[DOCS] Fix typo in API for custom InputFormats based on the “new” MapReduce API

This looks like a simple typo ```SparkContext.newHadoopRDD``` instead of ```SparkContext.newAPIHadoopRDD``` as in actual http://spark.apache.org/docs/1.2.1/api/scala/index.html#org.apache.spark.SparkContext

Author: Alexander <abezzubov@nflabs.com>

Closes #4718 from bzz/hadoop-InputFormats-doc-fix and squashes the following commits:

680a4c4 [Alexander] Fix typo in docs on custom Hadoop InputFormats

docs/programming-guide.md

@@ -335,7 +335,7 @@ Apart from text files, Spark's Scala API also supports several other data format
 
 * For [SequenceFiles](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/mapred/SequenceFileInputFormat.html), use SparkContext's `sequenceFile[K, V]` method where `K` and `V` are the types of key and values in the file. These should be subclasses of Hadoop's [Writable](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/io/Writable.html) interface, like [IntWritable](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/io/IntWritable.html) and [Text](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/io/Text.html). In addition, Spark allows you to specify native types for a few common Writables; for example, `sequenceFile[Int, String]` will automatically read IntWritables and Texts.
 
-* For other Hadoop InputFormats, you can use the `SparkContext.hadoopRDD` method, which takes an arbitrary `JobConf` and input format class, key class and value class. Set these the same way you would for a Hadoop job with your input source. You can also use `SparkContext.newHadoopRDD` for InputFormats based on the "new" MapReduce API (`org.apache.hadoop.mapreduce`).
+* For other Hadoop InputFormats, you can use the `SparkContext.hadoopRDD` method, which takes an arbitrary `JobConf` and input format class, key class and value class. Set these the same way you would for a Hadoop job with your input source. You can also use `SparkContext.newAPIHadoopRDD` for InputFormats based on the "new" MapReduce API (`org.apache.hadoop.mapreduce`).
 
 * `RDD.saveAsObjectFile` and `SparkContext.objectFile` support saving an RDD in a simple format consisting of serialized Java objects. While this is not as efficient as specialized formats like Avro, it offers an easy way to save any RDD.
 
@@ -367,7 +367,7 @@ Apart from text files, Spark's Java API also supports several other data formats
 
 * For [SequenceFiles](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/mapred/SequenceFileInputFormat.html), use SparkContext's `sequenceFile[K, V]` method where `K` and `V` are the types of key and values in the file. These should be subclasses of Hadoop's [Writable](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/io/Writable.html) interface, like [IntWritable](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/io/IntWritable.html) and [Text](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/io/Text.html).
 
-* For other Hadoop InputFormats, you can use the `JavaSparkContext.hadoopRDD` method, which takes an arbitrary `JobConf` and input format class, key class and value class. Set these the same way you would for a Hadoop job with your input source. You can also use `JavaSparkContext.newHadoopRDD` for InputFormats based on the "new" MapReduce API (`org.apache.hadoop.mapreduce`).
+* For other Hadoop InputFormats, you can use the `JavaSparkContext.hadoopRDD` method, which takes an arbitrary `JobConf` and input format class, key class and value class. Set these the same way you would for a Hadoop job with your input source. You can also use `JavaSparkContext.newAPIHadoopRDD` for InputFormats based on the "new" MapReduce API (`org.apache.hadoop.mapreduce`).
 
 * `JavaRDD.saveAsObjectFile` and `JavaSparkContext.objectFile` support saving an RDD in a simple format consisting of serialized Java objects. While this is not as efficient as specialized formats like Avro, it offers an easy way to save any RDD.