worker.py

from __future__ import annotations

import asyncio
import bisect
import builtins
import concurrent.futures
import errno
import heapq
import logging
import os
import random
import sys
import threading
import warnings
import weakref
from collections import defaultdict, deque, namedtuple
from collections.abc import Callable, Hashable, Iterable, MutableMapping
from contextlib import suppress
from datetime import timedelta
from inspect import isawaitable
from pickle import PicklingError
from typing import TYPE_CHECKING, Any, ClassVar

if TYPE_CHECKING:
    from .client import Client

from tlz import first, keymap, merge, pluck  # noqa: F401
from tornado.ioloop import IOLoop, PeriodicCallback

import dask
from dask.core import istask
from dask.system import CPU_COUNT
from dask.utils import (
    apply,
    format_bytes,
    funcname,
    parse_bytes,
    parse_timedelta,
    stringify,
    typename,
)

from . import comm, preloading, profile, system, utils
from .batched import BatchedSend
from .comm import connect, get_address_host
from .comm.addressing import address_from_user_args, parse_address
from .comm.utils import OFFLOAD_THRESHOLD
from .core import (
    CommClosedError,
    Status,
    coerce_to_address,
    error_message,
    pingpong,
    send_recv,
)
from .diagnostics import nvml
from .diagnostics.plugin import _get_plugin_name
from .diskutils import WorkSpace
from .http import get_handlers
from .metrics import time
from .node import ServerNode
from .proctitle import setproctitle
from .protocol import pickle, to_serialize
from .pubsub import PubSubWorkerExtension
from .security import Security
from .sizeof import safe_sizeof as sizeof
from .threadpoolexecutor import ThreadPoolExecutor
from .threadpoolexecutor import secede as tpe_secede
from .utils import (
    LRU,
    TimeoutError,
    _maybe_complex,
    get_ip,
    has_arg,
    import_file,
    iscoroutinefunction,
    json_load_robust,
    key_split,
    log_errors,
    offload,
    parse_ports,
    silence_logging,
    thread_state,
    warn_on_duration,
)
from .utils_comm import gather_from_workers, pack_data, retry_operation
from .utils_perf import ThrottledGC, disable_gc_diagnosis, enable_gc_diagnosis
from .versions import get_versions

logger = logging.getLogger(__name__)

LOG_PDB = dask.config.get("distributed.admin.pdb-on-err")

no_value = "--no-value-sentinel--"

# TaskState.status subsets
PROCESSING = {
    "waiting",
    "ready",
    "constrained",
    "executing",
    "long-running",
    "cancelled",
    "resumed",
}
READY = {"ready", "constrained"}

# Worker.status subsets
RUNNING = {Status.running, Status.paused, Status.closing_gracefully}

DEFAULT_EXTENSIONS: list[type] = [PubSubWorkerExtension]

DEFAULT_METRICS: dict[str, Callable[[Worker], Any]] = {}

DEFAULT_STARTUP_INFORMATION: dict[str, Callable[[Worker], Any]] = {}

DEFAULT_DATA_SIZE = parse_bytes(
    dask.config.get("distributed.scheduler.default-data-size")
)

SerializedTask = namedtuple("SerializedTask", ["function", "args", "kwargs", "task"])


class InvalidTransition(Exception):
    pass


class TaskState:
    """Holds volatile state relating to an individual Dask task


    * **dependencies**: ``set(TaskState instances)``
        The data needed by this key to run
    * **dependents**: ``set(TaskState instances)``
        The keys that use this dependency.
    * **duration**: ``float``
        Expected duration the a task
    * **priority**: ``tuple``
        The priority this task given by the scheduler.  Determines run order.
    * **state**: ``str``
        The current state of the task. One of ["waiting", "ready", "executing",
        "fetch", "memory", "flight", "long-running", "rescheduled", "error"]
    * **who_has**: ``set(worker)``
        Workers that we believe have this data
    * **coming_from**: ``str``
        The worker that current task data is coming from if task is in flight
    * **waiting_for_data**: ``set(keys of dependencies)``
        A dynamic version of dependencies.  All dependencies that we still don't
        have for a particular key.
    * **resource_restrictions**: ``{str: number}``
        Abstract resources required to run a task
    * **exception**: ``str``
        The exception caused by running a task if it erred
    * **traceback**: ``str``
        The exception caused by running a task if it erred
    * **type**: ``type``
        The type of a particular piece of data
    * **suspicious_count**: ``int``
        The number of times a dependency has not been where we expected it
    * **startstops**: ``[{startstop}]``
        Log of transfer, load, and compute times for a task
    * **start_time**: ``float``
        Time at which task begins running
    * **stop_time**: ``float``
        Time at which task finishes running
    * **metadata**: ``dict``
        Metadata related to task. Stored metadata should be msgpack
        serializable (e.g. int, string, list, dict).
    * **nbytes**: ``int``
        The size of a particular piece of data
    * **annotations**: ``dict``
        Task annotations

    Parameters
    ----------
    key: str
    runspec: SerializedTask
        A named tuple containing the ``function``, ``args``, ``kwargs`` and
        ``task`` associated with this `TaskState` instance. This defaults to
        ``None`` and can remain empty if it is a dependency that this worker
        will receive from another worker.

    """

    def __init__(self, key, runspec=None):
        assert key is not None
        self.key = key
        self.runspec = runspec
        self.dependencies = set()
        self.dependents = set()
        self.duration = None
        self.priority = None
        self.state = "released"
        self.who_has = set()
        self.coming_from = None
        self.waiting_for_data = set()
        self.waiters = set()
        self.resource_restrictions = {}
        self.exception = None
        self.exception_text = ""
        self.traceback = None
        self.traceback_text = ""
        self.type = None
        self.suspicious_count = 0
        self.startstops = []
        self.start_time = None
        self.stop_time = None
        self.metadata = {}
        self.nbytes = None
        self.annotations = None
        self.done = False
        self._next = None

    def __repr__(self):
        return f"<Task {self.key!r} {self.state}>"

    def get_nbytes(self) -> int:
        nbytes = self.nbytes
        return nbytes if nbytes is not None else DEFAULT_DATA_SIZE

    def is_protected(self) -> bool:
        return self.state in PROCESSING or any(
            dep_ts.state in PROCESSING for dep_ts in self.dependents
        )


class Worker(ServerNode):
    """Worker node in a Dask distributed cluster

    Workers perform two functions:

    1.  **Serve data** from a local dictionary
    2.  **Perform computation** on that data and on data from peers

    Workers keep the scheduler informed of their data and use that scheduler to
    gather data from other workers when necessary to perform a computation.

    You can start a worker with the ``dask-worker`` command line application::

        $ dask-worker scheduler-ip:port

    Use the ``--help`` flag to see more options::

        $ dask-worker --help

    The rest of this docstring is about the internal state the the worker uses
    to manage and track internal computations.

    **State**

    **Informational State**

    These attributes don't change significantly during execution.

    * **nthreads:** ``int``:
        Number of nthreads used by this worker process
    * **executors:** ``Dict[str, concurrent.futures.Executor]``:
        Executors used to perform computation. Always contains the default
        executor.
    * **local_directory:** ``path``:
        Path on local machine to store temporary files
    * **scheduler:** ``rpc``:
        Location of scheduler.  See ``.ip/.port`` attributes.
    * **name:** ``string``:
        Alias
    * **services:** ``{str: Server}``:
        Auxiliary web servers running on this worker
    * **service_ports:** ``{str: port}``:
    * **total_out_connections**: ``int``
        The maximum number of concurrent outgoing requests for data
    * **total_in_connections**: ``int``
        The maximum number of concurrent incoming requests for data
    * **comm_threshold_bytes**: ``int``
        As long as the total number of bytes in flight is below this threshold
        we will not limit the number of outgoing connections for a single tasks
        dependency fetch.
    * **batched_stream**: ``BatchedSend``
        A batched stream along which we communicate to the scheduler
    * **log**: ``[(message)]``
        A structured and queryable log.  See ``Worker.story``

    **Volatile State**

    These attributes track the progress of tasks that this worker is trying to
    complete.  In the descriptions below a ``key`` is the name of a task that
    we want to compute and ``dep`` is the name of a piece of dependent data
    that we want to collect from others.

    * **tasks**: ``{key: TaskState}``
        The tasks currently executing on this worker (and any dependencies of those tasks)
    * **data:** ``{key: object}``:
        Prefer using the **host** attribute instead of this, unless
        memory_limit and at least one of memory_target_fraction or
        memory_spill_fraction values are defined, in that case, this attribute
        is a zict.Buffer, from which information on LRU cache can be queried.
    * **data.memory:** ``{key: object}``:
        Dictionary mapping keys to actual values stored in memory. Only
        available if condition for **data** being a zict.Buffer is met.
    * **data.disk:** ``{key: object}``:
        Dictionary mapping keys to actual values stored on disk. Only
        available if condition for **data** being a zict.Buffer is met.
    * **data_needed**: deque(keys)
        The keys which still require data in order to execute, arranged in a deque
    * **ready**: [keys]
        Keys that are ready to run.  Stored in a LIFO stack
    * **constrained**: [keys]
        Keys for which we have the data to run, but are waiting on abstract
        resources like GPUs.  Stored in a FIFO deque
    * **executing_count**: ``int``
        A count of tasks currently executing on this worker
    * **executed_count**: int
        A number of tasks that this worker has run in its lifetime
    * **long_running**: {keys}
        A set of keys of tasks that are running and have started their own
        long-running clients.
    * **has_what**: ``{worker: {deps}}``
        The data that we care about that we think a worker has
    * **pending_data_per_worker**: ``{worker: [dep]}``
        The data on each worker that we still want, prioritized as a deque
    * **in_flight_tasks**: ``int``
        A count of the number of tasks that are coming to us in current
        peer-to-peer connections
    * **in_flight_workers**: ``{worker: {task}}``
        The workers from which we are currently gathering data and the
        dependencies we expect from those connections
    * **comm_bytes**: ``int``
        The total number of bytes in flight
    * **threads**: ``{key: int}``
        The ID of the thread on which the task ran
    * **active_threads**: ``{int: key}``
        The keys currently running on active threads
    * **waiting_for_data_count**: ``int``
        A count of how many tasks are currently waiting for data


    Parameters
    ----------
    scheduler_ip: str
    scheduler_port: int
    ip: str, optional
    data: MutableMapping, type, None
        The object to use for storage, builds a disk-backed LRU dict by default
    nthreads: int, optional
    loop: tornado.ioloop.IOLoop
    local_directory: str, optional
        Directory where we place local resources
    name: str, optional
    memory_limit: int, float, string
        Number of bytes of memory that this worker should use.
        Set to zero for no limit.  Set to 'auto' to calculate
        as system.MEMORY_LIMIT * min(1, nthreads / total_cores)
        Use strings or numbers like 5GB or 5e9
    memory_target_fraction: float
        Fraction of memory to try to stay beneath
    memory_spill_fraction: float
        Fraction of memory at which we start spilling to disk
    memory_pause_fraction: float
        Fraction of memory at which we stop running new tasks
    executor: concurrent.futures.Executor, dict[str, concurrent.futures.Executor], str
        The executor(s) to use. Depending on the type, it has the following meanings:
            - Executor instance: The default executor.
            - Dict[str, Executor]: mapping names to Executor instances. If the
              "default" key isn't in the dict, a "default" executor will be created
              using ``ThreadPoolExecutor(nthreads)``.
            - Str: The string "offload", which refer to the same thread pool used for
              offloading communications. This results in the same thread being used
              for deserialization and computation.
    resources: dict
        Resources that this worker has like ``{'GPU': 2}``
    nanny: str
        Address on which to contact nanny, if it exists
    lifetime: str
        Amount of time like "1 hour" after which we gracefully shut down the worker.
        This defaults to None, meaning no explicit shutdown time.
    lifetime_stagger: str
        Amount of time like "5 minutes" to stagger the lifetime value
        The actual lifetime will be selected uniformly at random between
        lifetime +/- lifetime_stagger
    lifetime_restart: bool
        Whether or not to restart a worker after it has reached its lifetime
        Default False

    Examples
    --------

    Use the command line to start a worker::

        $ dask-scheduler
        Start scheduler at 127.0.0.1:8786

        $ dask-worker 127.0.0.1:8786
        Start worker at:               127.0.0.1:1234
        Registered with scheduler at:  127.0.0.1:8786

    See Also
    --------
    distributed.scheduler.Scheduler
    distributed.nanny.Nanny
    """

    _instances: ClassVar[weakref.WeakSet[Worker]] = weakref.WeakSet()
    _initialized_clients: ClassVar[weakref.WeakSet[Client]] = weakref.WeakSet()

    def __init__(
        self,
        scheduler_ip=None,
        scheduler_port=None,
        scheduler_file=None,
        ncores=None,
        nthreads=None,
        loop=None,
        local_dir=None,
        local_directory=None,
        services=None,
        service_ports=None,
        service_kwargs=None,
        name=None,
        reconnect=True,
        memory_limit="auto",
        executor=None,
        resources=None,
        silence_logs=None,
        death_timeout=None,
        preload=None,
        preload_argv=None,
        security=None,
        contact_address=None,
        memory_monitor_interval="200ms",
        extensions=None,
        metrics=DEFAULT_METRICS,
        startup_information=DEFAULT_STARTUP_INFORMATION,
        data=None,
        interface=None,
        host=None,
        port=None,
        protocol=None,
        dashboard_address=None,
        dashboard=False,
        http_prefix="/",
        nanny=None,
        plugins=(),
        low_level_profiler=dask.config.get("distributed.worker.profile.low-level"),
        validate=None,
        profile_cycle_interval=None,
        lifetime=None,
        lifetime_stagger=None,
        lifetime_restart=None,
        **kwargs,
    ):
        self.tasks = {}
        self.waiting_for_data_count = 0
        self.has_what = defaultdict(set)
        self.pending_data_per_worker = defaultdict(deque)
        self.nanny = nanny
        self._lock = threading.Lock()

        self.data_needed = []

        self.in_flight_workers = {}
        self.total_out_connections = dask.config.get(
            "distributed.worker.connections.outgoing"
        )
        self.total_in_connections = dask.config.get(
            "distributed.worker.connections.incoming"
        )
        self.comm_threshold_bytes = 10e6
        self.comm_nbytes = 0
        self._missing_dep_flight = set()

        self.threads = {}

        self.active_threads_lock = threading.Lock()
        self.active_threads = {}
        self.active_keys = set()
        self.profile_keys = defaultdict(profile.create)
        self.profile_keys_history = deque(maxlen=3600)
        self.profile_recent = profile.create()
        self.profile_history = deque(maxlen=3600)

        self.generation = 0

        self.ready = []
        self.constrained = deque()
        self._executing = set()
        self._in_flight_tasks = set()
        self.executed_count = 0
        self.long_running = set()

        self.recent_messages_log = deque(
            maxlen=dask.config.get("distributed.comm.recent-messages-log-length")
        )
        self.target_message_size = 50e6  # 50 MB

