import asyncio
import heapq
import inspect
import itertools
import json
import logging
import math
import operator
import os
import random
import sys
import uuid
import warnings
import weakref
from collections import defaultdict, deque
from collections.abc import (
Callable,
Collection,
Hashable,
Iterable,
Iterator,
Mapping,
Set,
)
from contextlib import suppress
from datetime import timedelta
from functools import partial
from numbers import Number
from typing import ClassVar
from typing import cast as pep484_cast
import psutil
from sortedcontainers import SortedDict, SortedSet
from tlz import (
compose,
first,
groupby,
merge,
merge_sorted,
merge_with,
pluck,
second,
valmap,
)
from tornado.ioloop import IOLoop, PeriodicCallback
import dask
from dask.highlevelgraph import HighLevelGraph
from dask.utils import format_bytes, format_time, parse_bytes, parse_timedelta, tmpfile
from dask.widgets import get_template
from . import preloading, profile
from . import versions as version_module
from .active_memory_manager import ActiveMemoryManagerExtension
from .batched import BatchedSend
from .comm import (
get_address_host,
normalize_address,
resolve_address,
unparse_host_port,
)
from .comm.addressing import addresses_from_user_args
from .core import CommClosedError, Status, clean_exception, rpc, send_recv
from .diagnostics.plugin import SchedulerPlugin, _get_plugin_name
from .event import EventExtension
from .http import get_handlers
from .lock import LockExtension
from .metrics import time
from .multi_lock import MultiLockExtension
from .node import ServerNode
from .proctitle import setproctitle
from .protocol.pickle import loads
from .publish import PublishExtension
from .pubsub import PubSubSchedulerExtension
from .queues import QueueExtension
from .recreate_tasks import ReplayTaskScheduler
from .security import Security
from .semaphore import SemaphoreExtension
from .stealing import WorkStealing
from .utils import (
All,
TimeoutError,
empty_context,
get_fileno_limit,
key_split,
key_split_group,
log_errors,
no_default,
validate_key,
)
from .utils_comm import gather_from_workers, retry_operation, scatter_to_workers
from .utils_perf import disable_gc_diagnosis, enable_gc_diagnosis
from .variable import VariableExtension
try:
from cython import compiled
except ImportError:
compiled = False
if compiled:
from cython import (
Py_hash_t,
Py_ssize_t,
bint,
cast,
ccall,
cclass,
cfunc,
declare,
double,
exceptval,
final,
inline,
nogil,
)
else:
from ctypes import c_double as double
from ctypes import c_ssize_t as Py_hash_t
from ctypes import c_ssize_t as Py_ssize_t
bint = bool
def cast(T, v, *a, **k):
return v
def ccall(func):
return func
def cclass(cls):
return cls
def cfunc(func):
return func
def declare(*a, **k):
if len(a) == 2:
return a[1]
else:
pass
def exceptval(*a, **k):
def wrapper(func):
return func
return wrapper
def final(cls):
return cls
def inline(func):
return func
def nogil(func):
return func
if sys.version_info < (3, 8):
try:
import pickle5 as pickle
except ImportError:
import pickle
else:
import pickle
logger = logging.getLogger(__name__)
LOG_PDB = dask.config.get("distributed.admin.pdb-on-err")
DEFAULT_DATA_SIZE = declare(
Py_ssize_t, parse_bytes(dask.config.get("distributed.scheduler.default-data-size"))
)
DEFAULT_EXTENSIONS = [
LockExtension,
MultiLockExtension,
PublishExtension,
ReplayTaskScheduler,
QueueExtension,
VariableExtension,
PubSubSchedulerExtension,
SemaphoreExtension,
EventExtension,
ActiveMemoryManagerExtension,
]
ALL_TASK_STATES = declare(
set, {"released", "waiting", "no-worker", "processing", "erred", "memory"}
)
globals()["ALL_TASK_STATES"] = ALL_TASK_STATES
COMPILED = declare(bint, compiled)
globals()["COMPILED"] = COMPILED
[docs]@final
@cclass
class ClientState:
"""
A simple object holding information about a client.
.. attribute:: client_key: str
A unique identifier for this client. This is generally an opaque
string generated by the client itself.
.. attribute:: wants_what: {TaskState}
A set of tasks this client wants kept in memory, so that it can
download its result when desired. This is the reverse mapping of
:class:`TaskState.who_wants`.
Tasks are typically removed from this set when the corresponding
object in the client's space (for example a ``Future`` or a Dask
collection) gets garbage-collected.
"""
_client_key: str
_hash: Py_hash_t
_wants_what: set
_last_seen: double
_versions: dict
__slots__ = ("_client_key", "_hash", "_wants_what", "_last_seen", "_versions")
def __init__(self, client: str, versions: dict = None):
self._client_key = client
self._hash = hash(client)
self._wants_what = set()
self._last_seen = time()
self._versions = versions or {}
def __hash__(self):
return self._hash
def __eq__(self, other):
typ_self: type = type(self)
typ_other: type = type(other)
if typ_self == typ_other:
other_cs: ClientState = other
return self._client_key == other_cs._client_key
else:
return False
def __repr__(self):
return "<Client '%s'>" % self._client_key
def __str__(self):
return self._client_key
@property
def client_key(self):
return self._client_key
@property
def wants_what(self):
return self._wants_what
@property
def last_seen(self):
return self._last_seen
@property
def versions(self):
return self._versions
@final
@cclass
class MemoryState:
"""Memory readings on a worker or on the whole cluster.
managed
Sum of the output of sizeof() for all dask keys held by the worker, both in
memory and spilled to disk
managed_in_memory
Sum of the output of sizeof() for the dask keys held in RAM
managed_spilled
Sum of the output of sizeof() for the dask keys spilled to the hard drive.
Note that this is the size in memory; serialized size may be different.
process
Total RSS memory measured by the OS on the worker process.
This is always exactly equal to managed_in_memory + unmanaged.
unmanaged
process - managed_in_memory. This is the sum of
- Python interpreter and modules
- global variables
- memory temporarily allocated by the dask tasks that are currently running
- memory fragmentation
- memory leaks
- memory not yet garbage collected
- memory not yet free()'d by the Python memory manager to the OS
unmanaged_old
Minimum of the 'unmanaged' measures over the last
``distributed.memory.recent-to-old-time`` seconds
unmanaged_recent
unmanaged - unmanaged_old; in other words process memory that has been recently
allocated but is not accounted for by dask; hopefully it's mostly a temporary
spike.
optimistic
managed_in_memory + unmanaged_old; in other words the memory held long-term by
the process under the hopeful assumption that all unmanaged_recent memory is a
temporary spike
"""
__slots__ = ("_process", "_managed_in_memory", "_managed_spilled", "_unmanaged_old")
_process: Py_ssize_t
_managed_in_memory: Py_ssize_t
_managed_spilled: Py_ssize_t
_unmanaged_old: Py_ssize_t
def __init__(
self,
*,
process: Py_ssize_t,
unmanaged_old: Py_ssize_t,
managed: Py_ssize_t,
managed_spilled: Py_ssize_t,
):
# Some data arrives with the heartbeat, some other arrives in realtime as the
# tasks progress. Also, sizeof() is not guaranteed to return correct results.
# This can cause glitches where a partial measure is larger than the whole, so
# we need to force all numbers to add up exactly by definition.
self._process = process
self._managed_spilled = min(managed_spilled, managed)
# Subtractions between unsigned ints guaranteed by construction to be >= 0
self._managed_in_memory = min(managed - self._managed_spilled, process)
self._unmanaged_old = min(unmanaged_old, process - self._managed_in_memory)
@property
def process(self) -> Py_ssize_t:
return self._process
@property
def managed_in_memory(self) -> Py_ssize_t:
return self._managed_in_memory
@property
def managed_spilled(self) -> Py_ssize_t:
return self._managed_spilled
@property
def unmanaged_old(self) -> Py_ssize_t:
return self._unmanaged_old
@classmethod
def sum(cls, *infos: "MemoryState") -> "MemoryState":
out = MemoryState(process=0, unmanaged_old=0, managed=0, managed_spilled=0)
ms: MemoryState
for ms in infos:
out._process += ms._process
out._managed_spilled += ms._managed_spilled
out._managed_in_memory += ms._managed_in_memory
out._unmanaged_old += ms._unmanaged_old
return out
@property
def managed(self) -> Py_ssize_t:
return self._managed_in_memory + self._managed_spilled
@property
def unmanaged(self) -> Py_ssize_t:
# This is never negative thanks to __init__
return self._process - self._managed_in_memory
@property
def unmanaged_recent(self) -> Py_ssize_t:
# This is never negative thanks to __init__
return self._process - self._managed_in_memory - self._unmanaged_old
@property
def optimistic(self) -> Py_ssize_t:
return self._managed_in_memory + self._unmanaged_old
def __repr__(self) -> str:
return (
f"Process memory (RSS) : {format_bytes(self._process)}\n"
f" - managed by Dask : {format_bytes(self._managed_in_memory)}\n"
f" - unmanaged (old) : {format_bytes(self._unmanaged_old)}\n"
f" - unmanaged (recent): {format_bytes(self.unmanaged_recent)}\n"
f"Spilled to disk : {format_bytes(self._managed_spilled)}\n"
)
[docs]@final
@cclass
class WorkerState:
"""
A simple object holding information about a worker.
.. attribute:: address: str
This worker's unique key. This can be its connected address
(such as ``'tcp://127.0.0.1:8891'``) or an alias (such as ``'alice'``).
.. attribute:: processing: {TaskState: cost}
A dictionary of tasks that have been submitted to this worker.
Each task state is associated with the expected cost in seconds
of running that task, summing both the task's expected computation
time and the expected communication time of its result.
If a task is already executing on the worker and the excecution time is
twice the learned average TaskGroup duration, this will be set to twice
the current executing time. If the task is unknown, the default task
duration is used instead of the TaskGroup average.
Multiple tasks may be submitted to a worker in advance and the worker
will run them eventually, depending on its execution resources
(but see :doc:`work-stealing`).
All the tasks here are in the "processing" state.
This attribute is kept in sync with :attr:`TaskState.processing_on`.
.. attribute:: executing: {TaskState: duration}
A dictionary of tasks that are currently being run on this worker.
Each task state is asssociated with the duration in seconds which
the task has been running.
.. attribute:: has_what: {TaskState}
An insertion-sorted set-like of tasks which currently reside on this worker.
All the tasks here are in the "memory" state.
This is the reverse mapping of :class:`TaskState.who_has`.
.. attribute:: nbytes: int
The total memory size, in bytes, used by the tasks this worker
holds in memory (i.e. the tasks in this worker's :attr:`has_what`).
.. attribute:: nthreads: int
The number of CPU threads made available on this worker.
.. attribute:: resources: {str: Number}
The available resources on this worker like ``{'gpu': 2}``.
These are abstract quantities that constrain certain tasks from
running at the same time on this worker.
.. attribute:: used_resources: {str: Number}
The sum of each resource used by all tasks allocated to this worker.
The numbers in this dictionary can only be less or equal than
those in this worker's :attr:`resources`.
.. attribute:: occupancy: double
The total expected runtime, in seconds, of all tasks currently
processing on this worker. This is the sum of all the costs in
this worker's :attr:`processing` dictionary.
.. attribute:: status: Status
Read-only worker status, synced one way from the remote Worker object
.. attribute:: nanny: str
Address of the associated Nanny, if present
.. attribute:: last_seen: Py_ssize_t
The last time we received a heartbeat from this worker, in local
scheduler time.
.. attribute:: actors: {TaskState}
A set of all TaskStates on this worker that are actors. This only
includes those actors whose state actually lives on this worker, not
actors to which this worker has a reference.
"""
# XXX need a state field to signal active/removed?
_actors: set
_address: str
_bandwidth: double
_executing: dict
_extra: dict
# _has_what is a dict with all values set to None as rebalance() relies on the
# property of Python >=3.7 dicts to be insertion-sorted.
_has_what: dict
_hash: Py_hash_t
_last_seen: double
_local_directory: str
_memory_limit: Py_ssize_t
_memory_other_history: "deque[tuple[float, Py_ssize_t]]"
_memory_unmanaged_old: Py_ssize_t
_metrics: dict
_name: object
_nanny: str
_nbytes: Py_ssize_t
_nthreads: Py_ssize_t
_occupancy: double
_pid: Py_ssize_t
_processing: dict
_resources: dict
_services: dict
_status: Status
_time_delay: double
_used_resources: dict
_versions: dict
__slots__ = (
"_actors",
"_address",
"_bandwidth",
"_extra",
"_executing",
"_has_what",
"_hash",
"_last_seen",
"_local_directory",
"_memory_limit",
"_memory_other_history",
"_memory_unmanaged_old",
"_metrics",
"_name",
"_nanny",
"_nbytes",
"_nthreads",
"_occupancy",
"_pid",
"_processing",
"_resources",
"_services",
"_status",
"_time_delay",
"_used_resources",
"_versions",
)
def __init__(
self,
*,
address: str,
pid: Py_ssize_t,
name: object,
nthreads: Py_ssize_t = 0,
memory_limit: Py_ssize_t,
local_directory: str,
nanny: str,
services: "dict | None" = None,
versions: "dict | None" = None,
extra: "dict | None" = None,
):
self._address = address
self._pid = pid
self._name = name
self._nthreads = nthreads
self._memory_limit = memory_limit
self._local_directory = local_directory
self._services = services or {}
self._versions = versions or {}
self._nanny = nanny
self._hash = hash(address)
self._status = Status.undefined
self._nbytes = 0
self._occupancy = 0
self._memory_unmanaged_old = 0
self._memory_other_history = deque()
self._metrics = {}
self._last_seen = 0
self._time_delay = 0
self._bandwidth = float(
parse_bytes(dask.config.get("distributed.scheduler.bandwidth"))
)
self._actors = set()
self._has_what = {}
self._processing = {}
self._executing = {}
self._resources = {}
self._used_resources = {}
self._extra = extra or {}
def __hash__(self):
return self._hash
def __eq__(self, other):
typ_self: type = type(self)
typ_other: type = type(other)
if typ_self == typ_other:
other_ws: WorkerState = other
return self._address == other_ws._address
else:
return False
@property
def actors(self):
return self._actors
@property
def address(self) -> str:
return self._address
@property
def bandwidth(self):
return self._bandwidth
@property
def executing(self):
return self._executing
@property
def extra(self):
return self._extra
@property
def has_what(self) -> "Set[TaskState]":
return self._has_what.keys()
@property
def host(self):
return get_address_host(self._address)
@property
def last_seen(self):
return self._last_seen
@property
def local_directory(self):
return self._local_directory
@property
def memory_limit(self):
return self._memory_limit
@property
def metrics(self):
return self._metrics
@property
def memory(self) -> MemoryState:
return MemoryState(
# metrics["memory"] is None if the worker sent a heartbeat before its
# SystemMonitor ever had a chance to run
process=self._metrics["memory"] or 0,
managed=self._nbytes,
managed_spilled=self._metrics["spilled_nbytes"],
unmanaged_old=self._memory_unmanaged_old,
)
@property
def name(self):
return self._name
@property
def nanny(self):
return self._nanny
@property
def nbytes(self):
return self._nbytes
@nbytes.setter
def nbytes(self, v: Py_ssize_t):
self._nbytes = v
@property
def nthreads(self):
return self._nthreads
@property
def occupancy(self):
return self._occupancy
@occupancy.setter
def occupancy(self, v: double):
self._occupancy = v
@property
def pid(self):
return self._pid
@property
def processing(self):
return self._processing
@property
def resources(self):
return self._resources
@property
def services(self):
return self._services
@property
def status(self):
return self._status
@status.setter
def status(self, new_status):
if not isinstance(new_status, Status):
raise TypeError(f"Expected Status; got {new_status!r}")
self._status = new_status
@property
def time_delay(self):
return self._time_delay
@property
def used_resources(self):
return self._used_resources
@property
def versions(self):
return self._versions
@ccall
def clean(self):
"""Return a version of this object that is appropriate for serialization"""
ws: WorkerState = WorkerState(
address=self._address,
pid=self._pid,
name=self._name,
nthreads=self._nthreads,
memory_limit=self._memory_limit,
local_directory=self._local_directory,
services=self._services,
nanny=self._nanny,
extra=self._extra,
)
ts: TaskState
ws._processing = {ts._key: cost for ts, cost in self._processing.items()}
ws._executing = {ts._key: duration for ts, duration in self._executing.items()}
return ws
def __repr__(self):
return "<WorkerState %r, name: %s, memory: %d, processing: %d>" % (
self._address,
self._name,
len(self._has_what),
len(self._processing),
)
def _repr_html_(self):
return get_template("worker_state.html.j2").render(
address=self.address,
name=self.name,
has_what=self._has_what,
processing=self.processing,
)
@ccall
@exceptval(check=False)
def identity(self) -> dict:
return {
"type": "Worker",
"id": self._name,
"host": self.host,
"resources": self._resources,
"local_directory": self._local_directory,
"name": self._name,
"nthreads": self._nthreads,
"memory_limit": self._memory_limit,
"last_seen": self._last_seen,
"services": self._services,
"metrics": self._metrics,
"nanny": self._nanny,
**self._extra,
}
@property
def ncores(self):
warnings.warn("WorkerState.ncores has moved to WorkerState.nthreads")
return self._nthreads
@final
@cclass
class Computation:
"""
Collection tracking a single compute or persist call
See also
--------
TaskPrefix
TaskGroup
TaskState
"""
_start: double
_groups: set
_code: object
_id: object
def __init__(self):
self._start = time()
self._groups = set()
self._code = SortedSet()
self._id = uuid.uuid4()
@property
def code(self):
return self._code
@property
def start(self):
return self._start
@property
def stop(self):
if self.groups:
return max(tg.stop for tg in self.groups)
else:
return -1
@property
def states(self):
tg: TaskGroup
return merge_with(sum, [tg._states for tg in self._groups])
@property
def groups(self):
return self._groups
def __repr__(self):
return (
f"<Computation {self._id}: "
+ "Tasks: "
+ ", ".join(
"%s: %d" % (k, v) for (k, v) in sorted(self.states.items()) if v
)
+ ">"
)
def _repr_html_(self):
return get_template("computation.html.j2").render(
id=self._id,
start=self.start,
stop=self.stop,
groups=self.groups,
states=self.states,
code=self.code,
)
@final
@cclass
class TaskPrefix:
"""Collection tracking all tasks within a group
Keys often have a structure like ``("x-123", 0)``
A group takes the first section, like ``"x"``
.. attribute:: name: str
The name of a group of tasks.
For a task like ``("x-123", 0)`` this is the text ``"x"``
.. attribute:: states: Dict[str, int]
The number of tasks in each state,
like ``{"memory": 10, "processing": 3, "released": 4, ...}``
.. attribute:: duration_average: float
An exponentially weighted moving average duration of all tasks with this prefix
.. attribute:: suspicious: int
Numbers of times a task was marked as suspicious with this prefix
See Also
--------
TaskGroup
"""
_name: str
_all_durations: "defaultdict[str, float]"
_duration_average: double
_suspicious: Py_ssize_t
_groups: list
def __init__(self, name: str):
self._name = name
self._groups = []
# store timings for each prefix-action
self._all_durations = defaultdict(float)
task_durations = dask.config.get("distributed.scheduler.default-task-durations")
if self._name in task_durations:
self._duration_average = parse_timedelta(task_durations[self._name])
else:
self._duration_average = -1
self._suspicious = 0
@property
def name(self) -> str:
return self._name
@property
def all_durations(self) -> "defaultdict[str, float]":
return self._all_durations
@ccall
@exceptval(check=False)
def add_duration(self, action: str, start: double, stop: double):
duration = stop - start
self._all_durations[action] += duration
if action == "compute":
old = self._duration_average
if old < 0:
self._duration_average = duration
else:
self._duration_average = 0.5 * duration + 0.5 * old
@property
def duration_average(self) -> double:
return self._duration_average
@property
def suspicious(self) -> Py_ssize_t:
return self._suspicious
@property
def groups(self):
return self._groups
@property
def states(self):
tg: TaskGroup
return merge_with(sum, [tg._states for tg in self._groups])
@property
def active(self) -> "list[TaskGroup]":
tg: TaskGroup
return [
tg
for tg in self._groups
if any([v != 0 for k, v in tg._states.items() if k != "forgotten"])
]
@property
def active_states(self):
tg: TaskGroup
return merge_with(sum, [tg._states for tg in self.active])
def __repr__(self):
return (
"<"
+ self._name
+ ": "
+ ", ".join(
"%s: %d" % (k, v) for (k, v) in sorted(self.states.items()) if v
)
+ ">"
)
@property
def nbytes_total(self):
tg: TaskGroup
return sum([tg._nbytes_total for tg in self._groups])
def __len__(self):
return sum(map(len, self._groups))
@property
def duration(self):
tg: TaskGroup
return sum([tg._duration for tg in self._groups])
@property
def types(self):
tg: TaskGroup
return set().union(*[tg._types for tg in self._groups])
@final
@cclass
class TaskGroup:
"""Collection tracking all tasks within a group
Keys often have a structure like ``("x-123", 0)``
A group takes the first section, like ``"x-123"``
.. attribute:: name: str
The name of a group of tasks.
For a task like ``("x-123", 0)`` this is the text ``"x-123"``
.. attribute:: states: Dict[str, int]
The number of tasks in each state,
like ``{"memory": 10, "processing": 3, "released": 4, ...}``
.. attribute:: dependencies: Set[TaskGroup]
The other TaskGroups on which this one depends
.. attribute:: nbytes_total: int
The total number of bytes that this task group has produced
.. attribute:: duration: float
The total amount of time spent on all tasks in this TaskGroup
.. attribute:: types: Set[str]
The result types of this TaskGroup
.. attribute:: last_worker: WorkerState
The worker most recently assigned a task from this group, or None when the group
is not identified to be root-like by `SchedulerState.decide_worker`.
.. attribute:: last_worker_tasks_left: int
If `last_worker` is not None, the number of times that worker should be assigned
subsequent tasks until a new worker is chosen.
See also
--------
TaskPrefix
"""
_name: str
_prefix: TaskPrefix # TaskPrefix | None
_states: dict
_dependencies: set
_nbytes_total: Py_ssize_t
_duration: double
_types: set
_start: double
_stop: double
_all_durations: "defaultdict[str, float]"
_last_worker: WorkerState # WorkerState | None
_last_worker_tasks_left: Py_ssize_t
def __init__(self, name: str):
self._name = name
self._prefix = None # type: ignore
self._states = {state: 0 for state in ALL_TASK_STATES}
self._states["forgotten"] = 0
self._dependencies = set()
self._nbytes_total = 0
self._duration = 0
self._types = set()
self._start = 0.0
self._stop = 0.0
self._all_durations = defaultdict(float)
self._last_worker = None # type: ignore
self._last_worker_tasks_left = 0
@property
def name(self) -> str:
return self._name
@property
def prefix(self) -> "TaskPrefix | None":
return self._prefix
@property
def states(self) -> dict:
return self._states
@property
def dependencies(self) -> set:
return self._dependencies
@property
def nbytes_total(self):
return self._nbytes_total
@property
def duration(self) -> double:
return self._duration
@ccall
@exceptval(check=False)
def add_duration(self, action: str, start: double, stop: double):
duration = stop - start
self._all_durations[action] += duration
if action == "compute":
if self._stop < stop:
self._stop = stop
self._start = self._start or start
self._duration += duration
self._prefix.add_duration(action, start, stop)
@property
def types(self) -> set:
return self._types
@property
def all_durations(self) -> "defaultdict[str, float]":
return self._all_durations
@property
def start(self) -> double:
return self._start
@property
def stop(self) -> double:
return self._stop
@property
def last_worker(self) -> "WorkerState | None":
return self._last_worker
@property
def last_worker_tasks_left(self) -> int:
return self._last_worker_tasks_left
@ccall
def add(self, other: "TaskState"):
self._states[other._state] += 1
other._group = self
def __repr__(self):
return (
"<"
+ (self._name or "no-group")
+ ": "
+ ", ".join(
"%s: %d" % (k, v) for (k, v) in sorted(self._states.items()) if v
)
+ ">"
)
def __len__(self):
return sum(self._states.values())
[docs]@final
@cclass
class TaskState:
"""
A simple object holding information about a task.
.. attribute:: key: str
The key is the unique identifier of a task, generally formed
from the name of the function, followed by a hash of the function
and arguments, like ``'inc-ab31c010444977004d656610d2d421ec'``.
