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')]

def wholeTextFiles(self, path, minPartitions=None, use_unicode=True):
        """
        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')]
        """
        minPartitions = minPartitions or self.defaultMinPartitions
        return RDD(self._jsc.wholeTextFiles(path, minPartitions), self,
                   PairDeserializer(UTF8Deserializer(use_unicode), UTF8Deserializer(use_unicode)))