Source code for distributed.scheduler

from __future__ import print_function, division, absolute_import

from collections import defaultdict, deque, OrderedDict
from datetime import timedelta
from functools import partial
import itertools
import json
import logging
from numbers import Number
import operator
import os
import pickle
import random
import six
import warnings

import psutil
import sortedcontainers
try:
    from cytoolz import frequencies, merge, pluck, merge_sorted, first
except ImportError:
    from toolz import frequencies, merge, pluck, merge_sorted, first
from toolz import valmap, second, compose, groupby
from tornado import gen
from tornado.gen import Return
from tornado.ioloop import IOLoop

import dask

from .batched import BatchedSend
from .comm import (normalize_address, resolve_address,
                   get_address_host, unparse_host_port)
from .compatibility import finalize, unicode, Mapping, Set
from .core import (rpc, connect, send_recv,
                   clean_exception, CommClosedError)
from . import profile
from .metrics import time
from .node import ServerNode
from .proctitle import setproctitle
from .security import Security
from .utils import (All, ignoring, get_ip, get_fileno_limit, log_errors,
                    key_split, validate_key, no_default, DequeHandler,
                    parse_timedelta, PeriodicCallback, shutting_down)
from .utils_comm import (scatter_to_workers, gather_from_workers)
from .utils_perf import enable_gc_diagnosis, disable_gc_diagnosis

from .publish import PublishExtension
from .queues import QueueExtension
from .recreate_exceptions import ReplayExceptionScheduler
from .lock import LockExtension
from .pubsub import PubSubSchedulerExtension
from .stealing import WorkStealing
from .variable import VariableExtension


logger = logging.getLogger(__name__)


BANDWIDTH = dask.config.get('distributed.scheduler.bandwidth')
ALLOWED_FAILURES = dask.config.get('distributed.scheduler.allowed-failures')

LOG_PDB = dask.config.get('distributed.admin.pdb-on-err')
DEFAULT_DATA_SIZE = dask.config.get('distributed.scheduler.default-data-size')

DEFAULT_EXTENSIONS = [
    LockExtension,
    PublishExtension,
    ReplayExceptionScheduler,
    QueueExtension,
    VariableExtension,
    PubSubSchedulerExtension,
]

if dask.config.get('distributed.scheduler.work-stealing'):
    DEFAULT_EXTENSIONS.append(WorkStealing)

ALL_TASK_STATES = {'released', 'waiting', 'no-worker', 'processing', 'erred', 'memory'}


[docs]class ClientState(object): """ A simple object holding information about a client. .. attribute:: client_key: str A unique identifier for this client. This is generally an opaque string generated by the client itself. .. attribute:: wants_what: {TaskState} A set of tasks this client wants kept in memory, so that it can download its result when desired. This is the reverse mapping of :class:`TaskState.who_wants`. Tasks are typically removed from this set when the corresponding object in the client's space (for example a ``Future`` or a Dask collection) gets garbage-collected. """ __slots__ = ( 'client_key', 'wants_what', 'last_seen', ) def __init__(self, client): self.client_key = client self.wants_what = set() self.last_seen = time() def __repr__(self): return "<Client %r>" % (self.client_key,) def __str__(self): return self.client_key
[docs]class WorkerState(object): """ A simple object holding information about a worker. .. attribute:: address This worker's unique key. This can be its connected address (such as ``'tcp://127.0.0.1:8891'``) or an alias (such as ``'alice'``). .. attribute:: processing: {TaskState: cost} A dictionary of tasks that have been submitted to this worker. Each task state is asssociated with the expected cost in seconds of running that task, summing both the task's expected computation time and the expected communication time of its result. Multiple tasks may be submitted to a worker in advance and the worker will run them eventually, depending on its execution resources (but see :doc:`work-stealing`). All the tasks here are in the "processing" state. This attribute is kept in sync with :attr:`TaskState.processing_on`. .. attribute:: has_what: {TaskState} The set of tasks which currently reside on this worker. All the tasks here are in the "memory" state. This is the reverse mapping of :class:`TaskState.who_has`. .. attribute:: nbytes: int The total memory size, in bytes, used by the tasks this worker holds in memory (i.e. the tasks in this worker's :attr:`has_what`). .. attribute:: ncores: int The number of CPU cores made available on this worker. .. attribute:: resources: {str: Number} The available resources on this worker like ``{'gpu': 2}``. These are abstract quantities that constrain certain tasks from running at the same time on this worker. .. attribute:: used_resources: {str: Number} The sum of each resource used by all tasks allocated to this worker. The numbers in this dictionary can only be less or equal than those in this worker's :attr:`resources`. .. attribute:: occupancy: Number The total expected runtime, in seconds, of all tasks currently processing on this worker. This is the sum of all the costs in this worker's :attr:`processing` dictionary. .. attribute:: status: str The current status of the worker, either ``'running'`` or ``'closed'`` .. attribute:: last_seen: Number The last time we received a heartbeat from this worker, in local scheduler time. .. attribute:: actors: {TaskState} A set of all TaskStates on this worker that are actors. This only includes those actors whose state actually lives on this worker, not actors to which this worker has a reference. """ # XXX need a state field to signal active/removed? __slots__ = ( 'actors', 'address', 'has_what', 'last_seen', 'local_directory', 'memory_limit', 'metrics', 'name', 'nbytes', 'ncores', 'occupancy', 'pid', 'processing', 'resources', 'services', 'status', 'time_delay', 'used_resources', ) def __init__(self, address=None, pid=0, name=None, ncores=0, memory_limit=0, local_directory=None, services=None): self.address = address self.pid = pid self.name = name self.ncores = ncores self.memory_limit = memory_limit self.local_directory = local_directory self.services = services or {} self.status = 'running' self.nbytes = 0 self.occupancy = 0 self.metrics = {} self.last_seen = 0 self.time_delay = 0 self.actors = set() self.has_what = set() self.processing = {} self.resources = {} self.used_resources = {} @property def host(self): return get_address_host(self.address) def __repr__(self): return "<Worker %r, memory: %d, processing: %d>" % (self.address, len(self.has_what), len(self.processing)) def __str__(self): return self.address def identity(self): return { 'type': 'Worker', 'id': self.name, 'host': self.host, 'resources': self.resources, 'local_directory': self.local_directory, 'name': self.name, 'ncores': self.ncores, 'memory_limit': self.memory_limit, 'last_seen': self.last_seen, 'services': self.services, 'metrics': self.metrics }
[docs]class TaskState(object): """ A simple object holding information about a task. .. attribute:: key: str The key is the unique identifier of a task, generally formed from the name of the function, followed by a hash of the function and arguments, like ``'inc-ab31c010444977004d656610d2d421ec'``. .. attribute:: prefix: str The key prefix, used in certain calculations to get an estimate of the task's duration based on the duration of other tasks in the same "family" (for example ``'inc'``). .. attribute:: run_spec: object A specification of how to run the task. The type and meaning of this value is opaque to the scheduler, as it is only interpreted by the worker to which the task is sent for executing. As a special case, this attribute may also be ``None``, in which case the task is "pure data" (such as, for example, a piece of data loaded in the scheduler using :meth:`Client.scatter`). A "pure data" task cannot be computed again if its value is lost. .. attribute:: priority: tuple The priority provides each task with a relative ranking which is used to break ties when many tasks are being considered for execution. This ranking is generally a 2-item tuple. The first (and dominant) item corresponds to when it was submitted. Generally, earlier tasks take precedence. The second item is determined by the client, and is a way to prioritize tasks within a large graph that may be important, such as if they are on the critical path, or good to run in order to release many dependencies. This is explained further in :doc:`Scheduling Policy <scheduling-policies>`. .. attribute:: state: str This task's current state. Valid states include ``released``, ``waiting``, ``no-worker``, ``processing``, ``memory``, ``erred`` and ``forgotten``. If it is ``forgotten``, the task isn't stored in the ``tasks`` dictionary anymore and will probably disappear soon from memory. .. attribute:: dependencies: {TaskState} The set of tasks this task depends on for proper execution. Only tasks still alive are listed in this set. If, for whatever reason, this task also depends on a forgotten task, the :attr:`has_lost_dependencies` flag is set. A task can only be executed once all its dependencies have already been successfully executed and have their result stored on at least one worker. This is tracked by progressively draining the :attr:`waiting_on` set. .. attribute:: dependents: {TaskState} The set of tasks which depend on this task. Only tasks still alive are listed in this set. This is the reverse mapping of :attr:`dependencies`. .. attribute:: has_lost_dependencies: bool Whether any of the dependencies of this task has been forgotten. For memory consumption reasons, forgotten tasks are not kept in memory even though they may have dependent tasks. When a task is forgotten, therefore, each of its dependents has their :attr:`has_lost_dependencies` attribute set to ``True``. If :attr:`has_lost_dependencies` is true, this task cannot go into the "processing" state anymore. .. attribute:: waiting_on: {TaskState} The set of tasks this task is waiting on *before* it can be executed. This is always a subset of :attr:`dependencies`. Each time one of the dependencies has finished processing, it is removed from the :attr:`waiting_on` set. Once :attr:`waiting_on` becomes empty, this task can move from the "waiting" state to the "processing" state (unless one of the dependencies errored out, in which case this task is instead marked "erred"). .. attribute:: waiters: {TaskState} The set of tasks which need this task to remain alive. This is always a subset of :attr:`dependents`. Each time one of the dependents has finished processing, it is removed from the :attr:`waiters` set. Once both :attr:`waiters` and :attr:`who_wants` become empty, this task can be released (if it has a non-empty :attr:`run_spec`) or forgotten (otherwise) by the scheduler, and by any workers in :attr:`who_has`. .. note:: Counter-intuitively, :attr:`waiting_on` and :attr:`waiters` are not reverse mappings of each other. .. attribute:: who_wants: {ClientState} The set of clients who want this task's result to remain alive. This is the reverse mapping of :attr:`ClientState.wants_what`. When a client submits a graph to the scheduler it also specifies which output tasks it desires, such that their results are not released from memory. Once a task has finished executing (i.e. moves into the "memory" or "erred" state), the clients in :attr:`who_wants` are notified. Once both :attr:`waiters` and :attr:`who_wants` become empty, this task can be released (if it has a non-empty :attr:`run_spec`) or forgotten (otherwise) by the scheduler, and by any workers in :attr:`who_has`. .. attribute:: who_has: {WorkerState} The set of workers who have this task's result in memory. It is non-empty iff the task is in the "memory" state. There can be more than one worker in this set if, for example, :meth:`Client.scatter` or :meth:`Client.replicate` was used. This is the reverse mapping of :attr:`WorkerState.has_what`. .. attribute:: processing_on: WorkerState (or None) If this task is in the "processing" state, which worker is currently processing it. Otherwise this is ``None``. This attribute is kept in sync with :attr:`WorkerState.processing`. .. attribute:: retries: int The number of times this task can automatically be retried in case of failure. If a task fails executing (the worker returns with an error), its :attr:`retries` attribute is checked. If it is equal to 0, the task is marked "erred". If it is greater than 0, the :attr:`retries` attribute is decremented and execution is attempted again. .. attribute:: nbytes: int (or None) The number of bytes, as determined by ``sizeof``, of the result of a finished task. This number is used for diagnostics and to help prioritize work. .. attribute:: exception: object If this task failed executing, the exception object is stored here. Otherwise this is ``None``. .. attribute:: traceback: object If this task failed executing, the traceback object is stored here. Otherwise this is ``None``. .. attribute:: exception_blame: TaskState (or None) If this task or one of its dependencies failed executing, the failed task is stored here (possibly itself). Otherwise this is ``None``. .. attribute:: suspicious: int The number of times this task has been involved in a worker death. Some tasks may cause workers to die (such as calling ``os._exit(0)``). When a worker dies, all of the tasks on that worker are reassigned to others. This combination of behaviors can cause a bad task to catastrophically destroy all workers on the cluster, one after another. Whenever a worker dies, we mark each task currently processing on that worker (as recorded by :attr:`WorkerState.processing`) as suspicious. If a task is involved in three deaths (or some other fixed constant) then we mark the task as ``erred``. .. attribute:: host_restrictions: {hostnames} A set of hostnames where this task can be run (or ``None`` if empty). Usually this is empty unless the task has been specifically restricted to only run on certain hosts. A hostname may correspond to one or several connected workers. .. attribute:: worker_restrictions: {worker addresses} A set of complete worker addresses where this can be run (or ``None`` if empty). Usually this is empty unless the task has been specifically restricted to only run on certain workers. Note this is tracking worker addresses, not worker states, since the specific workers may not be connected at this time. .. attribute:: resource_restrictions: {resource: quantity} Resources required by