        self.log = deque(maxlen=100000)
        if validate is None:
            validate = dask.config.get("distributed.scheduler.validate")
        self.validate = validate
        self._transitions_table = {
            ("cancelled", "resumed"): self.transition_cancelled_resumed,
            ("cancelled", "fetch"): self.transition_cancelled_fetch,
            ("cancelled", "released"): self.transition_cancelled_released,
            ("cancelled", "waiting"): self.transition_cancelled_waiting,
            ("cancelled", "forgotten"): self.transition_cancelled_forgotten,
            ("cancelled", "memory"): self.transition_cancelled_memory,
            ("cancelled", "error"): self.transition_generic_error,
            ("resumed", "memory"): self.transition_generic_memory,
            ("resumed", "error"): self.transition_generic_error,
            ("resumed", "released"): self.transition_generic_released,
            ("resumed", "waiting"): self.transition_rescheduled_next,
            ("resumed", "fetch"): self.transition_rescheduled_next,
            ("constrained", "executing"): self.transition_constrained_executing,
            ("constrained", "released"): self.transition_constrained_released,
            ("error", "released"): self.transition_generic_released,
            ("executing", "error"): self.transition_executing_error,
            ("executing", "long-running"): self.transition_executing_long_running,
            ("executing", "memory"): self.transition_executing_memory,
            ("executing", "released"): self.transition_executing_released,
            ("executing", "rescheduled"): self.transition_executing_rescheduled,
            ("fetch", "flight"): self.transition_fetch_flight,
            ("fetch", "missing"): self.transition_fetch_missing,
            ("fetch", "released"): self.transition_generic_released,
            ("flight", "error"): self.transition_flight_error,
            ("flight", "fetch"): self.transition_flight_fetch,
            ("flight", "memory"): self.transition_flight_memory,
            ("flight", "released"): self.transition_flight_released,
            ("long-running", "error"): self.transition_generic_error,
            ("long-running", "memory"): self.transition_long_running_memory,
            ("long-running", "rescheduled"): self.transition_executing_rescheduled,
            ("long-running", "released"): self.transition_executing_released,
            ("memory", "released"): self.transition_memory_released,
            ("missing", "fetch"): self.transition_missing_fetch,
            ("missing", "released"): self.transition_missing_released,
            ("missing", "error"): self.transition_generic_error,
            ("ready", "error"): self.transition_generic_error,
            ("ready", "executing"): self.transition_ready_executing,
            ("ready", "released"): self.transition_generic_released,
            ("released", "error"): self.transition_generic_error,
            ("released", "fetch"): self.transition_released_fetch,
            ("released", "forgotten"): self.transition_released_forgotten,
            ("released", "memory"): self.transition_released_memory,
            ("released", "waiting"): self.transition_released_waiting,
            ("waiting", "constrained"): self.transition_waiting_constrained,
            ("waiting", "ready"): self.transition_waiting_ready,
            ("waiting", "released"): self.transition_generic_released,
        }

        self._transition_counter = 0
        self.incoming_transfer_log = deque(maxlen=100000)
        self.incoming_count = 0
        self.outgoing_transfer_log = deque(maxlen=100000)
        self.outgoing_count = 0
        self.outgoing_current_count = 0
        self.repetitively_busy = 0
        self.bandwidth = parse_bytes(dask.config.get("distributed.scheduler.bandwidth"))
        self.bandwidth_workers = defaultdict(
            lambda: (0, 0)
        )  # bw/count recent transfers
        self.bandwidth_types = defaultdict(lambda: (0, 0))  # bw/count recent transfers
        self.latency = 0.001
        self._client = None

        if profile_cycle_interval is None:
            profile_cycle_interval = dask.config.get("distributed.worker.profile.cycle")
        profile_cycle_interval = parse_timedelta(profile_cycle_interval, default="ms")

        self._setup_logging(logger)

        if local_dir is not None:
            warnings.warn("The local_dir keyword has moved to local_directory")
            local_directory = local_dir

        if not local_directory:
            local_directory = dask.config.get("temporary-directory") or os.getcwd()

        os.makedirs(local_directory, exist_ok=True)
        local_directory = os.path.join(local_directory, "dask-worker-space")

        with warn_on_duration(
            "1s",
            "Creating scratch directories is taking a surprisingly long time. "
            "This is often due to running workers on a network file system. "
            "Consider specifying a local-directory to point workers to write "
            "scratch data to a local disk.",
        ):
            self._workspace = WorkSpace(os.path.abspath(local_directory))
            self._workdir = self._workspace.new_work_dir(prefix="worker-")
            self.local_directory = self._workdir.dir_path

        if not preload:
            preload = dask.config.get("distributed.worker.preload")
        if not preload_argv:
            preload_argv = dask.config.get("distributed.worker.preload-argv")
        self.preloads = preloading.process_preloads(
            self, preload, preload_argv, file_dir=self.local_directory
        )

        if scheduler_file:
            cfg = json_load_robust(scheduler_file)
            scheduler_addr = cfg["address"]
        elif scheduler_ip is None and dask.config.get("scheduler-address", None):
            scheduler_addr = dask.config.get("scheduler-address")
        elif scheduler_port is None:
            scheduler_addr = coerce_to_address(scheduler_ip)
        else:
            scheduler_addr = coerce_to_address((scheduler_ip, scheduler_port))
        self.contact_address = contact_address

        if protocol is None:
            protocol_address = scheduler_addr.split("://")
            if len(protocol_address) == 2:
                protocol = protocol_address[0]

        self._start_port = port
        self._start_host = host
        if host:
            # Helpful error message if IPv6 specified incorrectly
            _, host_address = parse_address(host)
            if host_address.count(":") > 1 and not host_address.startswith("["):
                raise ValueError(
                    "Host address with IPv6 must be bracketed like '[::1]'; "
                    f"got {host_address}"
                )
        self._interface = interface
        self._protocol = protocol

        if ncores is not None:
            warnings.warn("the ncores= parameter has moved to nthreads=")
            nthreads = ncores

        self.nthreads = nthreads or CPU_COUNT
        if resources is None:
            resources = dask.config.get("distributed.worker.resources", None)

        self.total_resources = resources or {}
        self.available_resources = (resources or {}).copy()
        self.death_timeout = parse_timedelta(death_timeout)

        self.extensions = {}
        if silence_logs:
            silence_logging(level=silence_logs)

        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("worker")

        self.memory_limit = parse_memory_limit(memory_limit, self.nthreads)

        if "memory_target_fraction" in kwargs:
            self.memory_target_fraction = kwargs.pop("memory_target_fraction")
        else:
            self.memory_target_fraction = dask.config.get(
                "distributed.worker.memory.target"
            )
        if "memory_spill_fraction" in kwargs:
            self.memory_spill_fraction = kwargs.pop("memory_spill_fraction")
        else:
            self.memory_spill_fraction = dask.config.get(
                "distributed.worker.memory.spill"
            )
        if "memory_pause_fraction" in kwargs:
            self.memory_pause_fraction = kwargs.pop("memory_pause_fraction")
        else:
            self.memory_pause_fraction = dask.config.get(
                "distributed.worker.memory.pause"
            )

        if isinstance(data, MutableMapping):
            self.data = data
        elif callable(data):
            self.data = data()
        elif isinstance(data, tuple):
            self.data = data[0](**data[1])
        elif self.memory_limit and (
            self.memory_target_fraction or self.memory_spill_fraction
        ):
            from .spill import SpillBuffer

            self.data = SpillBuffer(
                os.path.join(self.local_directory, "storage"),
                target=int(
                    self.memory_limit
                    * (self.memory_target_fraction or self.memory_spill_fraction)
                )
                or sys.maxsize,
            )
        else:
            self.data = {}

        self.actors = {}
        self.loop = loop or IOLoop.current()
        self.reconnect = reconnect

        # Common executors always available
        self.executors: dict[str, concurrent.futures.Executor] = {
            "offload": utils._offload_executor,
            "actor": ThreadPoolExecutor(1, thread_name_prefix="Dask-Actor-Threads"),
        }
        if nvml.device_get_count() > 0:
            self.executors["gpu"] = ThreadPoolExecutor(
                1, thread_name_prefix="Dask-GPU-Threads"
            )

        # Find the default executor
        if executor == "offload":
            self.executors["default"] = self.executors["offload"]
        elif isinstance(executor, dict):
            self.executors.update(executor)
        elif executor is not None:
            self.executors["default"] = executor
        if "default" not in self.executors:
            self.executors["default"] = ThreadPoolExecutor(
                self.nthreads, thread_name_prefix="Dask-Default-Threads"
            )

        self.batched_stream = BatchedSend(interval="2ms", loop=self.loop)
        self.name = name
        self.scheduler_delay = 0
        self.stream_comms = {}
        self.heartbeat_active = False
        self._ipython_kernel = None

        if self.local_directory not in sys.path:
            sys.path.insert(0, self.local_directory)

        self.services = {}
        self.service_specs = services or {}

        self._dashboard_address = dashboard_address
        self._dashboard = dashboard
        self._http_prefix = http_prefix

        self.metrics = dict(metrics) if metrics else {}
        self.startup_information = (
            dict(startup_information) if startup_information else {}
        )

        self.low_level_profiler = low_level_profiler

        handlers = {
            "gather": self.gather,
            "run": self.run,
            "run_coroutine": self.run_coroutine,
            "get_data": self.get_data,
            "update_data": self.update_data,
            "free_keys": self.handle_free_keys,
            "terminate": self.close,
            "ping": pingpong,
            "upload_file": self.upload_file,
            "start_ipython": self.start_ipython,
            "call_stack": self.get_call_stack,
            "profile": self.get_profile,
            "profile_metadata": self.get_profile_metadata,
            "get_logs": self.get_logs,
            "keys": self.keys,
            "versions": self.versions,
            "actor_execute": self.actor_execute,
            "actor_attribute": self.actor_attribute,
            "plugin-add": self.plugin_add,
            "plugin-remove": self.plugin_remove,
            "get_monitor_info": self.get_monitor_info,
        }

        stream_handlers = {
            "close": self.close,
            "cancel-compute": self.handle_cancel_compute,
            "acquire-replicas": self.handle_acquire_replicas,
            "compute-task": self.handle_compute_task,
            "free-keys": self.handle_free_keys,
            "remove-replicas": self.handle_remove_replicas,
            "steal-request": self.handle_steal_request,
            "worker-status-change": self.handle_worker_status_change,
        }

        super().__init__(
            handlers=handlers,
            stream_handlers=stream_handlers,
            io_loop=self.loop,
            connection_args=self.connection_args,
            **kwargs,
        )

        self.scheduler = self.rpc(scheduler_addr)
        self.execution_state = {
            "scheduler": self.scheduler.address,
            "ioloop": self.loop,
            "worker": self,
        }

        pc = PeriodicCallback(self.heartbeat, 1000)
        self.periodic_callbacks["heartbeat"] = pc
        pc = PeriodicCallback(
            lambda: self.batched_stream.send({"op": "keep-alive"}), 60000
        )
        self.periodic_callbacks["keep-alive"] = pc

        pc = PeriodicCallback(self.find_missing, 1000)
        self.periodic_callbacks["find-missing"] = pc

        self._suspicious_count_limit = 10
        self._address = contact_address

        self.memory_monitor_interval = parse_timedelta(
            memory_monitor_interval, default="ms"
        )
        if self.memory_limit:
            self._memory_monitoring = False
            pc = PeriodicCallback(
                self.memory_monitor, self.memory_monitor_interval * 1000
            )
            self.periodic_callbacks["memory"] = pc

        if extensions is None:
            extensions = DEFAULT_EXTENSIONS
        for ext in extensions:
            ext(self)

        self._throttled_gc = ThrottledGC(logger=logger)

        setproctitle("dask-worker [not started]")

        profile_trigger_interval = parse_timedelta(
            dask.config.get("distributed.worker.profile.interval"), default="ms"
        )
        pc = PeriodicCallback(self.trigger_profile, profile_trigger_interval * 1000)
        self.periodic_callbacks["profile"] = pc

        pc = PeriodicCallback(self.cycle_profile, profile_cycle_interval * 1000)
        self.periodic_callbacks["profile-cycle"] = pc

        self.plugins = {}
        self._pending_plugins = plugins

        self.lifetime = lifetime or dask.config.get(
            "distributed.worker.lifetime.duration"
        )
        lifetime_stagger = lifetime_stagger or dask.config.get(
            "distributed.worker.lifetime.stagger"
        )
        self.lifetime_restart = lifetime_restart or dask.config.get(
            "distributed.worker.lifetime.restart"
        )
        if isinstance(self.lifetime, str):
            self.lifetime = parse_timedelta(self.lifetime)
        if isinstance(lifetime_stagger, str):
            lifetime_stagger = parse_timedelta(lifetime_stagger)
        if self.lifetime:
            self.lifetime += (random.random() * 2 - 1) * lifetime_stagger
            self.io_loop.call_later(self.lifetime, self.close_gracefully)

        Worker._instances.add(self)

    ##################
    # Administrative #
    ##################

    def __repr__(self):
        return "<%s: %r, %s, %s, stored: %d, running: %d/%d, ready: %d, comm: %d, waiting: %d>" % (
            self.__class__.__name__,
            self.address,
            self.name,
            self.status,
            len(self.data),
            self.executing_count,
            self.nthreads,
            len(self.ready),
            self.in_flight_tasks,
            self.waiting_for_data_count,
        )

    @property
    def logs(self):
        return self._deque_handler.deque

    def log_event(self, topic, msg):
        self.batched_stream.send(
            {
                "op": "log-event",
                "topic": topic,
                "msg": msg,
            }
        )

    @property
    def executing_count(self) -> int:
        return len(self._executing)

    @property
    def in_flight_tasks(self) -> int:
        return len(self._in_flight_tasks)

    @property
    def worker_address(self):
        """For API compatibility with Nanny"""
        return self.address

    @property
    def local_dir(self):
        """For API compatibility with Nanny"""
        warnings.warn(
            "The local_dir attribute has moved to local_directory", stacklevel=2
        )
        return self.local_directory

    @property
    def executor(self):
        return self.executors["default"]

    @ServerNode.status.setter  # type: ignore
    def status(self, value):
        """Override Server.status to notify the Scheduler of status changes"""
        ServerNode.status.__set__(self, value)
        self._send_worker_status_change()

    def _send_worker_status_change(self) -> None:
        if (
            self.batched_stream
            and self.batched_stream.comm
            and not self.batched_stream.comm.closed()
        ):
            self.batched_stream.send(
                {"op": "worker-status-change", "status": self._status.name}
            )
        elif self._status != Status.closed:
            self.loop.call_later(0.05, self._send_worker_status_change)

    async def get_metrics(self):
        out = dict(
            executing=self.executing_count,
            in_memory=len(self.data),
            ready=len(self.ready),
            in_flight=self.in_flight_tasks,
            bandwidth={
                "total": self.bandwidth,
                "workers": dict(self.bandwidth_workers),
                "types": keymap(typename, self.bandwidth_types),
            },
            spilled_nbytes=getattr(self.data, "spilled_total", 0),
        )
        out.update(self.monitor.recent())

        for k, metric in self.metrics.items():
            try:
                result = metric(self)
                if isawaitable(result):
                    result = await result
                # In case of collision, prefer core metrics
                out.setdefault(k, result)
            except Exception:  # TODO: log error once
                pass

        return out

    async def get_startup_information(self):
        result = {}
        for k, f in self.startup_information.items():
            try:
                v = f(self)
                if isawaitable(v):
                    v = await v
                result[k] = v
            except Exception:  # TODO: log error once
                pass

        return result

    def identity(self, comm=None):
        return {
            "type": type(self).__name__,
            "id": self.id,
            "scheduler": self.scheduler.address,
            "nthreads": self.nthreads,
            "ncores": self.nthreads,  # backwards compatibility
            "memory_limit": self.memory_limit,
        }

