change SparkContext textFile method - apache-spark

Question

I'm trying to change textFile method in spark source code to return an RDD of multi-line strings instead of rdd of line strings. I want to find the implementation that reads file contents from disk in spark source code.
SparkConf sparkConf = new SparkConf().setAppName("MyJavaApp");
JavaSparkContext ctx = new JavaSparkContext(sparkConf);
JavaRDD<String> lines = ctx.textFile(args[0], 1);
But when I follow the textFile call chain, I just reach to HadoopRDD and RDD class.
The call chain is as follow:
in JavaSparkContext.scala
def textFile(path: String, minPartitions: Int): JavaRDD[String] =
sc.textFile(path, minPartitions)
and in SparkContext.scala
def textFile(path: String, minPartitions: Int = defaultMinPartitions): RDD[String] = {
hadoopFile(path, classOf[TextInputFormat], classOf[LongWritable], classOf[Text],
minPartitions).map(pair => pair._2.toString).setName(path)}
and
def hadoopFile[K, V](path: String, ...): RDD[(K, V)] = {
val confBroadcast = broadcast(new SerializableWritable(hadoopConfiguration))
val setInputPathsFunc = (jobConf: JobConf) => FileInputFormat.setInputPaths(jobConf, path)
new HadoopRDD(this,...).setName(path) }
and in HadoopRDD.scala
class HadoopRDD[K, V](
sc: SparkContext,
broadcastedConf: Broadcast[SerializableWritable[Configuration]],
initLocalJobConfFuncOpt: Option[JobConf => Unit],
inputFormatClass: Class[_ <: InputFormat[K, V]],
keyClass: Class[K],
valueClass: Class[V],
minPartitions: Int) extends RDD[(K, V)](sc, Nil) with Logging {...
I don't want to use a map function (as a overhead) to make my customized rdd from rdd of lines.
any help?