Source code for distributed.queues

from __future__ import print_function, division, absolute_import

from collections import defaultdict
import datetime
import logging
import uuid

from tornado import gen
import tornado.queues

from .client import Future, _get_global_client, Client
from .utils import tokey, sync, thread_state
from .worker import get_client

logger = logging.getLogger(__name__)

class QueueExtension(object):
    """ An extension for the scheduler to manage queues

    This adds the following routes to the scheduler

    *  queue_create
    *  queue_release
    *  queue_put
    *  queue_get
    *  queue_size

    def __init__(self, scheduler):
        self.scheduler = scheduler
        self.queues = dict()
        self.client_refcount = dict()
        self.future_refcount = defaultdict(lambda: 0)

            'queue_create': self.create,
            'queue_put': self.put,
            'queue_get': self.get,
            'queue_qsize': self.qsize}

            'queue-future-release': self.future_release,
            'queue_release': self.release,

        self.scheduler.extensions['queues'] = self

    def create(self, stream=None, name=None, client=None, maxsize=0):
        if name not in self.queues:
            self.queues[name] = tornado.queues.Queue(maxsize=maxsize)
            self.client_refcount[name] = 1
            self.client_refcount[name] += 1

    def release(self, stream=None, name=None, client=None):
        if name not in self.queues:

        self.client_refcount[name] -= 1
        if self.client_refcount[name] == 0:
            del self.client_refcount[name]
            futures = self.queues[name]._queue
            del self.queues[name]
                    keys=[d['value'] for d in futures if d['type'] == 'Future'],
                    client='queue-%s' % name

    def put(self, stream=None, name=None, key=None, data=None, client=None, timeout=None):
        if key is not None:
            record = {'type': 'Future', 'value': key}
            self.future_refcount[name, key] += 1
            self.scheduler.client_desires_keys(keys=[key], client='queue-%s' % name)
            record = {'type': 'msgpack', 'value': data}
        if timeout is not None:
            timeout = datetime.timedelta(seconds=(timeout))
        yield self.queues[name].put(record, timeout=timeout)

    def future_release(self, name=None, key=None, client=None):
        self.future_refcount[name, key] -= 1
        if self.future_refcount[name, key] == 0:
                                                client='queue-%s' % name)
            del self.future_refcount[name, key]

    def get(self, stream=None, name=None, client=None, timeout=None,
        def process(record):
            """ Add task status if known """
            if record['type'] == 'Future':
                record = record.copy()
                key = record['value']
                ts = self.scheduler.tasks.get(key)
                state = ts.state if ts is not None else 'lost'

                record['state'] = state
                if state == 'erred':
                    record['exception'] = ts.exception_blame.exception
                    record['traceback'] = ts.exception_blame.traceback

            return record

        if batch:
            q = self.queues[name]
            out = []
            if batch is True:
                while not q.empty():
                    record = yield q.get()
                if timeout is not None:
                    msg = ("Dask queues don't support simultaneous use of "
                           "integer batch sizes and timeouts")
                    raise NotImplementedError(msg)
                for i in range(batch):
                    record = yield q.get()
            out = [process(o) for o in out]
            raise gen.Return(out)
            if timeout is not None:
                timeout = datetime.timedelta(seconds=timeout)
            record = yield self.queues[name].get(timeout=timeout)
            record = process(record)
            raise gen.Return(record)

    def qsize(self, stream=None, name=None, client=None):
        return self.queues[name].qsize()

[docs]class Queue(object): """ Distributed Queue This allows multiple clients to share futures or small bits of data between each other with a multi-producer/multi-consumer queue. All metadata is sequentialized through the scheduler. Elements of the Queue must be either Futures or msgpack-encodable data (ints, strings, lists, dicts). All data is sent through the scheduler so it is wise not to send large objects. To share large objects scatter the data and share the future instead. .. warning:: This object is experimental and has known issues in Python 2 Examples -------- >>> from dask.distributed import Client, Queue # doctest: +SKIP >>> client = Client() # doctest: +SKIP >>> queue = Queue('x') # doctest: +SKIP >>> future = client.submit(f, x) # doctest: +SKIP >>> queue.put(future) # doctest: +SKIP See Also -------- Variable: shared variable between clients """ def __init__(self, name=None, client=None, maxsize=0): self.client = client or _get_global_client() = name or 'queue-' + uuid.uuid4().hex if self.client.asynchronous or getattr(thread_state, 'on_event_loop_thread', False): self._started = self.client.scheduler.queue_create(, maxsize=maxsize) else: sync(self.client.loop, self.client.scheduler.queue_create,, maxsize=maxsize) self._started = gen.moment def __await__(self): @gen.coroutine def _(): yield self._started raise gen.Return(self) return _().__await__() @gen.coroutine def _put(self, value, timeout=None): if isinstance(value, Future): yield self.client.scheduler.queue_put(key=tokey(value.key), timeout=timeout, else: yield self.client.scheduler.queue_put(data=value, timeout=timeout,
[docs] def put(self, value, timeout=None, **kwargs): """ Put data into the queue """ return self.client.sync(self._put, value, timeout=timeout, **kwargs)
[docs] def get(self, timeout=None, batch=False, **kwargs): """ Get data from the queue Parameters ---------- timeout: Number (optional) Time in seconds to wait before timing out batch: boolean, int (optional) If True then return all elements currently waiting in the queue. If an integer than return that many elements from the queue If False (default) then return one item at a time """ return self.client.sync(self._get, timeout=timeout, batch=batch, **kwargs)
[docs] def qsize(self, **kwargs): """ Current number of elements in the queue """ return self.client.sync(self._qsize, **kwargs)
@gen.coroutine def _get(self, timeout=None, batch=False): resp = yield self.client.scheduler.queue_get(timeout=timeout,, batch=batch) def process(d): if d['type'] == 'Future': value = Future(d['value'], self.client, inform=True, state=d['state']) if d['state'] == 'erred': value._state.set_error(d['exception'], d['traceback']) self.client._send_to_scheduler({'op': 'queue-future-release', 'name':, 'key': d['value']}) else: value = d['value'] return value if batch is False: result = process(resp) else: result = list(map(process, resp)) raise gen.Return(result) @gen.coroutine def _qsize(self): result = yield self.client.scheduler.queue_qsize( raise gen.Return(result) def close(self): if self.client.status == 'running': # TODO: can leave zombie futures self.client._send_to_scheduler({'op': 'queue_release', 'name':}) def __getstate__(self): return (, self.client.scheduler.address) def __setstate__(self, state): name, address = state try: client = get_client(address) assert client.scheduler.address == address except (AttributeError, AssertionError): client = Client(address, set_as_default=False) self.__init__(name=name, client=client)