spark-instrumented-optimizer/python/pyspark/__init__.py

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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.
  - :class:`TaskContext`:
      Information about the current running task, available on the workers and experimental.
  - :class:`RDDBarrier`:
      Wraps an RDD under a barrier stage for barrier execution.
  - :class:`BarrierTaskContext`:
      A :class:`TaskContext` that provides extra info and tooling for barrier execution.
  - :class:`BarrierTaskInfo`:
      Information about a barrier task.
  - :class:`InheritableThread`:
      A inheritable thread to use in Spark when the pinned thread mode is on.
"""

from functools import wraps
import types

from pyspark.conf import SparkConf
from pyspark.rdd import RDD, RDDBarrier
from pyspark.files import SparkFiles
from pyspark.status import StatusTracker, SparkJobInfo, SparkStageInfo
from pyspark.util import InheritableThread
from pyspark.storagelevel import StorageLevel
from pyspark.accumulators import Accumulator, AccumulatorParam
from pyspark.broadcast import Broadcast
from pyspark.serializers import MarshalSerializer, PickleSerializer
from pyspark.taskcontext import TaskContext, BarrierTaskContext, BarrierTaskInfo
from pyspark.profiler import Profiler, BasicProfiler
from pyspark.version import __version__  # noqa: F401
from pyspark._globals import _NoValue  # noqa: F401


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
    # http://stackoverflow.com/questions/6527633/how-can-i-make-a-deepcopy-of-a-function-in-python
    fn = types.FunctionType(f.__code__, f.__globals__, name or f.__name__, f.__defaults__,
                            f.__closure__)
    # in case f was given attrs (note this dict is a shallow copy):
    fn.__dict__.update(f.__dict__)
    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`.

    .. note:: Should only be used to wrap a method where first arg is `self`
    """
    @wraps(func)
    def wrapper(self, *args, **kwargs):
        if len(args) > 0:
            raise TypeError("Method %s forces keyword arguments." % func.__name__)
        self._input_kwargs = kwargs
        return func(self, **kwargs)
    return wrapper

# To avoid circular dependencies
from pyspark.context import SparkContext

# for back compatibility
from pyspark.sql import SQLContext, HiveContext, Row  # noqa: F401

__all__ = [
    "SparkConf", "SparkContext", "SparkFiles", "RDD", "StorageLevel", "Broadcast",
    "Accumulator", "AccumulatorParam", "MarshalSerializer", "PickleSerializer",
    "StatusTracker", "SparkJobInfo", "SparkStageInfo", "Profiler", "BasicProfiler", "TaskContext",
    "RDDBarrier", "BarrierTaskContext", "BarrierTaskInfo", "InheritableThread",
]