File: C:/Users/fred/anaconda3/Lib/site-packages/distributed/worker_memory.py
"""Encapsulated manager for in-memory tasks on a worker.
This module covers:
- spill/unspill data depending on the 'distributed.worker.memory.target' threshold
- spill/unspill data depending on the 'distributed.worker.memory.spill' threshold
- pause/unpause the worker depending on the 'distributed.worker.memory.pause' threshold
- kill the worker depending on the 'distributed.worker.memory.terminate' threshold
This module does *not* cover:
- Changes in behaviour in Worker, Scheduler, task stealing, Active Memory Manager, etc.
caused by the Worker being in paused status
- Worker restart after it's been killed
- Scheduler-side heuristics regarding memory usage, e.g. the Active Memory Manager
See also:
- :mod:`distributed.spill`, which implements the spill-to-disk mechanism and is wrapped
by this module. Unlike this module, :mod:`distributed.spill` is agnostic to the
Worker.
- :mod:`distributed.active_memory_manager`, which runs on the scheduler side
"""
from __future__ import annotations
import asyncio
import logging
import os
import sys
import warnings
from collections.abc import Callable, Container, Hashable, MutableMapping
from contextlib import suppress
from functools import partial
from typing import TYPE_CHECKING, Any, Literal, Union, cast
import psutil
import dask.config
from dask.system import CPU_COUNT
from dask.typing import Key
from dask.utils import format_bytes, parse_bytes, parse_timedelta
from distributed import system
from distributed.compatibility import WINDOWS, PeriodicCallback
from distributed.core import Status
from distributed.gc import ThrottledGC
from distributed.metrics import context_meter, monotonic
from distributed.spill import ManualEvictProto, SpillBuffer
from distributed.utils import RateLimiterFilter, has_arg, log_errors
if TYPE_CHECKING:
# TODO import from typing (requires Python >=3.10)
from typing_extensions import TypeAlias
# Circular imports
from distributed.nanny import Nanny
from distributed.worker import Worker
WorkerDataParameter: TypeAlias = Union[
# pre-initialized
MutableMapping[Key, object],
# constructor
Callable[[], MutableMapping[Key, object]],
# constructor, passed worker.local_directory
Callable[[str], MutableMapping[Key, object]],
# (constructor, kwargs to constructor)
tuple[Callable[..., MutableMapping[Key, object]], dict[str, Any]],
# initialize internally
None,
]
worker_logger = logging.getLogger("distributed.worker.memory")
worker_logger.addFilter(RateLimiterFilter(r"Unmanaged memory use is high", rate="300s"))
nanny_logger = logging.getLogger("distributed.nanny.memory")
class WorkerMemoryManager:
"""Management of worker memory usage
Parameters
----------
worker
Worker to manage
For meaning of the remaining parameters, see the matching
parameter names in :class:`~.distributed.worker.Worker`.
Notes
-----
If data is a callable and has the argument ``worker_local_directory`` in its
signature, it will be filled with the worker's attr:``local_directory``.
"""
data: MutableMapping[Key, object] # {task key: task payload}
memory_limit: int | None
memory_target_fraction: float | Literal[False]
memory_spill_fraction: float | Literal[False]
memory_pause_fraction: float | Literal[False]
max_spill: int | Literal[False]
memory_monitor_interval: float
_throttled_gc: ThrottledGC
def __init__(
self,
worker: Worker,
*,
nthreads: int,
memory_limit: str | float = "auto",
# This should be None most of the times, short of a power user replacing the
# SpillBuffer with their own custom dict-like
data: WorkerDataParameter = None,
# Deprecated parameters; use dask.config instead
memory_target_fraction: float | Literal[False] | None = None,
memory_spill_fraction: float | Literal[False] | None = None,
memory_pause_fraction: float | Literal[False] | None = None,
):
self.memory_limit = parse_memory_limit(
memory_limit, nthreads, logger=worker_logger
)
self.memory_target_fraction = _parse_threshold(
"distributed.worker.memory.target",
"memory_target_fraction",
memory_target_fraction,
)
self.memory_spill_fraction = _parse_threshold(
"distributed.worker.memory.spill",
"memory_spill_fraction",
memory_spill_fraction,
)
self.memory_pause_fraction = _parse_threshold(
"distributed.worker.memory.pause",
"memory_pause_fraction",
memory_pause_fraction,
)
max_spill = dask.config.get("distributed.worker.memory.max-spill")
self.max_spill = False if max_spill is False else parse_bytes(max_spill)
if isinstance(data, MutableMapping):
self.data = data
elif callable(data):
if has_arg(data, "worker_local_directory"):
data = cast("Callable[[str], MutableMapping[Key, object]]", data)
self.data = data(worker.local_directory)
else:
data = cast("Callable[[], MutableMapping[Key, object]]", data)
self.data = data()
elif isinstance(data, tuple):
func, kwargs = data
if not callable(func):
raise ValueError("Expecting a callable")
if has_arg(func, "worker_local_directory"):
self.data = func(
worker_local_directory=worker.local_directory, **kwargs
)
else:
self.data = func(**kwargs)
elif self.memory_limit and (
self.memory_target_fraction or self.memory_spill_fraction
):
if self.memory_target_fraction:
target = int(
self.memory_limit
* (self.memory_target_fraction or self.memory_spill_fraction)
)
else:
target = sys.maxsize
self.data = SpillBuffer(
os.path.join(worker.local_directory, "storage"),
target=target,
max_spill=self.max_spill,
)
else:
self.data = {}
if not isinstance(self.data, MutableMapping):
raise TypeError(f"Worker.data must be a MutableMapping; got {self.data}")
if self.data:
raise ValueError("Worker.data must be empty at initialization time")
self.memory_monitor_interval = parse_timedelta(
dask.config.get("distributed.worker.memory.monitor-interval"),
default=False,
)
assert isinstance(self.memory_monitor_interval, (int, float))
if self.memory_limit and (
self.memory_spill_fraction is not False
or self.memory_pause_fraction is not False
):
