Source code for pyspark

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PySpark is the Python API for Spark.

Public classes:

  - :class:`SparkContext`:
      Main entry point for Spark functionality.
  - :class:`RDD`:
      A Resilient Distributed Dataset (RDD), the basic abstraction in Spark.
  - :class:`Broadcast`:
      A broadcast variable that gets reused across tasks.
  - :class:`Accumulator`:
      An "add-only" shared variable that tasks can only add values to.
  - :class:`SparkConf`:
      For configuring Spark.
  - :class:`SparkFiles`:
      Access files shipped with jobs.
  - :class:`StorageLevel`:
      Finer-grained cache persistence levels.


from functools import wraps
import types

from pyspark.conf import SparkConf
from pyspark.context import SparkContext
from pyspark.rdd import RDD
from pyspark.files import SparkFiles
from pyspark.storagelevel import StorageLevel
from pyspark.accumulators import Accumulator, AccumulatorParam
from pyspark.broadcast import Broadcast
from pyspark.serializers import MarshalSerializer, PickleSerializer
from pyspark.status import *
from pyspark.profiler import Profiler, BasicProfiler

def since(version):
    A decorator that annotates a function to append the version of Spark the function was added.
    import re
    indent_p = re.compile(r'\n( +)')

    def deco(f):
        indents = indent_p.findall(f.__doc__)
        indent = ' ' * (min(len(m) for m in indents) if indents else 0)
        f.__doc__ = f.__doc__.rstrip() + "\n\n%s.. versionadded:: %s" % (indent, version)
        return f
    return deco

def copy_func(f, name=None, sinceversion=None, doc=None):
    Returns a function with same code, globals, defaults, closure, and
    name (or provide a new name).
    # See
    fn = types.FunctionType(f.__code__, f.__globals__, name or f.__name__, f.__defaults__,
    # in case f was given attrs (note this dict is a shallow copy):
    if doc is not None:
        fn.__doc__ = doc
    if sinceversion is not None:
        fn = since(sinceversion)(fn)
    return fn

def keyword_only(func):
    A decorator that forces keyword arguments in the wrapped method
    and saves actual input keyword arguments in `_input_kwargs`.
    def wrapper(*args, **kwargs):
        if len(args) > 1:
            raise TypeError("Method %s forces keyword arguments." % func.__name__)
        wrapper._input_kwargs = kwargs
        return func(*args, **kwargs)
    return wrapper

# for back compatibility
from pyspark.sql import SQLContext, HiveContext, Row

__all__ = [
    "SparkConf", "SparkContext", "SparkFiles", "RDD", "StorageLevel", "Broadcast",
    "Accumulator", "AccumulatorParam", "MarshalSerializer", "PickleSerializer",
    "StatusTracker", "SparkJobInfo", "SparkStageInfo", "Profiler", "BasicProfiler",