.. attribute:: prefix: TaskPrefix
The broad class of tasks to which this task belongs like "inc" or
"read_csv"
.. attribute:: run_spec: object
A specification of how to run the task. The type and meaning of this
value is opaque to the scheduler, as it is only interpreted by the
worker to which the task is sent for executing.
As a special case, this attribute may also be ``None``, in which case
the task is "pure data" (such as, for example, a piece of data loaded
in the scheduler using :meth:`Client.scatter`). A "pure data" task
cannot be computed again if its value is lost.
.. attribute:: priority: tuple
The priority provides each task with a relative ranking which is used
to break ties when many tasks are being considered for execution.
This ranking is generally a 2-item tuple. The first (and dominant)
item corresponds to when it was submitted. Generally, earlier tasks
take precedence. The second item is determined by the client, and is
a way to prioritize tasks within a large graph that may be important,
such as if they are on the critical path, or good to run in order to
release many dependencies. This is explained further in
:doc:`Scheduling Policy <scheduling-policies>`.
.. attribute:: state: str
This task's current state. Valid states include ``released``,
``waiting``, ``no-worker``, ``processing``, ``memory``, ``erred``
and ``forgotten``. If it is ``forgotten``, the task isn't stored
in the ``tasks`` dictionary anymore and will probably disappear
soon from memory.
.. attribute:: dependencies: {TaskState}
The set of tasks this task depends on for proper execution. Only
tasks still alive are listed in this set. If, for whatever reason,
this task also depends on a forgotten task, the
:attr:`has_lost_dependencies` flag is set.
A task can only be executed once all its dependencies have already
been successfully executed and have their result stored on at least
one worker. This is tracked by progressively draining the
:attr:`waiting_on` set.
.. attribute:: dependents: {TaskState}
The set of tasks which depend on this task. Only tasks still alive
are listed in this set.
This is the reverse mapping of :attr:`dependencies`.
.. attribute:: has_lost_dependencies: bool
Whether any of the dependencies of this task has been forgotten.
For memory consumption reasons, forgotten tasks are not kept in
memory even though they may have dependent tasks. When a task is
forgotten, therefore, each of its dependents has their
:attr:`has_lost_dependencies` attribute set to ``True``.
If :attr:`has_lost_dependencies` is true, this task cannot go
into the "processing" state anymore.
.. attribute:: waiting_on: {TaskState}
The set of tasks this task is waiting on *before* it can be executed.
This is always a subset of :attr:`dependencies`. Each time one of the
dependencies has finished processing, it is removed from the
:attr:`waiting_on` set.
Once :attr:`waiting_on` becomes empty, this task can move from the
"waiting" state to the "processing" state (unless one of the
dependencies errored out, in which case this task is instead
marked "erred").
.. attribute:: waiters: {TaskState}
The set of tasks which need this task to remain alive. This is always
a subset of :attr:`dependents`. Each time one of the dependents
has finished processing, it is removed from the :attr:`waiters`
set.
Once both :attr:`waiters` and :attr:`who_wants` become empty, this
task can be released (if it has a non-empty :attr:`run_spec`) or
forgotten (otherwise) by the scheduler, and by any workers
in :attr:`who_has`.
.. note:: Counter-intuitively, :attr:`waiting_on` and
:attr:`waiters` are not reverse mappings of each other.
.. attribute:: who_wants: {ClientState}
The set of clients who want this task's result to remain alive.
This is the reverse mapping of :attr:`ClientState.wants_what`.
When a client submits a graph to the scheduler it also specifies
which output tasks it desires, such that their results are not released
from memory.
Once a task has finished executing (i.e. moves into the "memory"
or "erred" state), the clients in :attr:`who_wants` are notified.
Once both :attr:`waiters` and :attr:`who_wants` become empty, this
task can be released (if it has a non-empty :attr:`run_spec`) or
forgotten (otherwise) by the scheduler, and by any workers
in :attr:`who_has`.
.. attribute:: who_has: {WorkerState}
The set of workers who have this task's result in memory.
It is non-empty iff the task is in the "memory" state. There can be
more than one worker in this set if, for example, :meth:`Client.scatter`
or :meth:`Client.replicate` was used.
This is the reverse mapping of :attr:`WorkerState.has_what`.
.. attribute:: processing_on: WorkerState (or None)
If this task is in the "processing" state, which worker is currently
processing it. Otherwise this is ``None``.
This attribute is kept in sync with :attr:`WorkerState.processing`.
.. attribute:: retries: int
The number of times this task can automatically be retried in case
of failure. If a task fails executing (the worker returns with
an error), its :attr:`retries` attribute is checked. If it is
equal to 0, the task is marked "erred". If it is greater than 0,
the :attr:`retries` attribute is decremented and execution is
attempted again.
.. attribute:: nbytes: int (or None)
The number of bytes, as determined by ``sizeof``, of the result
of a finished task. This number is used for diagnostics and to
help prioritize work.
.. attribute:: type: str
The type of the object as a string. Only present for tasks that have
been computed.
.. attribute:: exception: object
If this task failed executing, the exception object is stored here.
Otherwise this is ``None``.
.. attribute:: traceback: object
If this task failed executing, the traceback object is stored here.
Otherwise this is ``None``.
.. attribute:: exception_blame: TaskState (or None)
If this task or one of its dependencies failed executing, the
failed task is stored here (possibly itself). Otherwise this
is ``None``.
.. attribute:: erred_on: set(str)
Worker addresses on which errors appeared causing this task to be in an error state.
.. attribute:: suspicious: int
The number of times this task has been involved in a worker death.
Some tasks may cause workers to die (such as calling ``os._exit(0)``).
When a worker dies, all of the tasks on that worker are reassigned
to others. This combination of behaviors can cause a bad task to
catastrophically destroy all workers on the cluster, one after
another. Whenever a worker dies, we mark each task currently
processing on that worker (as recorded by
:attr:`WorkerState.processing`) as suspicious.
If a task is involved in three deaths (or some other fixed constant)
then we mark the task as ``erred``.
.. attribute:: host_restrictions: {hostnames}
A set of hostnames where this task can be run (or ``None`` if empty).
Usually this is empty unless the task has been specifically restricted
to only run on certain hosts. A hostname may correspond to one or
several connected workers.
.. attribute:: worker_restrictions: {worker addresses}
A set of complete worker addresses where this can be run (or ``None``
if empty). Usually this is empty unless the task has been specifically
restricted to only run on certain workers.
Note this is tracking worker addresses, not worker states, since
the specific workers may not be connected at this time.
.. attribute:: resource_restrictions: {resource: quantity}
Resources required by this task, such as ``{'gpu': 1}`` or
``{'memory': 1e9}`` (or ``None`` if empty). These are user-defined
names and are matched against the contents of each
:attr:`WorkerState.resources` dictionary.
.. attribute:: loose_restrictions: bool
If ``False``, each of :attr:`host_restrictions`,
:attr:`worker_restrictions` and :attr:`resource_restrictions` is
a hard constraint: if no worker is available satisfying those
restrictions, the task cannot go into the "processing" state and
will instead go into the "no-worker" state.
If ``True``, the above restrictions are mere preferences: if no worker
is available satisfying those restrictions, the task can still go
into the "processing" state and be sent for execution to another
connected worker.
.. attribute:: metadata: dict
Metadata related to task.
.. attribute:: actor: bool
Whether or not this task is an Actor.
.. attribute:: group: TaskGroup
The group of tasks to which this one belongs.
.. attribute:: annotations: dict
Task annotations
"""
_key: str
_hash: Py_hash_t
_prefix: TaskPrefix
_run_spec: object
_priority: tuple # tuple | None
_state: str # str | None
_dependencies: set # set[TaskState]
_dependents: set # set[TaskState]
_has_lost_dependencies: bint
_waiting_on: set # set[TaskState]
_waiters: set # set[TaskState]
_who_wants: set # set[ClientState]
_who_has: set # set[WorkerState]
_processing_on: WorkerState # WorkerState | None
_retries: Py_ssize_t
_nbytes: Py_ssize_t
_type: str # str | None
_exception: object
_exception_text: str
_traceback: object
_traceback_text: str
_exception_blame: "TaskState" # TaskState | None"
_erred_on: set
_suspicious: Py_ssize_t
_host_restrictions: set # set[str] | None
_worker_restrictions: set # set[str] | None
_resource_restrictions: dict # dict | None
_loose_restrictions: bint
_metadata: dict
_annotations: dict
_actor: bint
_group: TaskGroup # TaskGroup | None
_group_key: str
__slots__ = (
# === General description ===
"_actor",
# Key name
"_key",
# Hash of the key name
"_hash",
# Key prefix (see key_split())
"_prefix",
# How to run the task (None if pure data)
"_run_spec",
# Alive dependents and dependencies
"_dependencies",
"_dependents",
# Compute priority
"_priority",
# Restrictions
"_host_restrictions",
"_worker_restrictions", # not WorkerStates but addresses
"_resource_restrictions",
"_loose_restrictions",
# === Task state ===
"_state",
# Whether some dependencies were forgotten
"_has_lost_dependencies",
# If in 'waiting' state, which tasks need to complete
# before we can run
"_waiting_on",
# If in 'waiting' or 'processing' state, which tasks needs us
# to complete before they can run
"_waiters",
# In in 'processing' state, which worker we are processing on
"_processing_on",
# If in 'memory' state, Which workers have us
"_who_has",
# Which clients want us
"_who_wants",
"_exception",
"_exception_text",
"_traceback",
"_traceback_text",
"_erred_on",
"_exception_blame",
"_suspicious",
"_retries",
"_nbytes",
"_type",
"_group_key",
"_group",
"_metadata",
"_annotations",
)
def __init__(self, key: str, run_spec: object):
self._key = key
self._hash = hash(key)
self._run_spec = run_spec
self._state = None # type: ignore
self._exception = None
self._exception_blame = None # type: ignore
self._traceback = None
self._exception_text = ""
self._traceback_text = ""
self._suspicious = 0
self._retries = 0
self._nbytes = -1
self._priority = None # type: ignore
self._who_wants = set()
self._dependencies = set()
self._dependents = set()
self._waiting_on = set()
self._waiters = set()
self._who_has = set()
self._processing_on = None # type: ignore
self._has_lost_dependencies = False
self._host_restrictions = None # type: ignore
self._worker_restrictions = None # type: ignore
self._resource_restrictions = None # type: ignore
self._loose_restrictions = False
self._actor = False
self._type = None # type: ignore
self._group_key = key_split_group(key)
self._group = None # type: ignore
self._metadata = {}
self._annotations = {}
self._erred_on = set()
def __hash__(self):
return self._hash
def __eq__(self, other):
typ_self: type = type(self)
typ_other: type = type(other)
if typ_self == typ_other:
other_ts: TaskState = other
return self._key == other_ts._key
else:
return False
@property
def key(self):
return self._key
@property
def prefix(self):
return self._prefix
@property
def run_spec(self):
return self._run_spec
@property
def priority(self) -> "tuple | None":
return self._priority
@property
def state(self) -> "str | None":
return self._state
@state.setter
def state(self, value: str):
self._group._states[self._state] -= 1
self._group._states[value] += 1
self._state = value
@property
def dependencies(self) -> "set[TaskState]":
return self._dependencies
@property
def dependents(self) -> "set[TaskState]":
return self._dependents
@property
def has_lost_dependencies(self):
return self._has_lost_dependencies
@property
def waiting_on(self) -> "set[TaskState]":
return self._waiting_on
@property
def waiters(self) -> "set[TaskState]":
return self._waiters
@property
def who_wants(self) -> "set[ClientState]":
return self._who_wants
@property
def who_has(self) -> "set[WorkerState]":
return self._who_has
@property
def processing_on(self) -> "WorkerState | None":
return self._processing_on
@processing_on.setter
def processing_on(self, v: WorkerState) -> None:
self._processing_on = v
@property
def retries(self):
return self._retries
@property
def nbytes(self):
return self._nbytes
@nbytes.setter
def nbytes(self, v: Py_ssize_t):
self._nbytes = v
@property
def type(self) -> "str | None":
return self._type
@property
def exception(self):
return self._exception
@property
def exception_text(self):
return self._exception_text
@property
def traceback(self):
return self._traceback
@property
def traceback_text(self):
return self._traceback_text
@property
def exception_blame(self) -> "TaskState | None":
return self._exception_blame
@property
def suspicious(self):
return self._suspicious
@property
def host_restrictions(self) -> "set[str] | None":
return self._host_restrictions
@property
def worker_restrictions(self) -> "set[str] | None":
return self._worker_restrictions
@property
def resource_restrictions(self) -> "dict | None":
return self._resource_restrictions
@property
def loose_restrictions(self):
return self._loose_restrictions
@property
def metadata(self):
return self._metadata
@property
def annotations(self):
return self._annotations
@property
def actor(self):
return self._actor
@property
def group(self) -> "TaskGroup | None":
return self._group
@property
def group_key(self) -> str:
return self._group_key
@property
def prefix_key(self):
return self._prefix._name
@property
def erred_on(self):
return self._erred_on
@ccall
def add_dependency(self, other: "TaskState"):
"""Add another task as a dependency of this task"""
self._dependencies.add(other)
self._group._dependencies.add(other._group)
other._dependents.add(self)
@ccall
@inline
@nogil
def get_nbytes(self) -> Py_ssize_t:
return self._nbytes if self._nbytes >= 0 else DEFAULT_DATA_SIZE
@ccall
def set_nbytes(self, nbytes: Py_ssize_t):
diff: Py_ssize_t = nbytes
old_nbytes: Py_ssize_t = self._nbytes
if old_nbytes >= 0:
diff -= old_nbytes
self._group._nbytes_total += diff
ws: WorkerState
for ws in self._who_has:
ws._nbytes += diff
self._nbytes = nbytes
def __repr__(self):
return f"<TaskState {self._key!r} {self._state}>"
def _repr_html_(self):
return get_template("task_state.html.j2").render(
state=self._state,
nbytes=self._nbytes,
key=self._key,
)
@ccall
def validate(self):
try:
for cs in self._who_wants:
assert isinstance(cs, ClientState), (repr(cs), self._who_wants)
for ws in self._who_has:
assert isinstance(ws, WorkerState), (repr(ws), self._who_has)
for ts in self._dependencies:
assert isinstance(ts, TaskState), (repr(ts), self._dependencies)
for ts in self._dependents:
assert isinstance(ts, TaskState), (repr(ts), self._dependents)
validate_task_state(self)
except Exception as e:
logger.exception(e)
if LOG_PDB:
import pdb
pdb.set_trace()
def get_nbytes_deps(self):
nbytes: Py_ssize_t = 0
ts: TaskState
for ts in self._dependencies:
nbytes += ts.get_nbytes()
return nbytes
class _StateLegacyMapping(Mapping):
"""
A mapping interface mimicking the former Scheduler state dictionaries.
"""
def __init__(self, states, accessor):
self._states = states
self._accessor = accessor
def __iter__(self):
return iter(self._states)
def __len__(self):
return len(self._states)
def __getitem__(self, key):
return self._accessor(self._states[key])
def __repr__(self):
return f"{self.__class__}({dict(self)})"
class _OptionalStateLegacyMapping(_StateLegacyMapping):
"""
Similar to _StateLegacyMapping, but a false-y value is interpreted
as a missing key.
"""
# For tasks etc.
def __iter__(self):
accessor = self._accessor
for k, v in self._states.items():
if accessor(v):
yield k
def __len__(self):
accessor = self._accessor
return sum(bool(accessor(v)) for v in self._states.values())
def __getitem__(self, key):
v = self._accessor(self._states[key])
if v:
return v
else:
raise KeyError
class _StateLegacySet(Set):
"""
Similar to _StateLegacyMapping, but exposes a set containing
all values with a true value.
"""
# For loose_restrictions
def __init__(self, states, accessor):
self._states = states
self._accessor = accessor
def __iter__(self):
return (k for k, v in self._states.items() if self._accessor(v))
def __len__(self):
return sum(map(bool, map(self._accessor, self._states.values())))
def __contains__(self, k):
st = self._states.get(k)
return st is not None and bool(self._accessor(st))
def __repr__(self):
return f"{self.__class__}({set(self)})"
def _legacy_task_key_set(tasks):
"""
Transform a set of task states into a set of task keys.
"""
ts: TaskState
return {ts._key for ts in tasks}
def _legacy_client_key_set(clients):
"""
Transform a set of client states into a set of client keys.
"""
cs: ClientState
return {cs._client_key for cs in clients}
def _legacy_worker_key_set(workers):
"""
Transform a set of worker states into a set of worker keys.
"""
ws: WorkerState
return {ws._address for ws in workers}
def _legacy_task_key_dict(task_dict: dict):
"""
Transform a dict of {task state: value} into a dict of {task key: value}.
"""
ts: TaskState
return {ts._key: value for ts, value in task_dict.items()}
def _task_key_or_none(task: TaskState):
return task._key if task is not None else None
@cclass
class SchedulerState:
"""Underlying task state of dynamic scheduler
Tracks the current state of workers, data, and computations.
Handles transitions between different task states. Notifies the
Scheduler of changes by messaging passing through Queues, which the
Scheduler listens to responds accordingly.
All events are handled quickly, in linear time with respect to their
input (which is often of constant size) and generally within a
millisecond. Additionally when Cythonized, this can be faster still.
To accomplish this the scheduler tracks a lot of state. Every
operation maintains the consistency of this state.
Users typically do not interact with ``Transitions`` directly. Instead
users interact with the ``Client``, which in turn engages the
``Scheduler`` affecting different transitions here under-the-hood. In
the background ``Worker``s also engage with the ``Scheduler``
affecting these state transitions as well.
**State**
The ``Transitions`` object contains the following state variables.
Each variable is listed along with what it stores and a brief
description.
* **tasks:** ``{task key: TaskState}``
Tasks currently known to the scheduler
* **unrunnable:** ``{TaskState}``
Tasks in the "no-worker" state
* **workers:** ``{worker key: WorkerState}``
Workers currently connected to the scheduler
* **idle:** ``{WorkerState}``:
Set of workers that are not fully utilized
* **saturated:** ``{WorkerState}``:
Set of workers that are not over-utilized
* **clients:** ``{client key: ClientState}``
Clients currently connected to the scheduler
* **task_duration:** ``{key-prefix: time}``
Time we expect certain functions to take, e.g. ``{'sum': 0.25}``
"""
_aliases: dict
_bandwidth: double
_clients: dict # dict[str, ClientState]
_computations: object
_extensions: dict
_host_info: dict
_idle: "SortedDict[str, WorkerState]"
_idle_dv: dict # dict[str, WorkerState]
_n_tasks: Py_ssize_t
_resources: dict
_saturated: set
_tasks: dict
_task_groups: dict
_task_prefixes: dict
_task_metadata: dict
_replicated_tasks: set
_total_nthreads: Py_ssize_t
_total_occupancy: double
_transitions_table: dict
_unknown_durations: dict
_unrunnable: set
_validate: bint
_workers: "SortedDict[str, WorkerState]"
_workers_dv: dict # dict[str, WorkerState]
_transition_counter: Py_ssize_t
_plugins: dict # dict[str, SchedulerPlugin]
# Variables from dask.config, cached by __init__ for performance
UNKNOWN_TASK_DURATION: double
MEMORY_RECENT_TO_OLD_TIME: double
MEMORY_REBALANCE_MEASURE: str
MEMORY_REBALANCE_SENDER_MIN: double
MEMORY_REBALANCE_RECIPIENT_MAX: double
MEMORY_REBALANCE_HALF_GAP: double
def __init__(
self,
aliases: dict,
clients: "dict[str, ClientState]",
workers: "SortedDict[str, WorkerState]",
host_info: dict,
resources: dict,
tasks: dict,
unrunnable: set,
validate: bint,
plugins: "Iterable[SchedulerPlugin]" = (),
**kwargs, # Passed verbatim to Server.__init__()
):
self._aliases = aliases
self._bandwidth = parse_bytes(
dask.config.get("distributed.scheduler.bandwidth")
)
self._clients = clients
self._clients["fire-and-forget"] = ClientState("fire-and-forget")
self._extensions = {}
self._host_info = host_info
self._idle = SortedDict()
# Note: cython.cast, not typing.cast!
self._idle_dv = cast(dict, self._idle)
self._n_tasks = 0
self._resources = resources
self._saturated = set()
self._tasks = tasks
self._replicated_tasks = {
ts for ts in self._tasks.values() if len(ts._who_has) > 1
}
self._computations = deque(
maxlen=dask.config.get("distributed.diagnostics.computations.max-history")
)
