SPARK-1483: Rename minSplits to minPartitions in public APIs

https://issues.apache.org/jira/browse/SPARK-1483

From the original JIRA: " The parameter name is part of the public API in Scala and Python, since you can pass named parameters to a method, so we should name it to this more descriptive term. Everywhere else we refer to "splits" as partitions." - @mateiz

Author: CodingCat <zhunansjtu@gmail.com>

Closes #430 from CodingCat/SPARK-1483 and squashes the following commits:

4b60541 [CodingCat] deprecate defaultMinSplits
ba2c663 [CodingCat] Rename minSplits to minPartitions in public APIs

5 files changed, 60 insertions, 45 deletions

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 3ddc0d5eee..ee5637371f 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -427,9 +427,9 @@ class SparkContext(config: SparkConf) extends Logging {
    * Read a text file from HDFS, a local file system (available on all nodes), or any
    * Hadoop-supported file system URI, and return it as an RDD of Strings.
    */
-  def textFile(path: String, minSplits: Int = defaultMinSplits): RDD[String] = {
+  def textFile(path: String, minPartitions: Int = defaultMinPartitions): RDD[String] = {
     hadoopFile(path, classOf[TextInputFormat], classOf[LongWritable], classOf[Text],
-      minSplits).map(pair => pair._2.toString)
+      minPartitions).map(pair => pair._2.toString)
   }
 
   /**
@@ -457,9 +457,10 @@ class SparkContext(config: SparkConf) extends Logging {
    *
    * @note Small files are preferred, large file is also allowable, but may cause bad performance.
    *
-   * @param minSplits A suggestion value of the minimal splitting number for input data.
+   * @param minPartitions A suggestion value of the minimal splitting number for input data.
    */
-  def wholeTextFiles(path: String, minSplits: Int = defaultMinSplits): RDD[(String, String)] = {
+  def wholeTextFiles(path: String, minPartitions: Int = defaultMinPartitions):
+  RDD[(String, String)] = {
     val job = new NewHadoopJob(hadoopConfiguration)
     NewFileInputFormat.addInputPath(job, new Path(path))
     val updateConf = job.getConfiguration
@@ -469,7 +470,7 @@ class SparkContext(config: SparkConf) extends Logging {
       classOf[String],
       classOf[String],
       updateConf,
-      minSplits)
+      minPartitions)
   }
 
   /**
@@ -481,7 +482,7 @@ class SparkContext(config: SparkConf) extends Logging {
    * @param inputFormatClass Class of the InputFormat
    * @param keyClass Class of the keys
    * @param valueClass Class of the values
-   * @param minSplits Minimum number of Hadoop Splits to generate.
+   * @param minPartitions Minimum number of Hadoop Splits to generate.
    *
    * '''Note:''' Because Hadoop's RecordReader class re-uses the same Writable object for each
    * record, directly caching the returned RDD will create many references to the same object.
@@ -493,11 +494,11 @@ class SparkContext(config: SparkConf) extends Logging {
       inputFormatClass: Class[_ <: InputFormat[K, V]],
       keyClass: Class[K],
       valueClass: Class[V],
-      minSplits: Int = defaultMinSplits
+      minPartitions: Int = defaultMinPartitions
       ): RDD[(K, V)] = {
     // Add necessary security credentials to the JobConf before broadcasting it.
     SparkHadoopUtil.get.addCredentials(conf)
-    new HadoopRDD(this, conf, inputFormatClass, keyClass, valueClass, minSplits)
+    new HadoopRDD(this, conf, inputFormatClass, keyClass, valueClass, minPartitions)
   }
 
