move whole text file API to Spark core

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

@@ -37,6 +37,7 @@ import org.apache.mesos.MesosNativeLibrary
 
 import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.deploy.{LocalSparkCluster, SparkHadoopUtil}
+import org.apache.spark.input.WholeTextFileInputFormat
 import org.apache.spark.partial.{ApproximateEvaluator, PartialResult}
 import org.apache.spark.rdd._
 import org.apache.spark.scheduler._
@@ -371,6 +372,37 @@ class SparkContext(
       minSplits).map(pair => pair._2.toString)
   }
 
+  /**
+   * Read a directory of text files from HDFS, a local file system (available on all nodes), or any
+   * Hadoop-supported file system URI. 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:
+   * {{{
+   *   hdfs://a-hdfs-path/part-00000
+   *   hdfs://a-hdfs-path/part-00001
+   *   ...
+   *   hdfs://a-hdfs-path/part-nnnnn
+   * }}}
+   *
+   * Do `val rdd = mlContext.wholeTextFile("hdfs://a-hdfs-path")`,
+   *
+   * <p> then `rdd` contains
+   * {{{
+   *   (a-hdfs-path/part-00000, its content)
+   *   (a-hdfs-path/part-00001, its content)
+   *   ...
+   *   (a-hdfs-path/part-nnnnn, its content)
+   * }}}
+   */
+  def wholeTextFiles(path: String): RDD[(String, String)] = {
+    newAPIHadoopFile(
+      path,
+      classOf[WholeTextFileInputFormat],
+      classOf[String],
+      classOf[String])
+  }
+
   /**
    * Get an RDD for a Hadoop-readable dataset from a Hadoop JobConf given its InputFormat and other
    * necessary info (e.g. file name for a filesystem-based dataset, table name for HyperTable),

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

@@ -154,6 +154,31 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork
    */
   def textFile(path: String, minSplits: Int): JavaRDD[String] = sc.textFile(path, minSplits)
 
+  /**
+   * Read a directory of text files from HDFS, a local file system (available on all nodes), or any
+   * Hadoop-supported file system URI. 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:
+   * {{{
+   *   hdfs://a-hdfs-path/part-00000
+   *   hdfs://a-hdfs-path/part-00001
+   *   ...
+   *   hdfs://a-hdfs-path/part-nnnnn
+   * }}}
+   *
+   * Do `val rdd = mlContext.wholeTextFile("hdfs://a-hdfs-path")`,
+   *
+   * <p> then `rdd` contains
+   * {{{
+   *   (a-hdfs-path/part-00000, its content)
+   *   (a-hdfs-path/part-00001, its content)
+   *   ...
+   *   (a-hdfs-path/part-nnnnn, its content)
+   * }}}
+   */
+  def wholeTextFiles(path: String): JavaRDD[(String, String)] = sc.wholeTextFiles(path)
+
   /** Get an RDD for a Hadoop SequenceFile with given key and value types.
     *
     * '''Note:''' Because Hadoop's RecordReader class re-uses the same Writable object for each

mllib/src/main/scala/org/apache/spark/mllib/input/WholeTextFileInputFormat.scala → core/src/main/scala/org/apache/spark/input/WholeTextFileInputFormat.scala

@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.mllib.input
+package org.apache.spark.input
 
 import org.apache.hadoop.fs.Path
 import org.apache.hadoop.mapreduce.InputSplit
@@ -32,7 +32,7 @@ import org.apache.hadoop.mapreduce.lib.input.CombineFileSplit
  * the value is the entire content of file.
  */
 
-private[mllib] class WholeTextFileInputFormat extends CombineFileInputFormat[String, String] {
+private[spark] class WholeTextFileInputFormat extends CombineFileInputFormat[String, String] {
   override protected def isSplitable(context: JobContext, file: Path): Boolean = false
 
   override def createRecordReader(

mllib/src/main/scala/org/apache/spark/mllib/input/WholeTextFileRecordReader.scala → core/src/main/scala/org/apache/spark/input/WholeTextFileRecordReader.scala

@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.mllib.input
+package org.apache.spark.input
 
 import com.google.common.io.{ByteStreams, Closeables}
 
@@ -30,7 +30,7 @@ import org.apache.hadoop.mapreduce.TaskAttemptContext
  * out in a key-value pair, where the key is the file path and the value is the entire content of
  * the file.
  */
-private[mllib] class WholeTextFileRecordReader(
+private[spark] class WholeTextFileRecordReader(
     split: CombineFileSplit,
     context: TaskAttemptContext,
     index: Integer)

mllib/src/test/scala/org/apache/spark/mllib/input/WholeTextFileRecordReaderSuite.scala → core/src/test/scala/org/apache/spark/input/WholeTextFileRecordReaderSuite.scala

@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.mllib.input
+package org.apache.spark.input
 
 import java.io.DataOutputStream
 import java.io.File
@@ -31,11 +31,10 @@ import org.scalatest.FunSuite
 import org.apache.hadoop.io.Text
 
 import org.apache.spark.SparkContext
-import org.apache.spark.mllib.MLContext._
 
 /**
  * Tests the correctness of
- * [[org.apache.spark.mllib.input.WholeTextFileRecordReader WholeTextFileRecordReader]]. A temporary
+ * [[org.apache.spark.input.WholeTextFileRecordReader WholeTextFileRecordReader]]. A temporary
  * directory is created as fake input. Temporal storage would be deleted in the end.
  */
 class WholeTextFileRecordReaderSuite extends FunSuite with BeforeAndAfterAll {
@@ -74,7 +73,7 @@ class WholeTextFileRecordReaderSuite extends FunSuite with BeforeAndAfterAll {
       createNativeFile(dir, filename, contents)
     }
 
-    val res = sc.wholeTextFile(dir.toString).collect()
+    val res = sc.wholeTextFiles(dir.toString).collect()
 
     assert(res.size === WholeTextFileRecordReaderSuite.fileNames.size,
       "Number of files read out does not fit with the actual value.")