this task, such as ``{'gpu': 1}`` or ``{'memory': 1e9}`` (or ``None`` if empty). These are user-defined names and are matched against the contents of each :attr:`WorkerState.resources` dictionary. .. attribute:: loose_restrictions: bool If ``False``, each of :attr:`host_restrictions`, :attr:`worker_restrictions` and :attr:`resource_restrictions` is a hard constraint: if no worker is available satisfying those restrictions, the task cannot go into the "processing" state and will instead go into the "no-worker" state. If ``True``, the above restrictions are mere preferences: if no worker is available satisfying those restrictions, the task can still go into the "processing" state and be sent for execution to another connected worker. .. attribute: actor: bool Whether or not this task is an Actor. """ __slots__ = ( # === General description === 'actor', # Key name 'key', # Key prefix (see key_split()) 'prefix', # How to run the task (None if pure data) 'run_spec', # Alive dependents and dependencies 'dependencies', 'dependents', # Compute priority 'priority', # Restrictions 'host_restrictions', 'worker_restrictions', # not WorkerStates but addresses 'resource_restrictions', 'loose_restrictions', # === Task state === 'state', # Whether some dependencies were forgotten 'has_lost_dependencies', # If in 'waiting' state, which tasks need to complete # before we can run 'waiting_on', # If in 'waiting' or 'processing' state, which tasks needs us # to complete before they can run 'waiters', # In in 'processing' state, which worker we are processing on 'processing_on', # If in 'memory' state, Which workers have us 'who_has', # Which clients want us 'who_wants', 'exception', 'traceback', 'exception_blame', 'suspicious', 'retries', 'nbytes', ) def __init__(self, key, run_spec): self.key = key self.prefix = key_split(key) self.run_spec = run_spec self.state = None self.exception = self.traceback = self.exception_blame = None self.suspicious = self.retries = 0 self.nbytes = None self.priority = None self.who_wants = set() self.dependencies = set() self.dependents = set() self.waiting_on = set() self.waiters = set() self.who_has = set() self.processing_on = None self.has_lost_dependencies = False self.host_restrictions = None self.worker_restrictions = None self.resource_restrictions = None self.loose_restrictions = False self.actor = None def get_nbytes(self): nbytes = self.nbytes return nbytes if nbytes is not None else DEFAULT_DATA_SIZE def set_nbytes(self, nbytes): old_nbytes = self.nbytes diff = nbytes - (old_nbytes or 0) for ws in self.who_has: ws.nbytes += diff self.nbytes = nbytes def __repr__(self): return "<Task %r %s>" % (self.key, self.state) def validate(self): try: for cs in self.who_wants: assert isinstance(cs, ClientState), (repr(cs), self.who_wants) for ws in self.who_has: assert isinstance(ws, WorkerState), (repr(ws), self.who_has) for ts in self.dependencies: assert isinstance(ts, TaskState), (repr(ts), self.dependencies) for ts in self.dependents: assert isinstance(ts, TaskState), (repr(ts), self.dependents) validate_task_state(self) except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace()
class _StateLegacyMapping(Mapping): """ A mapping interface mimicking the former Scheduler state dictionaries. """ def __init__(self, states, accessor): self._states = states self._accessor = accessor def __iter__(self): return iter(self._states) def __len__(self): return len(self._states) def __getitem__(self, key): return self._accessor(self._states[key]) def __repr__(self): return "%s(%s)" % (self.__class__, dict(self)) class _OptionalStateLegacyMapping(_StateLegacyMapping): """ Similar to _StateLegacyMapping, but a false-y value is interpreted as a missing key. """ # For tasks etc. def __iter__(self): accessor = self._accessor for k, v in self._states.items(): if accessor(v): yield k def __len__(self): accessor = self._accessor return sum(bool(accessor(v)) for v in self._states.values()) def __getitem__(self, key): v = self._accessor(self._states[key]) if v: return v else: raise KeyError class _StateLegacySet(Set): """ Similar to _StateLegacyMapping, but exposes a set containing all values with a true value. """ # For loose_restrictions def __init__(self, states, accessor): self._states = states self._accessor = accessor def __iter__(self): return (k for k, v in self._states.items() if self._accessor(v)) def __len__(self): return sum(map(bool, map(self._accessor, self._states.values()))) def __contains__(self, k): st = self._states.get(k) return st is not None and bool(self._accessor(st)) def __repr__(self): return "%s(%s)" % (self.__class__, set(self)) def _legacy_task_key_set(tasks): """ Transform a set of task states into a set of task keys. """ return {ts.key for ts in tasks} def _legacy_client_key_set(clients): """ Transform a set of client states into a set of client keys. """ return {cs.client_key for cs in clients} def _legacy_worker_key_set(workers): """ Transform a set of worker states into a set of worker keys. """ return {ws.address for ws in workers} def _legacy_task_key_dict(task_dict): """ Transform a dict of {task state: value} into a dict of {task key: value}. """ return {ts.key: value for ts, value in task_dict.items()} def _task_key_or_none(task): return task.key if task is not None else None
[docs]class Scheduler(ServerNode): """ Dynamic distributed task scheduler The scheduler tracks the current state of workers, data, and computations. The scheduler listens for events and responds by controlling workers appropriately. It continuously tries to use the workers to execute an ever growing dask graph. All events are handled quickly, in linear time with respect to their input (which is often of constant size) and generally within a millisecond. To accomplish this the scheduler tracks a lot of state. Every operation maintains the consistency of this state. The scheduler communicates with the outside world through Comm objects. It maintains a consistent and valid view of the world even when listening to several clients at once. A Scheduler is typically started either with the ``dask-scheduler`` executable:: $ dask-scheduler Scheduler started at 127.0.0.1:8786 Or within a LocalCluster a Client starts up without connection information:: >>> c = Client() # doctest: +SKIP >>> c.cluster.scheduler # doctest: +SKIP Scheduler(...) Users typically do not interact with the scheduler directly but rather with the client object ``Client``. **State** The scheduler contains the following state variables. Each variable is listed along with what it stores and a brief description. * **tasks:** ``{task key: TaskState}`` Tasks currently known to the scheduler * **unrunnable:** ``{TaskState}`` Tasks in the "no-worker" state * **workers:** ``{worker key: WorkerState}`` Workers currently connected to the scheduler * **idle:** ``{WorkerState}``: Set of workers that are not fully utilized * **saturated:** ``{WorkerState}``: Set of workers that are not over-utilized * **host_info:** ``{hostname: dict}``: Information about each worker host * **clients:** ``{client key: ClientState}`` Clients currently connected to the scheduler * **services:** ``{str: port}``: Other services running on this scheduler, like Bokeh * **loop:** ``IOLoop``: The running Tornado IOLoop * **client_comms:** ``{client key: Comm}`` For each client, a Comm object used to receive task requests and report task status updates. * **stream_comms:** ``{worker key: Comm}`` For each worker, a Comm object from which we both accept stimuli and report results * **task_duration:** ``{key-prefix: time}`` Time we expect certain functions to take, e.g. ``{'sum': 0.25}`` * **coroutines:** ``[Futures]``: A list of active futures that control operation """ default_port = 8786 def __init__( self, center=None, loop=None, delete_interval='500ms', synchronize_worker_interval='60s', services=None, allowed_failures=ALLOWED_FAILURES, extensions=None, validate=False, scheduler_file=None, security=None, worker_ttl=None, **kwargs): self._setup_logging() # Attributes self.allowed_failures = allowed_failures self.validate = validate self.status = None self.proc = psutil.Process() self.delete_interval = parse_timedelta(delete_interval, default='ms') self.synchronize_worker_interval = parse_timedelta(synchronize_worker_interval, default='ms') self.digests = None self.service_specs = services or {} self.services = {} self.scheduler_file = scheduler_file worker_ttl = worker_ttl or dask.config.get('distributed.scheduler.worker-ttl') self.worker_ttl = parse_timedelta(worker_ttl) if worker_ttl else None self.security = security or Security() assert isinstance(self.security, Security) self.connection_args = self.security.get_connection_args('scheduler') self.listen_args = self.security.get_listen_args('scheduler') # Communication state self.loop = loop or IOLoop.current() self.client_comms = dict() self.stream_comms = dict() self.coroutines = [] self._worker_coroutines = [] self._ipython_kernel = None # Task state self.tasks = dict() for old_attr, new_attr, wrap in [ ('priority', 'priority', None), ('dependencies', 'dependencies', _legacy_task_key_set), ('dependents', 'dependents', _legacy_task_key_set), ('retries', 'retries', None)]: func = operator.attrgetter(new_attr) if wrap is not None: func = compose(wrap, func) setattr(self, old_attr, _StateLegacyMapping(self.tasks, func)) for old_attr, new_attr, wrap in [ ('nbytes', 'nbytes', None), ('who_wants', 'who_wants', _legacy_client_key_set), ('who_has', 'who_has', _legacy_worker_key_set), ('waiting', 'waiting_on', _legacy_task_key_set), ('waiting_data', 'waiters', _legacy_task_key_set), ('rprocessing', 'processing_on', None), ('host_restrictions', 'host_restrictions', None), ('worker_restrictions', 'worker_restrictions', None), ('resource_restrictions', 'resource_restrictions', None), ('suspicious_tasks', 'suspicious', None), ('exceptions', 'exception', None), ('tracebacks', 'traceback', None), ('exceptions_blame', 'exception_blame', _task_key_or_none)]: func = operator.attrgetter(new_attr) if wrap is not None: func = compose(wrap, func) setattr(self, old_attr, _OptionalStateLegacyMapping(self.tasks, func)) for old_attr, new_attr, wrap in [('loose_restrictions', 'loose_restrictions', None)]: func = operator.attrgetter(new_attr) if wrap is not None: func = compose(wrap, func) setattr(self, old_attr, _StateLegacySet(self.tasks, func)) self.generation = 0 self._last_client = None self._last_time = 0 self.unrunnable = set() self.n_tasks = 0 self.task_metadata = dict() self.datasets = dict() # Prefix-keyed containers self.task_duration = {prefix: 0.00001 for prefix in fast_tasks} self.unknown_durations = defaultdict(set) # Client state self.clients = dict() for old_attr, new_attr, wrap in [('wants_what', 'wants_what', _legacy_task_key_set)]: func = operator.attrgetter(new_attr) if wrap is not None: func = compose(wrap, func) setattr(self, old_attr, _StateLegacyMapping(self.clients, func)) self.clients['fire-and-forget'] = ClientState('fire-and-forget') # Worker state self.workers = sortedcontainers.SortedDict() for old_attr, new_attr, wrap in [ ('ncores', 'ncores', None), ('worker_bytes', 'nbytes', None), ('worker_resources', 'resources', None), ('used_resources', 'used_resources', None), ('occupancy', 'occupancy', None), ('worker_info', 'metrics', None), ('processing', 'processing', _legacy_task_key_dict), ('has_what', 'has_what', _legacy_task_key_set)]: func = operator.attrgetter(new_attr) if wrap is not None: func = compose(wrap, func) setattr(self, old_attr, _StateLegacyMapping(self.workers, func)) self.idle = sortedcontainers.SortedSet(key=operator.attrgetter('address')) self.saturated = set() self.total_ncores = 0 self.total_occupancy = 0 self.host_info = defaultdict(dict) self.resources = defaultdict(dict) self.aliases = dict() self._task_state_collections = [self.unrunnable] self._worker_collections = [self.workers, self.host_info, self.resources, self.aliases] self.extensions = {} self.plugins = [] self.transition_log = deque(maxlen=dask.config.get('distributed.scheduler.transition-log-length')) self.log = deque(maxlen=dask.config.get('distributed.scheduler.transition-log-length')) self.worker_setups = [] worker_handlers = { 'task-finished': self.handle_task_finished, 'task-erred': self.handle_task_erred, 'release': self.handle_release_data, 'release-worker-data': self.release_worker_data, 'add-keys': self.add_keys, 'missing-data': self.handle_missing_data, 'long-running': self.handle_long_running, 'reschedule': self.reschedule } client_handlers = { 'update-graph': self.update_graph, 'client-desires-keys': self.client_desires_keys, 'update-data': self.update_data, 'report-key': self.report_on_key, 'client-releases-keys': self.client_releases_keys, 'heartbeat-client': self.client_heartbeat, 'close-client': self.remove_client, 'restart': self.restart } self.handlers = { 'register-client': self.add_client, 'scatter': self.scatter, 'register-worker': self.add_worker, 'unregister': self.remove_worker, 'gather': self.gather, 'cancel': self.stimulus_cancel, 'retry': self.stimulus_retry, 'feed': self.feed, 'terminate': self.close, 'broadcast': self.broadcast, 'proxy': self.proxy, 'ncores': self.get_ncores, 'has_what': self.get_has_what, 'who_has': self.get_who_has, 'processing': self.get_processing, 'call_stack': self.get_call_stack, 'profile': self.get_profile, 'logs': self.get_logs, 'worker_logs': self.get_worker_logs, 'nbytes': self.get_nbytes, 'versions': self.versions, 'add_keys': self.add_keys, 'rebalance': self.rebalance, 'replicate': self.replicate, 'start_ipython': self.start_ipython, 'run_function': self.run_function, 'update_data': self.update_data, 'set_resources': self.add_resources, 'retire_workers': self.retire_workers, 'get_metadata': self.get_metadata, 'set_metadata': self.set_metadata, 'heartbeat_worker': self.heartbeat_worker, 'get_task_status': self.get_task_status, 'get_task_stream': self.get_task_stream, 'register_worker_callbacks': self.register_worker_callbacks } self._transitions = { ('released', 'waiting'): self.transition_released_waiting, ('waiting', 'released'): self.transition_waiting_released, ('waiting', 'processing'): self.transition_waiting_processing, ('waiting', 'memory'): self.transition_waiting_memory, ('processing', 'released'): self.transition_processing_released, ('processing', 'memory'): self.transition_processing_memory, ('processing', 'erred'): self.transition_processing_erred, ('no-worker', 'released'): self.transition_no_worker_released, ('no-worker', 'waiting'): self.transition_no_worker_waiting, ('released', 'forgotten'): self.transition_released_forgotten, ('memory', 'forgotten'): self.transition_memory_forgotten, ('erred', 'forgotten'): self.transition_released_forgotten, ('erred', 'released'): self.transition_erred_released, ('memory', 'released'): self.transition_memory_released, ('released', 'erred'): self.transition_released_erred } connection_limit = get_fileno_limit() / 2 super(Scheduler, self).__init__( handlers=self.handlers, stream_handlers=merge(worker_handlers, client_handlers), io_loop=self.loop, connection_limit=connection_limit, deserialize=False, connection_args=self.connection_args, **kwargs) if self.worker_ttl: pc = PeriodicCallback(self.check_worker_ttl, self.worker_ttl, io_loop=loop) self.periodic_callbacks['worker-ttl'] = pc if extensions is None: extensions = DEFAULT_EXTENSIONS for ext in extensions: ext(self) setproctitle("dask-scheduler [not started]") ################## # Administration # ################## def __repr__(self): return '<Scheduler: "%s" processes: %d cores: %d>' % ( self.address, len(self.workers), self.total_ncores)