   /** Get an RDD for a Hadoop file with an arbitrary InputFormat
@@ -512,7 +513,7 @@ class SparkContext(config: SparkConf) extends Logging {
       inputFormatClass: Class[_ <: InputFormat[K, V]],
       keyClass: Class[K],
       valueClass: Class[V],
-      minSplits: Int = defaultMinSplits
+      minPartitions: Int = defaultMinPartitions
       ): RDD[(K, V)] = {
     // A Hadoop configuration can be about 10 KB, which is pretty big, so broadcast it.
     val confBroadcast = broadcast(new SerializableWritable(hadoopConfiguration))
@@ -524,7 +525,7 @@ class SparkContext(config: SparkConf) extends Logging {
       inputFormatClass,
       keyClass,
       valueClass,
-      minSplits)
+      minPartitions)
   }
 
   /**
@@ -532,7 +533,7 @@ class SparkContext(config: SparkConf) extends Logging {
    * values and the InputFormat so that users don't need to pass them directly. Instead, callers
    * can just write, for example,
    * {{{
-   * val file = sparkContext.hadoopFile[LongWritable, Text, TextInputFormat](path, minSplits)
+   * val file = sparkContext.hadoopFile[LongWritable, Text, TextInputFormat](path, minPartitions)
    * }}}
    *
    * '''Note:''' Because Hadoop's RecordReader class re-uses the same Writable object for each
@@ -541,13 +542,13 @@ class SparkContext(config: SparkConf) extends Logging {
    * a `map` function.
    */
   def hadoopFile[K, V, F <: InputFormat[K, V]]
-      (path: String, minSplits: Int)
+      (path: String, minPartitions: Int)
       (implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]): RDD[(K, V)] = {
     hadoopFile(path,
       fm.runtimeClass.asInstanceOf[Class[F]],
       km.runtimeClass.asInstanceOf[Class[K]],
       vm.runtimeClass.asInstanceOf[Class[V]],
-      minSplits)
+      minPartitions)
   }
 
   /**
@@ -565,7 +566,7 @@ class SparkContext(config: SparkConf) extends Logging {
    */
   def hadoopFile[K, V, F <: InputFormat[K, V]](path: String)
       (implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]): RDD[(K, V)] =
-    hadoopFile[K, V, F](path, defaultMinSplits)
+    hadoopFile[K, V, F](path, defaultMinPartitions)
 
   /** Get an RDD for a Hadoop file with an arbitrary new API InputFormat. */
   def newAPIHadoopFile[K, V, F <: NewInputFormat[K, V]]
@@ -626,10 +627,10 @@ class SparkContext(config: SparkConf) extends Logging {
   def sequenceFile[K, V](path: String,
       keyClass: Class[K],
       valueClass: Class[V],
-      minSplits: Int
+      minPartitions: Int
       ): RDD[(K, V)] = {
     val inputFormatClass = classOf[SequenceFileInputFormat[K, V]]
-    hadoopFile(path, inputFormatClass, keyClass, valueClass, minSplits)
+    hadoopFile(path, inputFormatClass, keyClass, valueClass, minPartitions)
   }
 
   /** Get an RDD for a Hadoop SequenceFile with given key and value types.
@@ -641,7 +642,7 @@ class SparkContext(config: SparkConf) extends Logging {
     * */
   def sequenceFile[K, V](path: String, keyClass: Class[K], valueClass: Class[V]
       ): RDD[(K, V)] =
-    sequenceFile(path, keyClass, valueClass, defaultMinSplits)
+    sequenceFile(path, keyClass, valueClass, defaultMinPartitions)
 
   /**
    * Version of sequenceFile() for types implicitly convertible to Writables through a
@@ -665,7 +666,7 @@ class SparkContext(config: SparkConf) extends Logging {
    * a `map` function.
    */
    def sequenceFile[K, V]
-       (path: String, minSplits: Int = defaultMinSplits)
+       (path: String, minPartitions: Int = defaultMinPartitions)
        (implicit km: ClassTag[K], vm: ClassTag[V],
         kcf: () => WritableConverter[K], vcf: () => WritableConverter[V])
       : RDD[(K, V)] = {
@@ -674,7 +675,7 @@ class SparkContext(config: SparkConf) extends Logging {
     val format = classOf[SequenceFileInputFormat[Writable, Writable]]
     val writables = hadoopFile(path, format,
         kc.writableClass(km).asInstanceOf[Class[Writable]],
-        vc.writableClass(vm).asInstanceOf[Class[Writable]], minSplits)
+        vc.writableClass(vm).asInstanceOf[Class[Writable]], minPartitions)
     writables.map { case (k, v) => (kc.convert(k), vc.convert(v)) }
   }
 