    #####################
    # External Services #
    #####################

    async def _register_with_scheduler(self):
        self.periodic_callbacks["keep-alive"].stop()
        self.periodic_callbacks["heartbeat"].stop()
        start = time()
        if self.contact_address is None:
            self.contact_address = self.address
        logger.info("-" * 49)
        while True:
            try:
                _start = time()
                comm = await connect(self.scheduler.address, **self.connection_args)
                comm.name = "Worker->Scheduler"
                comm._server = weakref.ref(self)
                await comm.write(
                    dict(
                        op="register-worker",
                        reply=False,
                        address=self.contact_address,
                        status=self.status.name,
                        keys=list(self.data),
                        nthreads=self.nthreads,
                        name=self.name,
                        nbytes={
                            ts.key: ts.get_nbytes()
                            for ts in self.tasks.values()
                            # Only if the task is in memory this is a sensible
                            # result since otherwise it simply submits the
                            # default value
                            if ts.state == "memory"
                        },
                        types={k: typename(v) for k, v in self.data.items()},
                        now=time(),
                        resources=self.total_resources,
                        memory_limit=self.memory_limit,
                        local_directory=self.local_directory,
                        services=self.service_ports,
                        nanny=self.nanny,
                        pid=os.getpid(),
                        versions=get_versions(),
                        metrics=await self.get_metrics(),
                        extra=await self.get_startup_information(),
                    ),
                    serializers=["msgpack"],
                )
                future = comm.read(deserializers=["msgpack"])

                response = await future
                if response.get("warning"):
                    logger.warning(response["warning"])

                _end = time()
                middle = (_start + _end) / 2
                self._update_latency(_end - start)
                self.scheduler_delay = response["time"] - middle
                self.status = Status.running
                break
            except OSError:
                logger.info("Waiting to connect to: %26s", self.scheduler.address)
                await asyncio.sleep(0.1)
            except TimeoutError:
                logger.info("Timed out when connecting to scheduler")
        if response["status"] != "OK":
            raise ValueError(f"Unexpected response from register: {response!r}")
        else:
            await asyncio.gather(
                *(
                    self.plugin_add(name=name, plugin=plugin)
                    for name, plugin in response["worker-plugins"].items()
                )
            )

            logger.info("        Registered to: %26s", self.scheduler.address)
            logger.info("-" * 49)

        self.batched_stream.start(comm)
        self.periodic_callbacks["keep-alive"].start()
        self.periodic_callbacks["heartbeat"].start()
        self.loop.add_callback(self.handle_scheduler, comm)

    def _update_latency(self, latency):
        self.latency = latency * 0.05 + self.latency * 0.95
        if self.digests is not None:
            self.digests["latency"].add(latency)

    async def heartbeat(self):
        if self.heartbeat_active:
            logger.debug("Heartbeat skipped: channel busy")
            return
        self.heartbeat_active = True
        logger.debug("Heartbeat: %s", self.address)
        try:
            start = time()
            response = await retry_operation(
                self.scheduler.heartbeat_worker,
                address=self.contact_address,
                now=start,
                metrics=await self.get_metrics(),
                executing={
                    key: start - self.tasks[key].start_time
                    for key in self.active_keys
                    if key in self.tasks
                },
            )
            end = time()
            middle = (start + end) / 2

            self._update_latency(end - start)

            if response["status"] == "missing":
                # If running, wait 0.5s and then re-register self. Otherwise just exit.
                start = time()
                while self.status in RUNNING:
                    if time() >= start + 0.5:
                        await self._register_with_scheduler()
                        return
                    await asyncio.sleep(0.01)
                return

            self.scheduler_delay = response["time"] - middle
            self.periodic_callbacks["heartbeat"].callback_time = (
                response["heartbeat-interval"] * 1000
            )
            self.bandwidth_workers.clear()
            self.bandwidth_types.clear()
        except CommClosedError:
            logger.warning("Heartbeat to scheduler failed", exc_info=True)
            if not self.reconnect:
                await self.close(report=False)
        except OSError as e:
            # Scheduler is gone. Respect distributed.comm.timeouts.connect
            if "Timed out trying to connect" in str(e):
                await self.close(report=False)
            else:
                raise e
        finally:
            self.heartbeat_active = False

    async def handle_scheduler(self, comm):
        try:
            await self.handle_stream(
                comm, every_cycle=[self.ensure_communicating, self.ensure_computing]
            )
        except Exception as e:
            logger.exception(e)
            raise
        finally:
            if self.reconnect and self.status in RUNNING:
                logger.info("Connection to scheduler broken.  Reconnecting...")
                self.loop.add_callback(self.heartbeat)
            else:
                await self.close(report=False)

    def start_ipython(self, comm):
        """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={"worker": self}, log=logger
            )
        return self._ipython_kernel.get_connection_info()

    async def upload_file(self, comm, filename=None, data=None, load=True):
        out_filename = os.path.join(self.local_directory, filename)

        def func(data):
            if isinstance(data, str):
                data = data.encode()
            with open(out_filename, "wb") as f:
                f.write(data)
                f.flush()
            return data

        if len(data) < 10000:
            data = func(data)
        else:
            data = await offload(func, data)

        if load:
            try:
                import_file(out_filename)
                cache_loads.data.clear()
            except Exception as e:
                logger.exception(e)
                raise e

        return {"status": "OK", "nbytes": len(data)}

    def keys(self, comm=None):
        return list(self.data)

    async def gather(self, comm=None, who_has=None):
        who_has = {
            k: [coerce_to_address(addr) for addr in v]
            for k, v in who_has.items()
            if k not in self.data
        }
        result, missing_keys, missing_workers = await gather_from_workers(
            who_has, rpc=self.rpc, who=self.address
        )
        self.update_data(data=result, report=False)
        if missing_keys:
            logger.warning(
                "Could not find data: %s on workers: %s (who_has: %s)",
                missing_keys,
                missing_workers,
                who_has,
            )
            return {"status": "partial-fail", "keys": missing_keys}
        else:
            return {"status": "OK"}

    def get_monitor_info(self, comm=None, recent=False, start=0):
        result = dict(
            range_query=(
                self.monitor.recent()
                if recent
                else self.monitor.range_query(start=start)
            ),
            count=self.monitor.count,
            last_time=self.monitor.last_time,
        )
        if nvml.device_get_count() > 0:
            result["gpu_name"] = self.monitor.gpu_name
            result["gpu_memory_total"] = self.monitor.gpu_memory_total
        return result

    #############
    # Lifecycle #
    #############

    async def start(self):
        if self.status and self.status in (
            Status.closed,
            Status.closing,
            Status.closing_gracefully,
        ):
            return
        assert self.status is Status.undefined, self.status

        await super().start()

        enable_gc_diagnosis()
        thread_state.on_event_loop_thread = True

        ports = parse_ports(self._start_port)
        for port in ports:
            start_address = address_from_user_args(
                host=self._start_host,
                port=port,
                interface=self._interface,
                protocol=self._protocol,
                security=self.security,
            )
            kwargs = self.security.get_listen_args("worker")
            if self._protocol in ("tcp", "tls"):
                kwargs = kwargs.copy()
                kwargs["default_host"] = get_ip(
                    get_address_host(self.scheduler.address)
                )
            try:
                await self.listen(start_address, **kwargs)
            except OSError as e:
                if len(ports) > 1 and e.errno == errno.EADDRINUSE:
                    continue
                else:
                    raise
            else:
                self._start_address = start_address
                break
        else:
            raise ValueError(
                f"Could not start Worker on host {self._start_host}"
                f"with port {self._start_port}"
            )

        # Start HTTP server associated with this Worker node
        routes = get_handlers(
            server=self,
            modules=dask.config.get("distributed.worker.http.routes"),
            prefix=self._http_prefix,
        )
        self.start_http_server(routes, self._dashboard_address)
        if self._dashboard:
            try:
                import distributed.dashboard.worker
            except ImportError:
                logger.debug("To start diagnostics web server please install Bokeh")
            else:
                distributed.dashboard.worker.connect(
                    self.http_application,
                    self.http_server,
                    self,
                    prefix=self._http_prefix,
                )
        self.ip = get_address_host(self.address)

        if self.name is None:
            self.name = self.address

        for preload in self.preloads:
            await preload.start()

        # Services listen on all addresses
        # Note Nanny is not a "real" service, just some metadata
        # passed in service_ports...
        self.start_services(self.ip)

        try:
            listening_address = "%s%s:%d" % (self.listener.prefix, self.ip, self.port)
        except Exception:
            listening_address = f"{self.listener.prefix}{self.ip}"

        logger.info("      Start worker at: %26s", self.address)
        logger.info("         Listening to: %26s", listening_address)
        for k, v in self.service_ports.items():
            logger.info("  {:>16} at: {:>26}".format(k, self.ip + ":" + str(v)))
        logger.info("Waiting to connect to: %26s", self.scheduler.address)
        logger.info("-" * 49)
        logger.info("              Threads: %26d", self.nthreads)
        if self.memory_limit:
            logger.info("               Memory: %26s", format_bytes(self.memory_limit))
        logger.info("      Local Directory: %26s", self.local_directory)

        setproctitle("dask-worker [%s]" % self.address)

        await asyncio.gather(
            *(self.plugin_add(plugin=plugin) for plugin in self._pending_plugins)
        )
        self._pending_plugins = ()

        await self._register_with_scheduler()

        self.start_periodic_callbacks()
        return self

    def _close(self, *args, **kwargs):
        warnings.warn("Worker._close has moved to Worker.close", stacklevel=2)
        return self.close(*args, **kwargs)

    async def close(
        self, report=True, timeout=30, nanny=True, executor_wait=True, safe=False
    ):
        with log_errors():
            if self.status in (Status.closed, Status.closing):
                await self.finished()
                return

            self.reconnect = False
            disable_gc_diagnosis()

            try:
                logger.info("Stopping worker at %s", self.address)
            except ValueError:  # address not available if already closed
                logger.info("Stopping worker")
            if self.status not in RUNNING:
                logger.info("Closed worker has not yet started: %s", self.status)
            self.status = Status.closing

            for preload in self.preloads:
                await preload.teardown()

            if nanny and self.nanny:
                with self.rpc(self.nanny) as r:
                    await r.close_gracefully()

            setproctitle("dask-worker [closing]")

            teardowns = [
                plugin.teardown(self)
                for plugin in self.plugins.values()
                if hasattr(plugin, "teardown")
            ]

            await asyncio.gather(*(td for td in teardowns if isawaitable(td)))

            for pc in self.periodic_callbacks.values():
                pc.stop()

            if self._client:
                # If this worker is the last one alive, clean up the worker
                # initialized clients
                if not any(
                    w for w in Worker._instances if w != self and w.status in RUNNING
                ):
                    for c in Worker._initialized_clients:
                        # Regardless of what the client was initialized with
                        # we'll require the result as a future. This is
                        # necessary since the heursitics of asynchronous are not
                        # reliable and we might deadlock here
                        c._asynchronous = True
                        if c.asynchronous:
                            await c.close()
                        else:
                            # There is still the chance that even with us
                            # telling the client to be async, itself will decide
                            # otherwise
                            c.close()

            with suppress(EnvironmentError, TimeoutError):
                if report and self.contact_address is not None:
                    await asyncio.wait_for(
                        self.scheduler.unregister(
                            address=self.contact_address, safe=safe
                        ),
                        timeout,
                    )
            await self.scheduler.close_rpc()
            self._workdir.release()

            self.stop_services()

            if (
                self.batched_stream
                and self.batched_stream.comm
                and not self.batched_stream.comm.closed()
            ):
                self.batched_stream.send({"op": "close-stream"})

            if self.batched_stream:
                with suppress(TimeoutError):
                    await self.batched_stream.close(timedelta(seconds=timeout))

            for executor in self.executors.values():
                if executor is utils._offload_executor:
                    continue  # Never shutdown the offload executor
                if isinstance(executor, ThreadPoolExecutor):
                    executor._work_queue.queue.clear()
                    executor.shutdown(wait=executor_wait, timeout=timeout)
                else:
                    executor.shutdown(wait=executor_wait)

            self.stop()
            await self.rpc.close()

            self.status = Status.closed
            await super().close()

            setproctitle("dask-worker [closed]")
        return "OK"

    async def close_gracefully(self, restart=None):
        """Gracefully shut down a worker

        This first informs the scheduler that we're shutting down, and asks it
        to move our data elsewhere.  Afterwards, we close as normal
        """
        if self.status in (Status.closing, Status.closing_gracefully):
            await self.finished()

        if self.status == Status.closed:
            return

        if restart is None:
            restart = self.lifetime_restart

        logger.info("Closing worker gracefully: %s", self.address)
        # Scheduler.retire_workers will set the status to closing_gracefully and push it
        # back to this worker. We're additionally setting it here immediately to prevent
        # race conditions during the round trip.
        self.status = Status.closing_gracefully
        await self.scheduler.retire_workers(workers=[self.address], remove=False)
        await self.close(safe=True, nanny=not restart)

    async def terminate(self, comm=None, report=True, **kwargs):
        await self.close(report=report, **kwargs)
        return "OK"

    async def wait_until_closed(self):
        warnings.warn("wait_until_closed has moved to finished()")
        await self.finished()
        assert self.status == Status.closed

    ################
    # Worker Peers #
    ################

    def send_to_worker(self, address, msg):
        if address not in self.stream_comms:
            bcomm = BatchedSend(interval="1ms", loop=self.loop)
            self.stream_comms[address] = bcomm

            async def batched_send_connect():
                comm = await connect(
                    address, **self.connection_args  # TODO, serialization
                )
                comm.name = "Worker->Worker"
                await comm.write({"op": "connection_stream"})

                bcomm.start(comm)

            self.loop.add_callback(batched_send_connect)

        self.stream_comms[address].send(msg)

    async def get_data(
        self, comm, keys=None, who=None, serializers=None, max_connections=None
    ):
        start = time()

        if max_connections is None:
            max_connections = self.total_in_connections

        # Allow same-host connections more liberally
        if (
            max_connections
            and comm
            and get_address_host(comm.peer_address) == get_address_host(self.address)
        ):
            max_connections = max_connections * 2

        if self.status == Status.paused:
            max_connections = 1
            throttle_msg = " Throttling outgoing connections because worker is paused."
        else:
            throttle_msg = ""

        if (
            max_connections is not False
            and self.outgoing_current_count >= max_connections
        ):
            logger.debug(
                "Worker %s has too many open connections to respond to data request "
                "from %s (%d/%d).%s",
                self.address,
                who,
                self.outgoing_current_count,
                max_connections,
                throttle_msg,
            )
            return {"status": "busy"}

        self.outgoing_current_count += 1
        data = {k: self.data[k] for k in keys if k in self.data}

        if len(data) < len(keys):
            for k in set(keys) - set(data):
                if k in self.actors:
                    from .actor import Actor

                    data[k] = Actor(type(self.actors[k]), self.address, k, worker=self)

        msg = {"status": "OK", "data": {k: to_serialize(v) for k, v in data.items()}}
        nbytes = {k: self.tasks[k].nbytes for k in data if k in self.tasks}
        stop = time()
        if self.digests is not None:
            self.digests["get-data-load-duration"].add(stop - start)
        start = time()

        try:
            compressed = await comm.write(msg, serializers=serializers)
            response = await comm.read(deserializers=serializers)
            assert response == "OK", response
        except OSError:
            logger.exception(
                "failed during get data with %s -> %s", self.address, who, exc_info=True
            )
            comm.abort()
            raise
        finally:
            self.outgoing_current_count -= 1
        stop = time()
        if self.digests is not None:
            self.digests["get-data-send-duration"].add(stop - start)

        total_bytes = sum(filter(None, nbytes.values()))

        self.outgoing_count += 1
        duration = (stop - start) or 0.5  # windows
        self.outgoing_transfer_log.append(
            {
                "start": start + self.scheduler_delay,
                "stop": stop + self.scheduler_delay,
                "middle": (start + stop) / 2,
                "duration": duration,
                "who": who,
                "keys": nbytes,
                "total": total_bytes,
                "compressed": compressed,
                "bandwidth": total_bytes / duration,
            }
        )

        return Status.dont_reply

    ###################
    # Local Execution #
    ###################

    def update_data(
        self, comm=None, data=None, report=True, serializers=None, stimulus_id=None
    ):
        if stimulus_id is None:
            stimulus_id = f"update-data-{time()}"
        recommendations = {}
        scheduler_messages = []
        for key, value in data.items():
            try:
                ts = self.tasks[key]
                recommendations[ts] = ("memory", value)
            except KeyError:
                self.tasks[key] = ts = TaskState(key)
                recs, smsgs = self._put_key_in_memory(
                    ts, value, stimulus_id=stimulus_id
                )
                recommendations.update(recs)
                scheduler_messages += smsgs
                ts.priority = None
                ts.duration = None

            self.log.append((key, "receive-from-scatter"))

        if report:
            scheduler_messages.append(
                {"op": "add-keys", "keys": list(data), "stimulus_id": stimulus_id}
            )

        self.transitions(recommendations, stimulus_id=stimulus_id)
        for msg in scheduler_messages:
            self.batched_stream.send(msg)
        return {"nbytes": {k: sizeof(v) for k, v in data.items()}, "status": "OK"}

    def handle_free_keys(self, comm=None, keys=None, reason=None):
        """
        Handler to be called by the scheduler.

