"""Implementation of the Active Memory Manager. This is a scheduler extension which sends drop/replicate suggestions to the worker. See also :mod:`distributed.worker_memory` and :mod:`distributed.spill`, which implement spill/pause/terminate mechanics on the Worker side. """ from __future__ import annotations import abc import logging from collections import defaultdict from collections.abc import Generator from typing import TYPE_CHECKING, Any, Literal, NamedTuple import dask from dask.utils import parse_timedelta from distributed.compatibility import PeriodicCallback from distributed.core import Status from distributed.metrics import time from distributed.utils import import_term, log_errors if TYPE_CHECKING: from distributed.client import Client from distributed.scheduler import Scheduler, TaskState, WorkerState # Main logger. This is reasonably terse also at DEBUG level. logger = logging.getLogger(__name__) # Per-task logging. Exceptionally verbose at DEBUG level. task_logger = logging.getLogger(__name__ + ".tasks") class ActiveMemoryManagerExtension: """Scheduler extension that optimizes memory usage across the cluster. It can be either triggered by hand or automatically every few seconds; at every iteration it performs one or both of the following: - create new replicas of in-memory tasks - destroy replicas of in-memory tasks; this never destroys the last available copy. There are no 'move' operations. A move is performed in two passes: first you create a copy and, in the next iteration, you delete the original (if the copy succeeded). This extension is configured by the dask config section ``distributed.scheduler.active-memory-manager``. """ #: Back-reference to the scheduler holding this extension scheduler: Scheduler #: All active policies policies: set[ActiveMemoryManagerPolicy] #: Memory measure to use. Must be one of the attributes or properties of #: :class:`distributed.scheduler.MemoryState`. measure: str #: Run automatically every this many seconds interval: float #: Current memory (in bytes) allocated on each worker, plus/minus pending actions #: This attribute only exist within the scope of self.run(). workers_memory: dict[WorkerState, int] #: Pending replications and deletions for each task #: This attribute only exist within the scope of self.run(). pending: dict[TaskState, tuple[set[WorkerState], set[WorkerState]]] def __init__( self, scheduler: Scheduler, # The following parameters are exposed so that one may create, run, and throw # away on the fly a specialized manager, separate from the main one. policies: set[ActiveMemoryManagerPolicy] | None = None, *, measure: str | None = None, register: bool = True, start: bool | None = None, interval: float | None = None, ): self.scheduler = scheduler self.policies = set() if policies is None: # Initialize policies from config policies = set() for kwargs in dask.config.get( "distributed.scheduler.active-memory-manager.policies" ): kwargs = kwargs.copy() cls = import_term(kwargs.pop("class")) policies.add(cls(**kwargs)) for policy in policies: self.add_policy(policy) if not measure: measure = dask.config.get( "distributed.scheduler.active-memory-manager.measure" ) mem = scheduler.memory measure_domain = { name for name in dir(mem) if not name.startswith("_") and isinstance(getattr(mem, name), int) } if not isinstance(measure, str) or measure not in measure_domain: raise ValueError( "distributed.scheduler.active-memory-manager.measure " "must be one of " + ", ".join(sorted(measure_domain)) ) self.measure = measure if register: scheduler.extensions["amm"] = self scheduler.handlers["amm_handler"] = self.amm_handler if interval is None: interval = parse_timedelta( dask.config.get("distributed.scheduler.active-memory-manager.interval") ) self.interval = interval if start is None: start = dask.config.get("distributed.scheduler.active-memory-manager.start") if start: self.start() def amm_handler(self, method: str) -> Any: """Scheduler handler, invoked from the Client by :class:`~distributed.active_memory_manager.AMMClientProxy` """ assert method in {"start", "stop", "run_once", "running"} out = getattr(self, method) return out() if callable(out) else out def start(self) -> None: """Start executing every ``self.interval`` seconds until scheduler shutdown""" if self.running: return pc = PeriodicCallback(self.run_once, self.interval * 1000.0) self.scheduler.periodic_callbacks[f"amm-{id(self)}"] = pc pc.start() def stop(self) -> None: """Stop periodic execution""" pc = self.scheduler.periodic_callbacks.pop(f"amm-{id(self)}", None) if pc: pc.stop() @property