def addFile(self, path, recursive=False):
        """
        Add a file to be downloaded with this Spark job on every node.
        The C{path} passed can be either a local file, a file in HDFS
        (or other Hadoop-supported filesystems), or an HTTP, HTTPS or
        FTP URI.

        To access the file in Spark jobs, use
        L{SparkFiles.get(fileName)<pyspark.files.SparkFiles.get>} with the
        filename to find its download location.

        A directory can be given if the recursive option is set to True.
        Currently directories are only supported for Hadoop-supported filesystems.

        .. note:: A path can be added only once. Subsequent additions of the same path are ignored.

        >>> from pyspark import SparkFiles
        >>> path = os.path.join(tempdir, "test.txt")
        >>> with open(path, "w") as testFile:
        ...    _ = testFile.write("100")
        >>> sc.addFile(path)
        >>> def func(iterator):
        ...    with open(SparkFiles.get("test.txt")) as testFile:
        ...        fileVal = int(testFile.readline())
        ...        return [x * fileVal for x in iterator]
        >>> sc.parallelize([1, 2, 3, 4]).mapPartitions(func).collect()
        [100, 200, 300, 400]
        """
        self._jsc.sc().addFile(path, recursive)
def addFile ( self , path , recursive = False ) : self . _jsc . sc ( ) . addFile ( path , recursive )
Add a file to be downloaded with this Spark job on every node.
        The C{path} passed can be either a local file, a file in HDFS
        (or other Hadoop-supported filesystems), or an HTTP, HTTPS or
        FTP URI.

        To access the file in Spark jobs, use
        L{SparkFiles.get(fileName)<pyspark.files.SparkFiles.get>} with the
        filename to find its download location.

        A directory can be given if the recursive option is set to True.
        Currently directories are only supported for Hadoop-supported filesystems.

        .. note:: A path can be added only once. Subsequent additions of the same path are ignored.

        >>> from pyspark import SparkFiles
        >>> path = os.path.join(tempdir, "test.txt")
        >>> with open(path, "w") as testFile:
        ...    _ = testFile.write("100")
        >>> sc.addFile(path)
        >>> def func(iterator):
        ...    with open(SparkFiles.get("test.txt")) as testFile:
        ...        fileVal = int(testFile.readline())
        ...        return [x * fileVal for x in iterator]
        >>> sc.parallelize([1, 2, 3, 4]).mapPartitions(func).collect()
        [100, 200, 300, 400]