from __future__ import annotations
import asyncio
import logging
import uuid
from collections import defaultdict
from dask.utils import parse_timedelta
from distributed.client import Future
from distributed.utils import wait_for
from distributed.worker import get_client
logger = logging.getLogger(__name__)
class QueueExtension:
"""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(int)
self.scheduler.handlers.update(
{
"queue_create": self.create,
"queue_put": self.put,
"queue_get": self.get,
"queue_qsize": self.qsize,
}
)
self.scheduler.stream_handlers.update(
{"queue-future-release": self.future_release, "queue_release": self.release}
)
def create(self, name=None, client=None, maxsize=0):
logger.debug(f"Queue name: {name}")
if name not in self.queues:
self.queues[name] = asyncio.Queue(maxsize=maxsize)
self.client_refcount[name] = 1
else:
self.client_refcount[name] += 1
def release(self, name=None, client=None):
if name not in self.queues:
return
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, 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)
else:
record = {"type": "msgpack", "value": data}
await wait_for(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, 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()
out.append(record)
else:
if timeout is not None:
msg = (
"Dask queues don't support simultaneous use of "
"integer batch sizes and timeouts"
)
raise NotImplementedError(msg)
for _ in range(batch):
record = await q.get()
out.append(record)
out = [process(o) for o in out]
return out
else:
record = await wait_for(self.queues[name].get(), timeout=timeout)
record = process(record)
return record
def qsize(self, name=None, client=None):
return self.queues[name].qsize()
[docs]class Queue:
"""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
Parameters
----------
name: string (optional)
Name used by other clients and the scheduler to identify the queue. If
not given, a random name will be generated.
client: Client (optional)
Client used for communication with the scheduler.
If not given, the default global client will be used.
maxsize: int (optional)
Number of items allowed in the queue. If 0 (the default), the queue
size is unbounded.
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
self.name = name or "queue-" + uuid.uuid4().hex
self.maxsize = maxsize
self._maybe_start()
def _maybe_start(self):
if self.client:
if self.client.asynchronous:
self._started = asyncio.ensure_future(self._start())
else:
self.client.sync(self._start)
@property
def client(self):
if not self._client:
try:
self._client = get_client()
except ValueError:
pass
return self._client
def _verify_running(self):
if not self.client:
raise RuntimeError(
f"{type(self)} object not properly initialized. This can happen"
" if the object is being deserialized outside of the context of"
" a Client or Worker."
)
async def _start(self):
await self.client.scheduler.queue_create(name=self.name, maxsize=self.maxsize)
return self
def __await__(self):
self._maybe_start()
if hasattr(self, "_started"):
return self._started.__await__()
else:
async def _():
return self
return _().__await__()
async def _put(self, value, timeout=None):
if isinstance(value, Future):
await self.client.scheduler.queue_put(
key=value.key, timeout=timeout, name=self.name
)
else:
await self.client.scheduler.queue_put(
data=value, timeout=timeout, name=self.name
)
[docs] def put(self, value, timeout=None, **kwargs):
"""Put data into the queue
Parameters
----------
timeout : number or string or timedelta, optional
Time in seconds to wait before timing out.
Instead of number of seconds, it is also possible to specify
a timedelta in string format, e.g. "200ms".
"""
self._verify_running()
timeout = parse_timedelta(timeout)
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 or string or timedelta, optional
Time in seconds to wait before timing out.
Instead of number of seconds, it is also possible to specify
a timedelta in string format, e.g. "200ms".
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
"""
self._verify_running()
timeout = parse_timedelta(timeout)
return self.client.sync(self._get, timeout=timeout, batch=batch, **kwargs)
[docs] def qsize(self, **kwargs):
"""Current number of elements in the queue"""
self._verify_running()
return self.client.sync(self._qsize, **kwargs)
async def _get(self, timeout=None, batch=False):
resp = await self.client.scheduler.queue_get(
timeout=timeout, name=self.name, 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": self.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(name=self.name)
return result
def close(self):
self._verify_running()
if self.client.status == "running": # TODO: can leave zombie futures
self.client._send_to_scheduler({"op": "queue_release", "name": self.name})
def __reduce__(self):
return type(self), (self.name, None, self.maxsize)