        The given keys are no longer referred to and required by the scheduler.
        The worker is now allowed to release the key, if applicable.

        This does not guarantee that the memory is released since the worker may
        still decide to hold on to the data and task since it is required by an
        upstream dependency.
        """
        self.log.append(("free-keys", keys, reason))
        recommendations = {}
        for key in keys:
            ts = self.tasks.get(key)
            if ts:
                recommendations[ts] = "released" if ts.dependents else "forgotten"

        self.transitions(recommendations, stimulus_id=reason)

    def handle_remove_replicas(self, keys, stimulus_id):
        """Stream handler notifying the worker that it might be holding unreferenced,
        superfluous data.

        This should not actually happen during ordinary operations and is only intended
        to correct any erroneous state. An example where this is necessary is if a
        worker fetches data for a downstream task but that task is released before the
        data arrives. In this case, the scheduler will notify the worker that it may be
        holding this unnecessary data, if the worker hasn't released the data itself,
        already.

        This handler does not guarantee the task nor the data to be actually
        released but only asks the worker to release the data on a best effort
        guarantee. This protects from race conditions where the given keys may
        already have been rescheduled for compute in which case the compute
        would win and this handler is ignored.

        For stronger guarantees, see handler free_keys
        """
        self.log.append(("remove-replicas", keys, stimulus_id))
        recommendations = {}

        rejected = []
        for key in keys:
            ts = self.tasks.get(key)
            if ts is None or ts.state != "memory":
                continue
            if not ts.is_protected():
                self.log.append(("remove-replica-confirmed", ts.key, stimulus_id))
                recommendations[ts] = "released" if ts.dependents else "forgotten"
            else:
                rejected.append(key)

        if rejected:
            self.log.append(("remove-replica-rejected", rejected, stimulus_id))
            self.batched_stream.send(
                {"op": "add-keys", "keys": rejected, "stimulus_id": stimulus_id}
            )

        self.transitions(recommendations=recommendations, stimulus_id=stimulus_id)

        return "OK"

    async def set_resources(self, **resources):
        for r, quantity in resources.items():
            if r in self.total_resources:
                self.available_resources[r] += quantity - self.total_resources[r]
            else:
                self.available_resources[r] = quantity
            self.total_resources[r] = quantity

        await retry_operation(
            self.scheduler.set_resources,
            resources=self.total_resources,
            worker=self.contact_address,
        )

    ###################
    # Task Management #
    ###################

    def handle_cancel_compute(self, key, reason):
        """
        Cancel a task on a best effort basis. This is only possible while a task
        is in state `waiting` or `ready`.
        Nothing will happen otherwise.
        """
        ts = self.tasks.get(key)
        if ts and ts.state in ("waiting", "ready"):
            self.log.append((key, "cancel-compute", reason))
            ts.scheduler_holds_ref = False
            # All possible dependents of TS should not be in state Processing on
            # scheduler side and therefore should not be assigned to a worker,
            # yet.
            assert not ts.dependents
            self.transition(ts, "released", stimulus_id=reason)

    def handle_acquire_replicas(
        self, comm=None, keys=None, priorities=None, who_has=None, stimulus_id=None
    ):
        recommendations = {}
        scheduler_msgs = []
        for k in keys:
            recs, smsgs = self.register_acquire_internal(
                k,
                stimulus_id=stimulus_id,
                priority=priorities[k],
            )
            recommendations.update(recs)
            scheduler_msgs += smsgs

        self.update_who_has(who_has, stimulus_id=stimulus_id)

        for msg in scheduler_msgs:
            self.batched_stream.send(msg)
        self.transitions(recommendations, stimulus_id=stimulus_id)

    def register_acquire_internal(self, key, priority, stimulus_id):
        try:
            ts = self.tasks[key]
            logger.debug(
                "Data task already known %s", {"task": ts, "stimulus_id": stimulus_id}
            )
        except KeyError:
            self.tasks[key] = ts = TaskState(key)

        self.log.append((key, "register-replica", ts.state, stimulus_id, time()))
        ts.priority = ts.priority or priority

        recommendations = {}
        scheduler_msgs = []
        if ts.state in ("released", "cancelled", "error"):
            recommendations[ts] = "fetch"

        return recommendations, scheduler_msgs

    def handle_compute_task(
        self,
        *,
        key,
        # FIXME: This will break protocol
        function=None,
        args=None,
        kwargs=None,
        task=no_value,
        who_has=None,
        nbytes=None,
        priority=None,
        duration=None,
        resource_restrictions=None,
        actor=False,
        annotations=None,
        stimulus_id=None,
    ):
        self.log.append((key, "compute-task", stimulus_id, time()))
        try:
            ts = self.tasks[key]
            logger.debug(
                "Asked to compute an already known task %s",
                {"task": ts, "stimulus_id": stimulus_id},
            )
        except KeyError:
            self.tasks[key] = ts = TaskState(key)

        ts.runspec = SerializedTask(function, args, kwargs, task)

        if priority is not None:
            priority = tuple(priority) + (self.generation,)
            self.generation -= 1

        if actor:
            self.actors[ts.key] = None

        ts.exception = None
        ts.traceback = None
        ts.exception_text = ""
        ts.traceback_text = ""
        ts.priority = priority
        ts.duration = duration
        if resource_restrictions:
            ts.resource_restrictions = resource_restrictions
        ts.annotations = annotations

        recommendations = {}
        scheduler_msgs = []
        for dependency in who_has:
            recs, smsgs = self.register_acquire_internal(
                key=dependency,
                stimulus_id=stimulus_id,
                priority=priority,
            )
            recommendations.update(recs)
            scheduler_msgs += smsgs
            dep_ts = self.tasks[dependency]

            # link up to child / parents
            ts.dependencies.add(dep_ts)
            dep_ts.dependents.add(ts)

        if ts.state in {"ready", "executing", "waiting", "resumed"}:
            pass
        elif ts.state == "memory":
            recommendations[ts] = "memory"
            scheduler_msgs.append(self.get_task_state_for_scheduler(ts))
        elif ts.state in {"released", "fetch", "flight", "missing", "cancelled"}:
            recommendations[ts] = "waiting"
        else:
            raise RuntimeError(f"Unexpected task state encountered {ts} {stimulus_id}")

        for msg in scheduler_msgs:
            self.batched_stream.send(msg)
        self.transitions(recommendations, stimulus_id=stimulus_id)

        # We received new info, that's great but not related to the compute-task
        # instruction
        self.update_who_has(who_has, stimulus_id=stimulus_id)
        if nbytes is not None:
            for key, value in nbytes.items():
                self.tasks[key].nbytes = value

    def transition_missing_fetch(self, ts, *, stimulus_id):
        self._missing_dep_flight.discard(ts)
        ts.state = "fetch"
        heapq.heappush(self.data_needed, (ts.priority, ts.key))
        return {}, []

    def transition_missing_released(self, ts, *, stimulus_id):
        self._missing_dep_flight.discard(ts)
        recommendations = self.release_key(ts.key, reason="missing->released")
        assert ts.key in self.tasks
        return recommendations, []

    def transition_fetch_missing(self, ts, *, stimulus_id):
        # handle_missing will append to self.data_needed if new workers are found
        ts.state = "missing"
        self._missing_dep_flight.add(ts)
        return {}, []

    def transition_released_fetch(self, ts, *, stimulus_id):
        for w in ts.who_has:
            self.pending_data_per_worker[w].append(ts.key)
        ts.state = "fetch"
        heapq.heappush(self.data_needed, (ts.priority, ts.key))
        return {}, []

    def transition_generic_released(self, ts, *, stimulus_id):
        recs = self.release_key(ts.key, reason=stimulus_id)
        return recs, []

    def transition_released_waiting(self, ts, *, stimulus_id):
        if self.validate:
            assert ts.state == "released"
            assert all(d.key in self.tasks for d in ts.dependencies)

        recommendations = {}
        ts.waiting_for_data.clear()
        for dep_ts in ts.dependencies:
            if not dep_ts.state == "memory":
                ts.waiting_for_data.add(dep_ts)
                dep_ts.waiters.add(ts)

        if ts.waiting_for_data:
            self.waiting_for_data_count += 1
        elif ts.resource_restrictions:
            recommendations[ts] = "constrained"
        else:
            recommendations[ts] = "ready"

        ts.state = "waiting"
        return recommendations, []

    def transition_fetch_flight(self, ts, worker, *, stimulus_id):
        if self.validate:
            assert ts.state == "fetch"
            assert ts.who_has
            assert ts.key not in self.data_needed

        ts.state = "flight"
        ts.coming_from = worker
        self._in_flight_tasks.add(ts)
        return {}, []

    def transition_memory_released(self, ts, *, stimulus_id):
        recs = self.release_key(ts.key, reason=stimulus_id)
        smsgs = [{"op": "release-worker-data", "key": ts.key}]
        return recs, smsgs

    def transition_waiting_constrained(self, ts, *, stimulus_id):
        if self.validate:
            assert ts.state == "waiting"
            assert not ts.waiting_for_data
            assert all(
                dep.key in self.data or dep.key in self.actors
                for dep in ts.dependencies
            )
            assert all(dep.state == "memory" for dep in ts.dependencies)
            assert ts.key not in self.ready
        ts.state = "constrained"
        self.constrained.append(ts.key)
        return {}, []

    def transition_long_running_rescheduled(self, ts, *, stimulus_id):
        recs = {ts: "released"}
        smsgs = [{"op": "reschedule", "key": ts.key, "worker": self.address}]
        return recs, smsgs

    def transition_executing_rescheduled(self, ts, *, stimulus_id):
        for resource, quantity in ts.resource_restrictions.items():
            self.available_resources[resource] += quantity
        self._executing.discard(ts)

        recs = {ts: "released"}
        smsgs = [{"op": "reschedule", "key": ts.key, "worker": self.address}]
        return recs, smsgs

    def transition_waiting_ready(self, ts, *, stimulus_id):
        if self.validate:
            assert ts.state == "waiting"
            assert ts.key not in self.ready
            assert not ts.waiting_for_data
            for dep in ts.dependencies:
                assert dep.key in self.data or dep.key in self.actors
                assert dep.state == "memory"

        ts.state = "ready"
        heapq.heappush(self.ready, (ts.priority, ts.key))

        return {}, []

    def transition_generic_error(
        self, ts, exception, traceback, exception_text, traceback_text, *, stimulus_id
    ):
        ts.exception = exception
        ts.traceback = traceback
        ts.exception_text = exception_text
        ts.traceback_text = traceback_text
        smsgs = [self.get_task_state_for_scheduler(ts)]
        ts.state = "error"
        return {}, smsgs

    def transition_executing_error(
        self, ts, exception, traceback, exception_text, traceback_text, *, stimulus_id
    ):
        for resource, quantity in ts.resource_restrictions.items():
            self.available_resources[resource] += quantity
        self._executing.discard(ts)
        return self.transition_generic_error(
            ts,
            exception,
            traceback,
            exception_text,
            traceback_text,
            stimulus_id=stimulus_id,
        )

    def transition_rescheduled_next(self, ts, *, stimulus_id):
        next_state = ts._next
        recs = self.release_key(ts.key, reason=stimulus_id)
        recs[ts] = next_state
        return recs, []

    def transition_cancelled_fetch(self, ts, *, stimulus_id):
        if ts.done:
            return {ts: "released"}, []
        elif ts._previous == "flight":
            ts.state = ts._previous
            return {}, []
        else:
            assert ts._previous == "executing"
            return {ts: ("resumed", "fetch")}, []

    def transition_cancelled_resumed(self, ts, next, *, stimulus_id):
        ts._next = next
        ts.state = "resumed"
        return {}, []

    def transition_cancelled_waiting(self, ts, *, stimulus_id):
        if ts.done:
            return {ts: "released"}, []
        elif ts._previous == "executing":
            ts.state = ts._previous
            return {}, []
        else:
            assert ts._previous == "flight"
            return {ts: ("resumed", "waiting")}, []

    def transition_cancelled_forgotten(self, ts, *, stimulus_id):
        ts._next = "forgotten"
        if not ts.done:
            return {}, []
        return {ts: "released"}, []

    def transition_cancelled_released(self, ts, *, stimulus_id):
        if not ts.done:
            ts._next = "released"
            return {}, []
        next_state = ts._next
        self._executing.discard(ts)
        self._in_flight_tasks.discard(ts)

        for resource, quantity in ts.resource_restrictions.items():
            self.available_resources[resource] += quantity
        recommendations = self.release_key(ts.key, reason=stimulus_id)
        recommendations[ts] = next_state or "released"
        return recommendations, []

    def transition_executing_released(self, ts, *, stimulus_id):
        ts._previous = ts.state
        # See https://github.com/dask/distributed/pull/5046#discussion_r685093940
        ts.state = "cancelled"
        ts.done = False
        return {}, []

    def transition_long_running_memory(self, ts, value=no_value, *, stimulus_id):
        self.executed_count += 1
        return self.transition_generic_memory(ts, value=value, stimulus_id=stimulus_id)

    def transition_generic_memory(self, ts, value=no_value, *, stimulus_id):
        if value is no_value and ts.key not in self.data:
            raise RuntimeError(
                f"Tried to transition task {ts} to `memory` without data available"
            )

        if ts.resource_restrictions is not None:
            for resource, quantity in ts.resource_restrictions.items():
                self.available_resources[resource] += quantity

        self._executing.discard(ts)
        self._in_flight_tasks.discard(ts)
        ts.coming_from = None

        recs, smsgs = self._put_key_in_memory(ts, value, stimulus_id=stimulus_id)
        smsgs.append(self.get_task_state_for_scheduler(ts))
        return recs, smsgs

    def transition_executing_memory(self, ts, value=no_value, *, stimulus_id):
        if self.validate:
            assert ts.state == "executing" or ts.key in self.long_running
            assert not ts.waiting_for_data
            assert ts.key not in self.ready

        self._executing.discard(ts)
        self.executed_count += 1
        return self.transition_generic_memory(ts, value=value, stimulus_id=stimulus_id)

    def transition_constrained_released(self, ts, *, stimulus_id):
        recs = self.release_key(ts.key, reason=stimulus_id)
        return recs, []

    def transition_constrained_executing(self, ts, *, stimulus_id):
        if self.validate:
            assert not ts.waiting_for_data
            assert ts.key not in self.data
            assert ts.state in READY
            assert ts.key not in self.ready
            for dep in ts.dependencies:
                assert dep.key in self.data or dep.key in self.actors