def running(self) -> bool: """Return True if the AMM is being triggered periodically; False otherwise""" return f"amm-{id(self)}" in self.scheduler.periodic_callbacks def add_policy(self, policy: ActiveMemoryManagerPolicy) -> None: if not isinstance(policy, ActiveMemoryManagerPolicy): raise TypeError(f"Expected ActiveMemoryManagerPolicy; got {policy!r}") self.policies.add(policy) policy.manager = self @log_errors def run_once(self) -> None: """Run all policies once and asynchronously (fire and forget) enact their recommendations to replicate/drop tasks """ ts_start = time() # This should never fail since this is a synchronous method assert not hasattr(self, "pending") self.pending = {} measure = self.measure self.workers_memory = { ws: getattr(ws.memory, measure) for ws in self.scheduler.workers.values() } try: # populate self.pending self._run_policies() if self.pending: self._enact_suggestions() finally: del self.workers_memory del self.pending ts_stop = time() logger.debug("Active Memory Manager run in %.0fms", (ts_stop - ts_start) * 1000) def _run_policies(self) -> None: """Sequentially run ActiveMemoryManagerPolicy.run() for all registered policies, obtain replicate/drop suggestions, and use them to populate self.pending. """ ws: WorkerState | None for policy in list(self.policies): # a policy may remove itself logger.debug("Running policy: %s", policy) policy_gen = policy.run() ws = None while True: try: suggestion = policy_gen.send(ws) except StopIteration: break # next policy if not isinstance(suggestion, Suggestion): # legacy: accept plain tuples suggestion = Suggestion(*suggestion) # type: ignore[unreachable] try: pending_repl, pending_drop = self.pending[suggestion.ts] except KeyError: pending_repl = set() pending_drop = set() self.pending[suggestion.ts] = pending_repl, pending_drop if suggestion.op == "replicate": ws = self._find_recipient( suggestion.ts, suggestion.candidates, pending_repl ) if ws: pending_repl.add(ws) self.workers_memory[ws] += suggestion.ts.nbytes elif suggestion.op == "drop": ws = self._find_dropper( suggestion.ts, suggestion.candidates, pending_drop ) if ws: pending_drop.add(ws) self.workers_memory[ws] = max( 0, self.workers_memory[ws] - suggestion.ts.nbytes ) else: raise ValueError(f"Unknown op: {suggestion.op}") # pragma: nocover def _find_recipient( self, ts: TaskState, candidates: set[WorkerState] | None, pending_repl: set[WorkerState], ) -> WorkerState | None: """Choose a worker to acquire a new replica of an in-memory task among a set of candidates. If candidates is None, default to all workers in the cluster. Regardless, workers that either already hold a replica or are scheduled to receive one at the end of this AMM iteration are not considered. Returns ------- The worker with the lowest memory usage (downstream of pending replications and drops), or None if no eligible candidates are available. """ orig_candidates = candidates def log_reject(msg: str) -> None: task_logger.debug( "(replicate, %s, %s) rejected: %s", ts, orig_candidates, msg ) if ts.state != "memory": log_reject(f"ts.state = {ts.state}") return None if ts.actor: log_reject("task is an actor") return None if candidates is None: candidates = self.scheduler.running.copy() else: # Don't modify orig_candidates candidates = candidates & self.scheduler.running if not candidates: log_reject("no running candidates") return None candidates -= ts.who_has if not candidates: log_reject("all candidates already own a replica") return None candidates -= pending_repl if not candidates: log_reject("already pending replication on all candidates") return None # Select candidate with the lowest memory usage choice = min(candidates, key=self.workers_memory.__getitem__) task_logger.debug( "(replicate, %s, %s): replicating to %s", ts, orig_candidates, choice ) return choice def _find_dropper( self, ts: TaskState, candidates: set[WorkerState] | None, pending_drop: set[WorkerState], ) -> WorkerState | None: """Choose a worker to drop its replica of an in-memory task among a set of candidates. If candidates is None, default to all workers in the cluster. Regardless, workers that either do not hold a replica or are already scheduled to drop theirs at the end of this AMM iteration are not considered. This method also ensures that a key will not lose its last replica. Returns ------- The worker with the highest memory usage (downstream of pending replications and drops), or None if no eligible candidates are available. """ orig_candidates = candidates def log_reject(msg: str) -> None: task_logger.debug("(drop, %s, %s) rejected: %s", ts, orig_candidates, msg) if len(ts.who_has) - len(pending_drop) < 2: log_reject("less than 2 replicas exist") return None if ts.actor: log_reject("task is an actor") return None if candidates is None: candidates = ts.who_has.copy() else: # Don't modify orig_candidates candidates = candidates & ts.who_has if not candidates: log_reject("no candidates suggested by the policy own a replica") return None candidates -= pending_drop if not candidates: log_reject("already pending drop on all candidates") return None # The `candidates &` bit could seem redundant with `candidates -=` immediately # below on first look, but beware of the second use of this variable later on! candidates_with_dependents_processing = candidates & { waiter_ts.processing_on for waiter_ts in ts.waiters } candidates -= candidates_with_dependents_processing if not candidates: log_reject("all candidates have dependent tasks queued or running on them") return None # Select candidate with the highest memory usage. # Drop from workers with status paused or closing_gracefully first. choice = max( candidates, key=lambda ws: (ws.status != Status.running, self.workers_memory[ws]), ) # IF there is only one candidate that could drop the key # AND the candidate has status=running # AND there were candidates with status=paused or closing_gracefully, but we # discarded them above because they have dependent tasks running on them, # THEN temporarily keep the extra replica on the candidate with status=running. # # This prevents a ping-pong effect between ReduceReplicas (or any other policy # that yields drop commands with multiple candidates) and RetireWorker: # 1. RetireWorker replicates in-memory tasks from worker A (very busy and being # retired) to worker B (idle) # 2. on the next AMM iteration 2 seconds later, ReduceReplicas drops the same # tasks from B (because the replicas on A have dependants on the same worker) # 3. on the third AMM iteration 2 seconds later, goto 1 in an infinite loop # which will last for as long as any tasks with dependencies are running on A if ( len(candidates) == 1 and choice.status == Status.running and candidates_with_dependents_processing and all( ws.status != Status.running for ws in candidates_with_dependents_processing ) ): log_reject( "there is only one replica on workers that aren't paused or retiring" ) return None task_logger.debug( "(drop, %s, %s): dropping from %s", ts, orig_candidates, choice ) return choice def _enact_suggestions(self) -> None: """Iterate through self.pending, which was filled by self._run_policies(), and push the suggestions to the workers through bulk comms. Return immediately. """ logger.debug("Enacting suggestions for %d tasks:", len(self.pending)) validate = self.scheduler.validate drop_by_worker: (defaultdict[WorkerState, list[str]]) = defaultdict(list) repl_by_worker: (defaultdict[WorkerState, list[str]]) = defaultdict(list) for ts, (pending_repl, pending_drop) in self.pending.items(): if not ts.who_has: continue if validate: # Never drop the last replica assert ts.who_has - pending_drop for ws in pending_repl: if validate: assert ws not in ts.who_has repl_by_worker[ws].append(ts.key) for ws in pending_drop: if validate: assert ws in ts.who_has drop_by_worker[ws].append(ts.key) stimulus_id = f"active_memory_manager-{time()}" for ws, keys in repl_by_worker.items(): logger.debug("- %s to acquire %d replicas", ws, len(keys)) self.scheduler.request_acquire_replicas( ws.address, keys, stimulus_id=stimulus_id ) for ws, keys in drop_by_worker.items(): logger.debug("- %s to drop %d replicas", ws, len(keys)) self.scheduler.request_remove_replicas( ws.address, keys, stimulus_id=stimulus_id ) class Suggestion(NamedTuple): op: Literal["replicate", "drop"] ts: TaskState candidates: set[WorkerState] | None = None if TYPE_CHECKING: # TODO import from typing (requires Python >=3.10) from typing_extensions import TypeAlias # TODO remove quotes (requires Python >=3.9) SuggestionGenerator: TypeAlias = Generator[Suggestion, "WorkerState | None", None] class ActiveMemoryManagerPolicy(abc.ABC): """Abstract parent class""" __slots__ = ("manager",) manager: ActiveMemoryManagerExtension def __repr__(self) -> str: return f"{self.__class__.