[docs] def identity(self, comm=None): """ Basic information about ourselves and our cluster """ d = {'type': type(self).__name__, 'id': str(self.id), 'address': self.address, 'services': {key: v.port for (key, v) in self.services.items()}, 'workers': {worker.address: worker.identity() for worker in self.workers.values()}} return d
[docs] def get_worker_service_addr(self, worker, service_name, protocol=False): """ Get the (host, port) address of the named service on the *worker*. Returns None if the service doesn't exist. Parameters ---------- worker : address service_name : str Common services include 'bokeh' and 'nanny' protocol : boolean Whether or not to include a full address with protocol (True) or just a (host, port) pair """ ws = self.workers[worker] port = ws.services.get(service_name) if port is None: return None elif protocol: return '%(protocol)s://%(host)s:%(port)d' % { 'protocol': ws.address.split('://')[0], 'host': ws.host, 'port': port } else: return ws.host, port
def start_services(self, listen_ip): for k, v in self.service_specs.items(): if isinstance(k, tuple): k, port = k else: port = 0 if isinstance(v, tuple): v, kwargs = v else: kwargs = {} if listen_ip == '0.0.0.0': listen_ip = '' # for IPv6 try: service = v(self, io_loop=self.loop, **kwargs) if isinstance(port, tuple): service.listen(port) else: service.listen((listen_ip, port)) self.services[k] = service except Exception as e: warnings.warn("\nCould not launch service '%s' on port %s. " % (k, port) + "Got the following message:\n\n" + str(e), stacklevel=3) def stop_services(self): for service in self.services.values(): service.stop()
[docs] def start(self, addr_or_port=8786, start_queues=True): """ Clear out old state and restart all running coroutines """ enable_gc_diagnosis() self.clear_task_state() with ignoring(AttributeError): for c in self._worker_coroutines: c.cancel() for cor in self.coroutines: if cor.done(): exc = cor.exception() if exc: raise exc if self.status != 'running': if isinstance(addr_or_port, int): # Listen on all interfaces. `get_ip()` is not suitable # as it would prevent connecting via 127.0.0.1. self.listen(('', addr_or_port), listen_args=self.listen_args) self.ip = get_ip() listen_ip = '' else: self.listen(addr_or_port, listen_args=self.listen_args) self.ip = get_address_host(self.listen_address) listen_ip = self.ip if listen_ip == '0.0.0.0': listen_ip = '' if isinstance(addr_or_port, str) and addr_or_port.startswith('inproc://'): listen_ip = 'localhost' # Services listen on all addresses self.start_services(listen_ip) self.status = 'running' logger.info(" Scheduler at: %25s", self.address) for k, v in self.services.items(): logger.info("%11s at: %25s", k, '%s:%d' % (listen_ip, v.port)) self.loop.add_callback(self.reevaluate_occupancy) if self.scheduler_file: with open(self.scheduler_file, 'w') as f: json.dump(self.identity(), f, indent=2) fn = self.scheduler_file # remove file when we close the process def del_scheduler_file(): if os.path.exists(fn): os.remove(fn) finalize(self, del_scheduler_file) self.start_periodic_callbacks() setproctitle("dask-scheduler [%s]" % (self.address,)) return self.finished()
[docs] @gen.coroutine def finished(self): """ Wait until all coroutines have ceased """ while any(not c.done() for c in self.coroutines): yield All(self.coroutines)
[docs] @gen.coroutine def close(self, comm=None, fast=False): """ Send cleanup signal to all coroutines then wait until finished See Also -------- Scheduler.cleanup """ if self.status.startswith('clos'): return self.status = 'closing' logger.info("Scheduler closing...") setproctitle("dask-scheduler [closing]") for pc in self.periodic_callbacks.values(): pc.stop() self.periodic_callbacks.clear() self.stop_services() for ext in self.extensions: with ignoring(AttributeError): ext.teardown() logger.info("Scheduler closing all comms") futures = [] for w, comm in list(self.stream_comms.items()): if not comm.closed(): comm.send({'op': 'close', 'report': False}) comm.send({'op': 'close-stream'}) with ignoring(AttributeError): futures.append(comm.close()) for future in futures: yield future if not fast: yield self.finished() for comm in self.client_comms.values(): comm.abort() self.rpc.close() self.status = 'closed' self.stop() yield super(Scheduler, self).close() setproctitle("dask-scheduler [closed]") disable_gc_diagnosis()
[docs] @gen.coroutine def close_worker(self, stream=None, worker=None, safe=None): """ Remove a worker from the cluster This both removes the worker from our local state and also sends a signal to the worker to shut down. This works regardless of whether or not the worker has a nanny process restarting it """ logger.info("Closing worker %s", worker) with log_errors(): self.log_event(worker, {'action': 'close-worker'}) nanny_addr = self.get_worker_service_addr(worker, 'nanny', protocol=True) address = nanny_addr or worker self.worker_send(worker, {'op': 'close', 'report': False}) self.remove_worker(address=worker, safe=safe)
def _setup_logging(self): self._deque_handler = DequeHandler(n=dask.config.get('distributed.admin.log-length')) self._deque_handler.setFormatter(logging.Formatter(dask.config.get('distributed.admin.log-format'))) logger.addHandler(self._deque_handler) finalize(self, logger.removeHandler, self._deque_handler) ########### # Stimuli # ########### @gen.coroutine def heartbeat_worker(self, comm=None, address=None, resolve_address=True, now=None, resources=None, host_info=None, metrics=None): address = self.coerce_address(address, resolve_address) address = normalize_address(address) host = get_address_host(address) local_now = time() now = now or time() metrics = metrics or {} host_info = host_info or {} self.host_info[host]['last-seen'] = local_now ws = self.workers.get(address) if not ws: return {'status': 'missing'} ws.last_seen = time() if metrics: ws.metrics = metrics if host_info: self.host_info[host].update(host_info) delay = time() - now ws.time_delay = delay if resources: self.add_resources(worker=address, resources=resources) self.log_event(address, merge({'action': 'heartbeat'}, metrics)) return {'status': 'OK', 'time': time(), 'heartbeat-interval': heartbeat_interval(len(self.workers))}
[docs] @gen.coroutine def add_worker(self, comm=None, address=None, keys=(), ncores=None, name=None, resolve_address=True, nbytes=None, now=None, resources=None, host_info=None, memory_limit=None, metrics=None, pid=0, services=None, local_directory=None): """ Add a new worker to the cluster """ with log_errors(): address = self.coerce_address(address, resolve_address) address = normalize_address(address) host = get_address_host(address) ws = self.workers.get(address) if ws is not None: raise ValueError("Worker already exists %s" % address) self.workers[address] = ws = WorkerState( address=address, pid=pid, ncores=ncores, memory_limit=memory_limit, name=name, local_directory=local_directory, services=services ) if name in self.aliases: msg = {'status': 'error', 'message': 'name taken, %s' % name, 'time': time()} yield comm.write(msg) return if 'addresses' not in self.host_info[host]: self.host_info[host].update({'addresses': set(), 'cores': 0}) self.host_info[host]['addresses'].add(address) self.host_info[host]['cores'] += ncores self.total_ncores += ncores self.aliases[name] = address response = self.heartbeat_worker(address=address, resolve_address=resolve_address, now=now, resources=resources, host_info=host_info, metrics=metrics) # Do not need to adjust self.total_occupancy as self.occupancy[ws] cannot exist before this. self.check_idle_saturated(ws) # for key in keys: # TODO # self.mark_key_in_memory(key, [address]) self.stream_comms[address] = BatchedSend(interval='5ms', loop=self.loop) if ws.ncores > len(ws.processing): self.idle.add(ws) for plugin in self.plugins[:]: try: plugin.add_worker(scheduler=self, worker=address) except Exception as e: logger.exception(e) if nbytes: for key in nbytes: ts = self.tasks.get(key) if ts is not None and ts.state in ('processing', 'waiting'): recommendations = self.transition(key, 'memory', worker=address, nbytes=nbytes[key]) self.transitions(recommendations) recommendations = {} for ts in list(self.unrunnable): valid = self.valid_workers(ts) if valid is True or ws in valid: recommendations[ts.key] = 'waiting' if recommendations: self.transitions(recommendations) self.log_event(address, {'action': 'add-worker'}) self.log_event('all', {'action': 'add-worker', 'worker': address}) logger.info("Register %s", str(address)) yield comm.write({'status': 'OK', 'time': time(), 'heartbeat-interval': heartbeat_interval(len(self.workers)), 'worker-setups': self.worker_setups}) yield self.handle_worker(comm=comm, worker=address)
[docs] def update_graph(self, client=None, tasks=None, keys=None, dependencies=None, restrictions=None, priority=None, loose_restrictions=None, resources=None, submitting_task=None, retries=None, user_priority=0, actors=None, fifo_timeout=0): """ Add new computations to the internal dask graph This happens whenever the Client calls submit, map, get, or compute. """ start = time() fifo_timeout = parse_timedelta(fifo_timeout) keys = set(keys) if len(tasks) > 1: self.log_event(['all', client], {'action': 'update_graph', 'count': len(tasks)}) # Remove aliases for k in list(tasks): if tasks[k] is k: del tasks[k] dependencies = dependencies or {} n = 0 while len(tasks) != n: # walk through new tasks, cancel any bad deps n = len(tasks) for k, deps in list(dependencies.items()): if any(dep not in self.tasks and dep not in tasks for dep in deps): # bad key logger.info('User asked for computation on lost data, %s', k) del tasks[k] del dependencies[k] if k in keys: keys.remove(k) self.report({'op': 'cancelled-key', 'key': k}, client=client) self.client_releases_keys(keys=[k], client=client) # Remove any self-dependencies (happens on test_publish_bag() and others) for k, v in dependencies.items(): deps = set(v) if k in deps: deps.remove(k) dependencies[k] = deps # Avoid computation that is already finished already_in_memory = set() # tasks that are already done for k, v in dependencies.items(): if v and k in self.tasks and self.tasks[k].state in ('memory', 'erred'): already_in_memory.add(k) if already_in_memory: dependents = dask.core.reverse_dict(dependencies) stack = list(already_in_memory) done = set(already_in_memory) while stack: # remove unnecessary dependencies key = stack.pop() ts = self.tasks[key] try: deps = dependencies[key] except KeyError: deps = self.dependencies[key] for dep in deps: if all(d in done for d in dependents[dep]): if dep in self.tasks: done.add(dep) stack.append(dep) for d in done: tasks.pop(d, None) dependencies.pop(d, None) # Get or create task states stack = list(keys) touched_keys = set() touched_tasks = [] while stack: k = stack.pop() if k in touched_keys: continue # XXX Have a method get_task_state(self, k) ? ts = self.tasks.get(k) if ts is None: ts = self.tasks[k] = TaskState(k, tasks.get(k)) ts.state = 'released' elif not ts.run_spec: ts.run_spec = tasks.get(k) touched_keys.add(k) touched_tasks.append(ts) stack.extend(dependencies.get(k, ())) self.client_desires_keys(keys=keys, client=client) # Add dependencies for key, deps in dependencies.items(): ts = self.tasks.get(key) if ts is None or ts.dependencies: continue for dep in deps: dts = self.tasks[dep] ts.dependencies.add(dts) dts.dependents.add(ts) # Compute priorities if isinstance(user_priority, Number): user_priority = {k: user_priority for k in tasks} # Add actors if actors is True: actors = list(keys) for actor in actors or []: self.tasks[actor].actor = True priority = priority or dask.order.order(tasks) # TODO: define order wrt old graph if submitting_task: # sub-tasks get better priority than parent tasks ts = self.tasks.get(submitting_task) if ts is not None: generation = ts.priority[0] - 0.01 else: # super-task already cleaned up generation = self.generation elif self._last_time + fifo_timeout < start: self.generation += 1 # older graph generations take precedence generation = self.generation self._last_time = start else: generation = self.generation for key in set(priority) & touched_keys: ts = self.tasks[key] if ts.priority is None: ts.priority = (-user_priority.get(key, 0), generation, priority[key]) # Ensure all runnables have a priority runnables = [ts for ts in touched_tasks if ts.run_spec] for ts in runnables: if ts.priority is None and ts.run_spec: ts.priority = (self.generation, 0) if restrictions: # *restrictions* is a dict keying task ids to lists of # restriction specifications (either worker names or addresses) for k, v in restrictions.items(): if v is None: continue ts = self.tasks.get(k) if ts is None: continue ts.host_restrictions = set() ts.worker_restrictions = set() for w in v: try: w = self.coerce_address(w) except ValueError: # Not a valid address, but perhaps it's a hostname ts.host_restrictions.add(w) else: ts.worker_restrictions.add(w) if loose_restrictions: for k in loose_restrictions: ts = self.tasks[k] ts.loose_restrictions = True if resources: for k, v in resources.items(): if v is None: continue assert isinstance(v, dict) ts = self.tasks.get(k) if ts is None: continue ts.resource_restrictions = v if retries: for k, v in retries.items(): assert isinstance(v, int) ts = self.tasks.get(k) if ts is None: continue ts.retries = v # Compute recommendations recommendations = OrderedDict() for ts in sorted(runnables, key=operator.attrgetter('priority'), reverse=True): if ts.state == 'released' and ts.run_spec: recommendations[ts.key] = 'waiting' for ts in touched_tasks: for dts in ts.dependencies: if dts.exception_blame: ts.exception_blame = dts.exception_blame recommendations[ts.key] = 'erred' break for plugin in self.plugins[:]: try: plugin.update_graph(self, client=client, tasks=tasks, keys=keys, restrictions=restrictions or {}, dependencies=dependencies, priority=priority, loose_restrictions=loose_restrictions) except Exception as e: logger.exception(e) self.transitions(recommendations) for ts in touched_tasks: if ts.state in ('memory', 'erred'): self.report_on_key(ts.key, client=client) end = time() if self.digests is not None: self.digests['update-graph-duration'].add(end - start)