        for resource, quantity in ts.resource_restrictions.items():
            self.available_resources[resource] -= quantity
        ts.state = "executing"
        self._executing.add(ts)
        self.loop.add_callback(self.execute, ts.key, stimulus_id=stimulus_id)
        return {}, []

    def transition_ready_executing(self, ts, *, stimulus_id):
        if self.validate:
            assert not ts.waiting_for_data
            assert ts.key not in self.data
            assert ts.state in READY
            assert ts.key not in self.ready
            assert all(
                dep.key in self.data or dep.key in self.actors
                for dep in ts.dependencies
            )

        ts.state = "executing"
        self._executing.add(ts)
        self.loop.add_callback(self.execute, ts.key, stimulus_id=stimulus_id)
        return {}, []

    def transition_flight_fetch(self, ts, *, stimulus_id):
        self._in_flight_tasks.discard(ts)
        ts.coming_from = None

        for w in ts.who_has:
            self.pending_data_per_worker[w].append(ts.key)
        ts.state = "fetch"
        heapq.heappush(self.data_needed, (ts.priority, ts.key))

        return {}, []

    def transition_flight_error(
        self, ts, exception, traceback, exception_text, traceback_text, *, stimulus_id
    ):
        self._in_flight_tasks.discard(ts)
        ts.coming_from = None
        return self.transition_generic_error(
            ts,
            exception,
            traceback,
            exception_text,
            traceback_text,
            stimulus_id=stimulus_id,
        )

    def transition_flight_released(self, ts, *, stimulus_id):
        ts._previous = "flight"
        # See https://github.com/dask/distributed/pull/5046#discussion_r685093940
        ts.state = "cancelled"
        return {}, []

    def transition_cancelled_memory(self, ts, value, *, stimulus_id):
        return {ts: ts._next}, []

    def transition_executing_long_running(self, ts, compute_duration, *, stimulus_id):
        ts.state = "long-running"
        self._executing.discard(ts)
        self.long_running.add(ts.key)
        smsgs = [
            {
                "op": "long-running",
                "key": ts.key,
                "compute_duration": compute_duration,
            }
        ]

        self.io_loop.add_callback(self.ensure_computing)
        return {}, smsgs

    def transition_released_memory(self, ts, value, *, stimulus_id):
        recs, smsgs = self._put_key_in_memory(ts, value, stimulus_id=stimulus_id)
        smsgs.append({"op": "add-keys", "keys": [ts.key], "stimulus_id": stimulus_id})
        return recs, smsgs

    def transition_flight_memory(self, ts, value, *, stimulus_id):
        self._in_flight_tasks.discard(ts)
        ts.coming_from = None
        recs, smsgs = self._put_key_in_memory(ts, value, stimulus_id=stimulus_id)
        smsgs.append({"op": "add-keys", "keys": [ts.key], "stimulus_id": stimulus_id})
        return recs, smsgs

    def transition_released_forgotten(self, ts, *, stimulus_id):
        recommendations = {}
        # Dependents _should_ be released by the scheduler before this
        if self.validate:
            assert not any(d.state != "forgotten" for d in ts.dependents)
        for dep in ts.dependencies:
            dep.dependents.discard(ts)
            if dep.state == "released" and not dep.dependents:
                recommendations[dep] = "forgotten"

        # Mark state as forgotten in case it is still referenced
        ts.state = "forgotten"
        self.tasks.pop(ts.key, None)
        return recommendations, []

    def _transition(self, ts, finish, *args, stimulus_id, **kwargs):
        if isinstance(finish, tuple):
            # the concatenated transition path might need to access the tuple
            assert not args
            finish, *args = finish

        if ts is None or ts.state == finish:
            return {}, []

        start = ts.state
        func = self._transitions_table.get((start, finish))

        if func is not None:
            self._transition_counter += 1
            recs, smsgs = func(ts, *args, stimulus_id=stimulus_id, **kwargs)
            self._notify_plugins("transition", ts.key, start, finish, **kwargs)

        elif "released" not in (start, finish):
            # start -> "released" -> finish
            try:
                recs, smsgs = self._transition(ts, "released", stimulus_id=stimulus_id)
                v = recs.get(ts, (finish, *args))
                if isinstance(v, tuple):
                    v_state, *v_args = v
                else:
                    v_state, v_args = v, ()
                b_recs, b_smsgs = self._transition(
                    ts, v_state, *v_args, stimulus_id=stimulus_id
                )
                recs.update(b_recs)
                smsgs += b_smsgs
            except InvalidTransition:
                raise InvalidTransition(
                    f"Impossible transition from {start} to {finish} for {ts.key}"
                ) from None

        else:
            raise InvalidTransition(
                f"Impossible transition from {start} to {finish} for {ts.key}"
            )

        self.log.append(
            (
                ts.key,
                start,
                ts.state,
                {ts.key: new for ts, new in recs.items()},
                stimulus_id,
                time(),
            )
        )
        return recs, smsgs

    def transition(self, ts, finish: str, *, stimulus_id, **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
        """
        recs, smsgs = self._transition(ts, finish, stimulus_id=stimulus_id, **kwargs)
        for msg in smsgs:
            self.batched_stream.send(msg)
        self.transitions(recs, stimulus_id=stimulus_id)

    def transitions(self, recommendations: dict, *, stimulus_id):
        """Process transitions until none are left

        This includes feedback from previous transitions and continues until we
        reach a steady state
        """
        smsgs = []

        remaining_recs = recommendations.copy()
        tasks = set()
        while remaining_recs:
            ts, finish = remaining_recs.popitem()
            tasks.add(ts)
            a_recs, a_smsgs = self._transition(ts, finish, stimulus_id=stimulus_id)
            remaining_recs.update(a_recs)
            smsgs += a_smsgs

        if self.validate:
            # Full state validation is very expensive
            for ts in tasks:
                self.validate_task(ts)

        if not self.batched_stream.closed():
            for msg in smsgs:
                self.batched_stream.send(msg)
        else:
            logger.debug(
                "BatchedSend closed while transitioning tasks. %d tasks not sent.",
                len(smsgs),
            )

    def maybe_transition_long_running(self, ts, *, stimulus_id, compute_duration=None):
        if ts.state == "executing":
            self.transition(
                ts,
                "long-running",
                compute_duration=compute_duration,
                stimulus_id=stimulus_id,
            )
            assert ts.state == "long-running"

    def stateof(self, key):
        ts = self.tasks[key]
        return {
            "executing": ts.state == "executing",
            "waiting_for_data": bool(ts.waiting_for_data),
            "heap": key in pluck(1, self.ready),
            "data": key in self.data,
        }

    def story(self, *keys):
        keys = [key.key if isinstance(key, TaskState) else key for key in keys]
        return [
            msg
            for msg in self.log
            if any(key in msg for key in keys)
            or any(
                key in c
                for key in keys
                for c in msg
                if isinstance(c, (tuple, list, set))
            )
        ]

    def ensure_communicating(self):
        stimulus_id = f"ensure-communicating-{time()}"
        skipped_worker_in_flight = []

        while self.data_needed and (
            len(self.in_flight_workers) < self.total_out_connections
            or self.comm_nbytes < self.comm_threshold_bytes
        ):
            logger.debug(
                "Ensure communicating. Pending: %d. Connections: %d/%d",
                len(self.data_needed),
                len(self.in_flight_workers),
                self.total_out_connections,
            )

            _, key = heapq.heappop(self.data_needed)

            try:
                ts = self.tasks[key]
            except KeyError:
                continue

            if ts.state != "fetch":
                continue

            if not ts.who_has:
                self.transition(ts, "missing", stimulus_id=stimulus_id)
                continue

            workers = [w for w in ts.who_has if w not in self.in_flight_workers]
            if not workers:
                skipped_worker_in_flight.append((ts.priority, ts.key))
                continue

            host = get_address_host(self.address)
            local = [w for w in workers if get_address_host(w) == host]
            if local:
                worker = random.choice(local)
            else:
                worker = random.choice(list(workers))
            assert worker != self.address

            to_gather, total_nbytes = self.select_keys_for_gather(worker, ts.key)

            self.log.append(
                ("gather-dependencies", worker, to_gather, "stimulus", time())
            )

            self.comm_nbytes += total_nbytes
            self.in_flight_workers[worker] = to_gather
            recommendations = {self.tasks[d]: ("flight", worker) for d in to_gather}
            self.transitions(recommendations=recommendations, stimulus_id=stimulus_id)

            self.loop.add_callback(
                self.gather_dep,
                worker=worker,
                to_gather=to_gather,
                total_nbytes=total_nbytes,
                stimulus_id=stimulus_id,
            )

        for el in skipped_worker_in_flight:
            heapq.heappush(self.data_needed, el)

    def get_task_state_for_scheduler(self, ts):
        if ts.key in self.data or self.actors.get(ts.key):
            typ = ts.type
            if ts.nbytes is None or typ is None:
                try:
                    value = self.data[ts.key]
                except KeyError:
                    value = self.actors[ts.key]
                ts.nbytes = sizeof(value)
                typ = ts.type = type(value)
                del value
            try:
                typ_serialized = dumps_function(typ)
            except PicklingError:
                # Some types fail pickling (example: _thread.lock objects),
                # send their name as a best effort.
                typ_serialized = pickle.dumps(typ.__name__, protocol=4)
            d = {
                "op": "task-finished",
                "status": "OK",
                "key": ts.key,
                "nbytes": ts.nbytes,
                "thread": self.threads.get(ts.key),
                "type": typ_serialized,
                "typename": typename(typ),
                "metadata": ts.metadata,
            }
        elif ts.exception is not None:
            d = {
                "op": "task-erred",
                "status": "error",
                "key": ts.key,
                "thread": self.threads.get(ts.key),
                "exception": ts.exception,
                "traceback": ts.traceback,
                "exception_text": ts.exception_text,
                "traceback_text": ts.traceback_text,
            }
        else:
            logger.error("Key not ready to send to worker, %s: %s", ts.key, ts.state)
            return None
        if ts.startstops:
            d["startstops"] = ts.startstops
        return d

    def _put_key_in_memory(self, ts, value, *, stimulus_id):
        if ts.key in self.data:
            ts.state = "memory"
            return {}, []

        recommendations = {}
        scheduler_messages = []
        if ts.key in self.actors:
            self.actors[ts.key] = value
        else:
            start = time()
            try:
                self.data[ts.key] = value
            except Exception as e:
                msg = error_message(e)
                ts.exception = msg["exception"]
                ts.traceback = msg["traceback"]
                recommendations[ts] = ("error", msg["exception"], msg["traceback"])
                return recommendations, []
            stop = time()
            if stop - start > 0.020:
                ts.startstops.append(
                    {"action": "disk-write", "start": start, "stop": stop}
                )

        ts.state = "memory"
        if ts.nbytes is None:
            ts.nbytes = sizeof(value)

        ts.type = type(value)

        for dep in ts.dependents:
            dep.waiting_for_data.discard(ts)
            if not dep.waiting_for_data and dep.state == "waiting":
                self.waiting_for_data_count -= 1
                recommendations[dep] = "ready"

        self.log.append((ts.key, "put-in-memory", stimulus_id, time()))
        return recommendations, scheduler_messages

    def select_keys_for_gather(self, worker, dep):
        assert isinstance(dep, str)
        deps = {dep}

        total_bytes = self.tasks[dep].get_nbytes()
        L = self.pending_data_per_worker[worker]

        while L:
            d = L.popleft()
            ts = self.tasks.get(d)
            if ts is None or ts.state != "fetch":
                continue
            if total_bytes + ts.get_nbytes() > self.target_message_size:
                break
            deps.add(d)
            total_bytes += ts.get_nbytes()

        return deps, total_bytes

    @property
    def total_comm_bytes(self):
        warnings.warn(
            "The attribute `Worker.total_comm_bytes` has been renamed to `comm_threshold_bytes`. "
            "Future versions will only support the new name.",
            DeprecationWarning,
        )
        return self.comm_threshold_bytes

    async def gather_dep(
        self,
        worker: str,
        to_gather: Iterable[str],
        total_nbytes: int,
        *,
        stimulus_id,
    ):
        """Gather dependencies for a task from a worker who has them

        Parameters
        ----------
        worker : str
            Address of worker to gather dependencies from
        to_gather : list
            Keys of dependencies to gather from worker -- this is not
            necessarily equivalent to the full list of dependencies of ``dep``
            as some dependencies may already be present on this worker.
        total_nbytes : int
            Total number of bytes for all the dependencies in to_gather combined
        """
        cause: TaskState | None = None
        if self.status not in RUNNING:
            return

        with log_errors():
            response = {}
            to_gather_keys = set()
            try:
                found_dependent_for_cause = False
                for dependency_key in to_gather:
                    dependency_ts = self.tasks.get(dependency_key)
                    if dependency_ts and dependency_ts.state == "flight":
                        to_gather_keys.add(dependency_key)
                        if not found_dependent_for_cause:
                            cause = dependency_ts
                            # For diagnostics we want to attach the transfer to
                            # a single task. this task is typically the next to
                            # be executed but since we're fetching tasks for
                            # potentially many dependents, an exact match is not
                            # possible. If there are no dependents, this is a
                            # pure replica fetch
                            for dependent in dependency_ts.dependents:
                                cause = dependent
                                found_dependent_for_cause = True
                                break

                if not to_gather_keys:
                    return
                assert cause

                # Keep namespace clean since this func is long and has many
                # dep*, *ts* variables
                del to_gather, dependency_key, dependency_ts

                self.log.append(
                    ("request-dep", worker, to_gather_keys, stimulus_id, time())
                )
                logger.debug(
                    "Request %d keys for task %s from %s",
                    len(to_gather_keys),
                    cause,
                    worker,
                )

                start = time()
                response = await get_data_from_worker(
                    self.rpc, to_gather_keys, worker, who=self.address
                )
                stop = time()
                if response["status"] == "busy":
                    return

                data = {k: v for k, v in response["data"].items() if k in self.tasks}
                lost_keys = response["data"].keys() - data.keys()

                if lost_keys:
                    self.log.append(("lost-during-gather", lost_keys, stimulus_id))

                total_bytes = sum(self.tasks[key].get_nbytes() for key in data)

                cause.startstops.append(
                    {
                        "action": "transfer",
                        "start": start + self.scheduler_delay,
                        "stop": stop + self.scheduler_delay,
                        "source": worker,
                    }
                )
                duration = (stop - start) or 0.010
                bandwidth = total_bytes / duration
                self.incoming_transfer_log.append(
                    {
                        "start": start + self.scheduler_delay,
                        "stop": stop + self.scheduler_delay,
                        "middle": (start + stop) / 2.0 + self.scheduler_delay,
                        "duration": duration,
                        "keys": {key: self.tasks[key].nbytes for key in data},
                        "total": total_bytes,
                        "bandwidth": bandwidth,
                        "who": worker,
                    }
                )
                if total_bytes > 1000000:
                    self.bandwidth = self.bandwidth * 0.95 + bandwidth * 0.05
                    bw, cnt = self.bandwidth_workers[worker]
                    self.bandwidth_workers[worker] = (bw + bandwidth, cnt + 1)

                    types = set(map(type, response["data"].values()))
                    if len(types) == 1:
                        [typ] = types
                        bw, cnt = self.bandwidth_types[typ]
                        self.bandwidth_types[typ] = (bw + bandwidth, cnt + 1)

                if self.digests is not None:
                    self.digests["transfer-bandwidth"].add(total_bytes / duration)
                    self.digests["transfer-duration"].add(duration)
                self.counters["transfer-count"].add(len(response["data"]))
                self.incoming_count += 1

                self.log.append(
                    ("receive-dep", worker, set(response["data"]), stimulus_id, time())
                )

