code: def wholeTextFiles(self, path, minPartitions=None, use_unicode=True):
          
          minPartitions = minPartitions or self.defaultMinPartitions
          return RDD(self._jsc.wholeTextFiles(path, minPartitions), self,
                     PairDeserializer(UTF8Deserializer(use_unicode), UTF8Deserializer(use_unicode)))
docs: 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.
          The text files must be encoded as UTF-8.
  
          If use_unicode is False, the strings will be kept as `str` (encoding
          as `utf-8`), which is faster and smaller than unicode. (Added in
          Spark 1.2)
  
          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 C{rdd = sparkContext.wholeTextFiles("hdfs://a-hdfs-path")},
          then C{rdd} contains::
  
            (a-hdfs-path/part-00000, its content)
            (a-hdfs-path/part-00001, its content)
            ...
            (a-hdfs-path/part-nnnnn, its content)
  
          .. note:: Small files are preferred, as each file will be loaded
              fully in memory.
  
          >>> dirPath = os.path.join(tempdir, "files")
          >>> os.mkdir(dirPath)
          >>> with open(os.path.join(dirPath, "1.txt"), "w") as file1:
          ...    _ = file1.write("1")
          >>> with open(os.path.join(dirPath, "2.txt"), "w") as file2:
          ...    _ = file2.write("2")
          >>> textFiles = sc.wholeTextFiles(dirPath)
          >>> sorted(textFiles.collect())
          [(u'.../1.txt', u'1'), (u'.../2.txt', u'2')]