# TODO: balance workers
[docs] def stimulus_task_finished(self, key=None, worker=None, **kwargs): """ Mark that a task has finished execution on a particular worker """ logger.debug("Stimulus task finished %s, %s", key, worker) ts = self.tasks.get(key) if ts is None: return {} ws = self.workers[worker] if ts.state == 'processing': recommendations = self.transition(key, 'memory', worker=worker, **kwargs) if ts.state == 'memory': assert ws in ts.who_has else: logger.debug("Received already computed task, worker: %s, state: %s" ", key: %s, who_has: %s", worker, ts.state, key, ts.who_has) if ws not in ts.who_has: self.worker_send(worker, {'op': 'release-task', 'key': key}) recommendations = {} return recommendations
[docs] def stimulus_task_erred(self, key=None, worker=None, exception=None, traceback=None, **kwargs): """ Mark that a task has erred on a particular worker """ logger.debug("Stimulus task erred %s, %s", key, worker) ts = self.tasks.get(key) if ts is None: return {} if ts.state == 'processing': retries = ts.retries if retries > 0: ts.retries = retries - 1 recommendations = self.transition(key, 'waiting') else: recommendations = self.transition(key, 'erred', cause=key, exception=exception, traceback=traceback, worker=worker, **kwargs) else: recommendations = {} return recommendations
[docs] def stimulus_missing_data(self, cause=None, key=None, worker=None, ensure=True, **kwargs): """ Mark that certain keys have gone missing. Recover. """ with log_errors(): logger.debug("Stimulus missing data %s, %s", key, worker) ts = self.tasks.get(key) if ts is None or ts.state == 'memory': return {} cts = self.tasks.get(cause) recommendations = OrderedDict() if cts is not None and cts.state == 'memory': # couldn't find this for ws in cts.who_has: # TODO: this behavior is extreme ws.has_what.remove(cts) ws.nbytes -= cts.get_nbytes() cts.who_has.clear() recommendations[cause] = 'released' if key: recommendations[key] = 'released' self.transitions(recommendations) if self.validate: assert cause not in self.who_has return {}
def stimulus_retry(self, comm=None, keys=None, client=None): logger.info("Client %s requests to retry %d keys", client, len(keys)) if client: self.log_event(client, {'action': 'retry', 'count': len(keys)}) stack = list(keys) seen = set() roots = [] while stack: key = stack.pop() seen.add(key) erred_deps = [dts.key for dts in self.tasks[key].dependencies if dts.state == 'erred'] if erred_deps: stack.extend(erred_deps) else: roots.append(key) recommendations = {key: 'waiting' for key in roots} self.transitions(recommendations) if self.validate: for key in seen: assert not self.tasks[key].exception_blame return tuple(seen)
[docs] def remove_worker(self, comm=None, address=None, safe=False, close=True): """ Remove worker from cluster We do this when a worker reports that it plans to leave or when it appears to be unresponsive. This may send its tasks back to a released state. """ with log_errors(): if self.status == 'closed': return if address not in self.workers: return 'already-removed' address = self.coerce_address(address) host = get_address_host(address) ws = self.workers[address] self.log_event(['all', address], {'action': 'remove-worker', 'worker': address, 'processing-tasks': dict(ws.processing)}) logger.info("Remove worker %s", address) if close: with ignoring(AttributeError, CommClosedError): self.stream_comms[address].send({'op': 'close', 'report': False}) self.remove_resources(address) self.host_info[host]['cores'] -= ws.ncores self.host_info[host]['addresses'].remove(address) self.total_ncores -= ws.ncores if not self.host_info[host]['addresses']: del self.host_info[host] self.rpc.remove(address) del self.stream_comms[address] del self.aliases[ws.name] self.idle.discard(ws) self.saturated.discard(ws) del self.workers[address] ws.status = 'closed' self.total_occupancy -= ws.occupancy recommendations = OrderedDict() for ts in list(ws.processing): k = ts.key recommendations[k] = 'released' if not safe: ts.suspicious += 1 if ts.suspicious > self.allowed_failures: del recommendations[k] e = pickle.dumps(KilledWorker(k, address)) r = self.transition(k, 'erred', exception=e, cause=k) recommendations.update(r) for ts in ws.has_what: ts.who_has.remove(ws) if not ts.who_has: if ts.run_spec: recommendations[ts.key] = 'released' else: # pure data recommendations[ts.key] = 'forgotten' ws.has_what.clear() self.transitions(recommendations) for plugin in self.plugins[:]: try: plugin.remove_worker(scheduler=self, worker=address) except Exception as e: logger.exception(e) if not self.workers: logger.info("Lost all workers") @gen.coroutine def remove_worker_from_events(): # If the worker isn't registered anymore after the delay, remove from events if address not in self.workers and address in self.events: del self.events[address] cleanup_delay = parse_timedelta(dask.config.get('distributed.scheduler.events-cleanup-delay')) self.loop.call_later( cleanup_delay, remove_worker_from_events ) logger.debug("Removed worker %s", address) return 'OK'
[docs] def stimulus_cancel(self, comm, keys=None, client=None, force=False): """ Stop execution on a list of keys """ logger.info("Client %s requests to cancel %d keys", client, len(keys)) if client: self.log_event(client, {'action': 'cancel', 'count': len(keys), 'force': force}) for key in keys: self.cancel_key(key, client, force=force)
[docs] def cancel_key(self, key, client, retries=5, force=False): """ Cancel a particular key and all dependents """ # TODO: this should be converted to use the transition mechanism ts = self.tasks.get(key) cs = self.clients[client] if ts is None or not ts.who_wants: # no key yet, lets try again in a moment if retries: self.loop.add_future(gen.sleep(0.2), lambda _: self.cancel_key(key, client, retries - 1)) return if force or ts.who_wants == {cs}: # no one else wants this key for dts in list(ts.dependents): self.cancel_key(dts.key, client, force=force) logger.info("Scheduler cancels key %s. Force=%s", key, force) self.report({'op': 'cancelled-key', 'key': key}) clients = list(ts.who_wants) if force else [cs] for c in clients: self.client_releases_keys(keys=[key], client=c.client_key)
def client_desires_keys(self, keys=None, client=None): cs = self.clients.get(client) if cs is None: # For publish, queues etc. cs = self.clients[client] = ClientState(client) for k in keys: ts = self.tasks.get(k) if ts is None: # For publish, queues etc. ts = self.tasks[k] = TaskState(k, None) ts.state = 'released' ts.who_wants.add(cs) cs.wants_what.add(ts) if ts.state in ('memory', 'erred'): self.report_on_key(k, client=client)
[docs] def client_releases_keys(self, keys=None, client=None): """ Remove keys from client desired list """ logger.debug("Client %s releases keys: %s", client, keys) cs = self.clients[client] tasks2 = set() for key in list(keys): ts = self.tasks.get(key) if ts is not None and ts in cs.wants_what: cs.wants_what.remove(ts) s = ts.who_wants s.remove(cs) if not s: tasks2.add(ts) recommendations = {} for ts in tasks2: if not ts.dependents: # No live dependents, can forget recommendations[ts.key] = 'forgotten' elif ts.state != 'erred' and not ts.waiters: recommendations[ts.key] = 'released' self.transitions(recommendations)
[docs] def client_heartbeat(self, client=None): """ Handle heartbeats from Client """ self.clients[client].last_seen = time()
################### # Task Validation # ################### def validate_released(self, key): ts = self.tasks[key] assert ts.state == 'released' assert not ts.waiters assert not ts.waiting_on assert not ts.who_has assert not ts.processing_on assert not any(ts in dts.waiters for dts in ts.dependencies) assert ts not in self.unrunnable def validate_waiting(self, key): ts = self.tasks[key] assert ts.waiting_on assert not ts.who_has assert not ts.processing_on assert ts not in self.unrunnable for dts in ts.dependencies: # We are waiting on a dependency iff it's not stored assert bool(dts.who_has) + (dts in ts.waiting_on) == 1 assert ts in dts.waiters # XXX even if dts.who_has? def validate_processing(self, key): ts = self.tasks[key] assert not ts.waiting_on ws = ts.processing_on assert ws assert ts in ws.processing assert not ts.who_has for dts in ts.dependencies: assert dts.who_has assert ts in dts.waiters def validate_memory(self, key): ts = self.tasks[key] assert ts.who_has assert not ts.processing_on assert not ts.waiting_on assert ts not in self.unrunnable for dts in ts.dependents: assert (dts in ts.waiters) == (dts.state in ('waiting', 'processing')) assert ts not in dts.waiting_on def validate_no_worker(self, key): ts = self.tasks[key] assert ts in self.unrunnable assert not ts.waiting_on assert ts in self.unrunnable assert not ts.processing_on assert not ts.who_has for dts in ts.dependencies: assert dts.who_has def validate_erred(self, key): ts = self.tasks[key] assert ts.exception_blame assert not ts.who_has def validate_key(self, key, ts=None): try: if ts is None: ts = self.tasks.get(key) if ts is None: logger.debug("Key lost: %s", key) else: ts.validate() try: func = getattr(self, 'validate_' + ts.state.replace('-', '_')) except AttributeError: logger.error("self.validate_%s not found", ts.state.replace('-', '_')) else: func(key) except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise def validate_state(self, allow_overlap=False): validate_state(self.tasks, self.workers, self.clients) if not (set(self.workers) == set(self.stream_comms)): raise ValueError("Workers not the same in all collections") for w, ws in self.workers.items(): assert isinstance(w, (str, unicode)), (type(w), w) assert isinstance(ws, WorkerState), (type(ws), ws) assert ws.address == w if not ws.processing: assert not ws.occupancy assert ws in self.idle for k, ts in self.tasks.items(): assert isinstance(ts, TaskState), (type(ts), ts) assert ts.key == k self.validate_key(k, ts) for c, cs in self.clients.items(): # client=None is often used in tests... assert c is None or isinstance(c, str), (type(c), c) assert isinstance(cs, ClientState), (type(cs), cs) assert cs.client_key == c a = {w: ws.nbytes for w, ws in self.workers.items()} b = {w: sum(ts.get_nbytes() for ts in ws.has_what) for w, ws in self.workers.items()} assert a == b, (a, b) actual_total_occupancy = 0 for worker, ws in self.workers.items(): assert abs(sum(ws.processing.values()) - ws.occupancy) < 1e-8 actual_total_occupancy += ws.occupancy assert abs(actual_total_occupancy - self.total_occupancy) < 1e-8, \ (actual_total_occupancy, self.total_occupancy) ################### # Manage Messages # ###################
[docs] def report(self, msg, ts=None, client=None): """ Publish updates to all listening Queues and Comms If the message contains a key then we only send the message to those comms that care about the key. """ if client is not None: try: comm = self.client_comms[client] comm.send(msg) except CommClosedError: if self.status == 'running': logger.critical("Tried writing to closed comm: %s", msg) except KeyError: pass if ts is None and 'key' in msg: ts = self.tasks.get(msg['key']) if ts is None: # Notify all clients comms = self.client_comms.values() else: # Notify clients interested in key comms = [self.client_comms[c.client_key] for c in ts.who_wants if c.client_key in self.client_comms] for c in comms: try: c.send(msg) # logger.debug("Scheduler sends message to client %s", msg) except CommClosedError: if self.status == 'running': logger.critical("Tried writing to closed comm: %s", msg)
[docs] @gen.coroutine def add_client(self, comm, client=None): """ Add client to network We listen to all future messages from this Comm. """ assert client is not None logger.info("Receive client connection: %s", client) self.log_event(['all', client], {'action': 'add-client', 'client': client}) self.clients[client] = ClientState(client) try: bcomm = BatchedSend(interval='2ms', loop=self.loop) bcomm.start(comm) self.client_comms[client] = bcomm bcomm.send({'op': 'stream-start'}) try: yield self.handle_stream(comm=comm, extra={'client': client}) finally: self.remove_client(client=client) logger.debug('Finished handling client %s', client) finally: if not comm.closed(): self.client_comms[client].send({'op': 'stream-closed'}) try: if not shutting_down(): yield self.client_comms[client].close() del self.client_comms[client] if self.status == 'running': logger.info("Close client connection: %s", client) except TypeError: # comm becomes None during GC pass
[docs] def remove_client(self, client=None): """ Remove client from network """ if self.status == 'running': logger.info("Remove client %s", client) self.log_event(['all', client], {'action': 'remove-client', 'client': client}) try: cs = self.clients[client] except KeyError: # XXX is this a legitimate condition? pass else: self.client_releases_keys(keys=[ts.key for ts in cs.wants_what], client=cs.client_key) del self.clients[client] @gen.coroutine def remove_client_from_events(): # If the client isn't registered anymore after the delay, remove from events if client not in self.clients and client in self.events: del self.events[client] cleanup_delay = parse_timedelta(dask.config.get('distributed.scheduler.events-cleanup-delay')) self.loop.call_later( cleanup_delay, remove_client_from_events )
[docs] def send_task_to_worker(self, worker, key): """ Send a single computational task to a worker """ try: ts = self.tasks[key] msg = {'op': 'compute-task', 'key': key, 'priority': ts.priority, 'duration': self.get_task_duration(ts)} if ts.resource_restrictions: msg['resource_restrictions'] = ts.resource_restrictions if ts.actor: msg['actor'] = True deps = ts.dependencies if deps: msg['who_has'] = {dep.key: [ws.address for ws in dep.who_has] for dep in deps} msg['nbytes'] = {dep.key: dep.nbytes for dep in deps} if self.validate and deps: assert all(msg['who_has'].values()) task = ts.run_spec if type(task) is dict: msg.update(task) else: msg['task'] = task self.worker_send(worker, msg) except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise
def handle_uncaught_error(self, **msg): logger.exception(clean_exception(**msg)[1]) def handle_task_finished(self, key=None, worker=None, **msg): if worker not in self.workers: return validate_key(key) r = self.stimulus_task_finished(key=key, worker=worker, **msg) self.transitions(r) def handle_task_erred(self, key=None, **msg): r = self.stimulus_task_erred(key=key, **msg) self.transitions(r) def handle_release_data(self, key=None, worker=None, client=None, **msg): ts = self.tasks.get(key) if ts is None: return ws = self.workers[worker] if ts.processing_on is not ws: return r = self.stimulus_missing_data(key=key, ensure=False, **msg) self.transitions(r) def handle_missing_data(self, key=None, errant_worker=None, **kwargs): logger.debug("handle missing data key=%s worker=%s", key, errant_worker) self.log.append(('missing', key, errant_worker)) ts = self.tasks.get(key) if ts is None or not ts.who_has: return if errant_worker in self.workers: ws = self.workers[errant_worker] if ws in ts.who_has: ts.who_has.remove(ws) ws.has_what.remove(ts) ws.nbytes -= ts.get_nbytes() if not ts.who_has: if ts.run_spec: self.transitions({key: 'released'}) else: self.transitions({key: 'forgotten'}) def release_worker_data(self, stream=None, keys=None, worker=None): ws = self.workers[worker] tasks = {self.tasks[k] for k in keys} removed_tasks = tasks & ws.has_what ws.has_what -= removed_tasks recommendations = {} for ts in removed_tasks: ws.nbytes -= ts.get_nbytes() wh = ts.who_has wh.remove(ws) if not wh: recommendations[ts.key] = 'released' if recommendations: self.transitions(recommendations)
[docs] def handle_long_running(self, key=None, worker=None, compute_duration=None): """ A task has seceded from the thread pool We stop the task from being stolen in the future, and change task duration accounting as if the task has stopped. """ ts = self.tasks[key] if 'stealing' in self.extensions: self.extensions['stealing'].remove_key_from_stealable(ts) ws = ts.processing_on if ws is None: logger.debug("Received long-running signal from duplicate task. " "Ignoring.") return if compute_duration: prefix = ts.prefix old_duration = self.task_duration.get(prefix, 0) new_duration = compute_duration if not old_duration: avg_duration = new_duration else: avg_duration = (0.5 * old_duration + 0.5 * new_duration) self.task_duration[prefix] = avg_duration ws.occupancy -= ws.processing[ts] self.total_occupancy -= ws.processing[ts] ws.processing[ts] = 0 self.check_idle_saturated(ws)
[docs] @gen.coroutine def handle_worker(self, comm=None, worker=None): """ Listen to responses from a single worker This is the main loop for scheduler-worker interaction See Also -------- Scheduler.handle_client: Equivalent coroutine for clients """ worker_comm = self.stream_comms[worker] worker_comm.start(comm) logger.info("Starting worker compute stream, %s", worker) try: yield self.handle_stream(comm=comm, extra={'worker': worker}) finally: if worker in self.stream_comms: worker_comm.abort() self.remove_worker(address=worker)
[docs] def add_plugin(self, plugin=None, idempotent=False, **kwargs): """ Add external plugin to scheduler See https://distributed.readthedocs.io/en/latest/plugins.html """ if isinstance(plugin, type): plugin = plugin(self, **kwargs) if idempotent and any(isinstance(p, type(plugin)) for p in self.plugins): return self.plugins.append(plugin)
[docs] def remove_plugin(self, plugin): """ Remove external plugin from scheduler """ self.plugins.remove(plugin)
[docs] def worker_send(self, worker, msg): """ Send message to worker This also handles connection failures by adding a callback to remove the worker on the next cycle. """ try: self.stream_comms[worker].send(msg) except (CommClosedError, AttributeError): self.loop.add_callback(self.remove_worker, address=worker)
############################ # Less common interactions # ############################
[docs] @gen.coroutine def scatter(self, comm=None, data=None, workers=None, client=None, broadcast=False, timeout=2): """ Send data out to workers See also -------- Scheduler.broadcast: """ start = time() while not self.workers: yield gen.sleep(0.2) if time() > start + timeout: raise gen.TimeoutError("No workers found") if workers is None: ncores = {w: ws.ncores for w, ws in self.workers.items()} else: workers = [self.coerce_address(w) for w in workers] ncores = {w: self.workers[w].ncores for w in workers} assert isinstance(data, dict) keys, who_has, nbytes = yield scatter_to_workers(ncores, data, rpc=self.rpc, report=False) self.update_data(who_has=who_has, nbytes=nbytes, client=client) if broadcast: if broadcast == True: # noqa: E712 n = len(ncores) else: n = broadcast yield self.replicate(keys=keys, workers=workers, n=n) self.log_event([client, 'all'], {'action': 'scatter', 'client': client, 'count': len(data)}) raise gen.Return(keys)
[docs] @gen.coroutine def gather(self, comm=None, keys=None, serializers=None): """ Collect data in from workers """ keys = list(keys) who_has = {} for key in keys: ts = self.tasks.get(key) if ts is not None: who_has[key] = [ws.address for ws in ts.who_has] else: who_has[key] = [] data, missing_keys, missing_workers = yield gather_from_workers( who_has, rpc=self.rpc, close=False, serializers=serializers) if not missing_keys: result = {'status': 'OK', 'data': data} else: missing_states = [(self.tasks[key].state if key in self.tasks else None) for key in missing_keys] logger.debug("Couldn't gather keys %s state: %s workers: %s", missing_keys, missing_states, missing_workers) result = {'status': 'error', 'keys': missing_keys} with log_errors(): for worker in missing_workers: self.remove_worker(address=worker) # this is extreme for key, workers in missing_keys.items(): if not workers: continue ts = self.tasks[key] logger.exception("Workers don't have promised key: %s, %s", str(workers), str(key)) for worker in workers: ws = self.workers.get(worker) if ws is not None and ts in ws.has_what: ws.has_what.remove(ts) ts.who_has.remove(ws) ws.nbytes -= ts.get_nbytes() self.transitions({key: 'released'}) self.log_event('all', {'action': 'gather', 'count': len(keys)}) raise gen.Return(result)
def clear_task_state(self): # XXX what about nested state such as ClientState.wants_what # (see also fire-and-forget...) logger.info("Clear task state") for collection in self._task_state_collections: collection.clear()
[docs] @gen.coroutine def restart(self, client=None, timeout=3): """ Restart all workers. Reset local state. """ with log_errors(): n_workers = len(self.workers) logger.info("Send lost future signal to clients") for cs in self.clients.values(): self.client_releases_keys(keys=[ts.key for ts in cs.wants_what], client=cs.client_key) nannies = {addr: self.get_worker_service_addr(addr, 'nanny', protocol=True) for addr in self.workers} for addr in list(self.workers): try: # Ask the worker to close if it doesn't have a nanny, # otherwise the nanny will kill it anyway self.remove_worker(address=addr, close=addr not in nannies) except Exception as e: logger.info("Exception while restarting. This is normal", exc_info=True) self.clear_task_state() for plugin in self.plugins[:]: try: plugin.restart(self) except Exception as e: logger.exception(e) logger.debug("Send kill signal to nannies: %s", nannies) nannies = [rpc(nanny_address, connection_args=self.connection_args) for nanny_address in nannies.values() if nanny_address is not None] try: resps = All([nanny.restart(close=True, timeout=timeout * 0.8, executor_wait=False) for nanny in nannies]) resps = yield gen.with_timeout(timedelta(seconds=timeout), resps) if not all(resp == 'OK' for resp in resps): logger.error("Not all workers responded positively: %s", resps, exc_info=True) except gen.TimeoutError: logger.error("Nannies didn't report back restarted within " "timeout. Continuuing with restart process") finally: for nanny in nannies: nanny.close_rpc() self.start() self.log_event([client, 'all'], {'action': 'restart', 'client': client}) start = time() while time() < start + 10 and len(self.workers) < n_workers: yield gen.sleep(0.01) self.report({'op': 'restart'})
[docs] @gen.coroutine def broadcast(self, comm=None, msg=None, workers=None, hosts=None, nanny=False, serializers=None): """ Broadcast message to workers, return all results """ if workers is None: if hosts is None: workers = list(self.workers) else: workers = [] if hosts is not None: for host in hosts: if host in self.host_info: workers.extend(self.host_info[host]['addresses']) # TODO replace with worker_list if nanny: addresses = [self.get_worker_service_addr(w, 'nanny', protocol=True) for w in workers] else: addresses = workers @gen.coroutine def send_message(addr): comm = yield connect(addr, deserialize=self.deserialize, connection_args=self.connection_args) resp = yield send_recv(comm, close=True, serializers=serializers, **msg) raise gen.Return(resp) results = yield All([send_message(address) for address in addresses if address is not None]) raise Return(dict(zip(workers, results)))
[docs] @gen.coroutine def proxy(self, comm=None, msg=None, worker=None, serializers=None): """ Proxy a communication through the scheduler to some other worker """ d = yield self.broadcast(comm=comm, msg=msg, workers=[worker], serializers=serializers) raise gen.Return(d[worker])
[docs] @gen.coroutine def rebalance(self, comm=None, keys=None, workers=None): """ Rebalance keys so that each worker stores roughly equal bytes **Policy** This orders the workers by what fraction of bytes of the existing keys they have. It walks down this list from most-to-least. At each worker it sends the largest results it can find and sends them to the least occupied worker until either the sender or the recipient are at the average expected load. """ with log_errors(): if keys: tasks = {self.tasks[k] for k in keys} missing_data = [ts.key for ts in tasks if not ts.who_has] if missing_data: raise Return({'status': 'missing-data', 'keys': missing_data}) else: tasks = set(self.tasks.values()) if workers: workers = {self.workers[w] for w in workers} workers_by_task = {ts: ts.who_has & workers for ts in tasks} else: workers = set(self.workers.values()) workers_by_task = {ts: ts.who_has for ts in tasks} tasks_by_worker = {ws: set() for ws in workers} for k, v in workers_by_task.items(): for vv in v: tasks_by_worker[vv].add(k) worker_bytes = {ws: sum(ts.get_nbytes() for ts in v) for ws, v in tasks_by_worker.items()} avg = sum(worker_bytes.values()) / len(worker_bytes) sorted_workers = list(map(first, sorted(worker_bytes.items(), key=second, reverse=True))) recipients = iter(reversed(sorted_workers)) recipient = next(recipients) msgs = [] # (sender, recipient, key) for sender in sorted_workers[:len(workers) // 2]: sender_keys = {ts: ts.get_nbytes() for ts in tasks_by_worker[sender]} sender_keys = iter(sorted(sender_keys.items(), key=second, reverse=True)) try: while worker_bytes[sender] > avg: while (worker_bytes[recipient] < avg and worker_bytes[sender] > avg): ts, nb = next(sender_keys) if ts not in tasks_by_worker[recipient]: tasks_by_worker[recipient].add(ts) # tasks_by_worker[sender].remove(ts) msgs.append((sender, recipient, ts)) worker_bytes[sender] -= nb worker_bytes[recipient] += nb if worker_bytes[sender] > avg: recipient = next(recipients) except StopIteration: break to_recipients = defaultdict(lambda: defaultdict(list)) to_senders = defaultdict(list) for sender, recipient, ts in msgs: to_recipients[recipient.address][ts.key].append(sender.address) to_senders[sender.address].append(ts.key) result = yield {r: self.rpc(addr=r).gather(who_has=v) for r, v in to_recipients.items()} for r, v in to_recipients.items(): self.log_event(r, {'action': 'rebalance', 'who_has': v}) self.log_event('all', {'action': 'rebalance', 'total-keys': len(tasks), 'senders': valmap(len, to_senders), 'recipients': valmap(len, to_recipients), 'moved_keys': len(msgs)}) if not all(r['status'] == 'OK' for r in result.values()): raise Return({'status': 'missing-data', 'keys': sum([r['keys'] for r in result if 'keys' in r], [])}) for sender, recipient, ts in msgs: assert ts.state == 'memory' ts.who_has.add(recipient) recipient.has_what.add(ts) recipient.nbytes += ts.get_nbytes() self.log.append(('rebalance', ts.key, time(), sender.address, recipient.address)) result = yield {r: self.rpc(addr=r).delete_data(keys=v, report=False) for r, v in to_senders.items()} for sender, recipient, ts in msgs: ts.who_has.remove(sender) sender.has_what.remove(ts) sender.nbytes -= ts.get_nbytes() raise Return({'status': 'OK'})
[docs] @gen.coroutine def replicate(self, comm=None, keys=None, n=None, workers=None, branching_factor=2, delete=True): """ Replicate data throughout cluster This performs a tree copy of the data throughout the network individually on each piece of data. Parameters ---------- keys: Iterable list of keys to replicate n: int Number of replications we expect to see within the cluster branching_factor: int, optional The number of workers that can copy data in each generation. The larger the branching factor, the more data we copy in a single step, but the more a given worker risks being swamped by data requests. See also -------- Scheduler.rebalance """ assert branching_factor > 0 workers = {self.workers[w] for w in self.workers_list(workers)} if n is None: n = len(workers) else: n = min(n, len(workers)) if n == 0: raise ValueError("Can not use replicate to delete data") tasks = {self.tasks[k] for k in keys} missing_data = [ts.key for ts in tasks if not ts.who_has] if missing_data: raise Return({'status': 'missing-data', 'keys': missing_data}) # Delete extraneous data if delete: del_worker_tasks = defaultdict(set) for ts in tasks: del_candidates = ts.who_has & workers if len(del_candidates) > n: for ws in random.sample(del_candidates, len(del_candidates) - n): del_worker_tasks[ws].add(ts) yield [self.rpc(addr=ws.address) .delete_data(keys=[ts.key for ts in tasks], report=False) for ws, tasks in del_worker_tasks.items()] for ws, tasks in del_worker_tasks.items(): ws.has_what -= tasks for ts in tasks: ts.who_has.remove(ws) ws.nbytes -= ts.get_nbytes() self.log_event(ws.address, {'action': 'replicate-remove', 'keys': [ts.key for ts in tasks]}) # Copy not-yet-filled data while tasks: gathers = defaultdict(dict) for ts in list(tasks): n_missing = n - len(ts.who_has & workers) if n_missing <= 0: # Already replicated enough tasks.remove(ts) continue count = min(n_missing, branching_factor * len(ts.who_has)) assert count > 0 for ws in random.sample(workers - ts.who_has, count): gathers[ws.address][ts.key] = [wws.address for wws in ts.who_has] results = yield {w: self.rpc(addr=w).gather(who_has=who_has) for w, who_has in gathers.items()} for w, v in results.items(): if v['status'] == 'OK': self.add_keys(worker=w, keys=list(gathers[w])) else: logger.warning("Communication failed during replication: %s", v) self.log_event(w, {'action': 'replicate-add', 'keys': gathers[w]}) self.log_event('all', {'action': 'replicate', 'workers': list(workers), 'key-count': len(keys), 'branching-factor': branching_factor})
