deb-python-taskflow/taskflow/types/futures.py

# -*- coding: utf-8 -*-

#    Copyright (C) 2014 Yahoo! Inc. All Rights Reserved.
#
#    Licensed under the Apache License, Version 2.0 (the "License"); you may
#    not use this file except in compliance with the License. You may obtain
#    a copy of the License at
#
#         http://www.apache.org/licenses/LICENSE-2.0
#
#    Unless required by applicable law or agreed to in writing, software
#    distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
#    WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
#    License for the specific language governing permissions and limitations
#    under the License.

import functools
import threading

from concurrent import futures as _futures
from concurrent.futures import process as _process
from concurrent.futures import thread as _thread
from oslo_utils import importutils
from oslo_utils import reflection

greenpatcher = importutils.try_import('eventlet.patcher')
greenpool = importutils.try_import('eventlet.greenpool')
greenqueue = importutils.try_import('eventlet.queue')
greenthreading = importutils.try_import('eventlet.green.threading')

from taskflow.types import timing
from taskflow.utils import eventlet_utils as eu
from taskflow.utils import threading_utils as tu


# NOTE(harlowja): Allows for simpler access to this type...
Future = _futures.Future


class _Gatherer(object):
    def __init__(self, submit_func,
                 lock_cls=threading.Lock, start_before_submit=False):
        self._submit_func = submit_func
        self._stats_lock = lock_cls()
        self._stats = ExecutorStatistics()
        self._start_before_submit = start_before_submit

    @property
    def statistics(self):
        return self._stats

    def clear(self):
        with self._stats_lock:
            self._stats = ExecutorStatistics()

    def _capture_stats(self, watch, fut):
        watch.stop()
        with self._stats_lock:
            # Use a new collection and lock so that all mutations are seen as
            # atomic and not overlapping and corrupting with other
            # mutations (the clone ensures that others reading the current
            # values will not see a mutated/corrupted one). Since futures may
            # be completed by different threads we need to be extra careful to
            # gather this data in a way that is thread-safe...
            (failures, executed, runtime, cancelled) = (self._stats.failures,
                                                        self._stats.executed,
                                                        self._stats.runtime,
                                                        self._stats.cancelled)
            if fut.cancelled():
                cancelled += 1
            else:
                executed += 1
                if fut.exception() is not None:
                    failures += 1
                runtime += watch.elapsed()
            self._stats = ExecutorStatistics(failures=failures,
                                             executed=executed,
                                             runtime=runtime,
                                             cancelled=cancelled)

    def submit(self, fn, *args, **kwargs):
        watch = timing.StopWatch()
        if self._start_before_submit:
            watch.start()
        fut = self._submit_func(fn, *args, **kwargs)
        if not self._start_before_submit:
            watch.start()
        fut.add_done_callback(functools.partial(self._capture_stats, watch))
        return fut


class ThreadPoolExecutor(_thread.ThreadPoolExecutor):
    """Executor that uses a thread pool to execute calls asynchronously.

    It gathers statistics about the submissions executed for post-analysis...

    See: https://docs.python.org/dev/library/concurrent.futures.html
    """
    def __init__(self, max_workers=None):
        if max_workers is None:
            max_workers = tu.get_optimal_thread_count()
        super(ThreadPoolExecutor, self).__init__(max_workers=max_workers)
        if self._max_workers <= 0:
            raise ValueError("Max workers must be greater than zero")
        self._gatherer = _Gatherer(
            # Since our submit will use this gatherer we have to reference
            # the parent submit, bound to this instance (which is what we
            # really want to use anyway).
            super(ThreadPoolExecutor, self).submit)

    @property
    def statistics(self):
        """:class:`.ExecutorStatistics` about the executors executions."""
        return self._gatherer.statistics

    @property
    def alive(self):
        """Accessor to determine if the executor is alive/active."""
        return not self._shutdown

    def submit(self, fn, *args, **kwargs):
        """Submit some work to be executed (and gather statistics)."""
        return self._gatherer.submit(fn, *args, **kwargs)


class ProcessPoolExecutor(_process.ProcessPoolExecutor):
    """Executor that uses a process pool to execute calls asynchronously.

