Source code for distributed.queues

from collections import defaultdict
import datetime
import logging
import uuid

import tornado.queues
from tornado.locks import Event

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):
        logger.debug("Queue name: {}".format(name))
        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"]
            if keys:
                self.scheduler.client_releases_keys(keys=keys, client="queue-%s" % name)

    async 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)
        await 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:
            self.scheduler.client_releases_keys(keys=[key], client="queue-%s" % name)
            del self.future_refcount[name, key]

    async def get(self, stream=None, name=None, client=None, timeout=None, batch=False):
        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 = await 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 = await q.get()
            out = [process(o) for o in out]
            return out
            if timeout is not None:
                timeout = datetime.timedelta(seconds=timeout)
            record = await self.queues[name].get(timeout=timeout)
            record = process(record)
            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 self._event_started = Event() if self.client.asynchronous or getattr( thread_state, "on_event_loop_thread", False ): async def _create_queue(): await self.client.scheduler.queue_create(, maxsize=maxsize ) self._event_started.set() self.client.loop.add_callback(_create_queue) else: sync( self.client.loop, self.client.scheduler.queue_create,, maxsize=maxsize, ) self._event_started.set() def __await__(self): async def _(): await self._event_started.wait() return self return _().__await__() async def _put(self, value, timeout=None): if isinstance(value, Future): await self.client.scheduler.queue_put( key=tokey(value.key), timeout=timeout, ) else: await 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)
async def _get(self, timeout=None, batch=False): resp = await 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)) return result async def _qsize(self): result = await self.client.scheduler.queue_qsize( 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)