self._task_groups = {}
self._task_prefixes = {}
self._task_metadata = {}
self._total_nthreads = 0
self._total_occupancy = 0
self._transitions_table = {
("released", "waiting"): self.transition_released_waiting,
("waiting", "released"): self.transition_waiting_released,
("waiting", "processing"): self.transition_waiting_processing,
("waiting", "memory"): self.transition_waiting_memory,
("processing", "released"): self.transition_processing_released,
("processing", "memory"): self.transition_processing_memory,
("processing", "erred"): self.transition_processing_erred,
("no-worker", "released"): self.transition_no_worker_released,
("no-worker", "waiting"): self.transition_no_worker_waiting,
("no-worker", "memory"): self.transition_no_worker_memory,
("released", "forgotten"): self.transition_released_forgotten,
("memory", "forgotten"): self.transition_memory_forgotten,
("erred", "released"): self.transition_erred_released,
("memory", "released"): self.transition_memory_released,
("released", "erred"): self.transition_released_erred,
}
self._unknown_durations = {}
self._unrunnable = unrunnable
self._validate = validate
self._workers = workers
# Note: cython.cast, not typing.cast!
self._workers_dv = cast(dict, self._workers)
self._plugins = {} if not plugins else {_get_plugin_name(p): p for p in plugins}
# Variables from dask.config, cached by __init__ for performance
self.UNKNOWN_TASK_DURATION = parse_timedelta(
dask.config.get("distributed.scheduler.unknown-task-duration")
)
self.MEMORY_RECENT_TO_OLD_TIME = parse_timedelta(
dask.config.get("distributed.worker.memory.recent-to-old-time")
)
self.MEMORY_REBALANCE_MEASURE = dask.config.get(
"distributed.worker.memory.rebalance.measure"
)
self.MEMORY_REBALANCE_SENDER_MIN = dask.config.get(
"distributed.worker.memory.rebalance.sender-min"
)
self.MEMORY_REBALANCE_RECIPIENT_MAX = dask.config.get(
"distributed.worker.memory.rebalance.recipient-max"
)
self.MEMORY_REBALANCE_HALF_GAP = (
dask.config.get("distributed.worker.memory.rebalance.sender-recipient-gap")
/ 2.0
)
self._transition_counter = 0
# Call Server.__init__()
super().__init__(**kwargs) # type: ignore
@property
def aliases(self):
return self._aliases
@property
def bandwidth(self):
return self._bandwidth
@property
def clients(self):
return self._clients
@property
def computations(self):
return self._computations
@property
def extensions(self):
return self._extensions
@property
def host_info(self):
return self._host_info
@property
def idle(self):
return self._idle
@property
def n_tasks(self):
return self._n_tasks
@property
def resources(self):
return self._resources
@property
def saturated(self):
return self._saturated
@property
def tasks(self):
return self._tasks
@property
def task_groups(self):
return self._task_groups
@property
def task_prefixes(self):
return self._task_prefixes
@property
def task_metadata(self):
return self._task_metadata
@property
def replicated_tasks(self):
return self._replicated_tasks
@property
def total_nthreads(self):
return self._total_nthreads
@property
def total_occupancy(self):
return self._total_occupancy
@total_occupancy.setter
def total_occupancy(self, v: double):
self._total_occupancy = v
@property
def transition_counter(self):
return self._transition_counter
@property
def unknown_durations(self):
return self._unknown_durations
@property
def unrunnable(self):
return self._unrunnable
@property
def validate(self):
return self._validate
@validate.setter
def validate(self, v: bint):
self._validate = v
@property
def workers(self):
return self._workers
@property
def plugins(self) -> "dict[str, SchedulerPlugin]":
return self._plugins
@property
def memory(self) -> MemoryState:
return MemoryState.sum(*(w.memory for w in self.workers.values()))
@property
def __pdict__(self):
return {
"bandwidth": self._bandwidth,
"resources": self._resources,
"saturated": self._saturated,
"unrunnable": self._unrunnable,
"n_tasks": self._n_tasks,
"unknown_durations": self._unknown_durations,
"validate": self._validate,
"tasks": self._tasks,
"task_groups": self._task_groups,
"task_prefixes": self._task_prefixes,
"total_nthreads": self._total_nthreads,
"total_occupancy": self._total_occupancy,
"extensions": self._extensions,
"clients": self._clients,
"workers": self._workers,
"idle": self._idle,
"host_info": self._host_info,
}
@ccall
@exceptval(check=False)
def new_task(
self, key: str, spec: object, state: str, computation: Computation = None
) -> TaskState:
"""Create a new task, and associated states"""
ts: TaskState = TaskState(key, spec)
ts._state = state
tp: TaskPrefix
prefix_key = key_split(key)
tp = self._task_prefixes.get(prefix_key) # type: ignore
if tp is None:
self._task_prefixes[prefix_key] = tp = TaskPrefix(prefix_key)
ts._prefix = tp
group_key = ts._group_key
tg: TaskGroup = self._task_groups.get(group_key) # type: ignore
if tg is None:
self._task_groups[group_key] = tg = TaskGroup(group_key)
if computation:
computation.groups.add(tg)
tg._prefix = tp
tp._groups.append(tg)
tg.add(ts)
self._tasks[key] = ts
return ts
#####################
# State Transitions #
#####################
def _transition(self, key, finish: str, *args, **kwargs):
"""Transition a key from its current state to the finish state
Examples
--------
>>> self._transition('x', 'waiting')
{'x': 'processing'}
Returns
-------
Dictionary of recommendations for future transitions
See Also
--------
Scheduler.transitions : transitive version of this function
"""
parent: SchedulerState = cast(SchedulerState, self)
ts: TaskState
start: str
start_finish: tuple
finish2: str
recommendations: dict
worker_msgs: dict
client_msgs: dict
msgs: list
new_msgs: list
dependents: set
dependencies: set
try:
recommendations = {}
worker_msgs = {}
client_msgs = {}
ts = parent._tasks.get(key) # type: ignore
if ts is None:
return recommendations, client_msgs, worker_msgs
start = ts._state
if start == finish:
return recommendations, client_msgs, worker_msgs
if self.plugins:
dependents = set(ts._dependents)
dependencies = set(ts._dependencies)
start_finish = (start, finish)
func = self._transitions_table.get(start_finish)
if func is not None:
recommendations, client_msgs, worker_msgs = func(key, *args, **kwargs)
self._transition_counter += 1
elif "released" not in start_finish:
assert not args and not kwargs, (args, kwargs, start_finish)
a_recs: dict
a_cmsgs: dict
a_wmsgs: dict
a: tuple = self._transition(key, "released")
a_recs, a_cmsgs, a_wmsgs = a
v = a_recs.get(key, finish)
func = self._transitions_table["released", v]
b_recs: dict
b_cmsgs: dict
b_wmsgs: dict
b: tuple = func(key)
b_recs, b_cmsgs, b_wmsgs = b
recommendations.update(a_recs)
for c, new_msgs in a_cmsgs.items():
msgs = client_msgs.get(c) # type: ignore
if msgs is not None:
msgs.extend(new_msgs)
else:
client_msgs[c] = new_msgs
for w, new_msgs in a_wmsgs.items():
msgs = worker_msgs.get(w) # type: ignore
if msgs is not None:
msgs.extend(new_msgs)
else:
worker_msgs[w] = new_msgs
recommendations.update(b_recs)
for c, new_msgs in b_cmsgs.items():
msgs = client_msgs.get(c) # type: ignore
if msgs is not None:
msgs.extend(new_msgs)
else:
client_msgs[c] = new_msgs
for w, new_msgs in b_wmsgs.items():
msgs = worker_msgs.get(w) # type: ignore
if msgs is not None:
msgs.extend(new_msgs)
else:
worker_msgs[w] = new_msgs
start = "released"
else:
raise RuntimeError("Impossible transition from %r to %r" % start_finish)
finish2 = ts._state
# FIXME downcast antipattern
scheduler = pep484_cast(Scheduler, self)
scheduler.transition_log.append(
(key, start, finish2, recommendations, time())
)
if parent._validate:
logger.debug(
"Transitioned %r %s->%s (actual: %s). Consequence: %s",
key,
start,
finish2,
ts._state,
dict(recommendations),
)
if self.plugins:
# Temporarily put back forgotten key for plugin to retrieve it
if ts._state == "forgotten":
ts._dependents = dependents
ts._dependencies = dependencies
parent._tasks[ts._key] = ts
for plugin in list(self.plugins.values()):
try:
plugin.transition(key, start, finish2, *args, **kwargs)
except Exception:
logger.info("Plugin failed with exception", exc_info=True)
if ts._state == "forgotten":
del parent._tasks[ts._key]
tg: TaskGroup = ts._group
if ts._state == "forgotten" and tg._name in parent._task_groups:
# Remove TaskGroup if all tasks are in the forgotten state
all_forgotten: bint = True
for s in ALL_TASK_STATES:
if tg._states.get(s):
all_forgotten = False
break
if all_forgotten:
ts._prefix._groups.remove(tg)
del parent._task_groups[tg._name]
return recommendations, client_msgs, worker_msgs
except Exception:
logger.exception("Error transitioning %r from %r to %r", key, start, finish)
if LOG_PDB:
import pdb
pdb.set_trace()
raise
def _transitions(self, recommendations: dict, client_msgs: dict, worker_msgs: dict):
"""Process transitions until none are left
This includes feedback from previous transitions and continues until we
reach a steady state
"""
keys: set = set()
recommendations = recommendations.copy()
msgs: list
new_msgs: list
new: tuple
new_recs: dict
new_cmsgs: dict
new_wmsgs: dict
while recommendations:
key, finish = recommendations.popitem()
keys.add(key)
new = self._transition(key, finish)
new_recs, new_cmsgs, new_wmsgs = new
recommendations.update(new_recs)
for c, new_msgs in new_cmsgs.items():
msgs = client_msgs.get(c) # type: ignore
if msgs is not None:
msgs.extend(new_msgs)
else:
client_msgs[c] = new_msgs
for w, new_msgs in new_wmsgs.items():
msgs = worker_msgs.get(w) # type: ignore
if msgs is not None:
msgs.extend(new_msgs)
else:
worker_msgs[w] = new_msgs
if self._validate:
# FIXME downcast antipattern
scheduler = pep484_cast(Scheduler, self)
for key in keys:
scheduler.validate_key(key)
def transition_released_waiting(self, key):
try:
ts: TaskState = self._tasks[key]
dts: TaskState
recommendations: dict = {}
client_msgs: dict = {}
worker_msgs: dict = {}
if self._validate:
assert ts._run_spec
assert not ts._waiting_on
assert not ts._who_has
assert not ts._processing_on
assert not any([dts._state == "forgotten" for dts in ts._dependencies])
if ts._has_lost_dependencies:
recommendations[key] = "forgotten"
return recommendations, client_msgs, worker_msgs
ts.state = "waiting"
dts: TaskState
for dts in ts._dependencies:
if dts._exception_blame:
ts._exception_blame = dts._exception_blame
recommendations[key] = "erred"
return recommendations, client_msgs, worker_msgs
for dts in ts._dependencies:
dep = dts._key
if not dts._who_has:
ts._waiting_on.add(dts)
if dts._state == "released":
recommendations[dep] = "waiting"
else:
dts._waiters.add(ts)
ts._waiters = {dts for dts in ts._dependents if dts._state == "waiting"}
if not ts._waiting_on:
if self._workers_dv:
recommendations[key] = "processing"
else:
self._unrunnable.add(ts)
ts.state = "no-worker"
return recommendations, client_msgs, worker_msgs
except Exception as e:
logger.exception(e)
if LOG_PDB:
import pdb
pdb.set_trace()
raise
def transition_no_worker_waiting(self, key):
try:
ts: TaskState = self._tasks[key]
dts: TaskState
recommendations: dict = {}
client_msgs: dict = {}
worker_msgs: dict = {}
if self._validate:
assert ts in self._unrunnable
assert not ts._waiting_on
assert not ts._who_has
assert not ts._processing_on
self._unrunnable.remove(ts)
if ts._has_lost_dependencies:
recommendations[key] = "forgotten"
return recommendations, client_msgs, worker_msgs
for dts in ts._dependencies:
dep = dts._key
if not dts._who_has:
ts._waiting_on.add(dts)
if dts._state == "released":
recommendations[dep] = "waiting"
else:
dts._waiters.add(ts)
ts.state = "waiting"
if not ts._waiting_on:
if self._workers_dv:
recommendations[key] = "processing"
else:
self._unrunnable.add(ts)
ts.state = "no-worker"
return recommendations, client_msgs, worker_msgs
except Exception as e:
logger.exception(e)
if LOG_PDB:
import pdb
pdb.set_trace()
raise
def transition_no_worker_memory(
self, key, nbytes=None, type=None, typename: str = None, worker=None
):
try:
ws: WorkerState = self._workers_dv[worker]
ts: TaskState = self._tasks[key]
recommendations: dict = {}
client_msgs: dict = {}
worker_msgs: dict = {}
if self._validate:
assert not ts._processing_on
assert not ts._waiting_on
assert ts._state == "no-worker"
self._unrunnable.remove(ts)
if nbytes is not None:
ts.set_nbytes(nbytes)
self.check_idle_saturated(ws)
_add_to_memory(
self, ts, ws, recommendations, client_msgs, type=type, typename=typename
)
ts.state = "memory"
return recommendations, client_msgs, worker_msgs
except Exception as e:
logger.exception(e)
if LOG_PDB:
import pdb
pdb.set_trace()
raise
@ccall
@exceptval(check=False)
def decide_worker(self, ts: TaskState) -> WorkerState: # -> WorkerState | None
"""
Decide on a worker for task *ts*. Return a WorkerState.
If it's a root or root-like task, we place it with its relatives to
reduce future data tansfer.
If it has dependencies or restrictions, we use
`decide_worker_from_deps_and_restrictions`.
Otherwise, we pick the least occupied worker, or pick from all workers
in a round-robin fashion.
"""
if not self._workers_dv:
return None # type: ignore
ws: WorkerState
tg: TaskGroup = ts._group
valid_workers: set = self.valid_workers(ts)
if (
valid_workers is not None
and not valid_workers
and not ts._loose_restrictions
):
self._unrunnable.add(ts)
ts.state = "no-worker"
return None # type: ignore
# Group is larger than cluster with few dependencies?
# Minimize future data transfers.
if (
valid_workers is None
and len(tg) > self._total_nthreads * 2
and len(tg._dependencies) < 5
and sum(map(len, tg._dependencies)) < 5
):
ws = tg._last_worker
if not (
ws and tg._last_worker_tasks_left and ws._address in self._workers_dv
):
# Last-used worker is full or unknown; pick a new worker for the next few tasks
ws = min(
(self._idle_dv or self._workers_dv).values(),
key=partial(self.worker_objective, ts),
)
tg._last_worker_tasks_left = math.floor(
(len(tg) / self._total_nthreads) * ws._nthreads
)
# Record `last_worker`, or clear it on the final task
tg._last_worker = (
ws if tg.states["released"] + tg.states["waiting"] > 1 else None
)
tg._last_worker_tasks_left -= 1
return ws
if ts._dependencies or valid_workers is not None:
ws = decide_worker(
ts,
self._workers_dv.values(),
valid_workers,
partial(self.worker_objective, ts),
)
else:
# Fastpath when there are no related tasks or restrictions
worker_pool = self._idle or self._workers
# Note: cython.cast, not typing.cast!
worker_pool_dv = cast(dict, worker_pool)
wp_vals = worker_pool.values()
n_workers: Py_ssize_t = len(worker_pool_dv)
if n_workers < 20: # smart but linear in small case
ws = min(wp_vals, key=operator.attrgetter("occupancy"))
if ws._occupancy == 0:
# special case to use round-robin; linear search
# for next worker with zero occupancy (or just
# land back where we started).
wp_i: WorkerState
start: Py_ssize_t = self._n_tasks % n_workers
i: Py_ssize_t
for i in range(n_workers):
wp_i = wp_vals[(i + start) % n_workers]
if wp_i._occupancy == 0:
ws = wp_i
break
else: # dumb but fast in large case
ws = wp_vals[self._n_tasks % n_workers]
if self._validate:
assert ws is None or isinstance(ws, WorkerState), (
type(ws),
ws,
)
assert ws._address in self._workers_dv
return ws
@ccall
def set_duration_estimate(self, ts: TaskState, ws: WorkerState) -> double:
"""Estimate task duration using worker state and task state.
If a task takes longer than twice the current average duration we
estimate the task duration to be 2x current-runtime, otherwise we set it
to be the average duration.
See also ``_remove_from_processing``
"""
exec_time: double = ws._executing.get(ts, 0)
duration: double = self.get_task_duration(ts)
total_duration: double
if exec_time > 2 * duration:
total_duration = 2 * exec_time
else:
comm: double = self.get_comm_cost(ts, ws)
total_duration = duration + comm
old = ws._processing.get(ts, 0)
ws._processing[ts] = total_duration
self._total_occupancy += total_duration - old
ws._occupancy += total_duration - old
return total_duration
def transition_waiting_processing(self, key):
try:
ts: TaskState = self._tasks[key]
dts: TaskState
recommendations: dict = {}
client_msgs: dict = {}
worker_msgs: dict = {}
if self._validate:
assert not ts._waiting_on
assert not ts._who_has
assert not ts._exception_blame
assert not ts._processing_on
assert not ts._has_lost_dependencies
assert ts not in self._unrunnable
assert all([dts._who_has for dts in ts._dependencies])
ws: WorkerState = self.decide_worker(ts)
if ws is None:
return recommendations, client_msgs, worker_msgs
worker = ws._address
self.set_duration_estimate(ts, ws)
ts._processing_on = ws
ts.state = "processing"
self.consume_resources(ts, ws)
self.check_idle_saturated(ws)
self._n_tasks += 1
if ts._actor:
ws._actors.add(ts)
# logger.debug("Send job to worker: %s, %s", worker, key)
worker_msgs[worker] = [_task_to_msg(self, ts)]
return recommendations, client_msgs, worker_msgs
except Exception as e:
logger.exception(e)
if LOG_PDB:
import pdb
pdb.set_trace()
raise
def transition_waiting_memory(
self, key, nbytes=None, type=None, typename: str = None, worker=None, **kwargs
):
try:
ws: WorkerState = self._workers_dv[worker]
ts: TaskState = self._tasks[key]
recommendations: dict = {}
client_msgs: dict = {}
worker_msgs: dict = {}
if self._validate:
assert not ts._processing_on
assert ts._waiting_on
assert ts._state == "waiting"
ts._waiting_on.clear()
if nbytes is not None:
ts.set_nbytes(nbytes)
self.check_idle_saturated(ws)
_add_to_memory(
self, ts, ws, recommendations, client_msgs, type=type, typename=typename
)
if self._validate:
assert not ts._processing_on
assert not ts._waiting_on
assert ts._who_has
return recommendations, client_msgs, worker_msgs
except Exception as e:
logger.exception(e)
if LOG_PDB:
import pdb
pdb.set_trace()
raise
def transition_processing_memory(
self,
key,
nbytes=None,
type=None,
typename: str = None,
worker=None,
startstops=None,
**kwargs,
):
ws: WorkerState
wws: WorkerState
recommendations: dict = {}
client_msgs: dict = {}
worker_msgs: dict = {}
try:
ts: TaskState = self._tasks[key]
assert worker
assert isinstance(worker, str)
if self._validate:
assert ts._processing_on
ws = ts._processing_on
assert ts in ws._processing
assert not ts._waiting_on
assert not ts._who_has, (ts, ts._who_has)
assert not ts._exception_blame
assert ts._state == "processing"
ws = self._workers_dv.get(worker) # type: ignore
if ws is None:
recommendations[key] = "released"
return recommendations, client_msgs, worker_msgs
if ws != ts._processing_on: # someone else has this task
logger.info(
"Unexpected worker completed task. Expected: %s, Got: %s, Key: %s",
ts._processing_on,
ws,
key,
)
worker_msgs[ts._processing_on.address] = [
{
"op": "cancel-compute",
"key": key,
"reason": "Finished on different worker",
}
]
#############################
# Update Timing Information #
#############################
if startstops:
startstop: dict
for startstop in startstops:
ts._group.add_duration(
stop=startstop["stop"],
start=startstop["start"],
action=startstop["action"],
)
s: set = self._unknown_durations.pop(ts._prefix._name, set())
tts: TaskState
steal = self.extensions.get("stealing")
for tts in s:
if tts._processing_on:
self.set_duration_estimate(tts, tts._processing_on)
if steal:
steal.put_key_in_stealable(tts)
############################
# Update State Information #
############################
if nbytes is not None:
ts.set_nbytes(nbytes)
_remove_from_processing(self, ts)
_add_to_memory(
self, ts, ws, recommendations, client_msgs, type=type, typename=typename
)
if self._validate:
assert not ts._processing_on
assert not ts._waiting_on
return recommendations, client_msgs, worker_msgs
except Exception as e:
logger.exception(e)
if LOG_PDB:
import pdb
pdb.set_trace()
raise
def transition_memory_released(self, key, safe: bint = False):
ws: WorkerState
try:
ts: TaskState = self._tasks[key]
dts: TaskState
recommendations: dict = {}
client_msgs: dict = {}
worker_msgs: dict = {}
if self._validate:
assert not ts._waiting_on
assert not ts._processing_on
if safe:
assert not ts._waiters
if ts._actor:
for ws in ts._who_has:
ws._actors.discard(ts)
if ts._who_wants:
ts._exception_blame = ts
ts._exception = "Worker holding Actor was lost"
recommendations[ts._key] = "erred"
return (
recommendations,
client_msgs,
worker_msgs,
) # don't try to recreate
for dts in ts._waiters:
if dts._state in ("no-worker", "processing"):
recommendations[dts._key] = "waiting"
elif dts._state == "waiting":
dts._waiting_on.add(ts)
# XXX factor this out?
worker_msg = {
"op": "free-keys",
"keys": [key],
"stimulus_id": f"memory-released-{time()}",
}
for ws in ts._who_has:
worker_msgs[ws._address] = [worker_msg]
self.remove_all_replicas(ts)
ts.state = "released"
report_msg = {"op": "lost-data", "key": key}
cs: ClientState
for cs in ts._who_wants:
client_msgs[cs._client_key] = [report_msg]
if not ts._run_spec: # pure data
recommendations[key] = "forgotten"
elif ts._has_lost_dependencies:
recommendations[key] = "forgotten"
elif ts._who_wants or ts._waiters:
recommendations[key] = "waiting"
if self._validate:
assert not ts._waiting_on
return recommendations, client_msgs, worker_msgs
except Exception as e:
logger.exception(e)
if LOG_PDB:
import pdb
pdb.set_trace()
raise
def transition_released_erred(self, key):
try:
ts: TaskState = self._tasks[key]
dts: TaskState
failing_ts: TaskState
recommendations: dict = {}
client_msgs: dict = {}
worker_msgs: dict = {}
if self._validate:
with log_errors(pdb=LOG_PDB):
assert ts._exception_blame
assert not ts._who_has
assert not ts._waiting_on
assert not ts._waiters
failing_ts = ts._exception_blame
for dts in ts._dependents:
dts._exception_blame = failing_ts
if not dts._who_has:
recommendations[dts._key] = "erred"
report_msg = {
"op": "task-erred",
"key": key,
"exception": failing_ts._exception,
"traceback": failing_ts._traceback,
}
cs: ClientState
for cs in ts._who_wants:
client_msgs[cs._client_key] = [report_msg]
ts.state = "erred"
# TODO: waiting data?
return recommendations, client_msgs, worker_msgs
except Exception as e:
logger.exception(e)
if LOG_PDB:
import pdb
pdb.set_trace()
raise
def transition_erred_released(self, key):
try:
ts: TaskState = self._tasks[key]
dts: TaskState
recommendations: dict = {}
client_msgs: dict = {}
worker_msgs: dict = {}
if self._validate:
with log_errors(pdb=LOG_PDB):
assert ts._exception_blame
assert not ts._who_has
assert not ts._waiting_on
assert not ts._waiters
ts._exception = None
ts._exception_blame = None
ts._traceback = None
for dts in ts._dependents:
if dts._state == "erred":
recommendations[dts._key] = "waiting"
w_msg = {
"op": "free-keys",
"keys": [key],
"stimulus_id": f"erred-released-{time()}",
}
for ws_addr in ts._erred_on:
worker_msgs[ws_addr] = [w_msg]
ts._erred_on.clear()
report_msg = {"op": "task-retried", "key": key}
cs: ClientState
for cs in ts._who_wants:
client_msgs[cs._client_key] = [report_msg]
ts.state = "released"
return recommendations, client_msgs, worker_msgs
except Exception as e:
logger.exception(e)
if LOG_PDB:
import pdb
pdb.set_trace()
raise
def transition_waiting_released(self, key):
try:
ts: TaskState = self._tasks[key]
recommendations: dict = {}
client_msgs: dict = {}
worker_msgs: dict = {}
if self._validate:
assert not ts._who_has
assert not ts._processing_on
dts: TaskState
for dts in ts._dependencies:
if ts in dts._waiters:
dts._waiters.discard(ts)
if not dts._waiters and not dts._who_wants:
recommendations[dts._key] = "released"
ts._waiting_on.clear()
ts.state = "released"
if ts._has_lost_dependencies:
recommendations[key] = "forgotten"
elif not ts._exception_blame and (ts._who_wants or ts._waiters):
recommendations[key] = "waiting"
else:
ts._waiters.clear()
return recommendations, client_msgs, worker_msgs
except Exception as e:
logger.exception(e)
if LOG_PDB:
import pdb
pdb.set_trace()
raise
def transition_processing_released(self, key):
try:
ts: TaskState = self._tasks[key]
dts: TaskState
recommendations: dict = {}
client_msgs: dict = {}
worker_msgs: dict = {}
if self._validate:
assert ts._processing_on
assert not ts._who_has
assert not ts._waiting_on
assert self._tasks[key].state == "processing"
w: str = _remove_from_processing(self, ts)
if w:
worker_msgs[w] = [
{
"op": "free-keys",
"keys": [key],
"stimulus_id": f"processing-released-{time()}",
}
]
ts.state = "released"
if ts._has_lost_dependencies:
recommendations[key] = "forgotten"
elif ts._waiters or ts._who_wants:
recommendations[key] = "waiting"
if recommendations.get(key) != "waiting":
for dts in ts._dependencies:
if dts._state != "released":
dts._waiters.discard(ts)
if not dts._waiters and not dts._who_wants:
recommendations[dts._key] = "released"
ts._waiters.clear()
if self._validate:
assert not ts._processing_on
return recommendations, client_msgs, worker_msgs
except Exception as e:
logger.exception(e)
if LOG_PDB:
import pdb
pdb.set_trace()
raise
def transition_processing_erred(
self,
key: str,
cause: str = None,
exception=None,
traceback=None,
exception_text: str = None,
traceback_text: str = None,
worker: str = None,
**kwargs,
):
ws: WorkerState
try:
ts: TaskState = self._tasks[key]
dts: TaskState
failing_ts: TaskState
recommendations: dict = {}
client_msgs: dict = {}
worker_msgs: dict = {}
if self._validate:
assert cause or ts._exception_blame
assert ts._processing_on
assert not ts._who_has
assert not ts._waiting_on
if ts._actor:
ws = ts._processing_on
ws._actors.remove(ts)
w = _remove_from_processing(self, ts)
ts._erred_on.add(w or worker)
if exception is not None:
ts._exception = exception
ts._exception_text = exception_text # type: ignore
if traceback is not None:
ts._traceback = traceback
ts._traceback_text = traceback_text # type: ignore
if cause is not None:
failing_ts = self._tasks[cause]
ts._exception_blame = failing_ts
else:
failing_ts = ts._exception_blame # type: ignore
for dts in ts._dependents:
dts._exception_blame = failing_ts
recommendations[dts._key] = "erred"
for dts in ts._dependencies:
dts._waiters.discard(ts)
if not dts._waiters and not dts._who_wants:
recommendations[dts._key] = "released"
ts._waiters.clear() # do anything with this?
ts.state = "erred"
report_msg = {
"op": "task-erred",
"key": key,
"exception": failing_ts._exception,
"traceback": failing_ts._traceback,
}
cs: ClientState
for cs in ts._who_wants:
client_msgs[cs._client_key] = [report_msg]
cs = self._clients["fire-and-forget"]
if ts in cs._wants_what:
_client_releases_keys(
self,
cs=cs,
keys=[key],
recommendations=recommendations,
)
if self._validate:
assert not ts._processing_on
return recommendations, client_msgs, worker_msgs
except Exception as e:
logger.exception(e)
if LOG_PDB:
import pdb
pdb.set_trace()
raise
def transition_no_worker_released(self, key):
try:
ts: TaskState = self._tasks[key]
dts: TaskState
recommendations: dict = {}
client_msgs: dict = {}
worker_msgs: dict = {}
if self._validate:
assert self._tasks[key].state == "no-worker"
assert not ts._who_has
assert not ts._waiting_on
self._unrunnable.remove(ts)
ts.state = "released"
for dts in ts._dependencies:
dts._waiters.discard(ts)
ts._waiters.clear()
return recommendations, client_msgs, worker_msgs
except Exception as e:
logger.exception(e)
if LOG_PDB:
import pdb
pdb.set_trace()
raise
@ccall
def remove_key(self, key):
ts: TaskState = self._tasks.pop(key)
assert ts._state == "forgotten"
self._unrunnable.discard(ts)
cs: ClientState
for cs in ts._who_wants:
cs._wants_what.remove(ts)
ts._who_wants.clear()
ts._processing_on = None
ts._exception_blame = ts._exception = ts._traceback = None
self._task_metadata.pop(key, None)
def transition_memory_forgotten(self, key):
ws: WorkerState
try:
ts: TaskState = self._tasks[key]
recommendations: dict = {}
client_msgs: dict = {}
worker_msgs: dict = {}
if self._validate:
assert ts._state == "memory"
assert not ts._processing_on
assert not ts._waiting_on
if not ts._run_spec:
# It's ok to forget a pure data task
pass
elif ts._has_lost_dependencies:
# It's ok to forget a task with forgotten dependencies
pass
elif not ts._who_wants and not ts._waiters and not ts._dependents:
# It's ok to forget a task that nobody needs
pass
else:
assert 0, (ts,)
if ts._actor:
for ws in ts._who_has:
ws._actors.discard(ts)
_propagate_forgotten(self, ts, recommendations, worker_msgs)
client_msgs = _task_to_client_msgs(self, ts)
self.remove_key(key)
return recommendations, client_msgs, worker_msgs
except Exception as e:
logger.exception(e)
if LOG_PDB:
import pdb
pdb.set_trace()
raise
def transition_released_forgotten(self, key):
try:
ts: TaskState = self._tasks[key]
recommendations: dict = {}
client_msgs: dict = {}
worker_msgs: dict = {}
if self._validate:
assert ts._state in ("released", "erred")
assert not ts._who_has
assert not ts._processing_on
assert not ts._waiting_on, (ts, ts._waiting_on)
if not ts._run_spec:
# It's ok to forget a pure data task
pass
elif ts._has_lost_dependencies:
# It's ok to forget a task with forgotten dependencies
pass
elif not ts._who_wants and not ts._waiters and not ts._dependents:
# It's ok to forget a task that nobody needs
pass
else:
assert 0, (ts,)
_propagate_forgotten(self, ts, recommendations, worker_msgs)
client_msgs = _task_to_client_msgs(self, ts)
self.remove_key(key)
return recommendations, client_msgs, worker_msgs
except Exception as e:
logger.exception(e)
if LOG_PDB:
import pdb
pdb.set_trace()
raise
##############################
# Assigning Tasks to Workers #
##############################
@ccall
@exceptval(check=False)
def check_idle_saturated(self, ws: WorkerState, occ: double = -1.0):
"""Update the status of the idle and saturated state
The scheduler keeps track of workers that are ..