            except OSError:
                logger.exception("Worker stream died during communication: %s", worker)
                has_what = self.has_what.pop(worker)
                self.pending_data_per_worker.pop(worker)
                self.log.append(
                    ("receive-dep-failed", worker, has_what, stimulus_id, time())
                )
                for d in has_what:
                    ts = self.tasks[d]
                    ts.who_has.remove(worker)

            except Exception as e:
                logger.exception(e)
                if self.batched_stream and LOG_PDB:
                    import pdb

                    pdb.set_trace()
                raise
            finally:
                self.comm_nbytes -= total_nbytes
                busy = response.get("status", "") == "busy"
                data = response.get("data", {})

                if busy:
                    self.log.append(
                        ("busy-gather", worker, to_gather_keys, stimulus_id, time())
                    )

                recommendations: dict[TaskState, str | tuple] = {}
                deps_to_iter = set(self.in_flight_workers.pop(worker)) & to_gather_keys

                for d in deps_to_iter:
                    ts = self.tasks.get(d)
                    assert ts, (d, self.story(d))
                    ts.done = True
                    if d in data:
                        recommendations[ts] = ("memory", data[d])
                    elif not busy:
                        ts.who_has.discard(worker)
                        self.has_what[worker].discard(ts.key)
                        self.log.append(("missing-dep", d))
                        self.batched_stream.send(
                            {"op": "missing-data", "errant_worker": worker, "key": d}
                        )

                    if ts.state != "memory" and ts not in recommendations:
                        recommendations[ts] = "fetch"

                del data, response
                self.transitions(
                    recommendations=recommendations, stimulus_id=stimulus_id
                )
                self.ensure_computing()

                if not busy:
                    self.repetitively_busy = 0
                else:
                    # Exponential backoff to avoid hammering scheduler/worker
                    self.repetitively_busy += 1
                    await asyncio.sleep(0.100 * 1.5 ** self.repetitively_busy)

                    await self.query_who_has(*to_gather_keys, stimulus_id=stimulus_id)

                self.ensure_communicating()

    async def find_missing(self):
        with log_errors():
            if not self._missing_dep_flight:
                return
            try:
                if self.validate:
                    for ts in self._missing_dep_flight:
                        assert not ts.who_has

                stimulus_id = f"find-missing-{time()}"
                who_has = await retry_operation(
                    self.scheduler.who_has,
                    keys=[ts.key for ts in self._missing_dep_flight],
                )
                who_has = {k: v for k, v in who_has.items() if v}
                self.update_who_has(who_has, stimulus_id=stimulus_id)

            finally:
                # This is quite arbitrary but the heartbeat has scaling implemented
                self.periodic_callbacks[
                    "find-missing"
                ].callback_time = self.periodic_callbacks["heartbeat"].callback_time
                self.ensure_communicating()
                self.ensure_computing()

    async def query_who_has(self, *deps, stimulus_id):
        with log_errors():
            who_has = await retry_operation(self.scheduler.who_has, keys=deps)
            self.update_who_has(who_has, stimulus_id=stimulus_id)
            return who_has

    def update_who_has(self, who_has, *, stimulus_id):
        try:
            recommendations = {}
            for dep, workers in who_has.items():
                if not workers:
                    continue

                if dep in self.tasks:
                    if self.address in workers and self.tasks[dep].state != "memory":
                        logger.debug(
                            "Scheduler claims worker %s holds data for task %s which is not true.",
                            self.name,
                            dep,
                        )
                        # Do not mutate the input dict. That's rude
                        workers = set(workers) - {self.address}
                    dep_ts = self.tasks[dep]
                    dep_ts.who_has.update(workers)

                    if dep_ts.state == "missing":
                        recommendations[dep_ts] = "fetch"

                    for worker in workers:
                        self.has_what[worker].add(dep)
                        if dep_ts.state in ("fetch", "flight", "missing"):
                            self.pending_data_per_worker[worker].append(dep_ts.key)

            self.transitions(recommendations=recommendations, stimulus_id=stimulus_id)
        except Exception as e:
            logger.exception(e)
            if LOG_PDB:
                import pdb

                pdb.set_trace()
            raise

    def handle_steal_request(self, key, stimulus_id):
        # There may be a race condition between stealing and releasing a task.
        # In this case the self.tasks is already cleared. The `None` will be
        # registered as `already-computing` on the other end
        ts = self.tasks.get(key)
        state = ts.state if ts is not None else None

        response = {
            "op": "steal-response",
            "key": key,
            "state": state,
            "stimulus_id": stimulus_id,
        }
        self.batched_stream.send(response)

        if state in {"ready", "waiting", "constrained"}:
            # If task is marked as "constrained" we haven't yet assigned it an
            # `available_resources` to run on, that happens in
            # `transition_constrained_executing`
            self.transition(ts, "forgotten", stimulus_id=stimulus_id)

    def handle_worker_status_change(self, status: str) -> None:
        new_status = Status.lookup[status]  # type: ignore

        if new_status == Status.closing_gracefully and self.status not in (
            Status.running,
            Status.paused,
        ):
            logger.error(
                "Invalid Worker.status transition: %s -> %s", self.status, new_status
            )
            # Reiterate the current status to the scheduler to restore sync
            # (see status.setter)
            self.status = self.status
            return

        # Update status and send confirmation to the Scheduler (see status.setter).
        self.status = new_status

    def release_key(
        self,
        key: Hashable,
        cause: TaskState | None = None,
        reason: str | None = None,
        report: bool = True,
    ):
        recommendations = {}
        try:
            if self.validate:
                assert not isinstance(key, TaskState)
            ts = self.tasks[key]
            # needed for legacy notification support
            state_before = ts.state
            ts.state = "released"

            logger.debug(
                "Release key %s", {"key": key, "cause": cause, "reason": reason}
            )
            if cause:
                self.log.append((key, "release-key", {"cause": cause}, reason))
            else:
                self.log.append((key, "release-key", reason))
            if key in self.data:
                try:
                    del self.data[key]
                except FileNotFoundError:
                    logger.error("Tried to delete %s but no file found", exc_info=True)
            if key in self.actors:
                del self.actors[key]

            for worker in ts.who_has:
                self.has_what[worker].discard(ts.key)
            ts.who_has.clear()

            if key in self.threads:
                del self.threads[key]

            if ts.resource_restrictions is not None:
                if ts.state == "executing":
                    for resource, quantity in ts.resource_restrictions.items():
                        self.available_resources[resource] += quantity

            for d in ts.dependencies:
                ts.waiting_for_data.discard(d)
                d.waiters.discard(ts)

                if not d.waiters and d.state in {"flight", "fetch", "missing"}:
                    recommendations[d] = "forgotten"

            ts.waiting_for_data.clear()
            ts.nbytes = None
            ts._previous = None
            ts._next = None
            ts.done = False

            self._executing.discard(ts)
            self._in_flight_tasks.discard(ts)

            self._notify_plugins(
                "release_key", key, state_before, cause, reason, report
            )
        except CommClosedError:
            # Batched stream send might raise if it was already closed
            pass
        except Exception as e:
            logger.exception(e)
            if LOG_PDB:
                import pdb

                pdb.set_trace()
            raise

        return recommendations

    ################
    # Execute Task #
    ################

    def run(self, comm, function, args=(), wait=True, kwargs=None):
        return run(self, comm, function=function, args=args, kwargs=kwargs, wait=wait)

    def run_coroutine(self, comm, function, args=(), kwargs=None, wait=True):
        return run(self, comm, function=function, args=args, kwargs=kwargs, wait=wait)

    async def plugin_add(self, comm=None, plugin=None, name=None):
        with log_errors(pdb=False):
            if isinstance(plugin, bytes):
                plugin = pickle.loads(plugin)

            if name is None:
                name = _get_plugin_name(plugin)

            assert name

            if name in self.plugins:
                await self.plugin_remove(comm=comm, name=name)

            self.plugins[name] = plugin

            logger.info("Starting Worker plugin %s" % name)
            if hasattr(plugin, "setup"):
                try:
                    result = plugin.setup(worker=self)
                    if isawaitable(result):
                        result = await result
                except Exception as e:
                    msg = error_message(e)
                    return msg

            return {"status": "OK"}

    async def plugin_remove(self, comm=None, name=None):
        with log_errors(pdb=False):
            logger.info(f"Removing Worker plugin {name}")
            try:
                plugin = self.plugins.pop(name)
                if hasattr(plugin, "teardown"):
                    result = plugin.teardown(worker=self)
                    if isawaitable(result):
                        result = await result
            except Exception as e:
                msg = error_message(e)
                return msg

            return {"status": "OK"}

    async def actor_execute(
        self,
        comm=None,
        actor=None,
        function=None,
        args=(),
        kwargs: dict | None = None,
    ):
        kwargs = kwargs or {}
        separate_thread = kwargs.pop("separate_thread", True)
        key = actor
        actor = self.actors[key]
        func = getattr(actor, function)
        name = key_split(key) + "." + function

        try:
            if iscoroutinefunction(func):
                result = await func(*args, **kwargs)
            elif separate_thread:
                result = await self.loop.run_in_executor(
                    self.executors["actor"],
                    apply_function_actor,
                    func,
                    args,
                    kwargs,
                    self.execution_state,
                    name,
                    self.active_threads,
                    self.active_threads_lock,
                )
            else:
                result = func(*args, **kwargs)
            return {"status": "OK", "result": to_serialize(result)}
        except Exception as ex:
            return {"status": "error", "exception": to_serialize(ex)}

    def actor_attribute(self, comm=None, actor=None, attribute=None):
        try:
            value = getattr(self.actors[actor], attribute)
            return {"status": "OK", "result": to_serialize(value)}
        except Exception as ex:
            return {"status": "error", "exception": to_serialize(ex)}

    def meets_resource_constraints(self, key: str) -> bool:
        ts = self.tasks[key]
        if not ts.resource_restrictions:
            return True
        for resource, needed in ts.resource_restrictions.items():
            if self.available_resources[resource] < needed:
                return False

        return True

    async def _maybe_deserialize_task(self, ts, *, stimulus_id):
        if not isinstance(ts.runspec, SerializedTask):
            return ts.runspec
        try:
            start = time()
            # Offload deserializing large tasks
            if sizeof(ts.runspec) > OFFLOAD_THRESHOLD:
                function, args, kwargs = await offload(_deserialize, *ts.runspec)
            else:
                function, args, kwargs = _deserialize(*ts.runspec)
            stop = time()

            if stop - start > 0.010:
                ts.startstops.append(
                    {"action": "deserialize", "start": start, "stop": stop}
                )
            return function, args, kwargs
        except Exception as e:
            logger.error("Could not deserialize task", exc_info=True)
            self.log.append((ts.key, "deserialize-error"))
            emsg = error_message(e)
            emsg.pop("status")
            self.transition(
                ts,
                "error",
                **emsg,
                stimulus_id=stimulus_id,
            )
            raise

    def ensure_computing(self):
        if self.status in (Status.paused, Status.closing_gracefully):
            # Pending tasks shall be stolen
            return
        try:
            stimulus_id = f"ensure-computing-{time()}"
            while self.constrained and self.executing_count < self.nthreads:
                key = self.constrained[0]
                ts = self.tasks.get(key, None)
                if ts is None or ts.state != "constrained":
                    self.constrained.popleft()
                    continue
                if self.meets_resource_constraints(key):
                    self.constrained.popleft()
                    self.transition(ts, "executing", stimulus_id=stimulus_id)
                else:
                    break
            while self.ready and self.executing_count < self.nthreads:
                priority, key = heapq.heappop(self.ready)
                ts = self.tasks.get(key)
                if ts is None:
                    # It is possible for tasks to be released while still remaining on
                    # `ready` The scheduler might have re-routed to a new worker and
                    # told this worker to release.  If the task has "disappeared" just
                    # continue through the heap
                    continue
                elif ts.key in self.data:
                    self.transition(ts, "memory", stimulus_id=stimulus_id)
                elif ts.state in READY:
                    self.transition(ts, "executing", stimulus_id=stimulus_id)
        except Exception as e:
            logger.exception(e)
            if LOG_PDB:
                import pdb

                pdb.set_trace()
            raise

    async def execute(self, key, *, stimulus_id):
        if self.status in (Status.closing, Status.closed, Status.closing_gracefully):
            return
        if key not in self.tasks:
            return
        ts = self.tasks[key]

        try:
            if ts.state == "cancelled":
                # This might happen if keys are canceled
                logger.debug(
                    "Trying to execute task %s which is not in executing state anymore",
                    ts,
                )
                ts.done = True
                self.transition(ts, "released", stimulus_id=stimulus_id)
                return

            if self.validate:
                assert not ts.waiting_for_data
                assert ts.state == "executing"
                assert ts.runspec is not None

            function, args, kwargs = await self._maybe_deserialize_task(
                ts, stimulus_id=stimulus_id
            )

            args2, kwargs2 = self._prepare_args_for_execution(ts, args, kwargs)

            if ts.annotations is not None and "executor" in ts.annotations:
                executor = ts.annotations["executor"]
            else:
                executor = "default"
            assert executor in self.executors
            assert key == ts.key
            self.active_keys.add(ts.key)

            result: dict
            try:
                e = self.executors[executor]
                ts.start_time = time()
                if iscoroutinefunction(function):
                    result = await apply_function_async(
                        function,
                        args2,
                        kwargs2,
                        self.scheduler_delay,
                    )
                elif "ThreadPoolExecutor" in str(type(e)):
                    result = await self.loop.run_in_executor(
                        e,
                        apply_function,
                        function,
                        args2,
                        kwargs2,
                        self.execution_state,
                        ts.key,
                        self.active_threads,
                        self.active_threads_lock,
                        self.scheduler_delay,
                    )
                else:
                    result = await self.loop.run_in_executor(
                        e,
                        apply_function_simple,
                        function,
                        args2,
                        kwargs2,
                        self.scheduler_delay,
                    )
            finally:
                self.active_keys.discard(ts.key)

            key = ts.key
            # key *must* be still in tasks. Releasing it direclty is forbidden
            # without going through cancelled
            ts = self.tasks.get(key)
            assert ts, self.story(key)
            ts.done = True
            result["key"] = ts.key
            value = result.pop("result", None)
            ts.startstops.append(
                {"action": "compute", "start": result["start"], "stop": result["stop"]}
            )
            self.threads[ts.key] = result["thread"]
            recommendations = {}
            if result["op"] == "task-finished":
                ts.nbytes = result["nbytes"]
                ts.type = result["type"]
                recommendations[ts] = ("memory", value)
                if self.digests is not None:
                    self.digests["task-duration"].add(result["stop"] - result["start"])
            elif isinstance(result.pop("actual-exception"), Reschedule):
                recommendations[ts] = "rescheduled"
            else:
                logger.warning(
                    "Compute Failed\n"
                    "Function:  %s\n"
                    "args:      %s\n"
                    "kwargs:    %s\n"
                    "Exception: %r\n",
                    str(funcname(function))[:1000],
                    convert_args_to_str(args2, max_len=1000),
                    convert_kwargs_to_str(kwargs2, max_len=1000),
                    result["exception_text"],
                )
                recommendations[ts] = (
                    "error",
                    result["exception"],
                    result["traceback"],
                    result["exception_text"],
                    result["traceback_text"],
                )

            self.transitions(recommendations, stimulus_id=stimulus_id)

            logger.debug("Send compute response to scheduler: %s, %s", ts.key, result)

            if self.validate:
                assert ts.state != "executing"
                assert not ts.waiting_for_data

        except Exception as exc:
            assert ts
            logger.error(
                "Exception during execution of task %s.", ts.key, exc_info=True
            )
            emsg = error_message(exc)
            emsg.pop("status")
            self.transition(
                ts,
                "error",
                **emsg,
                stimulus_id=stimulus_id,
            )
        finally:
            self.ensure_computing()
            self.ensure_communicating()

    def _prepare_args_for_execution(self, ts, args, kwargs):
        start = time()
        data = {}
        for dep in ts.dependencies:
            k = dep.key
            try:
                data[k] = self.data[k]
            except KeyError:
                from .actor import Actor  # TODO: create local actor

                data[k] = Actor(type(self.actors[k]), self.address, k, self)
        args2 = pack_data(args, data, key_types=(bytes, str))
        kwargs2 = pack_data(kwargs, data, key_types=(bytes, str))
        stop = time()
        if stop - start > 0.005:
            ts.startstops.append({"action": "disk-read", "start": start, "stop": stop})
            if self.digests is not None:
                self.digests["disk-load-duration"].add(stop - start)
        return args2, kwargs2

    ##################
    # Administrative #
    ##################

    async def memory_monitor(self):
        """Track this process's memory usage and act accordingly

        If we rise above 70% memory use, start dumping data to disk.