@@ -688,9 +689,9 @@ class SparkContext(config: SparkConf) extends Logging {
    */
   def objectFile[T: ClassTag](
       path: String,
-      minSplits: Int = defaultMinSplits
+      minPartitions: Int = defaultMinPartitions
       ): RDD[T] = {
-    sequenceFile(path, classOf[NullWritable], classOf[BytesWritable], minSplits)
+    sequenceFile(path, classOf[NullWritable], classOf[BytesWritable], minPartitions)
       .flatMap(x => Utils.deserialize[Array[T]](x._2.getBytes))
   }
 
@@ -1183,8 +1184,12 @@ class SparkContext(config: SparkConf) extends Logging {
   def defaultParallelism: Int = taskScheduler.defaultParallelism
 
   /** Default min number of partitions for Hadoop RDDs when not given by user */
+  @deprecated("use defaultMinPartitions", "1.0.0")
   def defaultMinSplits: Int = math.min(defaultParallelism, 2)
 
+  /** Default min number of partitions for Hadoop RDDs when not given by user */
+  def defaultMinPartitions: Int = math.min(defaultParallelism, 2)
+
   private val nextShuffleId = new AtomicInteger(0)
 
   private[spark] def newShuffleId(): Int = nextShuffleId.getAndIncrement()
diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
index e6a3f06b0e..cf30523ab5 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
@@ -109,9 +109,17 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork
   /** Default level of parallelism to use when not given by user (e.g. parallelize and makeRDD). */
   def defaultParallelism: java.lang.Integer = sc.defaultParallelism
 
-  /** Default min number of partitions for Hadoop RDDs when not given by user */
+  /**
+   * Default min number of partitions for Hadoop RDDs when not given by user.
+   * @deprecated As of Spark 1.0.0, defaultMinSplits is deprecated, use
+   *            {@link #defaultMinPartitions()} instead
+   */
+  @Deprecated
   def defaultMinSplits: java.lang.Integer = sc.defaultMinSplits
 
+  /** Default min number of partitions for Hadoop RDDs when not given by user */
+  def defaultMinPartitions: java.lang.Integer = sc.defaultMinPartitions
+
   /** Distribute a local Scala collection to form an RDD. */
   def parallelize[T](list: java.util.List[T], numSlices: Int): JavaRDD[T] = {
     implicit val ctag: ClassTag[T] = fakeClassTag
@@ -153,7 +161,8 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork
    * Read a text file from HDFS, a local file system (available on all nodes), or any
    * Hadoop-supported file system URI, and return it as an RDD of Strings.
    */
-  def textFile(path: String, minSplits: Int): JavaRDD[String] = sc.textFile(path, minSplits)
+  def textFile(path: String, minPartitions: Int): JavaRDD[String] =
+    sc.textFile(path, minPartitions)
 
   /**
    * Read a directory of text files from HDFS, a local file system (available on all nodes), or any
@@ -180,17 +189,17 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork
    *
    * @note Small files are preferred, large file is also allowable, but may cause bad performance.
    *
-   * @param minSplits A suggestion value of the minimal splitting number for input data.
+   * @param minPartitions A suggestion value of the minimal splitting number for input data.
    */
-  def wholeTextFiles(path: String, minSplits: Int): JavaPairRDD[String, String] =
-    new JavaPairRDD(sc.wholeTextFiles(path, minSplits))
+  def wholeTextFiles(path: String, minPartitions: Int): JavaPairRDD[String, String] =
+    new JavaPairRDD(sc.wholeTextFiles(path, minPartitions))
 