- Saturated: have enough work to stay busy
- Idle: do not have enough work to stay busy
They are considered saturated if they both have enough tasks to occupy
all of their threads, and if the expected runtime of those tasks is
large enough.
This is useful for load balancing and adaptivity.
"""
if self._total_nthreads == 0 or ws.status == Status.closed:
return
if occ < 0:
occ = ws._occupancy
nc: Py_ssize_t = ws._nthreads
p: Py_ssize_t = len(ws._processing)
avg: double = self._total_occupancy / self._total_nthreads
idle = self._idle
saturated: set = self._saturated
if p < nc or occ < nc * avg / 2:
idle[ws._address] = ws
saturated.discard(ws)
else:
idle.pop(ws._address, None)
if p > nc:
pending: double = occ * (p - nc) / (p * nc)
if 0.4 < pending > 1.9 * avg:
saturated.add(ws)
return
saturated.discard(ws)
@ccall
def get_comm_cost(self, ts: TaskState, ws: WorkerState) -> double:
"""
Get the estimated communication cost (in s.) to compute the task
on the given worker.
"""
dts: TaskState
deps: set = ts._dependencies.difference(ws._has_what)
nbytes: Py_ssize_t = 0
for dts in deps:
nbytes += dts._nbytes
return nbytes / self._bandwidth
@ccall
def get_task_duration(self, ts: TaskState) -> double:
"""Get the estimated computation cost of the given task (not including
any communication cost).
If no data has been observed, value of
`distributed.scheduler.default-task-durations` are used. If none is set
for this task, `distributed.scheduler.unknown-task-duration` is used
instead.
"""
duration: double = ts._prefix._duration_average
if duration >= 0:
return duration
s: set = self._unknown_durations.get(ts._prefix._name) # type: ignore
if s is None:
self._unknown_durations[ts._prefix._name] = s = set()
s.add(ts)
return self.UNKNOWN_TASK_DURATION
@ccall
@exceptval(check=False)
def valid_workers(self, ts: TaskState) -> set:
"""Return set of currently valid workers for key
If all workers are valid then this returns ``None``.
This checks tracks the following state:
* worker_restrictions
* host_restrictions
* resource_restrictions
"""
s: set = None # type: ignore
if ts._worker_restrictions:
s = {w for w in ts._worker_restrictions if w in self._workers_dv}
if ts._host_restrictions:
# Resolve the alias here rather than early, for the worker
# may not be connected when host_restrictions is populated
hr: list = [self.coerce_hostname(h) for h in ts._host_restrictions]
# XXX need HostState?
sl: list = []
for h in hr:
dh: dict = self._host_info.get(h) # type: ignore
if dh is not None:
sl.append(dh["addresses"])
ss: set = set.union(*sl) if sl else set()
if s is None:
s = ss
else:
s |= ss
if ts._resource_restrictions:
dw: dict = {}
for resource, required in ts._resource_restrictions.items():
dr: dict = self._resources.get(resource) # type: ignore
if dr is None:
self._resources[resource] = dr = {}
sw: set = set()
for w, supplied in dr.items():
if supplied >= required:
sw.add(w)
dw[resource] = sw
ww: set = set.intersection(*dw.values())
if s is None:
s = ww
else:
s &= ww
if s is not None:
s = {self._workers_dv[w] for w in s}
return s
@ccall
def consume_resources(self, ts: TaskState, ws: WorkerState):
if ts._resource_restrictions:
for r, required in ts._resource_restrictions.items():
ws._used_resources[r] += required
@ccall
def release_resources(self, ts: TaskState, ws: WorkerState):
if ts._resource_restrictions:
for r, required in ts._resource_restrictions.items():
ws._used_resources[r] -= required
@ccall
def coerce_hostname(self, host):
"""
Coerce the hostname of a worker.
"""
alias = self._aliases.get(host)
if alias is not None:
ws: WorkerState = self._workers_dv[alias]
return ws.host
else:
return host
@ccall
@exceptval(check=False)
def worker_objective(self, ts: TaskState, ws: WorkerState) -> tuple:
"""
Objective function to determine which worker should get the task
Minimize expected start time. If a tie then break with data storage.
"""
dts: TaskState
nbytes: Py_ssize_t
comm_bytes: Py_ssize_t = 0
for dts in ts._dependencies:
if ws not in dts._who_has:
nbytes = dts.get_nbytes()
comm_bytes += nbytes
stack_time: double = ws._occupancy / ws._nthreads
start_time: double = stack_time + comm_bytes / self._bandwidth
if ts._actor:
return (len(ws._actors), start_time, ws._nbytes)
else:
return (start_time, ws._nbytes)
@ccall
def add_replica(self, ts: TaskState, ws: WorkerState):
"""Note that a worker holds a replica of a task with state='memory'"""
if self._validate:
assert ws not in ts._who_has
assert ts not in ws._has_what
ws._nbytes += ts.get_nbytes()
ws._has_what[ts] = None
ts._who_has.add(ws)
if len(ts._who_has) == 2:
self._replicated_tasks.add(ts)
@ccall
def remove_replica(self, ts: TaskState, ws: WorkerState):
"""Note that a worker no longer holds a replica of a task"""
ws._nbytes -= ts.get_nbytes()
del ws._has_what[ts]
ts._who_has.remove(ws)
if len(ts._who_has) == 1:
self._replicated_tasks.remove(ts)
@ccall
def remove_all_replicas(self, ts: TaskState):
"""Remove all replicas of a task from all workers"""
ws: WorkerState
nbytes: Py_ssize_t = ts.get_nbytes()
for ws in ts._who_has:
ws._nbytes -= nbytes
del ws._has_what[ts]
if len(ts._who_has) > 1:
self._replicated_tasks.remove(ts)
ts._who_has.clear()
[docs]class Scheduler(SchedulerState, ServerNode):
"""Dynamic distributed task scheduler
The scheduler tracks the current state of workers, data, and computations.
The scheduler listens for events and responds by controlling workers
appropriately. It continuously tries to use the workers to execute an ever
growing dask graph.
All events are handled quickly, in linear time with respect to their input
(which is often of constant size) and generally within a millisecond. To
accomplish this the scheduler tracks a lot of state. Every operation
maintains the consistency of this state.
The scheduler communicates with the outside world through Comm objects.
It maintains a consistent and valid view of the world even when listening
to several clients at once.
A Scheduler is typically started either with the ``dask-scheduler``
executable::
$ dask-scheduler
Scheduler started at 127.0.0.1:8786
Or within a LocalCluster a Client starts up without connection
information::
>>> c = Client() # doctest: +SKIP
>>> c.cluster.scheduler # doctest: +SKIP
Scheduler(...)
Users typically do not interact with the scheduler directly but rather with
the client object ``Client``.
**State**
The scheduler contains the following state variables. Each variable is
listed along with what it stores and a brief description.
* **tasks:** ``{task key: TaskState}``
Tasks currently known to the scheduler
* **unrunnable:** ``{TaskState}``
Tasks in the "no-worker" state
* **workers:** ``{worker key: WorkerState}``
Workers currently connected to the scheduler
* **idle:** ``{WorkerState}``:
Set of workers that are not fully utilized
* **saturated:** ``{WorkerState}``:
Set of workers that are not over-utilized
* **host_info:** ``{hostname: dict}``:
Information about each worker host
* **clients:** ``{client key: ClientState}``
Clients currently connected to the scheduler
* **services:** ``{str: port}``:
Other services running on this scheduler, like Bokeh
* **loop:** ``IOLoop``:
The running Tornado IOLoop
* **client_comms:** ``{client key: Comm}``
For each client, a Comm object used to receive task requests and
report task status updates.
* **stream_comms:** ``{worker key: Comm}``
For each worker, a Comm object from which we both accept stimuli and
report results
* **task_duration:** ``{key-prefix: time}``
Time we expect certain functions to take, e.g. ``{'sum': 0.25}``
"""
default_port = 8786
_instances: "ClassVar[weakref.WeakSet[Scheduler]]" = weakref.WeakSet()
def __init__(
self,
loop=None,
delete_interval="500ms",
synchronize_worker_interval="60s",
services=None,
service_kwargs=None,
allowed_failures=None,
extensions=None,
validate=None,
scheduler_file=None,
security=None,
worker_ttl=None,
idle_timeout=None,
interface=None,
host=None,
port=0,
protocol=None,
dashboard_address=None,
dashboard=None,
http_prefix="/",
preload=None,
preload_argv=(),
plugins=(),
**kwargs,
):
self._setup_logging(logger)
# Attributes
if allowed_failures is None:
allowed_failures = dask.config.get("distributed.scheduler.allowed-failures")
self.allowed_failures = allowed_failures
if validate is None:
validate = dask.config.get("distributed.scheduler.validate")
self.proc = psutil.Process()
self.delete_interval = parse_timedelta(delete_interval, default="ms")
self.synchronize_worker_interval = parse_timedelta(
synchronize_worker_interval, default="ms"
)
self.digests = None
self.service_specs = services or {}
self.service_kwargs = service_kwargs or {}
self.services = {}
self.scheduler_file = scheduler_file
worker_ttl = worker_ttl or dask.config.get("distributed.scheduler.worker-ttl")
self.worker_ttl = parse_timedelta(worker_ttl) if worker_ttl else None
idle_timeout = idle_timeout or dask.config.get(
"distributed.scheduler.idle-timeout"
)
if idle_timeout:
self.idle_timeout = parse_timedelta(idle_timeout)
else:
self.idle_timeout = None
self.idle_since = time()
self.time_started = self.idle_since # compatibility for dask-gateway
self._lock = asyncio.Lock()
self.bandwidth_workers = defaultdict(float)
self.bandwidth_types = defaultdict(float)
if not preload:
preload = dask.config.get("distributed.scheduler.preload")
if not preload_argv:
preload_argv = dask.config.get("distributed.scheduler.preload-argv")
self.preloads = preloading.process_preloads(self, preload, preload_argv)
if isinstance(security, dict):
security = Security(**security)
self.security = security or Security()
assert isinstance(self.security, Security)
self.connection_args = self.security.get_connection_args("scheduler")
self.connection_args["handshake_overrides"] = { # common denominator
"pickle-protocol": 4
}
self._start_address = addresses_from_user_args(
host=host,
port=port,
interface=interface,
protocol=protocol,
security=security,
default_port=self.default_port,
)
http_server_modules = dask.config.get("distributed.scheduler.http.routes")
show_dashboard = dashboard or (dashboard is None and dashboard_address)
# install vanilla route if show_dashboard but bokeh is not installed
if show_dashboard:
try:
import distributed.dashboard.scheduler
except ImportError:
show_dashboard = False
http_server_modules.append("distributed.http.scheduler.missing_bokeh")
routes = get_handlers(
server=self, modules=http_server_modules, prefix=http_prefix
)
self.start_http_server(routes, dashboard_address, default_port=8787)
if show_dashboard:
distributed.dashboard.scheduler.connect(
self.http_application, self.http_server, self, prefix=http_prefix
)
# Communication state
self.loop = loop or IOLoop.current()
self.client_comms = {}
self.stream_comms = {}
self._worker_coroutines = []
self._ipython_kernel = None
# Task state
tasks = {}
for old_attr, new_attr, wrap in [
("priority", "priority", None),
("dependencies", "dependencies", _legacy_task_key_set),
("dependents", "dependents", _legacy_task_key_set),
("retries", "retries", None),
]:
func = operator.attrgetter(new_attr)
if wrap is not None:
func = compose(wrap, func)
setattr(self, old_attr, _StateLegacyMapping(tasks, func))
for old_attr, new_attr, wrap in [
("nbytes", "nbytes", None),
("who_wants", "who_wants", _legacy_client_key_set),
("who_has", "who_has", _legacy_worker_key_set),
("waiting", "waiting_on", _legacy_task_key_set),
("waiting_data", "waiters", _legacy_task_key_set),
("rprocessing", "processing_on", None),
("host_restrictions", "host_restrictions", None),
("worker_restrictions", "worker_restrictions", None),
("resource_restrictions", "resource_restrictions", None),
("suspicious_tasks", "suspicious", None),
("exceptions", "exception", None),
("tracebacks", "traceback", None),
("exceptions_blame", "exception_blame", _task_key_or_none),
]:
func = operator.attrgetter(new_attr)
if wrap is not None:
func = compose(wrap, func)
setattr(self, old_attr, _OptionalStateLegacyMapping(tasks, func))
for old_attr, new_attr, wrap in [
("loose_restrictions", "loose_restrictions", None)
]:
func = operator.attrgetter(new_attr)
if wrap is not None:
func = compose(wrap, func)
setattr(self, old_attr, _StateLegacySet(tasks, func))
self.generation = 0
self._last_client = None
self._last_time = 0
unrunnable = set()
self.datasets = {}
# Prefix-keyed containers
# Client state
clients = {}
for old_attr, new_attr, wrap in [
("wants_what", "wants_what", _legacy_task_key_set)
]:
func = operator.attrgetter(new_attr)
if wrap is not None:
func = compose(wrap, func)
setattr(self, old_attr, _StateLegacyMapping(clients, func))
# Worker state
workers = SortedDict()
for old_attr, new_attr, wrap in [
("nthreads", "nthreads", None),
("worker_bytes", "nbytes", None),
("worker_resources", "resources", None),
("used_resources", "used_resources", None),
("occupancy", "occupancy", None),
("worker_info", "metrics", None),
("processing", "processing", _legacy_task_key_dict),
("has_what", "has_what", _legacy_task_key_set),
]:
func = operator.attrgetter(new_attr)
if wrap is not None:
func = compose(wrap, func)
setattr(self, old_attr, _StateLegacyMapping(workers, func))
host_info = {}
resources = {}
aliases = {}
self._task_state_collections = [unrunnable]
self._worker_collections = [
workers,
host_info,
resources,
aliases,
]
self.transition_log = deque(
maxlen=dask.config.get("distributed.scheduler.transition-log-length")
)
self.log = deque(
maxlen=dask.config.get("distributed.scheduler.transition-log-length")
)
self.events = defaultdict(
partial(
deque, maxlen=dask.config.get("distributed.scheduler.events-log-length")
)
)
self.event_counts = defaultdict(int)
self.event_subscriber = defaultdict(set)
self.worker_plugins = {}
self.nanny_plugins = {}
worker_handlers = {
"task-finished": self.handle_task_finished,
"task-erred": self.handle_task_erred,
"release-worker-data": self.release_worker_data,
"add-keys": self.add_keys,
"missing-data": self.handle_missing_data,
"long-running": self.handle_long_running,
"reschedule": self.reschedule,
"keep-alive": lambda *args, **kwargs: None,
"log-event": self.log_worker_event,
"worker-status-change": self.handle_worker_status_change,
}
client_handlers = {
"update-graph": self.update_graph,
"update-graph-hlg": self.update_graph_hlg,
"client-desires-keys": self.client_desires_keys,
"update-data": self.update_data,
"report-key": self.report_on_key,
"client-releases-keys": self.client_releases_keys,
"heartbeat-client": self.client_heartbeat,
"close-client": self.remove_client,
"restart": self.restart,
"subscribe-topic": self.subscribe_topic,
"unsubscribe-topic": self.unsubscribe_topic,
}
self.handlers = {
"register-client": self.add_client,
"scatter": self.scatter,
"register-worker": self.add_worker,
"register_nanny": self.add_nanny,
"unregister": self.remove_worker,
"gather": self.gather,
"cancel": self.stimulus_cancel,
"retry": self.stimulus_retry,
"feed": self.feed,
"terminate": self.close,
"broadcast": self.broadcast,
"proxy": self.proxy,
"ncores": self.get_ncores,
"has_what": self.get_has_what,
"who_has": self.get_who_has,
"processing": self.get_processing,
"call_stack": self.get_call_stack,
"profile": self.get_profile,
"performance_report": self.performance_report,
"get_logs": self.get_logs,
"logs": self.get_logs,
"worker_logs": self.get_worker_logs,
"log_event": self.log_worker_event,
"events": self.get_events,
"nbytes": self.get_nbytes,
"versions": self.versions,
"add_keys": self.add_keys,
"rebalance": self.rebalance,
"replicate": self.replicate,
"start_ipython": self.start_ipython,
"run_function": self.run_function,
"update_data": self.update_data,
"set_resources": self.add_resources,
"retire_workers": self.retire_workers,
"get_metadata": self.get_metadata,
"set_metadata": self.set_metadata,
"set_restrictions": self.set_restrictions,
"heartbeat_worker": self.heartbeat_worker,
"get_task_status": self.get_task_status,
"get_task_stream": self.get_task_stream,
"register_scheduler_plugin": self.register_scheduler_plugin,
"register_worker_plugin": self.register_worker_plugin,
"unregister_worker_plugin": self.unregister_worker_plugin,
"register_nanny_plugin": self.register_nanny_plugin,
"unregister_nanny_plugin": self.unregister_nanny_plugin,
"adaptive_target": self.adaptive_target,
"workers_to_close": self.workers_to_close,
"subscribe_worker_status": self.subscribe_worker_status,
"start_task_metadata": self.start_task_metadata,
"stop_task_metadata": self.stop_task_metadata,
}
connection_limit = get_fileno_limit() / 2
super().__init__(
# Arguments to SchedulerState
aliases=aliases,
clients=clients,
workers=workers,
host_info=host_info,
resources=resources,
tasks=tasks,
unrunnable=unrunnable,
validate=validate,
plugins=plugins,
# Arguments to ServerNode
handlers=self.handlers,
stream_handlers=merge(worker_handlers, client_handlers),
io_loop=self.loop,
connection_limit=connection_limit,
deserialize=False,
connection_args=self.connection_args,
**kwargs,
)
if self.worker_ttl:
pc = PeriodicCallback(self.check_worker_ttl, self.worker_ttl)
self.periodic_callbacks["worker-ttl"] = pc
if self.idle_timeout:
pc = PeriodicCallback(self.check_idle, self.idle_timeout / 4)
self.periodic_callbacks["idle-timeout"] = pc
if extensions is None:
extensions = list(DEFAULT_EXTENSIONS)
if dask.config.get("distributed.scheduler.work-stealing"):
extensions.append(WorkStealing)
for ext in extensions:
ext(self)
setproctitle("dask-scheduler [not started]")
Scheduler._instances.add(self)
self.rpc.allow_offload = False
self.status = Status.undefined
##################
# Administration #
##################
def __repr__(self):
parent: SchedulerState = cast(SchedulerState, self)
return '<Scheduler: "%s" workers: %d cores: %d, tasks: %d>' % (
self.address,
len(parent._workers_dv),
parent._total_nthreads,
len(parent._tasks),
)
def _repr_html_(self):
parent: SchedulerState = cast(SchedulerState, self)
return get_template("scheduler.html.j2").render(
address=self.address,
workers=parent._workers_dv,
threads=parent._total_nthreads,
tasks=parent._tasks,
)
[docs] def identity(self, comm=None):
"""Basic information about ourselves and our cluster"""
parent: SchedulerState = cast(SchedulerState, self)
d = {
"type": type(self).__name__,
"id": str(self.id),
"address": self.address,
"services": {key: v.port for (key, v) in self.services.items()},
"started": self.time_started,
"workers": {
worker.address: worker.identity()
for worker in parent._workers_dv.values()
},
}
return d
[docs] def get_worker_service_addr(self, worker, service_name, protocol=False):
"""
Get the (host, port) address of the named service on the *worker*.
Returns None if the service doesn't exist.