    It gathers statistics about the submissions executed for post-analysis...

    See: https://docs.python.org/dev/library/concurrent.futures.html
    """
    def __init__(self, max_workers=None):
        if max_workers is None:
            max_workers = tu.get_optimal_thread_count()
        super(ProcessPoolExecutor, self).__init__(max_workers=max_workers)
        if self._max_workers <= 0:
            raise ValueError("Max workers must be greater than zero")
        self._gatherer = _Gatherer(
            # Since our submit will use this gatherer we have to reference
            # the parent submit, bound to this instance (which is what we
            # really want to use anyway).
            super(ProcessPoolExecutor, self).submit)

    @property
    def alive(self):
        """Accessor to determine if the executor is alive/active."""
        return not self._shutdown_thread

    @property
    def statistics(self):
        """:class:`.ExecutorStatistics` about the executors executions."""
        return self._gatherer.statistics

    def submit(self, fn, *args, **kwargs):
        """Submit some work to be executed (and gather statistics)."""
        return self._gatherer.submit(fn, *args, **kwargs)


class _WorkItem(object):
    def __init__(self, future, fn, args, kwargs):
        self.future = future
        self.fn = fn
        self.args = args
        self.kwargs = kwargs

    def run(self):
        if not self.future.set_running_or_notify_cancel():
            return
        try:
            result = self.fn(*self.args, **self.kwargs)
        except BaseException as e:
            self.future.set_exception(e)
        else:
            self.future.set_result(result)


class SynchronousExecutor(_futures.Executor):
    """Executor that uses the caller to execute calls synchronously.

    This provides an interface to a caller that looks like an executor but
    will execute the calls inside the caller thread instead of executing it
    in a external process/thread for when this type of functionality is
    useful to provide...

    It gathers statistics about the submissions executed for post-analysis...
    """

    def __init__(self):
        self._shutoff = False
        self._gatherer = _Gatherer(self._submit,
                                   start_before_submit=True)

    @property
    def alive(self):
        """Accessor to determine if the executor is alive/active."""
        return not self._shutoff

    def shutdown(self, wait=True):
        self._shutoff = True

    def restart(self):
        """Restarts this executor (*iff* previously shutoff/shutdown).

        NOTE(harlowja): clears any previously gathered statistics.
        """
        if self._shutoff:
            self._shutoff = False
            self._gatherer.clear()

    @property
    def statistics(self):
        """:class:`.ExecutorStatistics` about the executors executions."""
        return self._gatherer.statistics

    def submit(self, fn, *args, **kwargs):
        """Submit some work to be executed (and gather statistics)."""
        if self._shutoff:
            raise RuntimeError('Can not schedule new futures'
                               ' after being shutdown')
        return self._gatherer.submit(fn, *args, **kwargs)

    def _submit(self, fn, *args, **kwargs):
        f = Future()
        runner = _WorkItem(f, fn, args, kwargs)
        runner.run()
        return f


class _GreenWorker(object):
    def __init__(self, executor, work, work_queue):
        self.executor = executor
        self.work = work
        self.work_queue = work_queue

    def __call__(self):
        # Run our main piece of work.
        try:
            self.work.run()
        finally:
            # Consume any delayed work before finishing (this is how we finish
            # work that was to big for the pool size, but needs to be finished
            # no matter).
            while True:
                try:
                    w = self.work_queue.get_nowait()
                except greenqueue.Empty:
                    break
                else:
                    try:
                        w.run()
                    finally:
                        self.work_queue.task_done()


class GreenFuture(Future):
    def __init__(self):
        super(GreenFuture, self).__init__()
        eu.check_for_eventlet(RuntimeError('Eventlet is needed to use a green'
                                           ' future'))
        # NOTE(harlowja): replace the built-in condition with a greenthread
        # compatible one so that when getting the result of this future the
        # functions will correctly yield to eventlet. If this is not done then
        # waiting on the future never actually causes the greenthreads to run
        # and thus you wait for infinity.
        if not greenpatcher.is_monkey_patched('threading'):
            self._condition = greenthreading.Condition()


class GreenThreadPoolExecutor(_futures.Executor):
    """Executor that uses a green thread pool to execute calls asynchronously.