        If we rise above 80% memory use, stop execution of new tasks
        """
        if self._memory_monitoring:
            return
        self._memory_monitoring = True
        total = 0

        proc = self.monitor.proc
        memory = proc.memory_info().rss
        frac = memory / self.memory_limit

        def check_pause(memory):
            frac = memory / self.memory_limit
            # Pause worker threads if above 80% memory use
            if self.memory_pause_fraction and frac > self.memory_pause_fraction:
                # Try to free some memory while in paused state
                self._throttled_gc.collect()
                if self.status == Status.running:
                    logger.warning(
                        "Worker is at %d%% memory usage. Pausing worker.  "
                        "Process memory: %s -- Worker memory limit: %s",
                        int(frac * 100),
                        format_bytes(memory),
                        format_bytes(self.memory_limit)
                        if self.memory_limit is not None
                        else "None",
                    )
                    self.status = Status.paused
            elif self.status == Status.paused:
                logger.warning(
                    "Worker is at %d%% memory usage. Resuming worker. "
                    "Process memory: %s -- Worker memory limit: %s",
                    int(frac * 100),
                    format_bytes(memory),
                    format_bytes(self.memory_limit)
                    if self.memory_limit is not None
                    else "None",
                )
                self.status = Status.running
                self.ensure_computing()

        check_pause(memory)
        # Dump data to disk if above 70%
        if self.memory_spill_fraction and frac > self.memory_spill_fraction:
            logger.debug(
                "Worker is at %.0f%% memory usage. Start spilling data to disk.",
                frac * 100,
            )
            start = time()
            target = self.memory_limit * self.memory_target_fraction
            count = 0
            need = memory - target
            while memory > target:
                if not self.data.fast:
                    logger.warning(
                        "Unmanaged memory use is high. This may indicate a memory leak "
                        "or the memory may not be released to the OS; see "
                        "https://distributed.dask.org/en/latest/worker.html#memtrim "
                        "for more information. "
                        "-- Unmanaged memory: %s -- Worker memory limit: %s",
                        format_bytes(memory),
                        format_bytes(self.memory_limit),
                    )
                    break
                k, v, weight = self.data.fast.evict()
                del k, v
                total += weight
                count += 1
                # If the current buffer is filled with a lot of small values,
                # evicting one at a time is very slow and the worker might
                # generate new data faster than it is able to evict. Therefore,
                # only pass on control if we spent at least 0.5s evicting
                if time() - start > 0.5:
                    await asyncio.sleep(0)
                    start = time()
                memory = proc.memory_info().rss
                if total > need and memory > target:
                    # Issue a GC to ensure that the evicted data is actually
                    # freed from memory and taken into account by the monitor
                    # before trying to evict even more data.
                    self._throttled_gc.collect()
                    memory = proc.memory_info().rss
            check_pause(memory)
            if count:
                logger.debug(
                    "Moved %d tasks worth %s to disk",
                    count,
                    format_bytes(total),
                )

        self._memory_monitoring = False
        return total

    def cycle_profile(self):
        now = time() + self.scheduler_delay
        prof, self.profile_recent = self.profile_recent, profile.create()
        self.profile_history.append((now, prof))

        self.profile_keys_history.append((now, dict(self.profile_keys)))
        self.profile_keys.clear()

    def trigger_profile(self):
        """
        Get a frame from all actively computing threads

        Merge these frames into existing profile counts
        """
        if not self.active_threads:  # hope that this is thread-atomic?
            return
        start = time()
        with self.active_threads_lock:
            active_threads = self.active_threads.copy()
        frames = sys._current_frames()
        frames = {ident: frames[ident] for ident in active_threads}
        llframes = {}
        if self.low_level_profiler:
            llframes = {ident: profile.ll_get_stack(ident) for ident in active_threads}
        for ident, frame in frames.items():
            if frame is not None:
                key = key_split(active_threads[ident])
                llframe = llframes.get(ident)

                state = profile.process(
                    frame, True, self.profile_recent, stop="distributed/worker.py"
                )
                profile.llprocess(llframe, None, state)
                profile.process(
                    frame, True, self.profile_keys[key], stop="distributed/worker.py"
                )

        stop = time()
        if self.digests is not None:
            self.digests["profile-duration"].add(stop - start)

    async def get_profile(
        self, comm=None, start=None, stop=None, key=None, server=False
    ):
        now = time() + self.scheduler_delay
        if server:
            history = self.io_loop.profile
        elif key is None:
            history = self.profile_history
        else:
            history = [(t, d[key]) for t, d in self.profile_keys_history if key in d]

        if start is None:
            istart = 0
        else:
            istart = bisect.bisect_left(history, (start,))

        if stop is None:
            istop = None
        else:
            istop = bisect.bisect_right(history, (stop,)) + 1
            if istop >= len(history):
                istop = None  # include end

        if istart == 0 and istop is None:
            history = list(history)
        else:
            iistop = len(history) if istop is None else istop
            history = [history[i] for i in range(istart, iistop)]

        prof = profile.merge(*pluck(1, history))

        if not history:
            return profile.create()

        if istop is None and (start is None or start < now):
            if key is None:
                recent = self.profile_recent
            else:
                recent = self.profile_keys[key]
            prof = profile.merge(prof, recent)

        return prof

    async def get_profile_metadata(self, comm=None, start=0, stop=None):
        add_recent = stop is None
        now = time() + self.scheduler_delay
        stop = stop or now
        start = start or 0
        result = {
            "counts": [
                (t, d["count"]) for t, d in self.profile_history if start < t < stop
            ],
            "keys": [
                (t, {k: d["count"] for k, d in v.items()})
                for t, v in self.profile_keys_history
                if start < t < stop
            ],
        }
        if add_recent:
            result["counts"].append((now, self.profile_recent["count"]))
            result["keys"].append(
                (now, {k: v["count"] for k, v in self.profile_keys.items()})
            )
        return result

    def get_call_stack(self, comm=None, keys=None):
        with self.active_threads_lock:
            frames = sys._current_frames()
            active_threads = self.active_threads.copy()
            frames = {k: frames[ident] for ident, k in active_threads.items()}
        if keys is not None:
            frames = {k: frame for k, frame in frames.items() if k in keys}

        result = {k: profile.call_stack(frame) for k, frame in frames.items()}
        return result

    def _notify_plugins(self, method_name, *args, **kwargs):
        for name, plugin in self.plugins.items():
            if hasattr(plugin, method_name):
                if method_name == "release_key":
                    warnings.warn(
                        "The `WorkerPlugin.release_key` hook is depreacted and will be "
                        "removed in a future version. A similar event can now be "
                        "caught by filtering for a `finish=='released'` event in the "
                        "`WorkerPlugin.transition` hook.",
                        DeprecationWarning,
                    )

                try:
                    getattr(plugin, method_name)(*args, **kwargs)
                except Exception:
                    logger.info(
                        "Plugin '%s' failed with exception", name, exc_info=True
                    )

    ##############
    # Validation #
    ##############

    def validate_task_memory(self, ts):
        assert ts.key in self.data or ts.key in self.actors
        assert isinstance(ts.nbytes, int)
        assert not ts.waiting_for_data
        assert ts.key not in self.ready
        assert ts.state == "memory"

    def validate_task_executing(self, ts):
        assert ts.state == "executing"
        assert ts.runspec is not None
        assert ts.key not in self.data
        assert not ts.waiting_for_data
        for dep in ts.dependencies:
            assert dep.state == "memory", self.story(dep)
            assert dep.key in self.data or dep.key in self.actors

    def validate_task_ready(self, ts):
        assert ts.key in pluck(1, self.ready)
        assert ts.key not in self.data
        assert ts.state != "executing"
        assert not ts.waiting_for_data
        assert all(
            dep.key in self.data or dep.key in self.actors for dep in ts.dependencies
        )

    def validate_task_waiting(self, ts):
        assert ts.key not in self.data
        assert ts.state == "waiting"
        if ts.dependencies and ts.runspec:
            assert not all(dep.key in self.data for dep in ts.dependencies)

    def validate_task_flight(self, ts):
        assert ts.key not in self.data
        assert ts in self._in_flight_tasks
        assert not any(dep.key in self.ready for dep in ts.dependents)
        assert ts.coming_from
        assert ts.coming_from in self.in_flight_workers
        assert ts.key in self.in_flight_workers[ts.coming_from]

    def validate_task_fetch(self, ts):
        assert ts.key not in self.data
        assert self.address not in ts.who_has

        for w in ts.who_has:
            assert ts.key in self.has_what[w]

    def validate_task_missing(self, ts):
        assert ts.key not in self.data
        assert not ts.who_has
        assert not any(ts.key in has_what for has_what in self.has_what.values())
        assert ts.key in self._missing_dep_flight

    def validate_task_cancelled(self, ts):
        assert ts.key not in self.data
        assert ts._previous

    def validate_task_resumed(self, ts):
        assert ts.key not in self.data
        assert ts._next
        assert ts._previous

    def validate_task_released(self, ts):
        assert ts.key not in self.data
        assert not ts._next
        assert not ts._previous
        assert ts not in self._executing
        assert ts not in self._in_flight_tasks
        assert ts not in self._missing_dep_flight
        assert ts not in self._missing_dep_flight
        assert not ts.who_has
        assert not any(ts.key in has_what for has_what in self.has_what.values())
        assert not ts.waiting_for_data
        assert not ts.done
        assert not ts.exception
        assert not ts.traceback

    def validate_task(self, ts):
        try:
            if ts.key in self.tasks:
                assert self.tasks[ts.key] == ts
            if ts.state == "memory":
                self.validate_task_memory(ts)
            elif ts.state == "waiting":
                self.validate_task_waiting(ts)
            elif ts.state == "missing":
                self.validate_task_missing(ts)
            elif ts.state == "cancelled":
                self.validate_task_cancelled(ts)
            elif ts.state == "resumed":
                self.validate_task_resumed(ts)
            elif ts.state == "ready":
                self.validate_task_ready(ts)
            elif ts.state == "executing":
                self.validate_task_executing(ts)
            elif ts.state == "flight":
                self.validate_task_flight(ts)
            elif ts.state == "fetch":
                self.validate_task_fetch(ts)
            elif ts.state == "released":
                self.validate_task_released(ts)
        except Exception as e:
            logger.exception(e)
            if LOG_PDB:
                import pdb

                pdb.set_trace()
            raise

    def validate_state(self):
        if self.status not in RUNNING:
            return
        try:
            assert self.executing_count >= 0
            waiting_for_data_count = 0
            for ts in self.tasks.values():
                assert ts.state is not None
                # check that worker has task
                for worker in ts.who_has:
                    assert ts.key in self.has_what[worker]
                # check that deps have a set state and that dependency<->dependent links
                # are there
                for dep in ts.dependencies:
                    # self.tasks was just a dict of tasks
                    # and this check was originally that the key was in `task_state`
                    # so we may have popped the key out of `self.tasks` but the
                    # dependency can still be in `memory` before GC grabs it...?
                    # Might need better bookkeeping
                    assert dep.state is not None
                    assert ts in dep.dependents, ts
                if ts.waiting_for_data:
                    waiting_for_data_count += 1
                for ts_wait in ts.waiting_for_data:
                    assert ts_wait.key in self.tasks
                    assert (
                        ts_wait.state
                        in {"ready", "executing", "flight", "fetch", "missing"}
                        or ts_wait.key in self._missing_dep_flight
                        or ts_wait.who_has.issubset(self.in_flight_workers)
                    ), (ts, ts_wait, self.story(ts), self.story(ts_wait))
                if ts.state == "memory":
                    assert isinstance(ts.nbytes, int)
                    assert not ts.waiting_for_data
                    assert ts.key in self.data or ts.key in self.actors
            assert self.waiting_for_data_count == waiting_for_data_count
            for worker, keys in self.has_what.items():
                for k in keys:
                    assert worker in self.tasks[k].who_has

            for ts in self.tasks.values():
                self.validate_task(ts)

        except Exception as e:
            self.loop.add_callback(self.close)
            logger.exception(e)
            if LOG_PDB:
                import pdb

                pdb.set_trace()
            raise

    #######################################
    # Worker Clients (advanced workloads) #
    #######################################

    @property
    def client(self) -> Client:
        with self._lock:
            if self._client:
                return self._client
            else:
                return self._get_client()

    def _get_client(self, timeout=None) -> Client:
        """Get local client attached to this worker

        If no such client exists, create one

        See Also
        --------
        get_client
        """

        if timeout is None:
            timeout = dask.config.get("distributed.comm.timeouts.connect")

        timeout = parse_timedelta(timeout, "s")

        try:
            from .client import default_client

            client = default_client()
        except ValueError:  # no clients found, need to make a new one
            pass
        else:
            # must be lazy import otherwise cyclic import
            from distributed.deploy.cluster import Cluster

            if (
                client.scheduler
                and client.scheduler.address == self.scheduler.address
                # The below conditions should only happen in case a second
                # cluster is alive, e.g. if a submitted task spawned its onwn
                # LocalCluster, see gh4565
                or (
                    isinstance(client._start_arg, str)
                    and client._start_arg == self.scheduler.address
                    or isinstance(client._start_arg, Cluster)
                    and client._start_arg.scheduler_address == self.scheduler.address
                )
            ):
                self._client = client

        if not self._client:
            from .client import Client

            asynchronous = self.loop is IOLoop.current()
            self._client = Client(
                self.scheduler,
                loop=self.loop,
                security=self.security,
                set_as_default=True,
                asynchronous=asynchronous,
                direct_to_workers=True,
                name="worker",
                timeout=timeout,
            )
            Worker._initialized_clients.add(self._client)
            if not asynchronous:
                assert self._client.status == "running"

        return self._client

    def get_current_task(self):
        """Get the key of the task we are currently running

        This only makes sense to run within a task

        Examples
        --------
        >>> from dask.distributed import get_worker
        >>> def f():
        ...     return get_worker().get_current_task()

        >>> future = client.submit(f)  # doctest: +SKIP
        >>> future.result()  # doctest: +SKIP
        'f-1234'

        See Also
        --------
        get_worker
        """
        return self.active_threads[threading.get_ident()]


def get_worker() -> Worker:
    """Get the worker currently running this task

    Examples
    --------
    >>> def f():
    ...     worker = get_worker()  # The worker on which this task is running
    ...     return worker.address

    >>> future = client.submit(f)  # doctest: +SKIP
    >>> future.result()  # doctest: +SKIP
    'tcp://127.0.0.1:47373'

    See Also
    --------
    get_client
    worker_client
    """
    try:
        return thread_state.execution_state["worker"]
    except AttributeError:
        try:
            return first(
                w
                for w in Worker._instances
                if w.status in (Status.running, Status.paused)
            )
        except StopIteration:
            raise ValueError("No workers found")


def get_client(address=None, timeout=None, resolve_address=True) -> Client:
    """Get a client while within a task.