Parameters
----------
worker : address
service_name : str
Common services include 'bokeh' and 'nanny'
protocol : boolean
Whether or not to include a full address with protocol (True)
or just a (host, port) pair
"""
parent: SchedulerState = cast(SchedulerState, self)
ws: WorkerState = parent._workers_dv[worker]
port = ws._services.get(service_name)
if port is None:
return None
elif protocol:
return "%(protocol)s://%(host)s:%(port)d" % {
"protocol": ws._address.split("://")[0],
"host": ws.host,
"port": port,
}
else:
return ws.host, port
[docs] async def start(self):
"""Clear out old state and restart all running coroutines"""
await super().start()
assert self.status != Status.running
enable_gc_diagnosis()
self.clear_task_state()
with suppress(AttributeError):
for c in self._worker_coroutines:
c.cancel()
for addr in self._start_address:
await self.listen(
addr,
allow_offload=False,
handshake_overrides={"pickle-protocol": 4, "compression": None},
**self.security.get_listen_args("scheduler"),
)
self.ip = get_address_host(self.listen_address)
listen_ip = self.ip
if listen_ip == "0.0.0.0":
listen_ip = ""
if self.address.startswith("inproc://"):
listen_ip = "localhost"
# Services listen on all addresses
self.start_services(listen_ip)
for listener in self.listeners:
logger.info(" Scheduler at: %25s", listener.contact_address)
for k, v in self.services.items():
logger.info("%11s at: %25s", k, "%s:%d" % (listen_ip, v.port))
self.loop.add_callback(self.reevaluate_occupancy)
if self.scheduler_file:
with open(self.scheduler_file, "w") as f:
json.dump(self.identity(), f, indent=2)
fn = self.scheduler_file # remove file when we close the process
def del_scheduler_file():
if os.path.exists(fn):
os.remove(fn)
weakref.finalize(self, del_scheduler_file)
for preload in self.preloads:
await preload.start()
await asyncio.gather(
*[plugin.start(self) for plugin in list(self.plugins.values())]
)
self.start_periodic_callbacks()
setproctitle(f"dask-scheduler [{self.address}]")
return self
[docs] async def close(self, comm=None, fast=False, close_workers=False):
"""Send cleanup signal to all coroutines then wait until finished
See Also
--------
Scheduler.cleanup
"""
parent: SchedulerState = cast(SchedulerState, self)
if self.status in (Status.closing, Status.closed):
await self.finished()
return
self.status = Status.closing
logger.info("Scheduler closing...")
setproctitle("dask-scheduler [closing]")
for preload in self.preloads:
await preload.teardown()
if close_workers:
await self.broadcast(msg={"op": "close_gracefully"}, nanny=True)
for worker in parent._workers_dv:
# Report would require the worker to unregister with the
# currently closing scheduler. This is not necessary and might
# delay shutdown of the worker unnecessarily
self.worker_send(worker, {"op": "close", "report": False})
for i in range(20): # wait a second for send signals to clear
if parent._workers_dv:
await asyncio.sleep(0.05)
else:
break
await asyncio.gather(
*[plugin.close() for plugin in list(self.plugins.values())]
)
for pc in self.periodic_callbacks.values():
pc.stop()
self.periodic_callbacks.clear()
self.stop_services()
for ext in parent._extensions.values():
with suppress(AttributeError):
ext.teardown()
logger.info("Scheduler closing all comms")
futures = []
for w, comm in list(self.stream_comms.items()):
if not comm.closed():
comm.send({"op": "close", "report": False})
comm.send({"op": "close-stream"})
with suppress(AttributeError):
futures.append(comm.close())
for future in futures: # TODO: do all at once
await future
for comm in self.client_comms.values():
comm.abort()
await self.rpc.close()
self.status = Status.closed
self.stop()
await super().close()
setproctitle("dask-scheduler [closed]")
disable_gc_diagnosis()
[docs] async def close_worker(self, comm=None, worker=None, safe=None):
"""Remove a worker from the cluster
This both removes the worker from our local state and also sends a
signal to the worker to shut down. This works regardless of whether or
not the worker has a nanny process restarting it
"""
logger.info("Closing worker %s", worker)
with log_errors():
self.log_event(worker, {"action": "close-worker"})
# FIXME: This does not handle nannies
self.worker_send(worker, {"op": "close", "report": False})
await self.remove_worker(address=worker, safe=safe)
###########
# Stimuli #
###########
def heartbeat_worker(
self,
comm=None,
*,
address,
resolve_address: bool = True,
now: float = None,
resources: dict = None,
host_info: dict = None,
metrics: dict,
executing: dict = None,
):
parent: SchedulerState = cast(SchedulerState, self)
address = self.coerce_address(address, resolve_address)
address = normalize_address(address)
ws: WorkerState = parent._workers_dv.get(address) # type: ignore
if ws is None:
return {"status": "missing"}
host = get_address_host(address)
local_now = time()
host_info = host_info or {}
dh: dict = parent._host_info.setdefault(host, {})
dh["last-seen"] = local_now
frac = 1 / len(parent._workers_dv)
parent._bandwidth = (
parent._bandwidth * (1 - frac) + metrics["bandwidth"]["total"] * frac
)
for other, (bw, count) in metrics["bandwidth"]["workers"].items():
if (address, other) not in self.bandwidth_workers:
self.bandwidth_workers[address, other] = bw / count
else:
alpha = (1 - frac) ** count
self.bandwidth_workers[address, other] = self.bandwidth_workers[
address, other
] * alpha + bw * (1 - alpha)
for typ, (bw, count) in metrics["bandwidth"]["types"].items():
if typ not in self.bandwidth_types:
self.bandwidth_types[typ] = bw / count
else:
alpha = (1 - frac) ** count
self.bandwidth_types[typ] = self.bandwidth_types[typ] * alpha + bw * (
1 - alpha
)
ws._last_seen = local_now
if executing is not None:
ws._executing = {
parent._tasks[key]: duration
for key, duration in executing.items()
if key in parent._tasks
}
ws._metrics = metrics
# Calculate RSS - dask keys, separating "old" and "new" usage
# See MemoryState for details
max_memory_unmanaged_old_hist_age = local_now - parent.MEMORY_RECENT_TO_OLD_TIME
memory_unmanaged_old = ws._memory_unmanaged_old
while ws._memory_other_history:
timestamp, size = ws._memory_other_history[0]
if timestamp >= max_memory_unmanaged_old_hist_age:
break
ws._memory_other_history.popleft()
if size == memory_unmanaged_old:
memory_unmanaged_old = 0 # recalculate min()
# metrics["memory"] is None if the worker sent a heartbeat before its
# SystemMonitor ever had a chance to run.
# ws._nbytes is updated at a different time and sizeof() may not be accurate,
# so size may be (temporarily) negative; floor it to zero.
size = max(0, (metrics["memory"] or 0) - ws._nbytes + metrics["spilled_nbytes"])
ws._memory_other_history.append((local_now, size))
if not memory_unmanaged_old:
# The worker has just been started or the previous minimum has been expunged
# because too old.
# Note: this algorithm is capped to 200 * MEMORY_RECENT_TO_OLD_TIME elements
# cluster-wide by heartbeat_interval(), regardless of the number of workers
ws._memory_unmanaged_old = min(map(second, ws._memory_other_history))
elif size < memory_unmanaged_old:
ws._memory_unmanaged_old = size
if host_info:
dh = parent._host_info.setdefault(host, {})
dh.update(host_info)
if now:
ws._time_delay = local_now - now
if resources:
self.add_resources(worker=address, resources=resources)
self.log_event(address, merge({"action": "heartbeat"}, metrics))
return {
"status": "OK",
"time": local_now,
"heartbeat-interval": heartbeat_interval(len(parent._workers_dv)),
}
[docs] async def add_worker(
self,
comm=None,
address=None,
keys=(),
nthreads=None,
name=None,
resolve_address=True,
nbytes=None,
types=None,
now=None,
resources=None,
host_info=None,
memory_limit=None,
metrics=None,
pid=0,
services=None,
local_directory=None,
versions=None,
nanny=None,
extra=None,
):
"""Add a new worker to the cluster"""
parent: SchedulerState = cast(SchedulerState, self)
with log_errors():
address = self.coerce_address(address, resolve_address)
address = normalize_address(address)
host = get_address_host(address)
ws: WorkerState = parent._workers_dv.get(address)
if ws is not None:
raise ValueError("Worker already exists %s" % ws)
if name in parent._aliases:
logger.warning(
"Worker tried to connect with a duplicate name: %s", name
)
msg = {
"status": "error",
"message": "name taken, %s" % name,
"time": time(),
}
if comm:
await comm.write(msg)
return
parent._workers[address] = ws = WorkerState(
address=address,
pid=pid,
nthreads=nthreads,
memory_limit=memory_limit or 0,
name=name,
local_directory=local_directory,
services=services,
versions=versions,
nanny=nanny,
extra=extra,
)
dh: dict = parent._host_info.get(host)
if dh is None:
parent._host_info[host] = dh = {}
dh_addresses: set = dh.get("addresses")
if dh_addresses is None:
dh["addresses"] = dh_addresses = set()
dh["nthreads"] = 0
dh_addresses.add(address)
dh["nthreads"] += nthreads
parent._total_nthreads += nthreads
parent._aliases[name] = address
self.heartbeat_worker(
address=address,
resolve_address=resolve_address,
now=now,
resources=resources,
host_info=host_info,
metrics=metrics,
)
# Do not need to adjust parent._total_occupancy as self.occupancy[ws] cannot exist before this.
self.check_idle_saturated(ws)
# for key in keys: # TODO
# self.mark_key_in_memory(key, [address])
self.stream_comms[address] = BatchedSend(interval="5ms", loop=self.loop)
if ws._nthreads > len(ws._processing):
parent._idle[ws._address] = ws
for plugin in list(self.plugins.values()):
try:
result = plugin.add_worker(scheduler=self, worker=address)
if inspect.isawaitable(result):
await result
except Exception as e:
logger.exception(e)
recommendations: dict = {}
client_msgs: dict = {}
worker_msgs: dict = {}
if nbytes:
assert isinstance(nbytes, dict)
already_released_keys = []
for key in nbytes:
ts: TaskState = parent._tasks.get(key)
if ts is not None and ts.state != "released":
if ts.state == "memory":
self.add_keys(worker=address, keys=[key])
else:
t: tuple = parent._transition(
key,
"memory",
worker=address,
nbytes=nbytes[key],
typename=types[key],
)
recommendations, client_msgs, worker_msgs = t
parent._transitions(
recommendations, client_msgs, worker_msgs
)
recommendations = {}
else:
already_released_keys.append(key)
if already_released_keys:
if address not in worker_msgs:
worker_msgs[address] = []
worker_msgs[address].append(
{
"op": "remove-replicas",
"keys": already_released_keys,
"stimulus_id": f"reconnect-already-released-{time()}",
}
)
for ts in list(parent._unrunnable):
valid: set = self.valid_workers(ts)
if valid is None or ws in valid:
recommendations[ts._key] = "waiting"
if recommendations:
parent._transitions(recommendations, client_msgs, worker_msgs)
recommendations = {}
self.send_all(client_msgs, worker_msgs)
self.log_event(address, {"action": "add-worker"})
self.log_event("all", {"action": "add-worker", "worker": address})
logger.info("Register worker %s", ws)
msg = {
"status": "OK",
"time": time(),
"heartbeat-interval": heartbeat_interval(len(parent._workers_dv)),
"worker-plugins": self.worker_plugins,
}
cs: ClientState
version_warning = version_module.error_message(
version_module.get_versions(),
merge(
{w: ws._versions for w, ws in parent._workers_dv.items()},
{
c: cs._versions
for c, cs in parent._clients.items()
if cs._versions
},
),
versions,
client_name="This Worker",
)
msg.update(version_warning)
if comm:
await comm.write(msg)
await self.handle_worker(comm=comm, worker=address)
async def add_nanny(self, comm):
msg = {
"status": "OK",
"nanny-plugins": self.nanny_plugins,
}
return msg
def update_graph_hlg(
self,
client=None,
hlg=None,
keys=None,
dependencies=None,
restrictions=None,
priority=None,
loose_restrictions=None,
resources=None,
submitting_task=None,
retries=None,
user_priority=0,
actors=None,
fifo_timeout=0,
code=None,
):
unpacked_graph = HighLevelGraph.__dask_distributed_unpack__(hlg)
dsk = unpacked_graph["dsk"]
dependencies = unpacked_graph["deps"]
annotations = unpacked_graph["annotations"]
# Remove any self-dependencies (happens on test_publish_bag() and others)
for k, v in dependencies.items():
deps = set(v)
if k in deps:
deps.remove(k)
dependencies[k] = deps
if priority is None:
# Removing all non-local keys before calling order()
dsk_keys = set(dsk) # intersection() of sets is much faster than dict_keys
stripped_deps = {
k: v.intersection(dsk_keys)
for k, v in dependencies.items()
if k in dsk_keys
}
priority = dask.order.order(dsk, dependencies=stripped_deps)
return self.update_graph(
client,
dsk,
keys,
dependencies,
restrictions,
priority,
loose_restrictions,
resources,
submitting_task,
retries,
user_priority,
actors,
fifo_timeout,
annotations,
code=code,
)
[docs] def update_graph(
self,
client=None,
tasks=None,
keys=None,
dependencies=None,
restrictions=None,
priority=None,
loose_restrictions=None,
resources=None,
submitting_task=None,
retries=None,
user_priority=0,
actors=None,
fifo_timeout=0,
annotations=None,
code=None,
):
"""
Add new computations to the internal dask graph
This happens whenever the Client calls submit, map, get, or compute.
"""
parent: SchedulerState = cast(SchedulerState, self)
start = time()
fifo_timeout = parse_timedelta(fifo_timeout)
keys = set(keys)
if len(tasks) > 1:
self.log_event(
["all", client], {"action": "update_graph", "count": len(tasks)}
)
# Remove aliases
for k in list(tasks):
if tasks[k] is k:
del tasks[k]
dependencies = dependencies or {}
if parent._total_occupancy > 1e-9 and parent._computations:
# Still working on something. Assign new tasks to same computation
computation = cast(Computation, parent._computations[-1])
else:
computation = Computation()
parent._computations.append(computation)
if code and code not in computation._code: # add new code blocks
computation._code.add(code)
n = 0
while len(tasks) != n: # walk through new tasks, cancel any bad deps
n = len(tasks)
for k, deps in list(dependencies.items()):
if any(
dep not in parent._tasks and dep not in tasks for dep in deps
): # bad key
logger.info("User asked for computation on lost data, %s", k)
del tasks[k]
del dependencies[k]
if k in keys:
keys.remove(k)
self.report({"op": "cancelled-key", "key": k}, client=client)
self.client_releases_keys(keys=[k], client=client)
# Avoid computation that is already finished
ts: TaskState
already_in_memory = set() # tasks that are already done
for k, v in dependencies.items():
if v and k in parent._tasks:
ts = parent._tasks[k]
if ts._state in ("memory", "erred"):
already_in_memory.add(k)
dts: TaskState
if already_in_memory:
dependents = dask.core.reverse_dict(dependencies)
stack = list(already_in_memory)
done = set(already_in_memory)
while stack: # remove unnecessary dependencies
key = stack.pop()
ts = parent._tasks[key]
try:
deps = dependencies[key]
except KeyError:
deps = self.dependencies[key]
for dep in deps:
if dep in dependents:
child_deps = dependents[dep]
else:
child_deps = self.dependencies[dep]
if all(d in done for d in child_deps):
if dep in parent._tasks and dep not in done:
done.add(dep)
stack.append(dep)
for d in done:
tasks.pop(d, None)
dependencies.pop(d, None)
# Get or create task states
stack = list(keys)
touched_keys = set()
touched_tasks = []
while stack:
k = stack.pop()
if k in touched_keys:
continue
# XXX Have a method get_task_state(self, k) ?
ts = parent._tasks.get(k)
if ts is None:
ts = parent.new_task(
k, tasks.get(k), "released", computation=computation
)
elif not ts._run_spec:
ts._run_spec = tasks.get(k)
touched_keys.add(k)
touched_tasks.append(ts)
stack.extend(dependencies.get(k, ()))
self.client_desires_keys(keys=keys, client=client)
# Add dependencies
for key, deps in dependencies.items():
ts = parent._tasks.get(key)
if ts is None or ts._dependencies:
continue
for dep in deps:
dts = parent._tasks[dep]
ts.add_dependency(dts)
# Compute priorities
if isinstance(user_priority, Number):
user_priority = {k: user_priority for k in tasks}
annotations = annotations or {}
restrictions = restrictions or {}
loose_restrictions = loose_restrictions or []
resources = resources or {}
retries = retries or {}
# Override existing taxonomy with per task annotations
if annotations:
if "priority" in annotations:
user_priority.update(annotations["priority"])
if "workers" in annotations:
restrictions.update(annotations["workers"])
if "allow_other_workers" in annotations:
loose_restrictions.extend(
k for k, v in annotations["allow_other_workers"].items() if v
)
if "retries" in annotations:
retries.update(annotations["retries"])
if "resources" in annotations:
resources.update(annotations["resources"])
for a, kv in annotations.items():
for k, v in kv.items():
# Tasks might have been culled, in which case
# we have nothing to annotate.
ts = parent._tasks.get(k)
if ts is not None:
ts._annotations[a] = v
# Add actors
if actors is True:
actors = list(keys)
for actor in actors or []:
ts = parent._tasks[actor]
ts._actor = True
priority = priority or dask.order.order(
tasks
) # TODO: define order wrt old graph
if submitting_task: # sub-tasks get better priority than parent tasks
ts = parent._tasks.get(submitting_task)
if ts is not None:
generation = ts._priority[0] - 0.01
else: # super-task already cleaned up
generation = self.generation
elif self._last_time + fifo_timeout < start:
self.generation += 1 # older graph generations take precedence
generation = self.generation
self._last_time = start
else:
generation = self.generation
for key in set(priority) & touched_keys:
ts = parent._tasks[key]
if ts._priority is None:
ts._priority = (-(user_priority.get(key, 0)), generation, priority[key])
# Ensure all runnables have a priority
runnables = [ts for ts in touched_tasks if ts._run_spec]
for ts in runnables:
if ts._priority is None and ts._run_spec:
ts._priority = (self.generation, 0)
if restrictions:
# *restrictions* is a dict keying task ids to lists of
# restriction specifications (either worker names or addresses)
for k, v in restrictions.items():
if v is None:
continue
ts = parent._tasks.get(k)
if ts is None:
continue
ts._host_restrictions = set()
ts._worker_restrictions = set()
# Make sure `v` is a collection and not a single worker name / address
if not isinstance(v, (list, tuple, set)):
v = [v]
for w in v:
try:
w = self.coerce_address(w)
except ValueError:
# Not a valid address, but perhaps it's a hostname
ts._host_restrictions.add(w)
else:
ts._worker_restrictions.add(w)
if loose_restrictions:
for k in loose_restrictions:
ts = parent._tasks[k]
ts._loose_restrictions = True
if resources:
for k, v in resources.items():
if v is None:
continue
assert isinstance(v, dict)
ts = parent._tasks.get(k)
if ts is None:
continue
ts._resource_restrictions = v
if retries:
for k, v in retries.items():
assert isinstance(v, int)
ts = parent._tasks.get(k)
if ts is None:
continue
ts._retries = v
# Compute recommendations
recommendations: dict = {}
for ts in sorted(runnables, key=operator.attrgetter("priority"), reverse=True):
if ts._state == "released" and ts._run_spec:
recommendations[ts._key] = "waiting"
for ts in touched_tasks:
for dts in ts._dependencies:
if dts._exception_blame:
ts._exception_blame = dts._exception_blame
recommendations[ts._key] = "erred"
break
for plugin in list(self.plugins.values()):
try:
plugin.update_graph(
self,
client=client,
tasks=tasks,
keys=keys,
restrictions=restrictions or {},
dependencies=dependencies,
priority=priority,
loose_restrictions=loose_restrictions,
resources=resources,
annotations=annotations,
)
except Exception as e:
logger.exception(e)
self.transitions(recommendations)
for ts in touched_tasks:
if ts._state in ("memory", "erred"):
self.report_on_key(ts=ts, client=client)
end = time()
if self.digests is not None:
self.digests["update-graph-duration"].add(end - start)
# TODO: balance workers
[docs] def stimulus_task_finished(self, key=None, worker=None, **kwargs):
"""Mark that a task has finished execution on a particular worker"""
parent: SchedulerState = cast(SchedulerState, self)
logger.debug("Stimulus task finished %s, %s", key, worker)
recommendations: dict = {}
client_msgs: dict = {}
worker_msgs: dict = {}
ws: WorkerState = parent._workers_dv[worker]
ts: TaskState = parent._tasks.get(key)
if ts is None or ts._state == "released":
logger.debug(
"Received already computed task, worker: %s, state: %s"
", key: %s, who_has: %s",
worker,
ts._state if ts else "forgotten",
key,
ts._who_has if ts else {},
)
worker_msgs[worker] = [
{
"op": "free-keys",
"keys": [key],
"stimulus_id": f"already-released-or-forgotten-{time()}",
}
]
elif ts._state == "memory":
self.add_keys(worker=worker, keys=[key])
else:
ts._metadata.update(kwargs["metadata"])
r: tuple = parent._transition(key, "memory", worker=worker, **kwargs)
recommendations, client_msgs, worker_msgs = r
if ts._state == "memory":
assert ws in ts._who_has
return recommendations, client_msgs, worker_msgs
[docs] def stimulus_task_erred(
self, key=None, worker=None, exception=None, traceback=None, **kwargs
):
"""Mark that a task has erred on a particular worker"""
parent: SchedulerState = cast(SchedulerState, self)
logger.debug("Stimulus task erred %s, %s", key, worker)
ts: TaskState = parent._tasks.get(key)
if ts is None or ts._state != "processing":
return {}, {}, {}
if ts._retries > 0:
ts._retries -= 1
return parent._transition(key, "waiting")
else:
return parent._transition(
key,
"erred",
cause=key,
exception=exception,
traceback=traceback,
worker=worker,
**kwargs,
)
def stimulus_retry(self, comm=None, keys=None, client=None):
parent: SchedulerState = cast(SchedulerState, self)
logger.info("Client %s requests to retry %d keys", client, len(keys))
if client:
self.log_event(client, {"action": "retry", "count": len(keys)})
stack = list(keys)
seen = set()
roots = []
ts: TaskState
dts: TaskState
while stack:
key = stack.pop()
seen.add(key)
ts = parent._tasks[key]
erred_deps = [dts._key for dts in ts._dependencies if dts._state == "erred"]
if erred_deps:
stack.extend(erred_deps)
else:
roots.append(key)
recommendations: dict = {key: "waiting" for key in roots}
self.transitions(recommendations)
if parent._validate:
for key in seen:
assert not parent._tasks[key].exception_blame
return tuple(seen)
[docs] async def remove_worker(self, comm=None, address=None, safe=False, close=True):
"""
Remove worker from cluster
We do this when a worker reports that it plans to leave or when it
appears to be unresponsive. This may send its tasks back to a released
state.