    See: https://docs.python.org/dev/library/concurrent.futures.html
    and http://eventlet.net/doc/modules/greenpool.html for information on
    how this works.

    It gathers statistics about the submissions executed for post-analysis...
    """

    def __init__(self, max_workers=1000):
        eu.check_for_eventlet(RuntimeError('Eventlet is needed to use a green'
                                           ' executor'))
        if max_workers <= 0:
            raise ValueError("Max workers must be greater than zero")
        self._max_workers = max_workers
        self._pool = greenpool.GreenPool(self._max_workers)
        self._delayed_work = greenqueue.Queue()
        self._shutdown_lock = greenthreading.Lock()
        self._shutdown = False
        self._gatherer = _Gatherer(self._submit,
                                   lock_cls=greenthreading.Lock)

    @property
    def alive(self):
        """Accessor to determine if the executor is alive/active."""
        return not self._shutdown

    @property
    def statistics(self):
        """:class:`.ExecutorStatistics` about the executors executions."""
        return self._gatherer.statistics

    def submit(self, fn, *args, **kwargs):
        """Submit some work to be executed (and gather statistics)."""
        with self._shutdown_lock:
            if self._shutdown:
                raise RuntimeError('Can not schedule new futures'
                                   ' after being shutdown')
            return self._gatherer.submit(fn, *args, **kwargs)

    def _submit(self, fn, *args, **kwargs):
        f = GreenFuture()
        work = _WorkItem(f, fn, args, kwargs)
        if not self._spin_up(work):
            self._delayed_work.put(work)
        return f

    def _spin_up(self, work):
        alive = self._pool.running() + self._pool.waiting()
        if alive < self._max_workers:
            self._pool.spawn_n(_GreenWorker(self, work, self._delayed_work))
            return True
        return False

    def shutdown(self, wait=True):
        with self._shutdown_lock:
            if not self._shutdown:
                self._shutdown = True
                shutoff = True
            else:
                shutoff = False
        if wait and shutoff:
            self._pool.waitall()
            self._delayed_work.join()


class ExecutorStatistics(object):
    """Holds *immutable* information about a executors executions."""

    __slots__ = ['_failures', '_executed', '_runtime', '_cancelled']

    __repr_format = ("failures=%(failures)s, executed=%(executed)s, "
                     "runtime=%(runtime)s, cancelled=%(cancelled)s")

    def __init__(self, failures=0, executed=0, runtime=0.0, cancelled=0):
        self._failures = failures
        self._executed = executed
        self._runtime = runtime
        self._cancelled = cancelled

    @property
    def failures(self):
        """How many submissions ended up raising exceptions."""
        return self._failures

    @property
    def executed(self):
        """How many submissions were executed (failed or not)."""
        return self._executed

    @property
    def runtime(self):
        """Total runtime of all submissions executed (failed or not)."""
        return self._runtime

    @property
    def cancelled(self):
        """How many submissions were cancelled before executing."""
        return self._cancelled

    @property
    def average_runtime(self):
        """The average runtime of all submissions executed.

        :raises: ZeroDivisionError when no executions have occurred.
        """
        return self._runtime / self._executed

    def __repr__(self):
        r = reflection.get_class_name(self, fully_qualified=False)
        r += "("
        r += self.__repr_format % ({
            'failures': self._failures,
            'executed': self._executed,
            'runtime': self._runtime,
            'cancelled': self._cancelled,
        })
        r += ")"
        return r