[docs] def workers_to_close(self, memory_ratio=None, n=None, key=None, minimum=None): """ Find workers that we can close with low cost This returns a list of workers that are good candidates to retire. These workers are not running anything and are storing relatively little data relative to their peers. If all workers are idle then we still maintain enough workers to have enough RAM to store our data, with a comfortable buffer. This is for use with systems like ``distributed.deploy.adaptive``. Parameters ---------- memory_factor: Number Amount of extra space we want to have for our stored data. Defaults two 2, or that we want to have twice as much memory as we currently have data. n: int Number of workers to close minimum: int Minimum number of workers to keep around key: Callable(WorkerState) An optional callable mapping a WorkerState object to a group affiliation. Groups will be closed together. This is useful when closing workers must be done collectively, such as by hostname. Examples -------- >>> scheduler.workers_to_close() ['tcp://192.168.0.1:1234', 'tcp://192.168.0.2:1234'] Group workers by hostname prior to closing >>> scheduler.workers_to_close(key=lambda ws: ws.host) ['tcp://192.168.0.1:1234', 'tcp://192.168.0.1:4567'] Remove two workers >>> scheduler.workers_to_close(n=2) Keep enough workers to have twice as much memory as we we need. >>> scheduler.workers_to_close(memory_ratio=2) Returns ------- to_close: list of worker addresses that are OK to close See Also -------- Scheduler.retire_workers """ if n is None and memory_ratio is None: memory_ratio = 2 with log_errors(): if not n and all(ws.processing for ws in self.workers.values()): return [] if key is None: key = lambda ws: ws.address groups = groupby(key, self.workers.values()) limit_bytes = {k: sum(ws.memory_limit for ws in v) for k, v in groups.items()} group_bytes = {k: sum(ws.nbytes for ws in v) for k, v in groups.items()} limit = sum(limit_bytes.values()) total = sum(group_bytes.values()) def key(group): is_idle = not any(ws.processing for ws in groups[group]) bytes = -group_bytes[group] return (is_idle, bytes) idle = sorted(groups, key=key) to_close = [] n_remain = len(self.workers) while idle: group = idle.pop() if n is None and any(ws.processing for ws in groups[group]): break if minimum and n_remain - len(groups[group]) < minimum: break limit -= limit_bytes[group] if ((n is not None and len(to_close) < n) or (memory_ratio is not None and limit >= memory_ratio * total)): to_close.append(group) n_remain -= len(groups[group]) else: break result = [ws.address for g in to_close for ws in groups[g]] if result: logger.info("Suggest closing workers: %s", result) return result
[docs] @gen.coroutine def retire_workers(self, comm=None, workers=None, remove=True, close_workers=False, **kwargs): """ Gracefully retire workers from cluster Parameters ---------- workers: list (optional) List of worker IDs to retire. If not provided we call ``workers_to_close`` which finds a good set remove: bool (defaults to True) Whether or not to remove the worker metadata immediately or else wait for the worker to contact us close_workers: bool (defaults to False) Whether or not to actually close the worker explicitly from here. Otherwise we expect some external job scheduler to finish off the worker. **kwargs: dict Extra options to pass to workers_to_close to determine which workers we should drop Returns ------- Dictionary mapping worker ID/address to dictionary of information about that worker for each retired worker. See Also -------- Scheduler.workers_to_close """ with log_errors(): if workers is None: while True: try: workers = self.workers_to_close(**kwargs) if workers: workers = yield self.retire_workers(workers=workers, remove=remove, close_workers=close_workers) raise gen.Return(workers) except KeyError: # keys left during replicate pass workers = {self.workers[w] for w in workers} if len(workers) > 0: # Keys orphaned by retiring those workers keys = set.union(*[w.has_what for w in workers]) keys = {ts.key for ts in keys if ts.who_has.issubset(workers)} else: keys = set() other_workers = set(self.workers.values()) - workers if keys: if other_workers: yield self.replicate(keys=keys, workers=[ws.address for ws in other_workers], n=1, delete=False) else: raise gen.Return([]) worker_keys = {ws.address: ws.identity() for ws in workers} if close_workers and worker_keys: yield [self.close_worker(worker=w, safe=True) for w in worker_keys] if remove: for w in worker_keys: self.remove_worker(address=w, safe=True) self.log_event('all', {'action': 'retire-workers', 'workers': worker_keys, 'moved-keys': len(keys)}) self.log_event(list(worker_keys), {'action': 'retired'}) raise gen.Return(worker_keys)
[docs] def add_keys(self, comm=None, worker=None, keys=()): """ Learn that a worker has certain keys This should not be used in practice and is mostly here for legacy reasons. However, it is sent by workers from time to time. """ if worker not in self.workers: return 'not found' ws = self.workers[worker] for key in keys: ts = self.tasks.get(key) if ts is not None and ts.state == 'memory': if ts not in ws.has_what: ws.nbytes += ts.get_nbytes() ws.has_what.add(ts) ts.who_has.add(ws) else: self.worker_send(worker, {'op': 'delete-data', 'keys': [key], 'report': False}) return 'OK'
[docs] def update_data(self, comm=None, who_has=None, nbytes=None, client=None, serializers=None): """ Learn that new data has entered the network from an external source See Also -------- Scheduler.mark_key_in_memory """ with log_errors(): who_has = {k: [self.coerce_address(vv) for vv in v] for k, v in who_has.items()} logger.debug("Update data %s", who_has) for key, workers in who_has.items(): ts = self.tasks.get(key) if ts is None: ts = self.tasks[key] = TaskState(key, None) ts.state = 'memory' if key in nbytes: ts.set_nbytes(nbytes[key]) for w in workers: ws = self.workers[w] if ts not in ws.has_what: ws.nbytes += ts.get_nbytes() ws.has_what.add(ts) ts.who_has.add(ws) self.report({'op': 'key-in-memory', 'key': key, 'workers': list(workers)}) if client: self.client_desires_keys(keys=list(who_has), client=client)
def report_on_key(self, key=None, ts=None, client=None): assert (key is None) + (ts is None) == 1, (key, ts) if ts is None: try: ts = self.tasks[key] except KeyError: self.report({'op': 'cancelled-key', 'key': key}, client=client) return else: key = ts.key if ts.state == 'forgotten': self.report({'op': 'cancelled-key', 'key': key}, ts=ts, client=client) elif ts.state == 'memory': self.report({'op': 'key-in-memory', 'key': key}, ts=ts, client=client) elif ts.state == 'erred': failing_ts = ts.exception_blame self.report({'op': 'task-erred', 'key': key, 'exception': failing_ts.exception, 'traceback': failing_ts.traceback}, ts=ts, client=client)
[docs] @gen.coroutine def feed(self, comm, function=None, setup=None, teardown=None, interval='1s', **kwargs): """ Provides a data Comm to external requester Caution: this runs arbitrary Python code on the scheduler. This should eventually be phased out. It is mostly used by diagnostics. """ import pickle interval = parse_timedelta(interval) with log_errors(): if function: function = pickle.loads(function) if setup: setup = pickle.loads(setup) if teardown: teardown = pickle.loads(teardown) state = setup(self) if setup else None if isinstance(state, gen.Future): state = yield state try: while self.status == 'running': if state is None: response = function(self) else: response = function(self, state) yield comm.write(response) yield gen.sleep(interval) except (EnvironmentError, CommClosedError): pass finally: if teardown: teardown(self, state)
def get_processing(self, comm=None, workers=None): if workers is not None: workers = set(map(self.coerce_address, workers)) return {w: [ts.key for ts in self.workers[w].processing] for w in workers} else: return {w: [ts.key for ts in ws.processing] for w, ws in self.workers.items()} def get_who_has(self, comm=None, keys=None): if keys is not None: return {k: [ws.address for ws in self.tasks[k].who_has] if k in self.tasks else [] for k in keys} else: return {key: [ws.address for ws in ts.who_has] for key, ts in self.tasks.items()} def get_has_what(self, comm=None, workers=None): if workers is not None: workers = map(self.coerce_address, workers) return {w: [ts.key for ts in self.workers[w].has_what] if w in self.workers else [] for w in workers} else: return {w: [ts.key for ts in ws.has_what] for w, ws in self.workers.items()} def get_ncores(self, comm=None, workers=None): if workers is not None: workers = map(self.coerce_address, workers) return {w: self.workers[w].ncores for w in workers if w in self.workers} else: return {w: ws.ncores for w, ws in self.workers.items()} @gen.coroutine def get_call_stack(self, comm=None, keys=None): if keys is not None: stack = list(keys) processing = set() while stack: key = stack.pop() ts = self.tasks[key] if ts.state == 'waiting': stack.extend(dts.key for dts in ts.dependencies) elif ts.state == 'processing': processing.add(ts) workers = defaultdict(list) for ts in processing: if ts.processing_on: workers[ts.processing_on.address].append(ts.key) else: workers = {w: None for w in self.workers} if not workers: raise gen.Return({}) else: response = yield {w: self.rpc(w).call_stack(keys=v) for w, v in workers.items()} response = {k: v for k, v in response.items() if v} raise gen.Return(response) def get_nbytes(self, comm=None, keys=None, summary=True): with log_errors(): if keys is not None: result = {k: self.tasks[k].nbytes for k in keys} else: result = {k: ts.nbytes for k, ts in self.tasks.items() if ts.nbytes is not None} if summary: out = defaultdict(lambda: 0) for k, v in result.items(): out[key_split(k)] += v result = dict(out) return result
[docs] def get_comm_cost(self, ts, ws): """ Get the estimated communication cost (in s.) to compute the task on the given worker. """ return (sum(dts.nbytes for dts in ts.dependencies - ws.has_what) / BANDWIDTH)
[docs] def get_task_duration(self, ts, default=0.5): """ Get the estimated computation cost of the given task (not including any communication cost). """ prefix = ts.prefix try: return self.task_duration[prefix] except KeyError: self.unknown_durations[prefix].add(ts) return default
[docs] def run_function(self, stream, function, args=(), kwargs={}, wait=True): """ Run a function within this process See Also -------- Client.run_on_scheduler: """ from .worker import run self.log_event('all', {'action': 'run-function', 'function': function}) return run(self, stream, function=function, args=args, kwargs=kwargs, wait=wait)
def set_metadata(self, stream=None, keys=None, value=None): try: metadata = self.task_metadata for key in keys[:-1]: if key not in metadata or not isinstance(metadata[key], (dict, list)): metadata[key] = dict() metadata = metadata[key] metadata[keys[-1]] = value except Exception as e: import pdb; pdb.set_trace() def get_metadata(self, stream=None, keys=None, default=no_default): metadata = self.task_metadata for key in keys[:-1]: metadata = metadata[key] try: return metadata[keys[-1]] except KeyError: if default != no_default: return default else: raise def get_task_status(self, stream=None, keys=None): return {key: (self.tasks[key].state if key in self.tasks else None) for key in keys} def get_task_stream(self, comm=None, start=None, stop=None, count=None): from distributed.diagnostics.task_stream import TaskStreamPlugin self.add_plugin(TaskStreamPlugin, idempotent=True) ts = [p for p in self.plugins if isinstance(p, TaskStreamPlugin)][0] return ts.collect(start=start, stop=stop, count=count)
[docs] @gen.coroutine def register_worker_callbacks(self, comm, setup=None): """ Registers a setup function, and call it on every worker """ if setup is None: raise gen.Return({}) self.worker_setups.append(setup) responses = yield self.broadcast(msg=dict(op='run', function=setup)) raise gen.Return(responses)
##################### # State Transitions # ##################### def _remove_from_processing(self, ts, send_worker_msg=None): """ Remove *ts* from the set of processing tasks. """ ws = ts.processing_on ts.processing_on = None w = ws.address if w in self.workers: # may have been removed duration = ws.processing.pop(ts) if not ws.processing: self.total_occupancy -= ws.occupancy ws.occupancy = 0 else: self.total_occupancy -= duration ws.occupancy -= duration self.check_idle_saturated(ws) self.release_resources(ts, ws) if send_worker_msg: self.worker_send(w, send_worker_msg) def _add_to_memory(self, ts, ws, recommendations, type=None, **kwargs): """ Add *ts* to the set of in-memory tasks. """ if self.validate: assert ts not in ws.has_what ts.who_has.add(ws) ws.has_what.add(ts) ws.nbytes += ts.get_nbytes() deps = ts.dependents if len(deps) > 1: deps = sorted(deps, key=operator.attrgetter('priority'), reverse=True) for dts in deps: s = dts.waiting_on if ts in s: s.discard(ts) if not s: # new task ready to run recommendations[dts.key] = 'processing' for dts in ts.dependencies: s = dts.waiters s.discard(ts) if not s and not dts.who_wants: recommendations[dts.key] = 'released' if not ts.waiters and not ts.who_wants: recommendations[ts.key] = 'released' else: msg = {'op': 'key-in-memory', 'key': ts.key} if type is not None: msg['type'] = type self.report(msg) ts.state = 'memory' cs = self.clients['fire-and-forget'] if ts in cs.wants_what: self.client_releases_keys(client='fire-and-forget', keys=[ts.key]) def transition_released_waiting(self, key): try: ts = self.tasks[key] if self.validate: assert ts.run_spec assert not ts.waiting_on assert not ts.who_has assert not ts.processing_on assert not any(dts.state == 'forgotten' for dts in ts.dependencies) if ts.has_lost_dependencies: return {key: 'forgotten'} ts.state = 'waiting' recommendations = OrderedDict() for dts in ts.dependencies: if dts.exception_blame: ts.exception_blame = dts.exception_blame recommendations[key] = 'erred' return recommendations for dts in ts.dependencies: dep = dts.key if not dts.who_has: ts.waiting_on.add(dts) if dts.state == 'released': recommendations[dep] = 'waiting' else: dts.waiters.add(ts) ts.waiters = {dts for dts in ts.dependents if dts.state == 'waiting'} if not ts.waiting_on: if self.workers: recommendations[key] = 'processing' else: self.unrunnable.add(ts) ts.state = 'no-worker' return recommendations except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise def transition_no_worker_waiting(self, key): try: ts = self.tasks[key] if self.validate: assert ts in self.unrunnable assert not ts.waiting_on assert not ts.who_has assert not ts.processing_on self.unrunnable.remove(ts) if ts.has_lost_dependencies: return {key: 'forgotten'} recommendations = OrderedDict() for dts in ts.dependencies: dep = dts.key if not dts.who_has: ts.waiting_on.add(dep) if dts.state == 'released': recommendations[dep] = 'waiting' else: dts.waiters.add(ts) ts.state = 'waiting' if not ts.waiting_on: if self.workers: recommendations[key] = 'processing' else: self.unrunnable.add(ts) ts.state = 'no-worker' return recommendations except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise
[docs] def decide_worker(self, ts): """ Decide on a worker for task *ts*. Return a WorkerState. """ valid_workers = self.valid_workers(ts) if not valid_workers and not ts.loose_restrictions and self.workers: self.unrunnable.add(ts) ts.state = 'no-worker' return None if ts.dependencies or valid_workers is not True: worker = decide_worker(ts, self.workers.values(), valid_workers, partial(self.worker_objective, ts)) elif self.idle: if len(self.idle) < 20: # smart but linear in small case worker = min(self.idle, key=operator.attrgetter('occupancy')) else: # dumb but fast in large case worker = self.idle[self.n_tasks % len(self.idle)] else: if len(self.workers) < 20: # smart but linear in small case worker = min(self.workers.values(), key=operator.attrgetter('occupancy')) else: # dumb but fast in large case worker = self.workers.values()[self.n_tasks % len(self.workers)] if self.validate: assert worker is None or isinstance(worker, WorkerState), (type(worker), worker) assert worker.address in self.workers return worker
def transition_waiting_processing(self, key): try: ts = self.tasks[key] if self.validate: assert not ts.waiting_on assert not ts.who_has assert not ts.exception_blame assert not ts.processing_on assert not ts.has_lost_dependencies assert ts not in self.unrunnable assert all(dts.who_has for dts in ts.dependencies) ws = self.decide_worker(ts) if ws is None: return {} worker = ws.address duration = self.get_task_duration(ts) comm = self.get_comm_cost(ts, ws) ws.processing[ts] = duration + comm ts.processing_on = ws ws.occupancy += duration + comm self.total_occupancy += duration + comm ts.state = 'processing' self.consume_resources(ts, ws) self.check_idle_saturated(ws) self.n_tasks += 1 if ts.actor: ws.actors.add(ts) # logger.debug("Send job to worker: %s, %s", worker, key) self.send_task_to_worker(worker, key) return {} except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise def transition_waiting_memory(self, key, nbytes=None, worker=None, **kwargs): try: ws = self.workers[worker] ts = self.tasks[key] if self.validate: assert not ts.processing_on assert ts.waiting_on assert ts.state == 'waiting' ts.waiting_on.clear() if nbytes is not None: ts.set_nbytes(nbytes) self.check_idle_saturated(ws) recommendations = OrderedDict() self._add_to_memory(ts, ws, recommendations, **kwargs) if self.validate: assert not ts.processing_on assert not ts.waiting_on assert ts.who_has return recommendations except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise def transition_processing_memory(self, key, nbytes=None, type=None, worker=None, startstops=None, **kwargs): try: ts = self.tasks[key] assert worker assert isinstance(worker, (str, unicode)) if self.validate: assert ts.processing_on ws = ts.processing_on assert ts in ws.processing assert not ts.waiting_on assert not ts.who_has, (ts, ts.who_has) assert not ts.exception_blame assert ts.state == 'processing' ws = self.workers.get(worker) if ws is None: return {key: 'released'} if ws is not ts.processing_on: # someone else has this task logger.info("Unexpected worker completed task, likely due to" " work stealing. Expected: %s, Got: %s, Key: %s", ts.processing_on, ws, key) return {} if startstops: L = [(b, c) for a, b, c in startstops if a == 'compute'] if L: compute_start, compute_stop = L[0] else: # This is very rare compute_start = compute_stop = None else: compute_start = compute_stop = None ############################# # Update Timing Information # ############################# if compute_start and ws.processing.get(ts, True): # Update average task duration for worker prefix = ts.prefix old_duration = self.task_duration.get(prefix, 0) new_duration = compute_stop - compute_start if not old_duration: avg_duration = new_duration else: avg_duration = (0.5 * old_duration + 0.5 * new_duration) self.task_duration[prefix] = avg_duration for tts in self.unknown_durations.pop(prefix, ()): if tts.processing_on: wws = tts.processing_on old = wws.processing[tts] comm = self.get_comm_cost(tts, wws) wws.processing[tts] = avg_duration + comm wws.occupancy += avg_duration + comm - old self.total_occupancy += avg_duration + comm - old ############################ # Update State Information # ############################ if nbytes is not None: ts.set_nbytes(nbytes) recommendations = OrderedDict() self._remove_from_processing(ts) self._add_to_memory(ts, ws, recommendations, type=type) if self.validate: assert not ts.processing_on assert not ts.waiting_on return recommendations except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise def transition_memory_released(self, key, safe=False): try: ts = self.tasks[key] if self.validate: assert not ts.waiting_on assert not ts.processing_on if safe: assert not ts.waiters if ts.actor: for ws in ts.who_has: ws.actors.discard(ts) if ts.who_wants: ts.exception_blame = ts ts.exception = "Worker holding Actor was lost" return {ts.key: 'erred'} # don't try to recreate recommendations = OrderedDict() for dts in ts.waiters: if dts.state in ('no-worker', 'processing'): recommendations[dts.key] = 'waiting' elif dts.state == 'waiting': dts.waiting_on.add(ts) # XXX factor this out? for ws in ts.who_has: ws.has_what.remove(ts) ws.nbytes -= ts.get_nbytes() self.worker_send(ws.address, {'op': 'delete-data', 'keys': [key], 'report': False}) ts.who_has.clear() ts.state = 'released' self.report({'op': 'lost-data', 'key': key}) if not ts.run_spec: # pure data recommendations[key] = 'forgotten' elif ts.has_lost_dependencies: recommendations[key] = 'forgotten' elif ts.who_wants or ts.waiters: recommendations[key] = 'waiting' if self.validate: assert not ts.waiting_on return recommendations except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise def transition_released_erred(self, key): try: ts = self.tasks[key] if self.validate: with log_errors(pdb=LOG_PDB): assert ts.exception_blame assert not ts.who_has assert not ts.waiting_on assert not ts.waiters recommendations = {} failing_ts = ts.exception_blame for dts in ts.dependents: dts.exception_blame = failing_ts if not dts.who_has: recommendations[dts.key] = 'erred' self.report({'op': 'task-erred', 'key': key, 'exception': failing_ts.exception, 'traceback': failing_ts.traceback}) ts.state = 'erred' # TODO: waiting data? return recommendations except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise def transition_erred_released(self, key): try: ts = self.tasks[key] if self.validate: with log_errors(pdb=LOG_PDB): assert all(dts.state != 'erred' for dts in ts.dependencies) assert ts.exception_blame assert not ts.who_has assert not ts.waiting_on assert not ts.waiters recommendations = OrderedDict() ts.exception = None ts.exception_blame = None ts.traceback = None for dep in ts.dependents: if dep.state == 'erred': recommendations[dep.key] = 'waiting' self.report({'op': 'task-retried', 'key': key}) ts.state = 'released' return recommendations except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise def transition_waiting_released(self, key): try: ts = self.tasks[key] if self.validate: assert not ts.who_has assert not ts.processing_on recommendations = {} for dts in ts.dependencies: s = dts.waiters if ts in s: s.discard(ts) if not s and not dts.who_wants: recommendations[dts.key] = 'released' ts.waiting_on.clear() ts.state = 'released' if ts.has_lost_dependencies: recommendations[key] = 'forgotten' elif not ts.exception_blame and (ts.who_wants or ts.waiters): recommendations[key] = 'waiting' else: ts.waiters.clear() return recommendations except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise def transition_processing_released(self, key): try: ts = self.tasks[key] if self.validate: assert ts.processing_on assert not ts.who_has assert not ts.waiting_on assert self.tasks[key].state == 'processing' self._remove_from_processing(ts, send_worker_msg={'op': 'release-task', 'key': key}) ts.state = 'released' recommendations = OrderedDict() if ts.has_lost_dependencies: recommendations[key] = 'forgotten' elif ts.waiters or ts.who_wants: recommendations[key] = 'waiting' if recommendations.get(key) != 'waiting': for dts in ts.dependencies: if dts.state != 'released': s = dts.waiters s.discard(ts) if not s and not dts.who_wants: recommendations[dts.key] = 'released' ts.waiters.clear() if self.validate: assert not ts.processing_on return recommendations except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise def transition_processing_erred(self, key, cause=None, exception=None, traceback=None, **kwargs): try: ts = self.tasks[key] if self.validate: assert cause or ts.exception_blame assert ts.processing_on assert not ts.who_has assert not ts.waiting_on if ts.actor: ws = ts.processing_on ws.actors.remove(ts) self._remove_from_processing(ts) if exception is not None: ts.exception = exception if traceback is not None: ts.traceback = traceback if cause is not None: failing_ts = self.tasks[cause] ts.exception_blame = failing_ts else: failing_ts = ts.exception_blame recommendations = {} for dts in ts.dependents: dts.exception_blame = failing_ts recommendations[dts.key] = 'erred' for dts in ts.dependencies: s = dts.waiters s.discard(ts) if not s and not dts.who_wants: recommendations[dts.key] = 'released' ts.waiters.clear() # do anything with this? ts.state = 'erred' self.report({'op': 'task-erred', 'key': key, 'exception': failing_ts.exception, 'traceback': failing_ts.traceback}) cs = self.clients['fire-and-forget'] if ts in cs.wants_what: self.client_releases_keys(client='fire-and-forget', keys=[key]) if self.validate: assert not ts.processing_on return recommendations except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise def transition_no_worker_released(self, key): try: ts = self.tasks[key] if self.validate: assert self.tasks[key].state == 'no-worker' assert not ts.who_has assert not ts.waiting_on self.unrunnable.remove(ts) ts.state = 'released' for dts in ts.dependencies: dts.waiters.discard(ts) ts.waiters.clear() return {} except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise def remove_key(self, key): ts = self.tasks.pop(key) assert ts.state == 'forgotten' self.unrunnable.discard(ts) for cs in ts.who_wants: cs.wants_what.remove(ts) ts.who_wants.clear() ts.processing_on = None ts.exception_blame = ts.exception = ts.traceback = None if key in self.task_metadata: del self.task_metadata[key] def _propagate_forgotten(self, ts, recommendations): ts.state = 'forgotten' key = ts.key for dts in ts.dependents: dts.has_lost_dependencies = True dts.dependencies.remove(ts) dts.waiting_on.discard(ts) if dts.state not in ('memory', 'erred'): # Cannot compute task anymore recommendations[dts.key] = 'forgotten' ts.dependents.clear() ts.waiters.clear() for dts in ts.dependencies: dts.dependents.remove(ts) s = dts.waiters s.discard(ts) if not dts.dependents and not dts.who_wants: # Task not needed anymore assert dts is not ts recommendations[dts.key] = 'forgotten' ts.dependencies.clear() ts.waiting_on.clear() for ws in ts.who_has: ws.has_what.remove(ts) ws.nbytes -= ts.get_nbytes() w = ws.address if w in self.workers: # in case worker has died self.worker_send(w, {'op': 'delete-data', 'keys': [key], 'report': False}) ts.who_has.clear() def transition_memory_forgotten(self, key): try: ts = self.tasks[key] if self.validate: assert ts.state == 'memory' assert not ts.processing_on assert not ts.waiting_on if not ts.run_spec: # It's ok to forget a pure data task pass elif ts.has_lost_dependencies: # It's ok to forget a task with forgotten dependencies pass elif not ts.who_wants and not ts.waiters and not ts.dependents: # It's ok to forget a task that nobody needs pass else: assert 0, (ts,) recommendations = {} if ts.actor: for ws in ts.who_has: ws.actors.discard(ts) self._propagate_forgotten(ts, recommendations) self.report_on_key(ts=ts) self.remove_key(key) return recommendations except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise def transition_released_forgotten(self, key): try: ts = self.tasks[key] if self.validate: assert ts.state in ('released', 'erred') assert not ts.who_has assert not ts.processing_on assert not ts.waiting_on, (ts, ts.waiting_on) if not ts.run_spec: # It's ok to forget a pure data task pass elif ts.has_lost_dependencies: # It's ok to forget a task with forgotten dependencies pass elif not ts.who_wants and not ts.waiters and not ts.dependents: # It's ok to forget a task that nobody needs pass else: assert 0, (ts,) recommendations = {} self._propagate_forgotten(ts, recommendations) self.report_on_key(ts=ts) self.remove_key(key) return recommendations except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise
[docs] def transition(self, key, finish, *args, **kwargs): """ Transition a key from its current state to the finish state Examples -------- >>> self.transition('x', 'waiting') {'x': 'processing'} Returns ------- Dictionary of recommendations for future transitions See Also -------- Scheduler.transitions: transitive version of this function """ try: try: ts = self.tasks[key] except KeyError: return {} start = ts.state if start == finish: return {} if self.plugins: dependents = set(ts.dependents) dependencies = set(ts.dependencies) if (start, finish) in self._transitions: func = self._transitions[start, finish] recommendations = func(key, *args, **kwargs) elif 'released' not in (start, finish): func = self._transitions['released', finish] assert not args and not kwargs a = self.transition(key, 'released') if key in a: func = self._transitions['released', a[key]] b = func(key) a = a.copy() a.update(b) recommendations = a start = 'released' else: raise RuntimeError("Impossible transition from %r to %r" % (start, finish)) finish2 = ts.state self.transition_log.append((key, start, finish2, recommendations, time())) if self.validate: logger.debug("Transitioned %r %s->%s (actual: %s). Consequence: %s", key, start, finish2, ts.state, dict(recommendations)) if self.plugins: # Temporarily put back forgotten key for plugin to retrieve it if ts.state == 'forgotten': try: ts.dependents = dependents ts.dependencies = dependencies except KeyError: pass self.tasks[ts.key] = ts for plugin in self.plugins: try: plugin.transition(key, start, finish2, *args, **kwargs) except Exception: logger.info("Plugin failed with exception", exc_info=True) if ts.state == 'forgotten': del self.tasks[ts.key] return recommendations except Exception as e: logger.exception("Error transitioning %r from %r to %r", key, start, finish) if LOG_PDB: import pdb pdb.set_trace() raise