"""
parent: SchedulerState = cast(SchedulerState, self)
with log_errors():
if self.status == Status.closed:
return
address = self.coerce_address(address)
if address not in parent._workers_dv:
return "already-removed"
host = get_address_host(address)
ws: WorkerState = parent._workers_dv[address]
self.log_event(
["all", address],
{
"action": "remove-worker",
"processing-tasks": dict(ws._processing),
},
)
logger.info("Remove worker %s", ws)
if close:
with suppress(AttributeError, CommClosedError):
self.stream_comms[address].send({"op": "close", "report": False})
self.remove_resources(address)
dh: dict = parent._host_info.get(host)
if dh is None:
parent._host_info[host] = dh = {}
dh_addresses: set = dh["addresses"]
dh_addresses.remove(address)
dh["nthreads"] -= ws._nthreads
parent._total_nthreads -= ws._nthreads
if not dh_addresses:
dh = None
dh_addresses = None
del parent._host_info[host]
self.rpc.remove(address)
del self.stream_comms[address]
del parent._aliases[ws._name]
parent._idle.pop(ws._address, None)
parent._saturated.discard(ws)
del parent._workers[address]
ws.status = Status.closed
parent._total_occupancy -= ws._occupancy
recommendations: dict = {}
ts: TaskState
for ts in list(ws._processing):
k = ts._key
recommendations[k] = "released"
if not safe:
ts._suspicious += 1
ts._prefix._suspicious += 1
if ts._suspicious > self.allowed_failures:
del recommendations[k]
e = pickle.dumps(
KilledWorker(task=k, last_worker=ws.clean()), protocol=4
)
r = self.transition(k, "erred", exception=e, cause=k)
recommendations.update(r)
logger.info(
"Task %s marked as failed because %d workers died"
" while trying to run it",
ts._key,
self.allowed_failures,
)
for ts in list(ws._has_what):
parent.remove_replica(ts, ws)
if not ts._who_has:
if ts._run_spec:
recommendations[ts._key] = "released"
else: # pure data
recommendations[ts._key] = "forgotten"
self.transitions(recommendations)
for plugin in list(self.plugins.values()):
try:
result = plugin.remove_worker(scheduler=self, worker=address)
if inspect.isawaitable(result):
await result
except Exception as e:
logger.exception(e)
if not parent._workers_dv:
logger.info("Lost all workers")
for w in parent._workers_dv:
self.bandwidth_workers.pop((address, w), None)
self.bandwidth_workers.pop((w, address), None)
def remove_worker_from_events():
# If the worker isn't registered anymore after the delay, remove from events
if address not in parent._workers_dv and address in self.events:
del self.events[address]
cleanup_delay = parse_timedelta(
dask.config.get("distributed.scheduler.events-cleanup-delay")
)
self.loop.call_later(cleanup_delay, remove_worker_from_events)
logger.debug("Removed worker %s", ws)
return "OK"
[docs] def stimulus_cancel(self, comm, keys=None, client=None, force=False):
"""Stop execution on a list of keys"""
logger.info("Client %s requests to cancel %d keys", client, len(keys))
if client:
self.log_event(
client, {"action": "cancel", "count": len(keys), "force": force}
)
for key in keys:
self.cancel_key(key, client, force=force)
[docs] def cancel_key(self, key, client, retries=5, force=False):
"""Cancel a particular key and all dependents"""
# TODO: this should be converted to use the transition mechanism
parent: SchedulerState = cast(SchedulerState, self)
ts: TaskState = parent._tasks.get(key)
dts: TaskState
try:
cs: ClientState = parent._clients[client]
except KeyError:
return
if ts is None or not ts._who_wants: # no key yet, lets try again in a moment
if retries:
self.loop.call_later(
0.2, lambda: self.cancel_key(key, client, retries - 1)
)
return
if force or ts._who_wants == {cs}: # no one else wants this key
for dts in list(ts._dependents):
self.cancel_key(dts._key, client, force=force)
logger.info("Scheduler cancels key %s. Force=%s", key, force)
self.report({"op": "cancelled-key", "key": key})
clients = list(ts._who_wants) if force else [cs]
for cs in clients:
self.client_releases_keys(keys=[key], client=cs._client_key)
def client_desires_keys(self, keys=None, client=None):
parent: SchedulerState = cast(SchedulerState, self)
cs: ClientState = parent._clients.get(client)
if cs is None:
# For publish, queues etc.
parent._clients[client] = cs = ClientState(client)
ts: TaskState
for k in keys:
ts = parent._tasks.get(k)
if ts is None:
# For publish, queues etc.
ts = parent.new_task(k, None, "released")
ts._who_wants.add(cs)
cs._wants_what.add(ts)
if ts._state in ("memory", "erred"):
self.report_on_key(ts=ts, client=client)
[docs] def client_releases_keys(self, keys=None, client=None):
"""Remove keys from client desired list"""
parent: SchedulerState = cast(SchedulerState, self)
if not isinstance(keys, list):
keys = list(keys)
cs: ClientState = parent._clients[client]
recommendations: dict = {}
_client_releases_keys(parent, keys=keys, cs=cs, recommendations=recommendations)
self.transitions(recommendations)
[docs] def client_heartbeat(self, client=None):
"""Handle heartbeats from Client"""
parent: SchedulerState = cast(SchedulerState, self)
cs: ClientState = parent._clients[client]
cs._last_seen = time()
###################
# Task Validation #
###################
def validate_released(self, key):
parent: SchedulerState = cast(SchedulerState, self)
ts: TaskState = parent._tasks[key]
dts: TaskState
assert ts._state == "released"
assert not ts._waiters
assert not ts._waiting_on
assert not ts._who_has
assert not ts._processing_on
assert not any([ts in dts._waiters for dts in ts._dependencies])
assert ts not in parent._unrunnable
def validate_waiting(self, key):
parent: SchedulerState = cast(SchedulerState, self)
ts: TaskState = parent._tasks[key]
dts: TaskState
assert ts._waiting_on
assert not ts._who_has
assert not ts._processing_on
assert ts not in parent._unrunnable
for dts in ts._dependencies:
# We are waiting on a dependency iff it's not stored
assert bool(dts._who_has) != (dts in ts._waiting_on)
assert ts in dts._waiters # XXX even if dts._who_has?
def validate_processing(self, key):
parent: SchedulerState = cast(SchedulerState, self)
ts: TaskState = parent._tasks[key]
dts: TaskState
assert not ts._waiting_on
ws: WorkerState = ts._processing_on
assert ws
assert ts in ws._processing
assert not ts._who_has
for dts in ts._dependencies:
assert dts._who_has
assert ts in dts._waiters
def validate_memory(self, key):
parent: SchedulerState = cast(SchedulerState, self)
ts: TaskState = parent._tasks[key]
dts: TaskState
assert ts._who_has
assert bool(ts in parent._replicated_tasks) == (len(ts._who_has) > 1)
assert not ts._processing_on
assert not ts._waiting_on
assert ts not in parent._unrunnable
for dts in ts._dependents:
assert (dts in ts._waiters) == (dts._state in ("waiting", "processing"))
assert ts not in dts._waiting_on
def validate_no_worker(self, key):
parent: SchedulerState = cast(SchedulerState, self)
ts: TaskState = parent._tasks[key]
dts: TaskState
assert ts in parent._unrunnable
assert not ts._waiting_on
assert ts in parent._unrunnable
assert not ts._processing_on
assert not ts._who_has
for dts in ts._dependencies:
assert dts._who_has
def validate_erred(self, key):
parent: SchedulerState = cast(SchedulerState, self)
ts: TaskState = parent._tasks[key]
assert ts._exception_blame
assert not ts._who_has
def validate_key(self, key, ts: TaskState = None):
parent: SchedulerState = cast(SchedulerState, self)
try:
if ts is None:
ts = parent._tasks.get(key)
if ts is None:
logger.debug("Key lost: %s", key)
else:
ts.validate()
try:
func = getattr(self, "validate_" + ts._state.replace("-", "_"))
except AttributeError:
logger.error(
"self.validate_%s not found", ts._state.replace("-", "_")
)
else:
func(key)
except Exception as e:
logger.exception(e)
if LOG_PDB:
import pdb
pdb.set_trace()
raise
def validate_state(self, allow_overlap=False):
parent: SchedulerState = cast(SchedulerState, self)
validate_state(parent._tasks, parent._workers, parent._clients)
if not (set(parent._workers_dv) == set(self.stream_comms)):
raise ValueError("Workers not the same in all collections")
ws: WorkerState
for w, ws in parent._workers_dv.items():
assert isinstance(w, str), (type(w), w)
assert isinstance(ws, WorkerState), (type(ws), ws)
assert ws._address == w
if not ws._processing:
assert not ws._occupancy
assert ws._address in parent._idle_dv
ts: TaskState
for k, ts in parent._tasks.items():
assert isinstance(ts, TaskState), (type(ts), ts)
assert ts._key == k
assert bool(ts in parent._replicated_tasks) == (len(ts._who_has) > 1)
self.validate_key(k, ts)
for ts in parent._replicated_tasks:
assert ts._state == "memory"
assert ts._key in parent._tasks
c: str
cs: ClientState
for c, cs in parent._clients.items():
# client=None is often used in tests...
assert c is None or type(c) == str, (type(c), c)
assert type(cs) == ClientState, (type(cs), cs)
assert cs._client_key == c
a = {w: ws._nbytes for w, ws in parent._workers_dv.items()}
b = {
w: sum(ts.get_nbytes() for ts in ws._has_what)
for w, ws in parent._workers_dv.items()
}
assert a == b, (a, b)
actual_total_occupancy = 0
for worker, ws in parent._workers_dv.items():
assert abs(sum(ws._processing.values()) - ws._occupancy) < 1e-8
actual_total_occupancy += ws._occupancy
assert abs(actual_total_occupancy - parent._total_occupancy) < 1e-8, (
actual_total_occupancy,
parent._total_occupancy,
)
###################
# Manage Messages #
###################
[docs] def report(self, msg: dict, ts: TaskState = None, client: str = None):
"""
Publish updates to all listening Queues and Comms
If the message contains a key then we only send the message to those
comms that care about the key.
"""
parent: SchedulerState = cast(SchedulerState, self)
if ts is None:
msg_key = msg.get("key")
if msg_key is not None:
tasks: dict = parent._tasks
ts = tasks.get(msg_key)
cs: ClientState
client_comms: dict = self.client_comms
client_keys: list
if ts is None:
# Notify all clients
client_keys = list(client_comms)
elif client is None:
# Notify clients interested in key
client_keys = [cs._client_key for cs in ts._who_wants]
else:
# Notify clients interested in key (including `client`)
client_keys = [
cs._client_key for cs in ts._who_wants if cs._client_key != client
]
client_keys.append(client)
k: str
for k in client_keys:
c = client_comms.get(k)
if c is None:
continue
try:
c.send(msg)
# logger.debug("Scheduler sends message to client %s", msg)
except CommClosedError:
if self.status == Status.running:
logger.critical(
"Closed comm %r while trying to write %s", c, msg, exc_info=True
)
[docs] async def add_client(self, comm, client=None, versions=None):
"""Add client to network
We listen to all future messages from this Comm.
"""
parent: SchedulerState = cast(SchedulerState, self)
assert client is not None
comm.name = "Scheduler->Client"
logger.info("Receive client connection: %s", client)
self.log_event(["all", client], {"action": "add-client", "client": client})
parent._clients[client] = ClientState(client, versions=versions)
for plugin in list(self.plugins.values()):
try:
plugin.add_client(scheduler=self, client=client)
except Exception as e:
logger.exception(e)
try:
bcomm = BatchedSend(interval="2ms", loop=self.loop)
bcomm.start(comm)
self.client_comms[client] = bcomm
msg = {"op": "stream-start"}
ws: WorkerState
version_warning = version_module.error_message(
version_module.get_versions(),
{w: ws._versions for w, ws in parent._workers_dv.items()},
versions,
)
msg.update(version_warning)
bcomm.send(msg)
try:
await self.handle_stream(comm=comm, extra={"client": client})
finally:
self.remove_client(client=client)
logger.debug("Finished handling client %s", client)
finally:
if not comm.closed():
self.client_comms[client].send({"op": "stream-closed"})
try:
if not sys.is_finalizing():
await self.client_comms[client].close()
del self.client_comms[client]
if self.status == Status.running:
logger.info("Close client connection: %s", client)
except TypeError: # comm becomes None during GC
pass
[docs] def remove_client(self, client=None):
"""Remove client from network"""
parent: SchedulerState = cast(SchedulerState, self)
if self.status == Status.running:
logger.info("Remove client %s", client)
self.log_event(["all", client], {"action": "remove-client", "client": client})
try:
cs: ClientState = parent._clients[client]
except KeyError:
# XXX is this a legitimate condition?
pass
else:
ts: TaskState
self.client_releases_keys(
keys=[ts._key for ts in cs._wants_what], client=cs._client_key
)
del parent._clients[client]
for plugin in list(self.plugins.values()):
try:
plugin.remove_client(scheduler=self, client=client)
except Exception as e:
logger.exception(e)
def remove_client_from_events():
# If the client isn't registered anymore after the delay, remove from events
if client not in parent._clients and client in self.events:
del self.events[client]
cleanup_delay = parse_timedelta(
dask.config.get("distributed.scheduler.events-cleanup-delay")
)
self.loop.call_later(cleanup_delay, remove_client_from_events)
[docs] def send_task_to_worker(self, worker, ts: TaskState, duration: double = -1):
"""Send a single computational task to a worker"""
parent: SchedulerState = cast(SchedulerState, self)
try:
msg: dict = _task_to_msg(parent, ts, duration)
self.worker_send(worker, msg)
except Exception as e:
logger.exception(e)
if LOG_PDB:
import pdb
pdb.set_trace()
raise
def handle_uncaught_error(self, **msg):
logger.exception(clean_exception(**msg)[1])
def handle_task_finished(self, key=None, worker=None, **msg):
parent: SchedulerState = cast(SchedulerState, self)
if worker not in parent._workers_dv:
return
validate_key(key)
recommendations: dict
client_msgs: dict
worker_msgs: dict
r: tuple = self.stimulus_task_finished(key=key, worker=worker, **msg)
recommendations, client_msgs, worker_msgs = r
parent._transitions(recommendations, client_msgs, worker_msgs)
self.send_all(client_msgs, worker_msgs)
def handle_task_erred(self, key=None, **msg):
parent: SchedulerState = cast(SchedulerState, self)
recommendations: dict
client_msgs: dict
worker_msgs: dict
r: tuple = self.stimulus_task_erred(key=key, **msg)
recommendations, client_msgs, worker_msgs = r
parent._transitions(recommendations, client_msgs, worker_msgs)
self.send_all(client_msgs, worker_msgs)
def handle_missing_data(self, key=None, errant_worker=None, **kwargs):
parent: SchedulerState = cast(SchedulerState, self)
logger.debug("handle missing data key=%s worker=%s", key, errant_worker)
self.log.append(("missing", key, errant_worker))
ts: TaskState = parent._tasks.get(key)
if ts is None:
return
ws: WorkerState = parent._workers_dv.get(errant_worker)
if ws is not None and ws in ts._who_has:
parent.remove_replica(ts, ws)
if not ts._who_has:
if ts._run_spec:
self.transitions({key: "released"})
else:
self.transitions({key: "forgotten"})
def release_worker_data(self, comm=None, key=None, worker=None):
parent: SchedulerState = cast(SchedulerState, self)
ws: WorkerState = parent._workers_dv.get(worker)
ts: TaskState = parent._tasks.get(key)
if not ws or not ts:
return
recommendations: dict = {}
if ws in ts._who_has:
parent.remove_replica(ts, ws)
if not ts._who_has:
recommendations[ts._key] = "released"
if recommendations:
self.transitions(recommendations)
[docs] def handle_long_running(self, key=None, worker=None, compute_duration=None):
"""A task has seceded from the thread pool
We stop the task from being stolen in the future, and change task
duration accounting as if the task has stopped.
"""
parent: SchedulerState = cast(SchedulerState, self)
if key not in parent._tasks:
logger.debug("Skipping long_running since key %s was already released", key)
return
ts: TaskState = parent._tasks[key]
steal = parent._extensions.get("stealing")
if steal is not None:
steal.remove_key_from_stealable(ts)
ws: WorkerState = ts._processing_on
if ws is None:
logger.debug("Received long-running signal from duplicate task. Ignoring.")
return
if compute_duration:
old_duration: double = ts._prefix._duration_average
new_duration: double = compute_duration
avg_duration: double
if old_duration < 0:
avg_duration = new_duration
else:
avg_duration = 0.5 * old_duration + 0.5 * new_duration
ts._prefix._duration_average = avg_duration
occ: double = ws._processing[ts]
ws._occupancy -= occ
parent._total_occupancy -= occ
ws._processing[ts] = 0
self.check_idle_saturated(ws)
def handle_worker_status_change(self, status: str, worker: str):
parent: SchedulerState = cast(SchedulerState, self)
ws: WorkerState = parent._workers_dv.get(worker) # type: ignore
if not ws:
return
self.log_event(
ws._address,
{
"action": "worker-status-change",
"prev-status": ws._status.name,
"status": status,
},
)
ws._status = Status.lookup[status] # type: ignore
[docs] async def handle_worker(self, comm=None, worker=None):
"""
Listen to responses from a single worker
This is the main loop for scheduler-worker interaction
See Also
--------
Scheduler.handle_client: Equivalent coroutine for clients
"""
comm.name = "Scheduler connection to worker"
worker_comm = self.stream_comms[worker]
worker_comm.start(comm)
logger.info("Starting worker compute stream, %s", worker)
try:
await self.handle_stream(comm=comm, extra={"worker": worker})
finally:
if worker in self.stream_comms:
worker_comm.abort()
await self.remove_worker(address=worker)
[docs] def add_plugin(
self,
plugin: SchedulerPlugin,
*,
idempotent: bool = False,
name: "str | None" = None,
**kwargs,
):
"""Add external plugin to scheduler.
See https://distributed.readthedocs.io/en/latest/plugins.html
Parameters
----------
plugin : SchedulerPlugin
SchedulerPlugin instance to add
idempotent : bool
If true, the plugin is assumed to already exist and no
action is taken.
name : str
A name for the plugin, if None, the name attribute is
checked on the Plugin instance and generated if not
discovered.
**kwargs
Deprecated; additional arguments passed to the `plugin` class if it is
not already an instance
"""
if isinstance(plugin, type):
warnings.warn(
"Adding plugins by class is deprecated and will be disabled in a "
"future release. Please add plugins by instance instead.",
category=FutureWarning,
)
plugin = plugin(self, **kwargs) # type: ignore
elif kwargs:
raise ValueError("kwargs provided but plugin is already an instance")
if name is None:
name = _get_plugin_name(plugin)
if name in self.plugins:
if idempotent:
return
warnings.warn(
f"Scheduler already contains a plugin with name {name}; overwriting.",
category=UserWarning,
)
self.plugins[name] = plugin
[docs] def remove_plugin(
self,
name: "str | None" = None,
plugin: "SchedulerPlugin | None" = None,
) -> None:
"""Remove external plugin from scheduler
Parameters
----------
name : str
Name of the plugin to remove
plugin : SchedulerPlugin
Deprecated; use `name` argument instead. Instance of a
SchedulerPlugin class to remove;
"""
# TODO: Remove this block of code once removing plugins by value is disabled
if bool(name) == bool(plugin):
raise ValueError("Must provide plugin or name (mutually exclusive)")
if isinstance(name, SchedulerPlugin):
# Backwards compatibility - the sig used to be (plugin, name)
plugin = name
name = None
if plugin is not None:
warnings.warn(
"Removing scheduler plugins by value is deprecated and will be disabled "
"in a future release. Please remove scheduler plugins by name instead.",
category=FutureWarning,
)
if hasattr(plugin, "name"):
name = plugin.name # type: ignore
else:
names = [k for k, v in self.plugins.items() if v is plugin]
if not names:
raise ValueError(
f"Could not find {plugin} among the current scheduler plugins"
)
if len(names) > 1:
raise ValueError(
f"Multiple instances of {plugin} were found in the current "
"scheduler plugins; we cannot remove this plugin."
)
name = names[0]
assert name is not None
# End deprecated code
try:
del self.plugins[name]
except KeyError:
raise ValueError(
f"Could not find plugin {name!r} among the current scheduler plugins"
)
[docs] async def register_scheduler_plugin(self, comm=None, plugin=None, name=None):
"""Register a plugin on the scheduler."""
if not dask.config.get("distributed.scheduler.pickle"):
raise ValueError(
"Cannot register a scheduler plugin as the scheduler "
"has been explicitly disallowed from deserializing "
"arbitrary bytestrings using pickle via the "
"'distributed.scheduler.pickle' configuration setting."
)
plugin = loads(plugin)
if hasattr(plugin, "start"):
result = plugin.start(self)
if inspect.isawaitable(result):
await result
self.add_plugin(plugin, name=name)
[docs] def worker_send(self, worker, msg):
"""Send message to worker
This also handles connection failures by adding a callback to remove
the worker on the next cycle.
"""
stream_comms: dict = self.stream_comms
try:
stream_comms[worker].send(msg)
except (CommClosedError, AttributeError):
self.loop.add_callback(self.remove_worker, address=worker)
[docs] def client_send(self, client, msg):
"""Send message to client"""
client_comms: dict = self.client_comms
c = client_comms.get(client)
if c is None:
return
try:
c.send(msg)
except CommClosedError:
if self.status == Status.running:
logger.critical(
"Closed comm %r while trying to write %s", c, msg, exc_info=True
)
[docs] def send_all(self, client_msgs: dict, worker_msgs: dict):
"""Send messages to client and workers"""
client_comms: dict = self.client_comms
stream_comms: dict = self.stream_comms
msgs: list
for client, msgs in client_msgs.items():
c = client_comms.get(client)
if c is None:
continue
try:
c.send(*msgs)
except CommClosedError:
if self.status == Status.running:
logger.critical(
"Closed comm %r while trying to write %s",
c,
msgs,
exc_info=True,
)
for worker, msgs in worker_msgs.items():
try:
w = stream_comms[worker]
w.send(*msgs)
except KeyError:
# worker already gone
pass
except (CommClosedError, AttributeError):
self.loop.add_callback(self.remove_worker, address=worker)
############################
# Less common interactions #
############################
[docs] async def scatter(
self,
comm=None,
data=None,
workers=None,
client=None,
broadcast=False,
timeout=2,
):
"""Send data out to workers
See also
--------
Scheduler.broadcast:
"""
parent: SchedulerState = cast(SchedulerState, self)
start = time()
while not parent._workers_dv:
await asyncio.sleep(0.2)
if time() > start + timeout:
raise TimeoutError("No workers found")
if workers is None:
ws: WorkerState
nthreads = {w: ws._nthreads for w, ws in parent._workers_dv.items()}
else:
workers = [self.coerce_address(w) for w in workers]
nthreads = {w: parent._workers_dv[w].nthreads for w in workers}
assert isinstance(data, dict)
keys, who_has, nbytes = await scatter_to_workers(
nthreads, data, rpc=self.rpc, report=False
)
self.update_data(who_has=who_has, nbytes=nbytes, client=client)
if broadcast:
if broadcast == True: # noqa: E712
n = len(nthreads)
else:
n = broadcast
await self.replicate(keys=keys, workers=workers, n=n)
self.log_event(
[client, "all"], {"action": "scatter", "client": client, "count": len(data)}
)
return keys
[docs] async def gather(self, comm=None, keys=None, serializers=None):
"""Collect data from workers to the scheduler"""
parent: SchedulerState = cast(SchedulerState, self)
ws: WorkerState
keys = list(keys)
who_has = {}
for key in keys:
ts: TaskState = parent._tasks.get(key)
if ts is not None:
who_has[key] = [ws._address for ws in ts._who_has]
else:
who_has[key] = []
data, missing_keys, missing_workers = await gather_from_workers(
who_has, rpc=self.rpc, close=False, serializers=serializers
)
if not missing_keys:
result = {"status": "OK", "data": data}
else:
missing_states = [
(parent._tasks[key].state if key in parent._tasks else None)
for key in missing_keys
]
logger.exception(
"Couldn't gather keys %s state: %s workers: %s",
missing_keys,
missing_states,
missing_workers,
)
result = {"status": "error", "keys": missing_keys}
with log_errors():
# Remove suspicious workers from the scheduler but allow them to
# reconnect.
await asyncio.gather(
*(
self.remove_worker(address=worker, close=False)
for worker in missing_workers
)
)
recommendations: dict
client_msgs: dict = {}
worker_msgs: dict = {}
for key, workers in missing_keys.items():
# Task may already be gone if it was held by a
# `missing_worker`
ts: TaskState = parent._tasks.get(key)
logger.exception(
"Workers don't have promised key: %s, %s",
str(workers),
str(key),
)
if not workers or ts is None:
continue
recommendations: dict = {key: "released"}
for worker in workers:
ws = parent._workers_dv.get(worker)
if ws is not None and ws in ts._who_has:
parent.remove_replica(ts, ws)
parent._transitions(
recommendations, client_msgs, worker_msgs
)
self.send_all(client_msgs, worker_msgs)
self.log_event("all", {"action": "gather", "count": len(keys)})
return result
def clear_task_state(self):
# XXX what about nested state such as ClientState.wants_what
# (see also fire-and-forget...)
logger.info("Clear task state")
for collection in self._task_state_collections:
collection.clear()
[docs] async def restart(self, client=None, timeout=30):
"""Restart all workers. Reset local state."""