   /**
    * 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.
    *
-   * @see `wholeTextFiles(path: String, minSplits: Int)`.
+   * @see `wholeTextFiles(path: String, minPartitions: Int)`.
    */
   def wholeTextFiles(path: String): JavaPairRDD[String, String] =
     new JavaPairRDD(sc.wholeTextFiles(path))
@@ -205,11 +214,11 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork
   def sequenceFile[K, V](path: String,
     keyClass: Class[K],
     valueClass: Class[V],
-    minSplits: Int
+    minPartitions: Int
     ): JavaPairRDD[K, V] = {
     implicit val ctagK: ClassTag[K] = ClassTag(keyClass)
     implicit val ctagV: ClassTag[V] = ClassTag(valueClass)
-    new JavaPairRDD(sc.sequenceFile(path, keyClass, valueClass, minSplits))
+    new JavaPairRDD(sc.sequenceFile(path, keyClass, valueClass, minPartitions))
   }
 
   /** Get an RDD for a Hadoop SequenceFile.
@@ -233,9 +242,9 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork
    * slow if you use the default serializer (Java serialization), though the nice thing about it is
    * that there's very little effort required to save arbitrary objects.
    */
-  def objectFile[T](path: String, minSplits: Int): JavaRDD[T] = {
+  def objectFile[T](path: String, minPartitions: Int): JavaRDD[T] = {
     implicit val ctag: ClassTag[T] = fakeClassTag
-    sc.objectFile(path, minSplits)(ctag)
+    sc.objectFile(path, minPartitions)(ctag)
   }
 
   /**
@@ -265,11 +274,11 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork
     inputFormatClass: Class[F],
     keyClass: Class[K],
     valueClass: Class[V],
-    minSplits: Int
+    minPartitions: Int
     ): JavaPairRDD[K, V] = {
     implicit val ctagK: ClassTag[K] = ClassTag(keyClass)
     implicit val ctagV: ClassTag[V] = ClassTag(valueClass)
-    new JavaPairRDD(sc.hadoopRDD(conf, inputFormatClass, keyClass, valueClass, minSplits))
+    new JavaPairRDD(sc.hadoopRDD(conf, inputFormatClass, keyClass, valueClass, minPartitions))
   }
 
   /**
@@ -304,11 +313,11 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork
     inputFormatClass: Class[F],
     keyClass: Class[K],
     valueClass: Class[V],
-    minSplits: Int
+    minPartitions: Int
     ): JavaPairRDD[K, V] = {
     implicit val ctagK: ClassTag[K] = ClassTag(keyClass)
     implicit val ctagV: ClassTag[V] = ClassTag(valueClass)
-    new JavaPairRDD(sc.hadoopFile(path, inputFormatClass, keyClass, valueClass, minSplits))
+    new JavaPairRDD(sc.hadoopFile(path, inputFormatClass, keyClass, valueClass, minPartitions))
   }
 
   /** Get an RDD for a Hadoop file with an arbitrary InputFormat
diff --git a/core/src/main/scala/org/apache/spark/input/WholeTextFileInputFormat.scala b/core/src/main/scala/org/apache/spark/input/WholeTextFileInputFormat.scala
index 80d055a895..4cb4505777 100644
--- a/core/src/main/scala/org/apache/spark/input/WholeTextFileInputFormat.scala
+++ b/core/src/main/scala/org/apache/spark/input/WholeTextFileInputFormat.scala
@@ -48,14 +48,15 @@ private[spark] class WholeTextFileInputFormat extends CombineFileInputFormat[Str
   }
 