    This client connects to the same scheduler to which the worker is connected

    Parameters
    ----------
    address : str, optional
        The address of the scheduler to connect to. Defaults to the scheduler
        the worker is connected to.
    timeout : int or str
        Timeout (in seconds) for getting the Client. Defaults to the
        ``distributed.comm.timeouts.connect`` configuration value.
    resolve_address : bool, default True
        Whether to resolve `address` to its canonical form.

    Returns
    -------
    Client

    Examples
    --------
    >>> def f():
    ...     client = get_client(timeout="10s")
    ...     futures = client.map(lambda x: x + 1, range(10))  # spawn many tasks
    ...     results = client.gather(futures)
    ...     return sum(results)

    >>> future = client.submit(f)  # doctest: +SKIP
    >>> future.result()  # doctest: +SKIP
    55

    See Also
    --------
    get_worker
    worker_client
    secede
    """

    if timeout is None:
        timeout = dask.config.get("distributed.comm.timeouts.connect")

    timeout = parse_timedelta(timeout, "s")

    if address and resolve_address:
        address = comm.resolve_address(address)
    try:
        worker = get_worker()
    except ValueError:  # could not find worker
        pass
    else:
        if not address or worker.scheduler.address == address:
            return worker._get_client(timeout=timeout)

    from .client import Client

    try:
        client = Client.current()  # TODO: assumes the same scheduler
    except ValueError:
        client = None
    if client and (not address or client.scheduler.address == address):
        return client
    elif address:
        return Client(address, timeout=timeout)
    else:
        raise ValueError("No global client found and no address provided")


def secede():
    """
    Have this task secede from the worker's thread pool

    This opens up a new scheduling slot and a new thread for a new task. This
    enables the client to schedule tasks on this node, which is
    especially useful while waiting for other jobs to finish (e.g., with
    ``client.gather``).

    Examples
    --------
    >>> def mytask(x):
    ...     # do some work
    ...     client = get_client()
    ...     futures = client.map(...)  # do some remote work
    ...     secede()  # while that work happens, remove ourself from the pool
    ...     return client.gather(futures)  # return gathered results

    See Also
    --------
    get_client
    get_worker
    """
    worker = get_worker()
    tpe_secede()  # have this thread secede from the thread pool
    duration = time() - thread_state.start_time
    worker.loop.add_callback(
        worker.maybe_transition_long_running,
        worker.tasks[thread_state.key],
        compute_duration=duration,
        stimulus_id=f"secede-{thread_state.key}-{time()}",
    )


class Reschedule(Exception):
    """Reschedule this task

    Raising this exception will stop the current execution of the task and ask
    the scheduler to reschedule this task, possibly on a different machine.

    This does not guarantee that the task will move onto a different machine.
    The scheduler will proceed through its normal heuristics to determine the
    optimal machine to accept this task.  The machine will likely change if the
    load across the cluster has significantly changed since first scheduling
    the task.
    """


def parse_memory_limit(memory_limit, nthreads, total_cores=CPU_COUNT) -> int | None:
    if memory_limit is None:
        return None

    if memory_limit == "auto":
        memory_limit = int(system.MEMORY_LIMIT * min(1, nthreads / total_cores))
    with suppress(ValueError, TypeError):
        memory_limit = float(memory_limit)
        if isinstance(memory_limit, float) and memory_limit <= 1:
            memory_limit = int(memory_limit * system.MEMORY_LIMIT)

    if isinstance(memory_limit, str):
        memory_limit = parse_bytes(memory_limit)
    else:
        memory_limit = int(memory_limit)

    return min(memory_limit, system.MEMORY_LIMIT)


async def get_data_from_worker(
    rpc,
    keys,
    worker,
    who=None,
    max_connections=None,
    serializers=None,
    deserializers=None,
):
    """Get keys from worker

    The worker has a two step handshake to acknowledge when data has been fully
    delivered.  This function implements that handshake.

    See Also
    --------
    Worker.get_data
    Worker.gather_deps
    utils_comm.gather_data_from_workers
    """
    if serializers is None:
        serializers = rpc.serializers
    if deserializers is None:
        deserializers = rpc.deserializers

    async def _get_data():
        comm = await rpc.connect(worker)
        comm.name = "Ephemeral Worker->Worker for gather"
        try:
            response = await send_recv(
                comm,
                serializers=serializers,
                deserializers=deserializers,
                op="get_data",
                keys=keys,
                who=who,
                max_connections=max_connections,
            )
            try:
                status = response["status"]
            except KeyError:
                raise ValueError("Unexpected response", response)
            else:
                if status == "OK":
                    await comm.write("OK")
            return response
        finally:
            rpc.reuse(worker, comm)

    return await retry_operation(_get_data, operation="get_data_from_worker")


job_counter = [0]


cache_loads = LRU(maxsize=100)


def loads_function(bytes_object):
    """Load a function from bytes, cache bytes"""
    if len(bytes_object) < 100000:
        try:
            result = cache_loads[bytes_object]
        except KeyError:
            result = pickle.loads(bytes_object)
            cache_loads[bytes_object] = result
        return result
    return pickle.loads(bytes_object)


def _deserialize(function=None, args=None, kwargs=None, task=no_value):
    """Deserialize task inputs and regularize to func, args, kwargs"""
    if function is not None:
        function = loads_function(function)
    if args and isinstance(args, bytes):
        args = pickle.loads(args)
    if kwargs and isinstance(kwargs, bytes):
        kwargs = pickle.loads(kwargs)

    if task is not no_value:
        assert not function and not args and not kwargs
        function = execute_task
        args = (task,)

    return function, args or (), kwargs or {}


def execute_task(task):
    """Evaluate a nested task

    >>> inc = lambda x: x + 1
    >>> execute_task((inc, 1))
    2
    >>> execute_task((sum, [1, 2, (inc, 3)]))
    7
    """
    if istask(task):
        func, args = task[0], task[1:]
        return func(*map(execute_task, args))
    elif isinstance(task, list):
        return list(map(execute_task, task))
    else:
        return task


cache_dumps = LRU(maxsize=100)

_cache_lock = threading.Lock()


def dumps_function(func) -> bytes:
    """Dump a function to bytes, cache functions"""
    try:
        with _cache_lock:
            result = cache_dumps[func]
    except KeyError:
        result = pickle.dumps(func, protocol=4)
        if len(result) < 100000:
            with _cache_lock:
                cache_dumps[func] = result
    except TypeError:  # Unhashable function
        result = pickle.dumps(func, protocol=4)
    return result


def dumps_task(task):
    """Serialize a dask task

    Returns a dict of bytestrings that can each be loaded with ``loads``

    Examples
    --------
    Either returns a task as a function, args, kwargs dict

    >>> from operator import add
    >>> dumps_task((add, 1))  # doctest: +SKIP
    {'function': b'\x80\x04\x95\x00\x8c\t_operator\x94\x8c\x03add\x94\x93\x94.'
     'args': b'\x80\x04\x95\x07\x00\x00\x00K\x01K\x02\x86\x94.'}

    Or as a single task blob if it can't easily decompose the result.  This
    happens either if the task is highly nested, or if it isn't a task at all

    >>> dumps_task(1)  # doctest: +SKIP
    {'task': b'\x80\x04\x95\x03\x00\x00\x00\x00\x00\x00\x00K\x01.'}
    """
    if istask(task):
        if task[0] is apply and not any(map(_maybe_complex, task[2:])):
            d = {"function": dumps_function(task[1]), "args": warn_dumps(task[2])}
            if len(task) == 4:
                d["kwargs"] = warn_dumps(task[3])
            return d
        elif not any(map(_maybe_complex, task[1:])):
            return {"function": dumps_function(task[0]), "args": warn_dumps(task[1:])}
    return to_serialize(task)


_warn_dumps_warned = [False]


def warn_dumps(obj, dumps=pickle.dumps, limit=1e6):
    """Dump an object to bytes, warn if those bytes are large"""
    b = dumps(obj, protocol=4)
    if not _warn_dumps_warned[0] and len(b) > limit:
        _warn_dumps_warned[0] = True
        s = str(obj)
        if len(s) > 70:
            s = s[:50] + " ... " + s[-15:]
        warnings.warn(
            "Large object of size %s detected in task graph: \n"
            "  %s\n"
            "Consider scattering large objects ahead of time\n"
            "with client.scatter to reduce scheduler burden and \n"
            "keep data on workers\n\n"
            "    future = client.submit(func, big_data)    # bad\n\n"
            "    big_future = client.scatter(big_data)     # good\n"
            "    future = client.submit(func, big_future)  # good"
            % (format_bytes(len(b)), s)
        )
    return b


def apply_function(
    function,
    args,
    kwargs,
    execution_state,
    key,
    active_threads,
    active_threads_lock,
    time_delay,
):
    """Run a function, collect information

    Returns
    -------
    msg: dictionary with status, result/error, timings, etc..
    """
    ident = threading.get_ident()
    with active_threads_lock:
        active_threads[ident] = key
    thread_state.start_time = time()
    thread_state.execution_state = execution_state
    thread_state.key = key

    msg = apply_function_simple(function, args, kwargs, time_delay)

    with active_threads_lock:
        del active_threads[ident]
    return msg


def apply_function_simple(
    function,
    args,
    kwargs,
    time_delay,
):
    """Run a function, collect information

    Returns
    -------
    msg: dictionary with status, result/error, timings, etc..
    """
    ident = threading.get_ident()
    start = time()
    try:
        result = function(*args, **kwargs)
    except Exception as e:
        msg = error_message(e)
        msg["op"] = "task-erred"
        msg["actual-exception"] = e
    else:
        msg = {
            "op": "task-finished",
            "status": "OK",
            "result": result,
            "nbytes": sizeof(result),
            "type": type(result) if result is not None else None,
        }
    finally:
        end = time()
    msg["start"] = start + time_delay
    msg["stop"] = end + time_delay
    msg["thread"] = ident
    return msg


async def apply_function_async(
    function,
    args,
    kwargs,
    time_delay,
):
    """Run a function, collect information

    Returns
    -------
    msg: dictionary with status, result/error, timings, etc..
    """
    ident = threading.get_ident()
    start = time()
    try:
        result = await function(*args, **kwargs)
    except Exception as e:
        msg = error_message(e)
        msg["op"] = "task-erred"
        msg["actual-exception"] = e
    else:
        msg = {
            "op": "task-finished",
            "status": "OK",
            "result": result,
            "nbytes": sizeof(result),
            "type": type(result) if result is not None else None,
        }
    finally:
        end = time()
    msg["start"] = start + time_delay
    msg["stop"] = end + time_delay
    msg["thread"] = ident
    return msg


def apply_function_actor(
    function, args, kwargs, execution_state, key, active_threads, active_threads_lock
):
    """Run a function, collect information

    Returns
    -------
    msg: dictionary with status, result/error, timings, etc..
    """
    ident = threading.get_ident()

    with active_threads_lock:
        active_threads[ident] = key

    thread_state.execution_state = execution_state
    thread_state.key = key
    thread_state.actor = True

    result = function(*args, **kwargs)

    with active_threads_lock:
        del active_threads[ident]

    return result


def get_msg_safe_str(msg):
    """Make a worker msg, which contains args and kwargs, safe to cast to str:
    allowing for some arguments to raise exceptions during conversion and
    ignoring them.
    """

    class Repr:
        def __init__(self, f, val):
            self._f = f
            self._val = val

        def __repr__(self):
            return self._f(self._val)

    msg = msg.copy()
    if "args" in msg:
        msg["args"] = Repr(convert_args_to_str, msg["args"])
    if "kwargs" in msg:
        msg["kwargs"] = Repr(convert_kwargs_to_str, msg["kwargs"])
    return msg


def convert_args_to_str(args, max_len: int | None = None) -> str:
    """Convert args to a string, allowing for some arguments to raise
    exceptions during conversion and ignoring them.
    """
    length = 0
    strs = ["" for i in range(len(args))]
    for i, arg in enumerate(args):
        try:
            sarg = repr(arg)
        except Exception:
            sarg = "< could not convert arg to str >"
        strs[i] = sarg
        length += len(sarg) + 2
        if max_len is not None and length > max_len:
            return "({}".format(", ".join(strs[: i + 1]))[:max_len]
    else:
        return "({})".format(", ".join(strs))


def convert_kwargs_to_str(kwargs: dict, max_len: int | None = None) -> str:
    """Convert kwargs to a string, allowing for some arguments to raise
    exceptions during conversion and ignoring them.
    """
    length = 0
    strs = ["" for i in range(len(kwargs))]
    for i, (argname, arg) in enumerate(kwargs.items()):
        try:
            sarg = repr(arg)
        except Exception:
            sarg = "< could not convert arg to str >"
        skwarg = repr(argname) + ": " + sarg
        strs[i] = skwarg
        length += len(skwarg) + 2
        if max_len is not None and length > max_len:
            return "{{{}".format(", ".join(strs[: i + 1]))[:max_len]
    else:
        return "{{{}}}".format(", ".join(strs))


async def run(server, comm, function, args=(), kwargs=None, is_coro=None, wait=True):
    kwargs = kwargs or {}
    function = pickle.loads(function)
    if is_coro is None:
        is_coro = iscoroutinefunction(function)
    else:
        warnings.warn(
            "The is_coro= parameter is deprecated. "
            "We now automatically detect coroutines/async functions"
        )
    assert wait or is_coro, "Combination not supported"
    if args:
        args = pickle.loads(args)
    if kwargs:
        kwargs = pickle.loads(kwargs)
    if has_arg(function, "dask_worker"):
        kwargs["dask_worker"] = server
    if has_arg(function, "dask_scheduler"):
        kwargs["dask_scheduler"] = server
    logger.info("Run out-of-band function %r", funcname(function))
    try:
        if not is_coro:
            result = function(*args, **kwargs)
        else:
            if wait:
                result = await function(*args, **kwargs)
            else:
                server.loop.add_callback(function, *args, **kwargs)
                result = None

    except Exception as e:
        logger.warning(
            "Run Failed\nFunction: %s\nargs:     %s\nkwargs:   %s\n",
            str(funcname(function))[:1000],
            convert_args_to_str(args, max_len=1000),
            convert_kwargs_to_str(kwargs, max_len=1000),
            exc_info=True,
        )

        response = error_message(e)
    else:
        response = {"status": "OK", "result": to_serialize(result)}
    return response


_global_workers = Worker._instances

try:
    if nvml.device_get_count() < 1:
        raise RuntimeError
except (Exception, RuntimeError):
    pass
else:

    async def gpu_metric(worker):
        result = await offload(nvml.real_time)
        return result

    DEFAULT_METRICS["gpu"] = gpu_metric

    def gpu_startup(worker):
        return nvml.one_time()

    DEFAULT_STARTUP_INFORMATION["gpu"] = gpu_startup


def print(*args, **kwargs):
    """Dask print function
    This prints both wherever this function is run, and also in the user's
    client session
    """
    try:
        worker = get_worker()
    except ValueError:
        pass
    else:
        msg = {
            "args": tuple(stringify(arg) for arg in args),
            "kwargs": {k: stringify(v) for k, v in kwargs.items()},
        }
        worker.log_event("print", msg)

    builtins.print(*args, **kwargs)


def warn(*args, **kwargs):
    """Dask warn function
    This raises a warning both wherever this function is run, and also
    in the user's client session
    """
    try:
        worker = get_worker()
    except ValueError:
        pass
    else:
        worker.log_event("warn", {"args": args, "kwargs": kwargs})

    warnings.warn(*args, **kwargs)