[docs] def transitions(self, recommendations): """ Process transitions until none are left This includes feedback from previous transitions and continues until we reach a steady state """ keys = set() recommendations = recommendations.copy() while recommendations: key, finish = recommendations.popitem() keys.add(key) new = self.transition(key, finish) recommendations.update(new) if self.validate: for key in keys: self.validate_key(key)
[docs] def story(self, *keys): """ Get all transitions that touch one of the input keys """ keys = set(keys) return [t for t in self.transition_log if t[0] in keys or keys.intersection(t[3])]
transition_story = story
[docs] def reschedule(self, key=None, worker=None): """ Reschedule a task Things may have shifted and this task may now be better suited to run elsewhere """ ts = self.tasks[key] if ts.state != 'processing': return if worker and ts.processing_on.address != worker: return self.transitions({key: 'released'})
############################## # Assigning Tasks to Workers # ############################## def check_idle_saturated(self, ws, occ=None): if self.total_ncores == 0 or ws.status == 'closed': return if occ is None: occ = ws.occupancy nc = ws.ncores p = len(ws.processing) avg = self.total_occupancy / self.total_ncores if p < nc or occ / nc < avg / 2: self.idle.add(ws) self.saturated.discard(ws) else: self.idle.discard(ws) pending = occ * (p - nc) / p / nc if p > nc and pending > 0.4 and pending > 1.9 * avg: self.saturated.add(ws) else: self.saturated.discard(ws)
[docs] def valid_workers(self, ts): """ Return set of currently valid workers for key If all workers are valid then this returns ``True``. This checks tracks the following state: * worker_restrictions * host_restrictions * resource_restrictions """ s = True if ts.worker_restrictions: s = {w for w in ts.worker_restrictions if w in self.workers} if ts.host_restrictions: # Resolve the alias here rather than early, for the worker # may not be connected when host_restrictions is populated hr = [self.coerce_hostname(h) for h in ts.host_restrictions] # XXX need HostState? ss = [self.host_info[h]['addresses'] for h in hr if h in self.host_info] ss = set.union(*ss) if ss else set() if s is True: s = ss else: s |= ss if ts.resource_restrictions: w = {resource: {w for w, supplied in self.resources[resource].items() if supplied >= required} for resource, required in ts.resource_restrictions.items()} ww = set.intersection(*w.values()) if s is True: s = ww else: s &= ww if s is True: return s else: return {self.workers[w] for w in s}
def consume_resources(self, ts, ws): if ts.resource_restrictions: for r, required in ts.resource_restrictions.items(): ws.used_resources[r] += required def release_resources(self, ts, ws): if ts.resource_restrictions: for r, required in ts.resource_restrictions.items(): ws.used_resources[r] -= required ##################### # Utility functions # ##################### def add_resources(self, stream=None, worker=None, resources=None): ws = self.workers[worker] if resources: ws.resources.update(resources) ws.used_resources = {} for resource, quantity in ws.resources.items(): ws.used_resources[resource] = 0 self.resources[resource][worker] = quantity return 'OK' def remove_resources(self, worker): ws = self.workers[worker] for resource, quantity in ws.resources.items(): del self.resources[resource][worker]
[docs] def coerce_address(self, addr, resolve=True): """ Coerce possible input addresses to canonical form. *resolve* can be disabled for testing with fake hostnames. Handles strings, tuples, or aliases. """ # XXX how many address-parsing routines do we have? if addr in self.aliases: addr = self.aliases[addr] if isinstance(addr, tuple): addr = unparse_host_port(*addr) if not isinstance(addr, six.string_types): raise TypeError("addresses should be strings or tuples, got %r" % (addr,)) if resolve: addr = resolve_address(addr) else: addr = normalize_address(addr) return addr
[docs] def coerce_hostname(self, host): """ Coerce the hostname of a worker. """ if host in self.aliases: return self.workers[self.aliases[host]].host else: return host
[docs] def workers_list(self, workers): """ List of qualifying workers Takes a list of worker addresses or hostnames. Returns a list of all worker addresses that match """ if workers is None: return list(self.workers) out = set() for w in workers: if ':' in w: out.add(w) else: out.update({ww for ww in self.workers if w in ww}) # TODO: quadratic return list(out)
[docs] def start_ipython(self, comm=None): """Start an IPython kernel Returns Jupyter connection info dictionary. """ from ._ipython_utils import start_ipython if self._ipython_kernel is None: self._ipython_kernel = start_ipython( ip=self.ip, ns={'scheduler': self}, log=logger, ) return self._ipython_kernel.get_connection_info()
[docs] def worker_objective(self, ts, ws): """ Objective function to determine which worker should get the task Minimize expected start time. If a tie then break with data storage. """ comm_bytes = sum([dts.get_nbytes() for dts in ts.dependencies if ws not in dts.who_has]) stack_time = ws.occupancy / ws.ncores start_time = comm_bytes / BANDWIDTH + stack_time if ts.actor: return (len(ws.actors), start_time, ws.nbytes) else: return (start_time, ws.nbytes)
@gen.coroutine def get_profile(self, comm=None, workers=None, merge_workers=True, start=None, stop=None, key=None): if workers is None: workers = self.workers else: workers = set(self.workers) & set(workers) result = yield {w: self.rpc(w).profile(start=start, stop=stop, key=key) for w in workers} if merge_workers: result = profile.merge(*result.values()) raise gen.Return(result) @gen.coroutine def get_profile_metadata(self, comm=None, workers=None, merge_workers=True, start=None, stop=None, profile_cycle_interval=None): dt = profile_cycle_interval or dask.config.get('distributed.worker.profile.cycle') dt = parse_timedelta(dt, default='ms') if workers is None: workers = self.workers else: workers = set(self.workers) & set(workers) result = yield {w: self.rpc(w).profile_metadata(start=start, stop=stop) for w in workers} counts = [v['counts'] for v in result.values()] counts = itertools.groupby(merge_sorted(*counts), lambda t: t[0] // dt * dt) counts = [(time, sum(pluck(1, group))) for time, group in counts] keys = set() for v in result.values(): for t, d in v['keys']: for k in d: keys.add(k) keys = {k: [] for k in keys} groups1 = [v['keys'] for v in result.values()] groups2 = list(merge_sorted(*groups1, key=first)) last = 0 for t, d in groups2: tt = t // dt * dt if tt > last: last = tt for k, v in keys.items(): v.append([tt, 0]) for k, v in d.items(): keys[k][-1][1] += v raise gen.Return({'counts': counts, 'keys': keys}) def get_logs(self, comm=None, n=None): deque_handler = self._deque_handler if n is None: L = list(deque_handler.deque) else: L = deque_handler.deque L = [L[-i] for i in range(min(n, len(L)))] return [(msg.levelname, deque_handler.format(msg)) for msg in L] @gen.coroutine def get_worker_logs(self, comm=None, n=None, workers=None): results = yield self.broadcast(msg={'op': 'get_logs', 'n': n}, workers=workers) raise gen.Return(results) ########### # Cleanup # ###########
[docs] def reevaluate_occupancy(self, worker_index=0): """ Periodically reassess task duration time The expected duration of a task can change over time. Unfortunately we don't have a good constant-time way to propagate the effects of these changes out to the summaries that they affect, like the total expected runtime of each of the workers, or what tasks are stealable. In this coroutine we walk through all of the workers and re-align their estimates with the current state of tasks. We do this periodically rather than at every transition, and we only do it if the scheduler process isn't under load (using psutil.Process.cpu_percent()). This lets us avoid this fringe optimization when we have better things to think about. """ DELAY = 0.1 try: if self.status == 'closed': return last = time() next_time = timedelta(seconds=DELAY) if self.proc.cpu_percent() < 50: workers = list(self.workers.values()) for i in range(len(workers)): ws = workers[worker_index % len(workers)] worker_index += 1 try: if ws is None or not ws.processing: continue self._reevaluate_occupancy_worker(ws) finally: del ws # lose ref duration = time() - last if duration > 0.005: # 5ms since last release next_time = timedelta(seconds=duration * 5) # 25ms gap break self.loop.add_timeout(next_time, self.reevaluate_occupancy, worker_index=worker_index) except Exception: logger.error("Error in reevaluate occupancy", exc_info=True) raise
def _reevaluate_occupancy_worker(self, ws): """ See reevaluate_occupancy """ old = ws.occupancy new = 0 nbytes = 0 for ts in ws.processing: duration = self.get_task_duration(ts) comm = self.get_comm_cost(ts, ws) ws.processing[ts] = duration + comm new += duration + comm ws.occupancy = new self.total_occupancy += new - old self.check_idle_saturated(ws) # significant increase in duration if (new > old * 1.3) and ('stealing' in self.extensions): steal = self.extensions['stealing'] for ts in ws.processing: steal.remove_key_from_stealable(ts) steal.put_key_in_stealable(ts) def check_worker_ttl(self): now = time() for ws in self.workers.values(): if ws.last_seen < now - self.worker_ttl: logger.warning("Worker failed to heartbeat within %s seconds. " "Closing: %s", self.worker_ttl, ws) self.remove_worker(address=ws.address)
[docs]def decide_worker(ts, all_workers, valid_workers, objective): """ Decide which worker should take task *ts*. We choose the worker that has the data on which *ts* depends. If several workers have dependencies then we choose the less-busy worker. Optionally provide *valid_workers* of where jobs are allowed to occur (if all workers are allowed to take the task, pass True instead). If the task requires data communication because no eligible worker has all the dependencies already, then we choose to minimize the number of bytes sent between workers. This is determined by calling the *objective* function. """ deps = ts.dependencies assert all(dts.who_has for dts in deps) if ts.actor: candidates = all_workers else: candidates = frequencies([ws for dts in deps for ws in dts.who_has]) if valid_workers is True: if not candidates: candidates = all_workers else: candidates = valid_workers & set(candidates) if not candidates: candidates = valid_workers if not candidates: if ts.loose_restrictions: return decide_worker(ts, all_workers, True, objective) else: return None if not candidates: return None if len(candidates) == 1: return first(candidates) return min(candidates, key=objective)
def validate_task_state(ts): """ Validate the given TaskState. """ assert ts.state in ALL_TASK_STATES or ts.state == 'forgotten', ts if ts.waiting_on: assert ts.waiting_on.issubset(ts.dependencies), \ ("waiting not subset of dependencies", str(ts.waiting_on), str(ts.dependencies)) if ts.waiters: assert ts.waiters.issubset(ts.dependents), \ ("waiters not subset of dependents", str(ts.waiters), str(ts.dependents)) for dts in ts.waiting_on: assert not dts.who_has, \ ("waiting on in-memory dep", str(ts), str(dts)) assert dts.state != 'released', \ ("waiting on released dep", str(ts), str(dts)) for dts in ts.dependencies: assert ts in dts.dependents, \ ("not in dependency's dependents", str(ts), str(dts), str(dts.dependents)) if ts.state in ('waiting', 'processing'): assert dts in ts.waiting_on or dts.who_has, \ ("dep missing", str(ts), str(dts)) assert dts.state != 'forgotten' for dts in ts.waiters: assert dts.state in ('waiting', 'processing'), \ ("waiter not in play", str(ts), str(dts)) for dts in ts.dependents: assert ts in dts.dependencies, \ ("not in dependent's dependencies", str(ts), str(dts), str(dts.dependencies)) assert dts.state != 'forgotten' assert (ts.processing_on is not None) == (ts.state == 'processing') assert bool(ts.who_has) == (ts.state == 'memory'), (ts, ts.who_has) if ts.state == 'processing': assert all(dts.who_has for dts in ts.dependencies), \ ("task processing without all deps", str(ts), str(ts.dependencies)) assert not ts.waiting_on if ts.who_has: assert ts.waiters or ts.who_wants, \ ("unneeded task in memory", str(ts), str(ts.who_has)) assert not any(ts in dts.waiting_on for dts in ts.dependents) for ws in ts.who_has: assert ts in ws.has_what, \ ("not in who_has' has_what", str(ts), str(ws), str(ws.has_what)) if ts.who_wants: for cs in ts.who_wants: assert ts in cs.wants_what, \ ("not in who_wants' wants_what", str(ts), str(cs), str(cs.wants_what)) if ts.actor: if ts.state == 'memory': assert sum([ts in ws.actors for ws in ts.who_has]) == 1 if ts.state == 'processing': assert ts in ts.processing_on.actors def validate_worker_state(ws): for ts in ws.has_what: assert ws in ts.who_has, \ ("not in has_what' who_has", str(ws), str(ts), str(ts.who_has)) for ts in ws.actors: assert ts.state in ('memory', 'processing') def validate_state(tasks, workers, clients): """ Validate a current runtime state This performs a sequence of checks on the entire graph, running in about linear time. This raises assert errors if anything doesn't check out. """ for ts in tasks.values(): validate_task_state(ts) for ws in workers.values(): validate_worker_state(ws) for cs in clients.values(): for ts in cs.wants_what: assert cs in ts.who_wants, \ ("not in wants_what' who_wants", str(cs), str(ts), str(ts.who_wants)) _round_robin = [0] fast_tasks = {'rechunk-split', 'shuffle-split'} def heartbeat_interval(n): """ Interval in seconds that we desire heartbeats based on number of workers """ if n <= 10: return 0.5 elif n < 50: return 1 elif n < 200: return 2 else: return 5 class KilledWorker(Exception): pass