__name__}()" @abc.abstractmethod def run( self, ) -> SuggestionGenerator: """This method is invoked by the ActiveMemoryManager every few seconds, or whenever the user invokes ``client.amm.run_once``. It is an iterator that must emit :class:`~distributed.active_memory_manager.Suggestion` objects: - ``Suggestion("replicate", )`` - ``Suggestion("replicate", , {subset of potential workers to replicate to})`` - ``Suggeston("drop", )`` - ``Suggestion("drop", , {subset of potential workers to drop from})`` Each element yielded indicates the desire to create or destroy a single replica of a key. If a subset of workers is not provided, it defaults to all workers on the cluster. Either the ActiveMemoryManager or the Worker may later decide to disregard the request, e.g. because it would delete the last copy of a key or because the key is currently needed on that worker. You may optionally retrieve which worker it was decided the key will be replicated to or dropped from, as follows: .. code-block:: python choice = (yield Suggestion("replicate", ts)) ``choice`` is either a WorkerState or None; the latter is returned if the ActiveMemoryManager chose to disregard the request. The current pending (accepted) suggestions can be inspected on ``self.manager.pending``; this includes the suggestions previously yielded by this same method. The current memory usage on each worker, *downstream of all pending suggestions*, can be inspected on ``self.manager.workers_memory``. """ class AMMClientProxy: """Convenience accessors to operate the AMM from the dask client Usage: ``client.amm.start()`` etc. All methods are asynchronous if the client is asynchronous and synchronous if the client is synchronous. """ _client: Client def __init__(self, client: Client): self._client = client def _run(self, method: str) -> Any: """Remotely invoke ActiveMemoryManagerExtension.amm_handler""" return self._client.sync(self._client.scheduler.amm_handler, method=method) def start(self) -> Any: return self._run("start") def stop(self) -> Any: return self._run("stop") def run_once(self) -> Any: return self._run("run_once") def running(self) -> Any: return self._run("running") class ReduceReplicas(ActiveMemoryManagerPolicy): """Make sure that in-memory tasks are not replicated on more workers than desired; drop the excess replicas. """ def run(self) -> SuggestionGenerator: nkeys = 0 ndrop = 0 for ts in self.manager.scheduler.replicated_tasks: desired_replicas = 1 # TODO have a marker on TaskState # If a dependent task has not been assigned to a worker yet, err on the side # of caution and preserve an additional replica for it. # However, if two dependent tasks have been already assigned to the same # worker, don't double count them. nwaiters = len({waiter.processing_on or waiter for waiter in ts.waiters}) ndrop_key = len(ts.who_has) - max(desired_replicas, nwaiters) if ts in self.manager.pending: pending_repl, pending_drop = self.manager.pending[ts] ndrop_key += len(pending_repl) - len(pending_drop) if ndrop_key > 0: nkeys += 1 ndrop += ndrop_key for _ in range(ndrop_key): yield Suggestion("drop", ts) if ndrop: logger.debug( "ReduceReplicas: Dropping %d superfluous replicas of %d tasks", ndrop, nkeys, ) class RetireWorker(ActiveMemoryManagerPolicy): """Replicate somewhere else all unique in-memory tasks on a worker, preparing for its shutdown. At any given time, the AMM may have registered multiple instances of this policy, one for each worker currently being retired - meaning that most of the time no instances will be registered at all. For this reason, this policy doesn't figure in the dask config (:file:`distributed.yaml`). Instances are added by :meth:`distributed.Scheduler.retire_workers` and automatically remove themselves once the worker has been retired. If the AMM is disabled in the dask config, :meth:`~distributed.Scheduler.retire_workers` will start a temporary ad-hoc one. **Failure condition** There may not be any suitable workers to receive the tasks from the retiring worker. This happens in two use cases: 1. This is the only worker in the cluster, or 2. All workers are either paused or being retired at the same time In either case, this policy will fail to move out all keys and set the ``no_recipients`` boolean to True. :meth:`~distributed.Scheduler.retire_workers` will abort the retirement. There is a third use case, where a task fails to be replicated away for whatever reason, e.g. because its recipient is unresponsive but the Scheduler doesn't know yet. In this case we'll just wait for the next AMM iteration and try again (possibly with a different receiving worker, e.g. if the receiving worker was hung but not yet declared dead). **Retiring a worker with spilled tasks** On its very first iteration, this policy suggests that other workers should fetch all unique in-memory tasks of the retiring worker. Frequently, this means that in the next few moments the retiring worker will