parent: SchedulerState = cast(SchedulerState, self)
with log_errors():
n_workers = len(parent._workers_dv)
logger.info("Send lost future signal to clients")
cs: ClientState
ts: TaskState
for cs in parent._clients.values():
self.client_releases_keys(
keys=[ts._key for ts in cs._wants_what], client=cs._client_key
)
ws: WorkerState
nannies = {addr: ws._nanny for addr, ws in parent._workers_dv.items()}
for addr in list(parent._workers_dv):
try:
# Ask the worker to close if it doesn't have a nanny,
# otherwise the nanny will kill it anyway
await self.remove_worker(address=addr, close=addr not in nannies)
except Exception:
logger.info(
"Exception while restarting. This is normal", exc_info=True
)
self.clear_task_state()
for plugin in list(self.plugins.values()):
try:
plugin.restart(self)
except Exception as e:
logger.exception(e)
logger.debug("Send kill signal to nannies: %s", nannies)
nannies = [
rpc(nanny_address, connection_args=self.connection_args)
for nanny_address in nannies.values()
if nanny_address is not None
]
resps = All(
[
nanny.restart(
close=True, timeout=timeout * 0.8, executor_wait=False
)
for nanny in nannies
]
)
try:
resps = await asyncio.wait_for(resps, timeout)
except TimeoutError:
logger.error(
"Nannies didn't report back restarted within "
"timeout. Continuuing with restart process"
)
else:
if not all(resp == "OK" for resp in resps):
logger.error(
"Not all workers responded positively: %s", resps, exc_info=True
)
finally:
await asyncio.gather(*[nanny.close_rpc() for nanny in nannies])
self.clear_task_state()
with suppress(AttributeError):
for c in self._worker_coroutines:
c.cancel()
self.log_event([client, "all"], {"action": "restart", "client": client})
start = time()
while time() < start + 10 and len(parent._workers_dv) < n_workers:
await asyncio.sleep(0.01)
self.report({"op": "restart"})
[docs] async def broadcast(
self,
comm=None,
msg=None,
workers=None,
hosts=None,
nanny=False,
serializers=None,
):
"""Broadcast message to workers, return all results"""
parent: SchedulerState = cast(SchedulerState, self)
if workers is None or workers is True:
if hosts is None:
workers = list(parent._workers_dv)
else:
workers = []
if hosts is not None:
for host in hosts:
dh: dict = parent._host_info.get(host)
if dh is not None:
workers.extend(dh["addresses"])
# TODO replace with worker_list
if nanny:
addresses = [parent._workers_dv[w].nanny for w in workers]
else:
addresses = workers
async def send_message(addr):
comm = await self.rpc.connect(addr)
comm.name = "Scheduler Broadcast"
try:
resp = await send_recv(comm, close=True, serializers=serializers, **msg)
finally:
self.rpc.reuse(addr, comm)
return resp
results = await All(
[send_message(address) for address in addresses if address is not None]
)
return dict(zip(workers, results))
[docs] async def proxy(self, comm=None, msg=None, worker=None, serializers=None):
"""Proxy a communication through the scheduler to some other worker"""
d = await self.broadcast(
comm=comm, msg=msg, workers=[worker], serializers=serializers
)
return d[worker]
[docs] async def gather_on_worker(
self, worker_address: str, who_has: "dict[str, list[str]]"
) -> set:
"""Peer-to-peer copy of keys from multiple workers to a single worker
Parameters
----------
worker_address: str
Recipient worker address to copy keys to
who_has: dict[Hashable, list[str]]
{key: [sender address, sender address, ...], key: ...}
Returns
-------
returns:
set of keys that failed to be copied
"""
try:
result = await retry_operation(
self.rpc(addr=worker_address).gather, who_has=who_has
)
except OSError as e:
# This can happen e.g. if the worker is going through controlled shutdown;
# it doesn't necessarily mean that it went unexpectedly missing
logger.warning(
f"Communication with worker {worker_address} failed during "
f"replication: {e.__class__.__name__}: {e}"
)
return set(who_has)
parent: SchedulerState = cast(SchedulerState, self)
ws: WorkerState = parent._workers_dv.get(worker_address) # type: ignore
if ws is None:
logger.warning(f"Worker {worker_address} lost during replication")
return set(who_has)
elif result["status"] == "OK":
keys_failed = set()
keys_ok: Set = who_has.keys()
elif result["status"] == "partial-fail":
keys_failed = set(result["keys"])
keys_ok = who_has.keys() - keys_failed
logger.warning(
f"Worker {worker_address} failed to acquire keys: {result['keys']}"
)
else: # pragma: nocover
raise ValueError(f"Unexpected message from {worker_address}: {result}")
for key in keys_ok:
ts: TaskState = parent._tasks.get(key) # type: ignore
if ts is None or ts._state != "memory":
logger.warning(f"Key lost during replication: {key}")
continue
if ws not in ts._who_has:
parent.add_replica(ts, ws)
return keys_failed
[docs] async def delete_worker_data(
self, worker_address: str, keys: "Collection[str]"
) -> None:
"""Delete data from a worker and update the corresponding worker/task states
Parameters
----------
worker_address: str
Worker address to delete keys from
keys: list[str]
List of keys to delete on the specified worker
"""
parent: SchedulerState = cast(SchedulerState, self)
try:
await retry_operation(
self.rpc(addr=worker_address).free_keys,
keys=list(keys),
stimulus_id=f"delete-data-{time()}",
)
except OSError as e:
# This can happen e.g. if the worker is going through controlled shutdown;
# it doesn't necessarily mean that it went unexpectedly missing
logger.warning(
f"Communication with worker {worker_address} failed during "
f"replication: {e.__class__.__name__}: {e}"
)
return
ws: WorkerState = parent._workers_dv.get(worker_address) # type: ignore
if ws is None:
return
for key in keys:
ts: TaskState = parent._tasks.get(key) # type: ignore
if ts is not None and ws in ts._who_has:
assert ts._state == "memory"
parent.remove_replica(ts, ws)
if not ts._who_has:
# Last copy deleted
self.transitions({key: "released"})
self.log_event(ws._address, {"action": "remove-worker-data", "keys": keys})
[docs] async def rebalance(
self,
comm=None,
keys: "Iterable[Hashable]" = None,
workers: "Iterable[str]" = None,
) -> dict:
"""Rebalance keys so that each worker ends up with roughly the same process
memory (managed+unmanaged).
.. warning::
This operation is generally not well tested against normal operation of the
scheduler. It is not recommended to use it while waiting on computations.
**Algorithm**
#. Find the mean occupancy of the cluster, defined as data managed by dask +
unmanaged process memory that has been there for at least 30 seconds
(``distributed.worker.memory.recent-to-old-time``).
This lets us ignore temporary spikes caused by task heap usage.
Alternatively, you may change how memory is measured both for the individual
workers as well as to calculate the mean through
``distributed.worker.memory.rebalance.measure``. Namely, this can be useful
to disregard inaccurate OS memory measurements.
#. Discard workers whose occupancy is within 5% of the mean cluster occupancy
(``distributed.worker.memory.rebalance.sender-recipient-gap`` / 2).
This helps avoid data from bouncing around the cluster repeatedly.
#. Workers above the mean are senders; those below are recipients.
#. Discard senders whose absolute occupancy is below 30%
(``distributed.worker.memory.rebalance.sender-min``). In other words, no data
is moved regardless of imbalancing as long as all workers are below 30%.
#. Discard recipients whose absolute occupancy is above 60%
(``distributed.worker.memory.rebalance.recipient-max``).
Note that this threshold by default is the same as
``distributed.worker.memory.target`` to prevent workers from accepting data
and immediately spilling it out to disk.
#. Iteratively pick the sender and recipient that are farthest from the mean and
move the *least recently inserted* key between the two, until either all
senders or all recipients fall within 5% of the mean.
A recipient will be skipped if it already has a copy of the data. In other
words, this method does not degrade replication.
A key will be skipped if there are no recipients available with enough memory
to accept the key and that don't already hold a copy.
The least recently insertd (LRI) policy is a greedy choice with the advantage of
being O(1), trivial to implement (it relies on python dict insertion-sorting)
and hopefully good enough in most cases. Discarded alternative policies were:
- Largest first. O(n*log(n)) save for non-trivial additional data structures and
risks causing the largest chunks of data to repeatedly move around the
cluster like pinballs.
- Least recently used (LRU). This information is currently available on the
workers only and not trivial to replicate on the scheduler; transmitting it
over the network would be very expensive. Also, note that dask will go out of
its way to minimise the amount of time intermediate keys are held in memory,
so in such a case LRI is a close approximation of LRU.
Parameters
----------
keys: optional
whitelist of dask keys that should be considered for moving. All other keys
will be ignored. Note that this offers no guarantee that a key will actually
be moved (e.g. because it is unnecessary or because there are no viable
recipient workers for it).
workers: optional
whitelist of workers addresses to be considered as senders or recipients.
All other workers will be ignored. The mean cluster occupancy will be
calculated only using the whitelisted workers.
"""
parent: SchedulerState = cast(SchedulerState, self)
with log_errors():
wss: "Collection[WorkerState]"
if workers is not None:
wss = [parent._workers_dv[w] for w in workers]
else:
wss = parent._workers_dv.values()
if not wss:
return {"status": "OK"}
if keys is not None:
if not isinstance(keys, Set):
keys = set(keys) # unless already a set-like
if not keys:
return {"status": "OK"}
missing_data = [
k
for k in keys
if k not in parent._tasks or not parent._tasks[k].who_has
]
if missing_data:
return {"status": "partial-fail", "keys": missing_data}
msgs = self._rebalance_find_msgs(keys, wss)
if not msgs:
return {"status": "OK"}
async with self._lock:
result = await self._rebalance_move_data(msgs)
if result["status"] == "partial-fail" and keys is None:
# Only return failed keys if the client explicitly asked for them
result = {"status": "OK"}
return result
def _rebalance_find_msgs(
self,
keys: "Set[Hashable] | None",
workers: "Iterable[WorkerState]",
) -> "list[tuple[WorkerState, WorkerState, TaskState]]":
"""Identify workers that need to lose keys and those that can receive them,
together with how many bytes each needs to lose/receive. Then, pair a sender
worker with a recipient worker for each key, until the cluster is rebalanced.
This method only defines the work to be performed; it does not start any network
transfers itself.
The big-O complexity is O(wt + ke*log(we)), where
- wt is the total number of workers on the cluster (or the number of whitelisted
workers, if explicitly stated by the user)
- we is the number of workers that are eligible to be senders or recipients
- kt is the total number of keys on the cluster (or on the whitelisted workers)
- ke is the number of keys that need to be moved in order to achieve a balanced
cluster
There is a degenerate edge case O(wt + kt*log(we)) when kt is much greater than
the number of whitelisted keys, or when most keys are replicated or cannot be
moved for some other reason.
Returns list of tuples to feed into _rebalance_move_data:
- sender worker
- recipient worker
- task to be transferred
"""
parent: SchedulerState = cast(SchedulerState, self)
ts: TaskState
ws: WorkerState
# Heaps of workers, managed by the heapq module, that need to send/receive data,
# with how many bytes each needs to send/receive.
#
# Each element of the heap is a tuple constructed as follows:
# - snd_bytes_max/rec_bytes_max: maximum number of bytes to send or receive.
# This number is negative, so that the workers farthest from the cluster mean
# are at the top of the smallest-first heaps.
# - snd_bytes_min/rec_bytes_min: minimum number of bytes after sending/receiving
# which the worker should not be considered anymore. This is also negative.
# - arbitrary unique number, there just to to make sure that WorkerState objects
# are never used for sorting in the unlikely event that two processes have
# exactly the same number of bytes allocated.
# - WorkerState
# - iterator of all tasks in memory on the worker (senders only), insertion
# sorted (least recently inserted first).
# Note that this iterator will typically *not* be exhausted. It will only be
# exhausted if, after moving away from the worker all keys that can be moved,
# is insufficient to drop snd_bytes_min above 0.
senders: "list[tuple[int, int, int, WorkerState, Iterator[TaskState]]]" = []
recipients: "list[tuple[int, int, int, WorkerState]]" = []
# Output: [(sender, recipient, task), ...]
msgs: "list[tuple[WorkerState, WorkerState, TaskState]]" = []
# By default, this is the optimistic memory, meaning total process memory minus
# unmanaged memory that appeared over the last 30 seconds
# (distributed.worker.memory.recent-to-old-time).
# This lets us ignore temporary spikes caused by task heap usage.
memory_by_worker = [
(ws, getattr(ws.memory, parent.MEMORY_REBALANCE_MEASURE)) for ws in workers
]
mean_memory = sum(m for _, m in memory_by_worker) // len(memory_by_worker)
for ws, ws_memory in memory_by_worker:
if ws.memory_limit:
half_gap = int(parent.MEMORY_REBALANCE_HALF_GAP * ws.memory_limit)
sender_min = parent.MEMORY_REBALANCE_SENDER_MIN * ws.memory_limit
recipient_max = parent.MEMORY_REBALANCE_RECIPIENT_MAX * ws.memory_limit
else:
half_gap = 0
sender_min = 0.0
recipient_max = math.inf
if (
ws._has_what
and ws_memory >= mean_memory + half_gap
and ws_memory >= sender_min
):
# This may send the worker below sender_min (by design)
snd_bytes_max = mean_memory - ws_memory # negative
snd_bytes_min = snd_bytes_max + half_gap # negative
# See definition of senders above
senders.append(
(snd_bytes_max, snd_bytes_min, id(ws), ws, iter(ws._has_what))
)
elif ws_memory < mean_memory - half_gap and ws_memory < recipient_max:
# This may send the worker above recipient_max (by design)
rec_bytes_max = ws_memory - mean_memory # negative
rec_bytes_min = rec_bytes_max + half_gap # negative
# See definition of recipients above
recipients.append((rec_bytes_max, rec_bytes_min, id(ws), ws))
# Fast exit in case no transfers are necessary or possible
if not senders or not recipients:
self.log_event(
"all",
{
"action": "rebalance",
"senders": len(senders),
"recipients": len(recipients),
"moved_keys": 0,
},
)
return []
heapq.heapify(senders)
heapq.heapify(recipients)
snd_ws: WorkerState
rec_ws: WorkerState
while senders and recipients:
snd_bytes_max, snd_bytes_min, _, snd_ws, ts_iter = senders[0]
# Iterate through tasks in memory, least recently inserted first
for ts in ts_iter:
if keys is not None and ts.key not in keys:
continue
nbytes = ts.nbytes
if nbytes + snd_bytes_max > 0:
# Moving this task would cause the sender to go below mean and
# potentially risk becoming a recipient, which would cause tasks to
# bounce around. Move on to the next task of the same sender.
continue
# Find the recipient, farthest from the mean, which
# 1. has enough available RAM for this task, and
# 2. doesn't hold a copy of this task already
# There may not be any that satisfies these conditions; in this case
# this task won't be moved.
skipped_recipients = []
use_recipient = False
while recipients and not use_recipient:
rec_bytes_max, rec_bytes_min, _, rec_ws = recipients[0]
if nbytes + rec_bytes_max > 0:
# recipients are sorted by rec_bytes_max.
# The next ones will be worse; no reason to continue iterating
break
use_recipient = ts not in rec_ws._has_what
if not use_recipient:
skipped_recipients.append(heapq.heappop(recipients))
for recipient in skipped_recipients:
heapq.heappush(recipients, recipient)
if not use_recipient:
# This task has no recipients available. Leave it on the sender and
# move on to the next task of the same sender.
continue
# Schedule task for transfer from sender to recipient
msgs.append((snd_ws, rec_ws, ts))
# *_bytes_max/min are all negative for heap sorting
snd_bytes_max += nbytes
snd_bytes_min += nbytes
rec_bytes_max += nbytes
rec_bytes_min += nbytes
# Stop iterating on the tasks of this sender for now and, if it still
# has bytes to lose, push it back into the senders heap; it may or may
# not come back on top again.
if snd_bytes_min < 0:
# See definition of senders above
heapq.heapreplace(
senders,
(snd_bytes_max, snd_bytes_min, id(snd_ws), snd_ws, ts_iter),
)
else:
heapq.heappop(senders)
# If recipient still has bytes to gain, push it back into the recipients
# heap; it may or may not come back on top again.
if rec_bytes_min < 0:
# See definition of recipients above
heapq.heapreplace(
recipients,
(rec_bytes_max, rec_bytes_min, id(rec_ws), rec_ws),
)
else:
heapq.heappop(recipients)
# Move to next sender with the most data to lose.
# It may or may not be the same sender again.
break
else: # for ts in ts_iter
# Exhausted tasks on this sender
heapq.heappop(senders)
return msgs
async def _rebalance_move_data(
self, msgs: "list[tuple[WorkerState, WorkerState, TaskState]]"
) -> dict:
"""Perform the actual transfer of data across the network in rebalance().
Takes in input the output of _rebalance_find_msgs(), that is a list of tuples:
- sender worker
- recipient worker
- task to be transferred
FIXME this method is not robust when the cluster is not idle.
"""
snd_ws: WorkerState
rec_ws: WorkerState
ts: TaskState
to_recipients: "defaultdict[str, defaultdict[str, list[str]]]" = defaultdict(
lambda: defaultdict(list)
)
for snd_ws, rec_ws, ts in msgs:
to_recipients[rec_ws.address][ts._key].append(snd_ws.address)
failed_keys_by_recipient = dict(
zip(
to_recipients,
await asyncio.gather(
*(
# Note: this never raises exceptions
self.gather_on_worker(w, who_has)
for w, who_has in to_recipients.items()
)
),
)
)
to_senders = defaultdict(list)
for snd_ws, rec_ws, ts in msgs:
if ts._key not in failed_keys_by_recipient[rec_ws.address]:
to_senders[snd_ws.address].append(ts._key)
# Note: this never raises exceptions
await asyncio.gather(
*(self.delete_worker_data(r, v) for r, v in to_senders.items())
)
for r, v in to_recipients.items():
self.log_event(r, {"action": "rebalance", "who_has": v})
self.log_event(
"all",
{
"action": "rebalance",
"senders": valmap(len, to_senders),
"recipients": valmap(len, to_recipients),
"moved_keys": len(msgs),
},
)
missing_keys = {k for r in failed_keys_by_recipient.values() for k in r}
if missing_keys:
return {"status": "partial-fail", "keys": list(missing_keys)}
else:
return {"status": "OK"}
[docs] async def replicate(
self,
comm=None,
keys=None,
n=None,
workers=None,
branching_factor=2,
delete=True,
lock=True,
):
"""Replicate data throughout cluster
This performs a tree copy of the data throughout the network
individually on each piece of data.
Parameters
----------
keys: Iterable
list of keys to replicate
n: int
Number of replications we expect to see within the cluster
branching_factor: int, optional
The number of workers that can copy data in each generation.
The larger the branching factor, the more data we copy in
a single step, but the more a given worker risks being
swamped by data requests.
See also
--------
Scheduler.rebalance
"""
parent: SchedulerState = cast(SchedulerState, self)
ws: WorkerState
wws: WorkerState
ts: TaskState
assert branching_factor > 0
async with self._lock if lock else empty_context:
workers = {parent._workers_dv[w] for w in self.workers_list(workers)}
if n is None:
n = len(workers)
else:
n = min(n, len(workers))
if n == 0:
raise ValueError("Can not use replicate to delete data")
tasks = {parent._tasks[k] for k in keys}
missing_data = [ts._key for ts in tasks if not ts._who_has]
if missing_data:
return {"status": "partial-fail", "keys": missing_data}
# Delete extraneous data
if delete:
del_worker_tasks = defaultdict(set)
for ts in tasks:
del_candidates = tuple(ts._who_has & workers)
if len(del_candidates) > n:
for ws in random.sample(
del_candidates, len(del_candidates) - n
):
del_worker_tasks[ws].add(ts)
# Note: this never raises exceptions
await asyncio.gather(
*[
self.delete_worker_data(ws._address, [t.key for t in tasks])
for ws, tasks in del_worker_tasks.items()
]
)
# Copy not-yet-filled data
while tasks:
gathers = defaultdict(dict)
for ts in list(tasks):
if ts._state == "forgotten":
# task is no longer needed by any client or dependant task
tasks.remove(ts)
continue
n_missing = n - len(ts._who_has & workers)
if n_missing <= 0:
# Already replicated enough
tasks.remove(ts)
continue
count = min(n_missing, branching_factor * len(ts._who_has))
assert count > 0
for ws in random.sample(tuple(workers - ts._who_has), count):
gathers[ws._address][ts._key] = [
wws._address for wws in ts._who_has
]
await asyncio.gather(
*(
# Note: this never raises exceptions
self.gather_on_worker(w, who_has)
for w, who_has in gathers.items()
)
)
for r, v in gathers.items():
self.log_event(r, {"action": "replicate-add", "who_has": v})
self.log_event(
"all",
{
"action": "replicate",
"workers": list(workers),
"key-count": len(keys),
"branching-factor": branching_factor,
},
)
[docs] def workers_to_close(
self,
comm=None,
memory_ratio: "int | float | None" = None,
n: "int | None" = None,
key: "Callable[[WorkerState], Hashable] | None" = None,
minimum: "int | None" = None,
target: "int | None" = None,
attribute: str = "address",
) -> "list[str]":
"""
Find workers that we can close with low cost
This returns a list of workers that are good candidates to retire.
These workers are not running anything and are storing
relatively little data relative to their peers. If all workers are
idle then we still maintain enough workers to have enough RAM to store
our data, with a comfortable buffer.
This is for use with systems like ``distributed.deploy.adaptive``.
Parameters
----------
memory_ratio : Number
Amount of extra space we want to have for our stored data.
Defaults to 2, or that we want to have twice as much memory as we
currently have data.
n : int
Number of workers to close
minimum : int
Minimum number of workers to keep around
key : Callable(WorkerState)
An optional callable mapping a WorkerState object to a group
affiliation. Groups will be closed together. This is useful when
closing workers must be done collectively, such as by hostname.
target : int
Target number of workers to have after we close
attribute : str
The attribute of the WorkerState object to return, like "address"
or "name". Defaults to "address".
Examples
--------
>>> scheduler.workers_to_close()
['tcp://192.168.0.1:1234', 'tcp://192.168.0.2:1234']
Group workers by hostname prior to closing
>>> scheduler.workers_to_close(key=lambda ws: ws.host)
['tcp://192.168.0.1:1234', 'tcp://192.168.0.1:4567']
Remove two workers
>>> scheduler.workers_to_close(n=2)
Keep enough workers to have twice as much memory as we we need.
>>> scheduler.workers_to_close(memory_ratio=2)
Returns
-------
to_close: list of worker addresses that are OK to close
See Also
--------
Scheduler.retire_workers
"""
parent: SchedulerState = cast(SchedulerState, self)
if target is not None and n is None:
n = len(parent._workers_dv) - target
if n is not None:
if n < 0:
n = 0
target = len(parent._workers_dv) - n
if n is None and memory_ratio is None:
memory_ratio = 2
ws: WorkerState
with log_errors():
if not n and all([ws._processing for ws in parent._workers_dv.values()]):
return []
if key is None:
key = operator.attrgetter("address")
if isinstance(key, bytes) and dask.config.get(
"distributed.scheduler.pickle"
):
key = pickle.loads(key)
groups = groupby(key, parent._workers.values())
limit_bytes = {
k: sum([ws._memory_limit for ws in v]) for k, v in groups.items()
}
group_bytes = {k: sum([ws._nbytes for ws in v]) for k, v in groups.items()}
limit = sum(limit_bytes.values())
total = sum(group_bytes.values())
def _key(group):
wws: WorkerState
is_idle = not any([wws._processing for wws in groups[group]])
bytes = -group_bytes[group]
return (is_idle, bytes)
idle = sorted(groups, key=_key)
to_close = []
n_remain = len(parent._workers_dv)
while idle:
group = idle.pop()
if n is None and any([ws._processing for ws in groups[group]]):
break
if minimum and n_remain - len(groups[group]) < minimum:
break
limit -= limit_bytes[group]
if (
n is not None and n_remain - len(groups[group]) >= cast(int, target)
) or (memory_ratio is not None and limit >= memory_ratio * total):
to_close.append(group)
n_remain -= len(groups[group])
else:
break
result = [getattr(ws, attribute) for g in to_close for ws in groups[g]]
if result:
logger.debug("Suggest closing workers: %s", result)
return result
[docs] async def retire_workers(
self,
comm=None,
workers=None,
remove=True,
close_workers=False,
names=None,
lock=True,
**kwargs,
) -> dict:
"""Gracefully retire workers from cluster
Parameters
----------
workers: list (optional)
List of worker addresses to retire.
If not provided we call ``workers_to_close`` which finds a good set
names: list (optional)
List of worker names to retire.
remove: bool (defaults to True)
Whether or not to remove the worker metadata immediately or else
wait for the worker to contact us
close_workers: bool (defaults to False)
Whether or not to actually close the worker explicitly from here.
Otherwise we expect some external job scheduler to finish off the
worker.
**kwargs: dict
Extra options to pass to workers_to_close to determine which
workers we should drop
Returns
-------
Dictionary mapping worker ID/address to dictionary of information about
that worker for each retired worker.
See Also
--------
Scheduler.workers_to_close
"""
parent: SchedulerState = cast(SchedulerState, self)
ws: WorkerState
ts: TaskState
with log_errors():
async with self._lock if lock else empty_context:
if names is not None:
if workers is not None:
raise TypeError("names and workers are mutually exclusive")
if names:
logger.info("Retire worker names %s", names)
names = set(map(str, names))
workers = {
ws._address
for ws in parent._workers_dv.values()
if str(ws._name) in names
}
elif workers is None:
while True:
try:
workers = self.workers_to_close(**kwargs)
if not workers:
return {}
return await self.retire_workers(
workers=workers,
remove=remove,
close_workers=close_workers,
lock=False,
)
except KeyError: # keys left during replicate
pass
workers = {
parent._workers_dv[w] for w in workers if w in parent._workers_dv
}
if not workers:
return {}
logger.info("Retire workers %s", workers)
# Keys orphaned by retiring those workers
keys = {k for w in workers for k in w.has_what}
keys = {ts._key for ts in keys if ts._who_has.issubset(workers)}
if keys:
other_workers = set(parent._workers_dv.values()) - workers
if not other_workers:
return {}
logger.info("Moving %d keys to other workers", len(keys))
await self.replicate(
keys=keys,
workers=[ws._address for ws in other_workers],
n=1,
delete=False,
lock=False,
)
worker_keys = {ws._address: ws.identity() for ws in workers}
if close_workers:
await asyncio.gather(
*[self.close_worker(worker=w, safe=True) for w in worker_keys]
)
if remove:
await asyncio.gather(
*[self.remove_worker(address=w, safe=True) for w in worker_keys]
)
self.log_event(
"all",
{
"action": "retire-workers",
"workers": worker_keys,
"moved-keys": len(keys),
},
)
self.log_event(list(worker_keys), {"action": "retired"})
return worker_keys
[docs] def add_keys(self, comm=None, worker=None, keys=(), stimulus_id=None):
"""
Learn that a worker has certain keys
This should not be used in practice and is mostly here for legacy
reasons. However, it is sent by workers from time to time.
