213 lines
6.6 KiB
Python
213 lines
6.6 KiB
Python
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# This file is part of Pebble.
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# Copyright (c) 2013-2022, Matteo Cafasso
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# Pebble is free software: you can redistribute it and/or modify
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# it under the terms of the GNU Lesser General Public License
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# as published by the Free Software Foundation,
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# either version 3 of the License, or (at your option) any later version.
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# Pebble is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU Lesser General Public License for more details.
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# You should have received a copy of the GNU Lesser General Public License
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# along with Pebble. If not, see <http://www.gnu.org/licenses/>.
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import os
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import sys
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import types
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import signal
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import multiprocessing
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from itertools import count
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from functools import wraps
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from concurrent.futures import CancelledError, TimeoutError
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from pebble.common import ProcessExpired, ProcessFuture
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from pebble.common import launch_process, stop_process, SLEEP_UNIT
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from pebble.common import process_execute, launch_thread, send_result
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def process(*args, **kwargs):
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"""Runs the decorated function in a concurrent process,
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taking care of the result and error management.
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Decorated functions will return a concurrent.futures.Future object
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once called.
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The timeout parameter will set a maximum execution time
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for the decorated function. If the execution exceeds the timeout,
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the process will be stopped and the Future will raise TimeoutError.
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The name parameter will set the process name.
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The daemon parameter controls the underlying process daemon flag.
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Default is True.
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The context parameter allows to provide the multiprocessing.context
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object used for starting the process.
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"""
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timeout = kwargs.get('timeout')
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name = kwargs.get('name')
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daemon = kwargs.get('daemon', True)
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mp_context = kwargs.get('context')
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# decorator without parameters
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if not kwargs and len(args) == 1 and callable(args[0]):
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return _process_wrapper(args[0], timeout, name, daemon, multiprocessing)
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# decorator with parameters
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_validate_parameters(timeout, name, daemon, mp_context)
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mp_context = mp_context if mp_context is not None else multiprocessing
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# without @pie syntax
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if len(args) == 1 and callable(args[0]):
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return _process_wrapper(args[0], timeout, name, daemon, multiprocessing)
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# with @pie syntax
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def decorating_function(function):
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return _process_wrapper(function, timeout, name, daemon, mp_context)
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return decorating_function
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def _process_wrapper(function, timeout, name, daemon, mp_context):
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if isinstance(function, types.FunctionType):
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_register_function(function)
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if hasattr(mp_context, 'get_start_method'):
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start_method = mp_context.get_start_method()
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else:
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start_method = 'spawn' if os.name == 'nt' else 'fork'
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@wraps(function)
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def wrapper(*args, **kwargs):
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future = ProcessFuture()
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reader, writer = mp_context.Pipe(duplex=False)
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if isinstance(function, types.FunctionType) and start_method != 'fork':
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target = _trampoline
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args = [function.__qualname__, function.__module__] + list(args)
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else:
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target = function
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worker = launch_process(
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name, _function_handler, daemon, mp_context,
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target, args, kwargs, (reader, writer))
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writer.close()
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future.set_running_or_notify_cancel()
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launch_thread(name, _worker_handler, True, future, worker, reader, timeout)
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return future
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return wrapper
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def _worker_handler(future, worker, pipe, timeout):
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"""Worker lifecycle manager.
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Waits for the worker to be perform its task,
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collects result, runs the callback and cleans up the process.
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"""
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result = _get_result(future, pipe, timeout)
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if worker.is_alive():
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stop_process(worker)
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if isinstance(result, BaseException):
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if isinstance(result, ProcessExpired):
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result.exitcode = worker.exitcode
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if not isinstance(result, CancelledError):
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future.set_exception(result)
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else:
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future.set_result(result)
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def _function_handler(function, args, kwargs, pipe):
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"""Runs the actual function in separate process and returns its result."""
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signal.signal(signal.SIGINT, signal.SIG_IGN)
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reader, writer = pipe
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reader.close()
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result = process_execute(function, *args, **kwargs)
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send_result(writer, result)
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def _get_result(future, pipe, timeout):
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"""Waits for result and handles communication errors."""
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counter = count(step=SLEEP_UNIT)
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try:
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while not pipe.poll(SLEEP_UNIT):
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if timeout is not None and next(counter) >= timeout:
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return TimeoutError('Task Timeout', timeout)
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elif future.cancelled():
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return CancelledError()
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return pipe.recv()
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except (EOFError, OSError):
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return ProcessExpired('Abnormal termination')
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except Exception as error:
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return error
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def _validate_parameters(timeout, name, daemon, mp_context):
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if timeout is not None and not isinstance(timeout, (int, float)):
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raise TypeError('Timeout expected to be None or integer or float')
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if name is not None and not isinstance(name, str):
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raise TypeError('Name expected to be None or string')
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if daemon is not None and not isinstance(daemon, bool):
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raise TypeError('Daemon expected to be None or bool')
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################################################################################
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# Spawn process start method handling logic
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################################################################################
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_registered_functions = {}
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def _register_function(function):
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_registered_functions[function.__qualname__] = function
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return function
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def _trampoline(name, module, *args, **kwargs):
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"""Trampoline function for decorators.
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Lookups the function between the registered ones;
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if not found, forces its registering and then executes it.
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"""
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function = _function_lookup(name, module)
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return function(*args, **kwargs)
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def _function_lookup(name, module):
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"""Searches the function between the registered ones.
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If not found, it imports the module forcing its registration.
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"""
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try:
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return _registered_functions[name]
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except KeyError: # force function registering
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__import__(module)
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mod = sys.modules[module]
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function = getattr(mod, name)
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try:
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return _registered_functions[name]
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except KeyError: # decorator without @pie syntax
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return _register_function(function)
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