContext: TaskAttemptContext)=
   {
-    new CombineFileRecordReader[String,DataInputStream](split.asInstanceOf[CombineFileSplit],taContext,classOf[StreamRecordReader])
+    new CombineFileRecordReader[String,DataInputStream](
+      split.asInstanceOf[CombineFileSplit],taContext,classOf[StreamRecordReader]
+    )
   }
 }
 
 /**
- * A [[org.apache.hadoop.mapreduce.RecordReader RecordReader]] for reading a single whole binary file
+ * A [[org.apache.hadoop.mapreduce.RecordReader RecordReader]] for reading a single binary file
  * out in a key-value pair, where the key is the file path and the value is the entire content of
  * the file as a byte array
  */
@@ -150,12 +156,14 @@ class ByteRecordReader(
 }
 
 /**
- * A class for extracting the information from the file using the BinaryRecordReader (as Byte array)
+ * A class for reading the file using the BinaryRecordReader (as Byte array)
  */
 class ByteInputFormat extends StreamFileInputFormat[Array[Byte]] {
   override def createRecordReader(split: InputSplit, taContext: TaskAttemptContext)=
   {
-    new CombineFileRecordReader[String,Array[Byte]](split.asInstanceOf[CombineFileSplit],taContext,classOf[ByteRecordReader])
+    new CombineFileRecordReader[String,Array[Byte]](
+      split.asInstanceOf[CombineFileSplit],taContext,classOf[ByteRecordReader]
+    )
   }
 }
 

core/src/main/scala/org/apache/spark/rdd/BinaryFileRDD.scala

@@ -20,21 +20,10 @@ package org.apache.spark.rdd
 /** Allows better control of the partitioning
   *
   */
-import java.text.SimpleDateFormat
-import java.util.Date
-
 import org.apache.hadoop.conf.{Configurable, Configuration}
 import org.apache.hadoop.io.Writable
 import org.apache.hadoop.mapreduce._
-
-import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.input.WholeTextFileInputFormat
-import org.apache.spark.InterruptibleIterator
-import org.apache.spark.Logging
-import org.apache.spark.Partition
-import org.apache.spark.SerializableWritable
-import org.apache.spark.{SparkContext, TaskContext}
-
+import org.apache.spark.{Partition, SparkContext}
 import org.apache.spark.input.StreamFileInputFormat
 
 private[spark] class RawFileRDD[T](
@@ -58,7 +47,9 @@ private[spark] class RawFileRDD[T](
     val rawSplits = inputFormat.getSplits(jobContext).toArray
     val result = new Array[Partition](rawSplits.size)
     for (i <- 0 until rawSplits.size) {
-      result(i) = new NewHadoopPartition(id, i, rawSplits(i).asInstanceOf[InputSplit with Writable])
+      result(i) = new NewHadoopPartition(
+        id, i, rawSplits(i).asInstanceOf[InputSplit with Writable]
+      )
     }
     result
   }