"""
parent: SchedulerState = cast(SchedulerState, self)
if worker not in parent._workers_dv:
return "not found"
ws: WorkerState = parent._workers_dv[worker]
redundant_replicas = []
for key in keys:
ts: TaskState = parent._tasks.get(key)
if ts is not None and ts._state == "memory":
if ws not in ts._who_has:
parent.add_replica(ts, ws)
else:
redundant_replicas.append(key)
if redundant_replicas:
if not stimulus_id:
stimulus_id = f"redundant-replicas-{time()}"
self.worker_send(
worker,
{
"op": "remove-replicas",
"keys": redundant_replicas,
"stimulus_id": stimulus_id,
},
)
return "OK"
[docs] def update_data(
self,
comm=None,
*,
who_has: dict,
nbytes: dict,
client=None,
serializers=None,
):
"""
Learn that new data has entered the network from an external source
See Also
--------
Scheduler.mark_key_in_memory
"""
parent: SchedulerState = cast(SchedulerState, self)
with log_errors():
who_has = {
k: [self.coerce_address(vv) for vv in v] for k, v in who_has.items()
}
logger.debug("Update data %s", who_has)
for key, workers in who_has.items():
ts: TaskState = parent._tasks.get(key) # type: ignore
if ts is None:
ts = parent.new_task(key, None, "memory")
ts.state = "memory"
ts_nbytes = nbytes.get(key, -1)
if ts_nbytes >= 0:
ts.set_nbytes(ts_nbytes)
for w in workers:
ws: WorkerState = parent._workers_dv[w]
if ws not in ts._who_has:
parent.add_replica(ts, ws)
self.report(
{"op": "key-in-memory", "key": key, "workers": list(workers)}
)
if client:
self.client_desires_keys(keys=list(who_has), client=client)
def report_on_key(self, key: str = None, ts: TaskState = None, client: str = None):
parent: SchedulerState = cast(SchedulerState, self)
if ts is None:
ts = parent._tasks.get(key)
elif key is None:
key = ts._key
else:
assert False, (key, ts)
return
report_msg: dict
if ts is None:
report_msg = {"op": "cancelled-key", "key": key}
else:
report_msg = _task_to_report_msg(parent, ts)
if report_msg is not None:
self.report(report_msg, ts=ts, client=client)
[docs] async def feed(
self, comm, function=None, setup=None, teardown=None, interval="1s", **kwargs
):
"""
Provides a data Comm to external requester
Caution: this runs arbitrary Python code on the scheduler. This should
eventually be phased out. It is mostly used by diagnostics.
"""
if not dask.config.get("distributed.scheduler.pickle"):
logger.warn(
"Tried to call 'feed' route with custom functions, but "
"pickle is disallowed. Set the 'distributed.scheduler.pickle'"
"config value to True to use the 'feed' route (this is mostly "
"commonly used with progress bars)"
)
return
interval = parse_timedelta(interval)
with log_errors():
if function:
function = pickle.loads(function)
if setup:
setup = pickle.loads(setup)
if teardown:
teardown = pickle.loads(teardown)
state = setup(self) if setup else None
if inspect.isawaitable(state):
state = await state
try:
while self.status == Status.running:
if state is None:
response = function(self)
else:
response = function(self, state)
await comm.write(response)
await asyncio.sleep(interval)
except OSError:
pass
finally:
if teardown:
teardown(self, state)
def log_worker_event(self, worker=None, topic=None, msg=None):
self.log_event(topic, msg)
def subscribe_worker_status(self, comm=None):
WorkerStatusPlugin(self, comm)
ident = self.identity()
for v in ident["workers"].values():
del v["metrics"]
del v["last_seen"]
return ident
def get_processing(self, comm=None, workers=None):
parent: SchedulerState = cast(SchedulerState, self)
ws: WorkerState
ts: TaskState
if workers is not None:
workers = set(map(self.coerce_address, workers))
return {
w: [ts._key for ts in parent._workers_dv[w].processing] for w in workers
}
else:
return {
w: [ts._key for ts in ws._processing]
for w, ws in parent._workers_dv.items()
}
def get_who_has(self, comm=None, keys=None):
parent: SchedulerState = cast(SchedulerState, self)
ws: WorkerState
ts: TaskState
if keys is not None:
return {
k: [ws._address for ws in parent._tasks[k].who_has]
if k in parent._tasks
else []
for k in keys
}
else:
return {
key: [ws._address for ws in ts._who_has]
for key, ts in parent._tasks.items()
}
def get_has_what(self, comm=None, workers=None):
parent: SchedulerState = cast(SchedulerState, self)
ws: WorkerState
ts: TaskState
if workers is not None:
workers = map(self.coerce_address, workers)
return {
w: [ts._key for ts in parent._workers_dv[w].has_what]
if w in parent._workers_dv
else []
for w in workers
}
else:
return {
w: [ts._key for ts in ws.has_what]
for w, ws in parent._workers_dv.items()
}
def get_ncores(self, comm=None, workers=None):
parent: SchedulerState = cast(SchedulerState, self)
ws: WorkerState
if workers is not None:
workers = map(self.coerce_address, workers)
return {
w: parent._workers_dv[w].nthreads
for w in workers
if w in parent._workers_dv
}
else:
return {w: ws._nthreads for w, ws in parent._workers_dv.items()}
async def get_call_stack(self, comm=None, keys=None):
parent: SchedulerState = cast(SchedulerState, self)
ts: TaskState
dts: TaskState
if keys is not None:
stack = list(keys)
processing = set()
while stack:
key = stack.pop()
ts = parent._tasks[key]
if ts._state == "waiting":
stack.extend([dts._key for dts in ts._dependencies])
elif ts._state == "processing":
processing.add(ts)
workers = defaultdict(list)
for ts in processing:
if ts._processing_on:
workers[ts._processing_on.address].append(ts._key)
else:
workers = {w: None for w in parent._workers_dv}
if not workers:
return {}
results = await asyncio.gather(
*(self.rpc(w).call_stack(keys=v) for w, v in workers.items())
)
response = {w: r for w, r in zip(workers, results) if r}
return response
def get_nbytes(self, comm=None, keys=None, summary=True):
parent: SchedulerState = cast(SchedulerState, self)
ts: TaskState
with log_errors():
if keys is not None:
result = {k: parent._tasks[k].nbytes for k in keys}
else:
result = {
k: ts._nbytes for k, ts in parent._tasks.items() if ts._nbytes >= 0
}
if summary:
out = defaultdict(lambda: 0)
for k, v in result.items():
out[key_split(k)] += v
result = dict(out)
return result
[docs] def run_function(self, stream, function, args=(), kwargs={}, wait=True):
"""Run a function within this process
See Also
--------
Client.run_on_scheduler
"""
from .worker import run
if not dask.config.get("distributed.scheduler.pickle"):
raise ValueError(
"Cannot run function as the scheduler has been explicitly disallowed from "
"deserializing arbitrary bytestrings using pickle via the "
"'distributed.scheduler.pickle' configuration setting."
)
self.log_event("all", {"action": "run-function", "function": function})
return run(self, stream, function=function, args=args, kwargs=kwargs, wait=wait)
def set_metadata(self, comm=None, keys=None, value=None):
parent: SchedulerState = cast(SchedulerState, self)
try:
metadata = parent._task_metadata
for key in keys[:-1]:
if key not in metadata or not isinstance(metadata[key], (dict, list)):
metadata[key] = {}
metadata = metadata[key]
metadata[keys[-1]] = value
except Exception:
import pdb
pdb.set_trace()
def get_metadata(self, comm=None, keys=None, default=no_default):
parent: SchedulerState = cast(SchedulerState, self)
metadata = parent._task_metadata
for key in keys[:-1]:
metadata = metadata[key]
try:
return metadata[keys[-1]]
except KeyError:
if default != no_default:
return default
else:
raise
def set_restrictions(self, comm=None, worker=None):
ts: TaskState
for key, restrictions in worker.items():
ts = self.tasks[key]
if isinstance(restrictions, str):
restrictions = {restrictions}
ts._worker_restrictions = set(restrictions)
def get_task_status(self, comm=None, keys=None):
parent: SchedulerState = cast(SchedulerState, self)
return {
key: (parent._tasks[key].state if key in parent._tasks else None)
for key in keys
}
def get_task_stream(self, comm=None, start=None, stop=None, count=None):
from distributed.diagnostics.task_stream import TaskStreamPlugin
if TaskStreamPlugin.name not in self.plugins:
self.add_plugin(TaskStreamPlugin(self))
plugin = self.plugins[TaskStreamPlugin.name]
return plugin.collect(start=start, stop=stop, count=count)
def start_task_metadata(self, comm=None, name=None):
plugin = CollectTaskMetaDataPlugin(scheduler=self, name=name)
self.add_plugin(plugin)
def stop_task_metadata(self, comm=None, name=None):
plugins = [
p
for p in list(self.plugins.values())
if isinstance(p, CollectTaskMetaDataPlugin) and p.name == name
]
if len(plugins) != 1:
raise ValueError(
"Expected to find exactly one CollectTaskMetaDataPlugin "
f"with name {name} but found {len(plugins)}."
)
plugin = plugins[0]
self.remove_plugin(name=plugin.name)
return {"metadata": plugin.metadata, "state": plugin.state}
[docs] async def register_worker_plugin(self, comm, plugin, name=None):
"""Registers a setup function, and call it on every worker"""
self.worker_plugins[name] = plugin
responses = await self.broadcast(
msg=dict(op="plugin-add", plugin=plugin, name=name)
)
return responses
[docs] async def unregister_worker_plugin(self, comm, name):
"""Unregisters a worker plugin"""
try:
self.worker_plugins.pop(name)
except KeyError:
raise ValueError(f"The worker plugin {name} does not exists")
responses = await self.broadcast(msg=dict(op="plugin-remove", name=name))
return responses
[docs] async def register_nanny_plugin(self, comm, plugin, name=None):
"""Registers a setup function, and call it on every worker"""
self.nanny_plugins[name] = plugin
responses = await self.broadcast(
msg=dict(op="plugin_add", plugin=plugin, name=name),
nanny=True,
)
return responses
[docs] async def unregister_nanny_plugin(self, comm, name):
"""Unregisters a worker plugin"""
try:
self.nanny_plugins.pop(name)
except KeyError:
raise ValueError(f"The nanny plugin {name} does not exists")
responses = await self.broadcast(
msg=dict(op="plugin_remove", name=name), nanny=True
)
return responses
[docs] def transition(self, key, finish: str, *args, **kwargs):
"""Transition a key from its current state to the finish state
Examples
--------
>>> self.transition('x', 'waiting')
{'x': 'processing'}
Returns
-------
Dictionary of recommendations for future transitions
See Also
--------
Scheduler.transitions: transitive version of this function
"""
parent: SchedulerState = cast(SchedulerState, self)
recommendations: dict
worker_msgs: dict
client_msgs: dict
a: tuple = parent._transition(key, finish, *args, **kwargs)
recommendations, client_msgs, worker_msgs = a
self.send_all(client_msgs, worker_msgs)
return recommendations
[docs] def transitions(self, recommendations: dict):
"""Process transitions until none are left
This includes feedback from previous transitions and continues until we
reach a steady state
"""
parent: SchedulerState = cast(SchedulerState, self)
client_msgs: dict = {}
worker_msgs: dict = {}
parent._transitions(recommendations, client_msgs, worker_msgs)
self.send_all(client_msgs, worker_msgs)
[docs] def story(self, *keys):
"""Get all transitions that touch one of the input keys"""
keys = {key.key if isinstance(key, TaskState) else key for key in keys}
return [
t for t in self.transition_log if t[0] in keys or keys.intersection(t[3])
]
transition_story = story
[docs] def reschedule(self, key=None, worker=None):
"""Reschedule a task
Things may have shifted and this task may now be better suited to run
elsewhere
"""
parent: SchedulerState = cast(SchedulerState, self)
ts: TaskState
try:
ts = parent._tasks[key]
except KeyError:
logger.warning(
"Attempting to reschedule task {}, which was not "
"found on the scheduler. Aborting reschedule.".format(key)
)
return
if ts._state != "processing":
return
if worker and ts._processing_on.address != worker:
return
self.transitions({key: "released"})
#####################
# Utility functions #
#####################
def add_resources(self, comm=None, worker=None, resources=None):
parent: SchedulerState = cast(SchedulerState, self)
ws: WorkerState = parent._workers_dv[worker]
if resources:
ws._resources.update(resources)
ws._used_resources = {}
for resource, quantity in ws._resources.items():
ws._used_resources[resource] = 0
dr: dict = parent._resources.get(resource, None)
if dr is None:
parent._resources[resource] = dr = {}
dr[worker] = quantity
return "OK"
def remove_resources(self, worker):
parent: SchedulerState = cast(SchedulerState, self)
ws: WorkerState = parent._workers_dv[worker]
for resource, quantity in ws._resources.items():
dr: dict = parent._resources.get(resource, None)
if dr is None:
parent._resources[resource] = dr = {}
del dr[worker]
[docs] def coerce_address(self, addr, resolve=True):
"""
Coerce possible input addresses to canonical form.
*resolve* can be disabled for testing with fake hostnames.
Handles strings, tuples, or aliases.
"""
# XXX how many address-parsing routines do we have?
parent: SchedulerState = cast(SchedulerState, self)
if addr in parent._aliases:
addr = parent._aliases[addr]
if isinstance(addr, tuple):
addr = unparse_host_port(*addr)
if not isinstance(addr, str):
raise TypeError(f"addresses should be strings or tuples, got {addr!r}")
if resolve:
addr = resolve_address(addr)
else:
addr = normalize_address(addr)
return addr
[docs] def workers_list(self, workers):
"""
List of qualifying workers
Takes a list of worker addresses or hostnames.
Returns a list of all worker addresses that match
"""
parent: SchedulerState = cast(SchedulerState, self)
if workers is None:
return list(parent._workers)
out = set()
for w in workers:
if ":" in w:
out.add(w)
else:
out.update({ww for ww in parent._workers if w in ww}) # TODO: quadratic
return list(out)
[docs] def start_ipython(self, comm=None):
"""Start an IPython kernel
Returns Jupyter connection info dictionary.
"""
from ._ipython_utils import start_ipython
if self._ipython_kernel is None:
self._ipython_kernel = start_ipython(
ip=self.ip, ns={"scheduler": self}, log=logger
)
return self._ipython_kernel.get_connection_info()
async def get_profile(
self,
comm=None,
workers=None,
scheduler=False,
server=False,
merge_workers=True,
start=None,
stop=None,
key=None,
):
parent: SchedulerState = cast(SchedulerState, self)
if workers is None:
workers = parent._workers_dv
else:
workers = set(parent._workers_dv) & set(workers)
if scheduler:
return profile.get_profile(self.io_loop.profile, start=start, stop=stop)
results = await asyncio.gather(
*(
self.rpc(w).profile(start=start, stop=stop, key=key, server=server)
for w in workers
),
return_exceptions=True,
)
results = [r for r in results if not isinstance(r, Exception)]
if merge_workers:
response = profile.merge(*results)
else:
response = dict(zip(workers, results))
return response
async def get_profile_metadata(
self,
comm=None,
workers=None,
merge_workers=True,
start=None,
stop=None,
profile_cycle_interval=None,
):
parent: SchedulerState = cast(SchedulerState, self)
dt = profile_cycle_interval or dask.config.get(
"distributed.worker.profile.cycle"
)
dt = parse_timedelta(dt, default="ms")
if workers is None:
workers = parent._workers_dv
else:
workers = set(parent._workers_dv) & set(workers)
results = await asyncio.gather(
*(self.rpc(w).profile_metadata(start=start, stop=stop) for w in workers),
return_exceptions=True,
)
results = [r for r in results if not isinstance(r, Exception)]
counts = [v["counts"] for v in results]
counts = itertools.groupby(merge_sorted(*counts), lambda t: t[0] // dt * dt)
counts = [(time, sum(pluck(1, group))) for time, group in counts]
keys = set()
for v in results:
for t, d in v["keys"]:
for k in d:
keys.add(k)
keys = {k: [] for k in keys}
groups1 = [v["keys"] for v in results]
groups2 = list(merge_sorted(*groups1, key=first))
last = 0
for t, d in groups2:
tt = t // dt * dt
if tt > last:
last = tt
for k, v in keys.items():
v.append([tt, 0])
for k, v in d.items():
keys[k][-1][1] += v
return {"counts": counts, "keys": keys}
async def performance_report(
self, comm=None, start=None, last_count=None, code="", mode=None
):
parent: SchedulerState = cast(SchedulerState, self)
stop = time()
# Profiles
compute, scheduler, workers = await asyncio.gather(
*[
self.get_profile(start=start),
self.get_profile(scheduler=True, start=start),
self.get_profile(server=True, start=start),
]
)
from . import profile
def profile_to_figure(state):
data = profile.plot_data(state)
figure, source = profile.plot_figure(data, sizing_mode="stretch_both")
return figure
compute, scheduler, workers = map(
profile_to_figure, (compute, scheduler, workers)
)
# Task stream
task_stream = self.get_task_stream(start=start)
total_tasks = len(task_stream)
timespent = defaultdict(int)
for d in task_stream:
for x in d.get("startstops", []):
timespent[x["action"]] += x["stop"] - x["start"]
tasks_timings = ""
for k in sorted(timespent.keys()):
tasks_timings += f"\n<li> {k} time: {format_time(timespent[k])} </li>"
from .dashboard.components.scheduler import task_stream_figure
from .diagnostics.task_stream import rectangles
rects = rectangles(task_stream)
source, task_stream = task_stream_figure(sizing_mode="stretch_both")
source.data.update(rects)
# Bandwidth
from distributed.dashboard.components.scheduler import (
BandwidthTypes,
BandwidthWorkers,
)
bandwidth_workers = BandwidthWorkers(self, sizing_mode="stretch_both")
bandwidth_workers.update()
bandwidth_types = BandwidthTypes(self, sizing_mode="stretch_both")
bandwidth_types.update()
# System monitor
from distributed.dashboard.components.shared import SystemMonitor
sysmon = SystemMonitor(self, last_count=last_count, sizing_mode="stretch_both")
sysmon.update()
# Scheduler logs
from distributed.dashboard.components.scheduler import SchedulerLogs
logs = SchedulerLogs(self)
from bokeh.models import Div, Panel, Tabs
import distributed
# HTML
ws: WorkerState
html = """
<h1> Dask Performance Report </h1>
<i> Select different tabs on the top for additional information </i>
<h2> Duration: {time} </h2>
<h2> Tasks Information </h2>
<ul>
<li> number of tasks: {ntasks} </li>
{tasks_timings}
</ul>
<h2> Scheduler Information </h2>
<ul>
<li> Address: {address} </li>
<li> Workers: {nworkers} </li>
<li> Threads: {threads} </li>
<li> Memory: {memory} </li>
<li> Dask Version: {dask_version} </li>
<li> Dask.Distributed Version: {distributed_version} </li>
</ul>
<h2> Calling Code </h2>
<pre>
{code}
</pre>
""".format(
time=format_time(stop - start),
ntasks=total_tasks,
tasks_timings=tasks_timings,
address=self.address,
nworkers=len(parent._workers_dv),
threads=sum([ws._nthreads for ws in parent._workers_dv.values()]),
memory=format_bytes(
sum([ws._memory_limit for ws in parent._workers_dv.values()])
),
code=code,
dask_version=dask.__version__,
distributed_version=distributed.__version__,
)
html = Div(
text=html,
style={
"width": "100%",
"height": "100%",
"max-width": "1920px",
"max-height": "1080px",
"padding": "12px",
"border": "1px solid lightgray",
"box-shadow": "inset 1px 0 8px 0 lightgray",
"overflow": "auto",
},
)
html = Panel(child=html, title="Summary")
compute = Panel(child=compute, title="Worker Profile (compute)")
workers = Panel(child=workers, title="Worker Profile (administrative)")
scheduler = Panel(child=scheduler, title="Scheduler Profile (administrative)")
task_stream = Panel(child=task_stream, title="Task Stream")
bandwidth_workers = Panel(
child=bandwidth_workers.root, title="Bandwidth (Workers)"
)
bandwidth_types = Panel(child=bandwidth_types.root, title="Bandwidth (Types)")
system = Panel(child=sysmon.root, title="System")
logs = Panel(child=logs.root, title="Scheduler Logs")
tabs = Tabs(
tabs=[
html,
task_stream,
system,
logs,
compute,
workers,
scheduler,
bandwidth_workers,
bandwidth_types,
]
)
from bokeh.core.templates import get_env
from bokeh.plotting import output_file, save
with tmpfile(extension=".html") as fn:
output_file(filename=fn, title="Dask Performance Report", mode=mode)
template_directory = os.path.join(
os.path.dirname(os.path.abspath(__file__)), "dashboard", "templates"
)
template_environment = get_env()
template_environment.loader.searchpath.append(template_directory)
template = template_environment.get_template("performance_report.html")
save(tabs, filename=fn, template=template)
with open(fn) as f:
data = f.read()
return data
async def get_worker_logs(self, comm=None, n=None, workers=None, nanny=False):
results = await self.broadcast(
msg={"op": "get_logs", "n": n}, workers=workers, nanny=nanny
)
return results
def log_event(self, name, msg):
event = (time(), msg)
if isinstance(name, (list, tuple)):
for n in name:
self.events[n].append(event)
self.event_counts[n] += 1
self._report_event(n, event)
else:
self.events[name].append(event)
self.event_counts[name] += 1
self._report_event(name, event)
def _report_event(self, name, event):
for client in self.event_subscriber[name]:
self.report(
{
"op": "event",
"topic": name,
"event": event,
},
client=client,
)
def subscribe_topic(self, topic, client):
self.event_subscriber[topic].add(client)
def unsubscribe_topic(self, topic, client):
self.event_subscriber[topic].discard(client)
def get_events(self, comm=None, topic=None):
if topic is not None:
return tuple(self.events[topic])
else:
return valmap(tuple, self.events)
async def get_worker_monitor_info(self, recent=False, starts=None):
parent: SchedulerState = cast(SchedulerState, self)
if starts is None:
starts = {}
results = await asyncio.gather(
*(
self.rpc(w).get_monitor_info(recent=recent, start=starts.get(w, 0))
for w in parent._workers_dv
)
)
return dict(zip(parent._workers_dv, results))
###########
# Cleanup #
###########
[docs] def reevaluate_occupancy(self, worker_index: Py_ssize_t = 0):
"""Periodically reassess task duration time
The expected duration of a task can change over time. Unfortunately we
don't have a good constant-time way to propagate the effects of these
changes out to the summaries that they affect, like the total expected
runtime of each of the workers, or what tasks are stealable.
In this coroutine we walk through all of the workers and re-align their
estimates with the current state of tasks. We do this periodically
rather than at every transition, and we only do it if the scheduler
process isn't under load (using psutil.Process.cpu_percent()). This
lets us avoid this fringe optimization when we have better things to
think about.
"""
parent: SchedulerState = cast(SchedulerState, self)
try:
if self.status == Status.closed:
return
last = time()
next_time = timedelta(seconds=0.1)
if self.proc.cpu_percent() < 50:
workers: list = list(parent._workers.values())
nworkers: Py_ssize_t = len(workers)
i: Py_ssize_t
for i in range(nworkers):
ws: WorkerState = workers[worker_index % nworkers]
worker_index += 1
try:
if ws is None or not ws._processing:
continue
_reevaluate_occupancy_worker(parent, ws)
finally:
del ws # lose ref
duration = time() - last
if duration > 0.005: # 5ms since last release
next_time = timedelta(seconds=duration * 5) # 25ms gap
break
self.loop.add_timeout(
next_time, self.reevaluate_occupancy, worker_index=worker_index
)
except Exception:
logger.error("Error in reevaluate occupancy", exc_info=True)
raise
async def check_worker_ttl(self):
parent: SchedulerState = cast(SchedulerState, self)
ws: WorkerState
now = time()
for ws in parent._workers_dv.values():
if (ws._last_seen < now - self.worker_ttl) and (
ws._last_seen < now - 10 * heartbeat_interval(len(parent._workers_dv))
):
logger.warning(
"Worker failed to heartbeat within %s seconds. Closing: %s",
self.worker_ttl,
ws,
)
await self.remove_worker(address=ws._address)
def check_idle(self):
parent: SchedulerState = cast(SchedulerState, self)
ws: WorkerState
if (
any([ws._processing for ws in parent._workers_dv.values()])
or parent._unrunnable
):