   /**
-   * Allow minSplits set by end-user in order to keep compatibility with old Hadoop API.
+   * Allow minPartitions set by end-user in order to keep compatibility with old Hadoop API.
    */
-  def setMaxSplitSize(context: JobContext, minSplits: Int) {
+  def setMaxSplitSize(context: JobContext, minPartitions: Int) {
     val files = listStatus(context)
     val totalLen = files.map { file =>
       if (file.isDir) 0L else file.getLen
     }.sum
-    val maxSplitSize = Math.ceil(totalLen * 1.0 / (if (minSplits == 0) 1 else minSplits)).toLong
+    val maxSplitSize = Math.ceil(totalLen * 1.0 /
+      (if (minPartitions == 0) 1 else minPartitions)).toLong
     super.setMaxSplitSize(maxSplitSize)
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
index 6811e1abb8..6547755764 100644
--- a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
@@ -87,7 +87,7 @@ private[spark] class HadoopPartition(rddId: Int, idx: Int, @transient s: InputSp
  * @param inputFormatClass Storage format of the data to be read.
  * @param keyClass Class of the key associated with the inputFormatClass.
  * @param valueClass Class of the value associated with the inputFormatClass.
- * @param minSplits Minimum number of Hadoop Splits (HadoopRDD partitions) to generate.
+ * @param minPartitions Minimum number of HadoopRDD partitions (Hadoop Splits) to generate.
  */
 @DeveloperApi
 class HadoopRDD[K, V](
@@ -97,7 +97,7 @@ class HadoopRDD[K, V](
     inputFormatClass: Class[_ <: InputFormat[K, V]],
     keyClass: Class[K],
     valueClass: Class[V],
-    minSplits: Int)
+    minPartitions: Int)
   extends RDD[(K, V)](sc, Nil) with Logging {
 
   def this(
@@ -106,7 +106,7 @@ class HadoopRDD[K, V](
       inputFormatClass: Class[_ <: InputFormat[K, V]],
       keyClass: Class[K],
       valueClass: Class[V],
-      minSplits: Int) = {
+      minPartitions: Int) = {
     this(
       sc,
       sc.broadcast(new SerializableWritable(conf))
@@ -115,7 +115,7 @@ class HadoopRDD[K, V](
       inputFormatClass,
       keyClass,
       valueClass,
-      minSplits)
+      minPartitions)
   }
 
   protected val jobConfCacheKey = "rdd_%d_job_conf".format(id)
@@ -169,7 +169,7 @@ class HadoopRDD[K, V](
     if (inputFormat.isInstanceOf[Configurable]) {
       inputFormat.asInstanceOf[Configurable].setConf(jobConf)
     }
-    val inputSplits = inputFormat.getSplits(jobConf, minSplits)
+    val inputSplits = inputFormat.getSplits(jobConf, minPartitions)
     val array = new Array[Partition](inputSplits.size)
     for (i <- 0 until inputSplits.size) {
       array(i) = new HadoopPartition(id, i, inputSplits(i))
diff --git a/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala
index 8684b645bc..ac1ccc06f2 100644
--- a/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala
@@ -158,7 +158,7 @@ private[spark] class WholeTextFileRDD(
     keyClass: Class[String],
     valueClass: Class[String],
     @transient conf: Configuration,
-    minSplits: Int)
+    minPartitions: Int)
   extends NewHadoopRDD[String, String](sc, inputFormatClass, keyClass, valueClass, conf) {
 
   override def getPartitions: Array[Partition] = {
@@ -169,7 +169,7 @@ private[spark] class WholeTextFileRDD(
       case _ =>
     }
     val jobContext = newJobContext(conf, jobId)
-    inputFormat.setMaxSplitSize(jobContext, minSplits)
+    inputFormat.setMaxSplitSize(jobContext, minPartitions)
     val rawSplits = inputFormat.getSplits(jobContext).toArray
     val result = new Array[Partition](rawSplits.size)
     for (i <- 0 until rawSplits.size) {