be bombarded by :meth:`distributed.worker.Worker.get_data` calls from the rest of the cluster. This can be a problem if most of the managed memory of the worker has been spilled out, as it could send the worker above its terminate threshold. Two measures are in place in order to prevent this: - At every iteration, this policy drops all tasks on the retiring worker that have already been replicated somewhere else. This makes room for further tasks to be moved out of the spill file in order to be replicated onto another worker. - Once the worker passes the ``pause`` threshold, :meth:`~distributed.worker.Worker.get_data` throttles the number of outgoing connections to 1. Parameters ========== address: str URI of the worker to be retired """ __slots__ = ("address", "no_recipients") address: str no_recipients: bool def __init__(self, address: str): self.address = address self.no_recipients = False def __repr__(self) -> str: return f"RetireWorker({self.address!r})" def run(self) -> SuggestionGenerator: """""" ws = self.manager.scheduler.workers.get(self.address) if ws is None: logger.debug("Removing policy %s: Worker no longer in cluster", self) self.manager.policies.remove(self) return if ws.actors: logger.warning( f"Tried retiring worker {self.address}, but it holds actor(s) " f"{set(ws.actors)}, which can't be moved." "The worker will not be retired." ) self.no_recipients = True self.manager.policies.remove(self) return nrepl = 0 nno_rec = 0 logger.debug("Retiring %s", ws) for ts in ws.has_what: if ts.actor: # This is just a proxy Actor object; if there were any originals we # would have stopped earlier continue if len(ts.who_has) > 1: # There are already replicas of this key on other workers. # Suggest dropping the replica from this worker. # Use cases: # 1. The suggestion is accepted by the AMM and by the Worker. # The replica on this worker is dropped. # 2. The suggestion is accepted by the AMM, but rejected by the Worker. # We'll try again at the next AMM iteration. # 3. The suggestion is rejected by the AMM, because another policy # (e.g. ReduceReplicas) already suggested the same for this worker # 4. The suggestion is rejected by the AMM, because the task has # dependents queued or running on the same worker. # We'll try again at the next AMM iteration. # 5. The suggestion is rejected by the AMM, because all replicas of the # key are on workers being retired and the other RetireWorker # instances already made the same suggestion. We need to deal with # this case and create a replica elsewhere. drop_ws = yield Suggestion("drop", ts, {ws}) if drop_ws: continue # Use case 1 or 2 if ts.who_has & self.manager.scheduler.running: continue # Use case 3 or 4 # Use case 5 # Either the worker holds the only replica or all replicas are being held # by workers that are being retired nrepl += 1 # Don't create an unnecessary additional replica if another policy already # asked for one try: has_pending_repl = bool(self.manager.pending[ts][0]) except KeyError: has_pending_repl = False if not has_pending_repl: rec_ws = yield Suggestion("replicate", ts) if not rec_ws: # replication was rejected by the AMM (see _find_recipient) nno_rec += 1 if nno_rec: # All workers are paused or closing_gracefully. # Scheduler.retire_workers will read this flag and exit immediately. # TODO after we implement the automatic transition of workers from paused # to closing_gracefully after a timeout expires, we should revisit this # code to wait for paused workers and only exit immediately if all # workers are in closing_gracefully status. self.no_recipients = True logger.warning( f"Tried retiring worker {self.address}, but {nno_rec} tasks could not " "be moved as there are no suitable workers to receive them. " "The worker will not be retired." ) self.manager.policies.remove(self) elif nrepl: logger.info( f"Retiring worker {self.address}; {nrepl} keys are being moved away.", ) else: logger.info( f"Retiring worker {self.address}; no unique keys need to be moved away." ) self.manager.policies.remove(self) def done(self) -> bool: """Return True if it is safe to close the worker down; False otherwise""" if self not in self.manager.policies: # Either the no_recipients flag has been raised, or there were no unique # replicas as of the latest AMM run. Note that due to tasks transitioning # from running to memory there may be some now; it's OK to lose them and # just recompute them somewhere else. return True ws = self.manager.scheduler.workers.get(self.address) if ws is None: return True return all(len(ts.who_has) > 1 for ts in ws.has_what)