Add more docs for Hadoop Configuration

Author: zsxwing

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

@@ -242,7 +242,11 @@ class SparkContext(config: SparkConf) extends SparkStatusAPI with Logging {
   // the bound port to the cluster manager properly
   ui.foreach(_.bind())
 
-  /** A default Hadoop Configuration for the Hadoop code (e.g. file systems) that we reuse. */
+  /** A default Hadoop Configuration for the Hadoop code (e.g. file systems) that we reuse.
+    *
+    * '''Note:''' As it will be reused in all Hadoop RDDs, it's better not to modify it unless you
+    * plan to set some global configurations for all Hadoop RDDs.
+    */
   val hadoopConfiguration = SparkHadoopUtil.get.newConfiguration(conf)
 
   val startTime = System.currentTimeMillis()
@@ -630,7 +634,10 @@ class SparkContext(config: SparkConf) extends SparkStatusAPI with Logging {
    * necessary info (e.g. file name for a filesystem-based dataset, table name for HyperTable),
    * using the older MapReduce API (`org.apache.hadoop.mapred`).
    *
-   * @param conf JobConf for setting up the dataset
+   * @param conf JobConf for setting up the dataset. Note: This will be put into a Broadcast.
+   *             Therefore if you plan to reuse this conf to create multiple RDDs, you need to make
+   *             sure you won't modify the conf. A safe approach is always creating a new conf for
+   *             a new RDD.
    * @param inputFormatClass Class of the InputFormat
    * @param keyClass Class of the keys
    * @param valueClass Class of the values
@@ -756,6 +763,14 @@ class SparkContext(config: SparkConf) extends SparkStatusAPI with Logging {
    * Get an RDD for a given Hadoop file with an arbitrary new API InputFormat
    * and extra configuration options to pass to the input format.
    *
+   * @param conf Configuration for setting up the dataset. Note: This will be put into a Broadcast.
+   *             Therefore if you plan to reuse this conf to create multiple RDDs, you need to make
+   *             sure you won't modify the conf. A safe approach is always creating a new conf for
+   *             a new RDD.
+   * @param fClass Class of the InputFormat
+   * @param kClass Class of the keys
+   * @param vClass Class of the values
+   *
    * '''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.
    * If you plan to directly cache Hadoop writable objects, you should first copy them using

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

@@ -387,6 +387,15 @@ class JavaSparkContext(val sc: SparkContext)
    * other necessary info (e.g. file name for a filesystem-based dataset, table name for HyperTable,
    * etc).
    *
+   * @param conf JobConf for setting up the dataset. Note: This will be put into a Broadcast.
+   *             Therefore if you plan to reuse this conf to create multiple RDDs, you need to make
+   *             sure you won't modify the conf. A safe approach is always creating a new conf for
+   *             a new RDD.
+   * @param inputFormatClass Class of the InputFormat
+   * @param keyClass Class of the keys
+   * @param valueClass Class of the values
+   * @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.
    * If you plan to directly cache Hadoop writable objects, you should first copy them using
@@ -409,6 +418,14 @@ class JavaSparkContext(val sc: SparkContext)
    * Get an RDD for a Hadoop-readable dataset from a Hadooop JobConf giving its InputFormat and any
    * other necessary info (e.g. file name for a filesystem-based dataset, table name for HyperTable,
    *
+   * @param conf JobConf for setting up the dataset. Note: This will be put into a Broadcast.
+   *             Therefore if you plan to reuse this conf to create multiple RDDs, you need to make
+   *             sure you won't modify the conf. A safe approach is always creating a new conf for
+   *             a new RDD.
+   * @param inputFormatClass Class of the InputFormat
+   * @param keyClass Class of the keys
+   * @param valueClass Class of the values
+   *
    * '''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.
    * If you plan to directly cache Hadoop writable objects, you should first copy them using
@@ -490,6 +507,14 @@ class JavaSparkContext(val sc: SparkContext)
    * Get an RDD for a given Hadoop file with an arbitrary new API InputFormat
    * and extra configuration options to pass to the input format.
    *
+   * @param conf Configuration for setting up the dataset. Note: This will be put into a Broadcast.
+   *             Therefore if you plan to reuse this conf to create multiple RDDs, you need to make
+   *             sure you won't modify the conf. A safe approach is always creating a new conf for
+   *             a new RDD.
+   * @param fClass Class of the InputFormat
+   * @param kClass Class of the keys
+   * @param vClass Class of the values
+   *
    * '''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.
    * If you plan to directly cache Hadoop writable objects, you should first copy them using
@@ -689,6 +714,9 @@ class JavaSparkContext(val sc: SparkContext)
 
   /**
    * Returns the Hadoop configuration used for the Hadoop code (e.g. file systems) we reuse.
+   *
+   * '''Note:''' As it will be reused in all Hadoop RDDs, it's better not to modify it unless you
+   * plan to set some global configurations for all Hadoop RDDs.
    */
   def hadoopConfiguration(): Configuration = {
     sc.hadoopConfiguration