assert self.memory_monitor_interval is not None
pc = PeriodicCallback(
# Don't store worker as self.worker to avoid creating a circular
# dependency. We could have alternatively used a weakref.
partial(self.memory_monitor, worker),
self.memory_monitor_interval * 1000,
)
worker.periodic_callbacks["memory_monitor"] = pc
self._throttled_gc = ThrottledGC(logger=worker_logger)
@log_errors
async def memory_monitor(self, worker: Worker) -> None:
"""Track this process's memory usage and act accordingly.
If process memory rises above the spill threshold (70%), start dumping data to
disk until it goes below the target threshold (60%).
If process memory rises above the pause threshold (80%), stop execution of new
tasks.
"""
# Don't use psutil directly; instead read from the same API that is used
# to send info to the Scheduler (e.g. for the benefit of Active Memory
# Manager) and which can be easily mocked in unit tests.
memory = worker.monitor.get_process_memory()
self._maybe_pause_or_unpause(worker, memory)
await self._maybe_spill(worker, memory)
def _maybe_pause_or_unpause(self, worker: Worker, memory: int) -> None:
if self.memory_pause_fraction is False:
return
assert self.memory_limit
frac = memory / self.memory_limit
# Pause worker threads if above 80% memory use
if frac > self.memory_pause_fraction:
# Try to free some memory while in paused state
self._throttled_gc.collect()
if worker.status == Status.running:
worker_logger.warning(
"Worker is at %d%% memory usage. Pausing worker. "
"Process memory: %s -- Worker memory limit: %s",
int(frac * 100),
format_bytes(memory),
format_bytes(self.memory_limit)
if self.memory_limit is not None
else "None",
)
worker.status = Status.paused
elif worker.status == Status.paused:
worker_logger.warning(
"Worker is at %d%% memory usage. Resuming worker. "
"Process memory: %s -- Worker memory limit: %s",
int(frac * 100),
format_bytes(memory),
format_bytes(self.memory_limit)
if self.memory_limit is not None
else "None",
)
worker.status = Status.running
async def _maybe_spill(self, worker: Worker, memory: int) -> None:
"""If process memory is above the ``spill`` threshold, evict keys until it goes
below the ``target`` threshold
"""
if self.memory_spill_fraction is False:
return
# SpillBuffer or a duct-type compatible MutableMapping which offers the
# fast property and evict() methods. Dask-CUDA uses this.
if not hasattr(self.data, "fast") or not hasattr(self.data, "evict"):
return
assert self.memory_limit
frac = memory / self.memory_limit
if frac <= self.memory_spill_fraction:
return
worker_logger.debug(
"Worker is at %.0f%% memory usage. Start spilling data to disk.",
frac * 100,
)
def metrics_callback(label: Hashable, value: float, unit: str) -> None:
if not isinstance(label, tuple):
label = (label,)
worker.digest_metric(("memory-monitor", *label, unit), value)
# Work around bug with Tornado 6.2 PeriodicCallback, which does not properly
# insulate contextvars. Without this hack, you would see metrics that are
# clearly emitted by Worker.execute labelled with 'memory-monitor'.So we're
# wrapping our change in contextvars (inside add_callback) inside create_task(),
# which copies and insulates the context.
async def _() -> None:
with context_meter.add_callback(metrics_callback):
# Measure delta between the measures from the SpillBuffer and the total
# end-to-end duration of _spill
await self._spill(worker, memory)
await asyncio.create_task(_(), name="memory-monitor-spill")
# End work around
async def _spill(self, worker: Worker, memory: int) -> None:
"""Evict keys until the process memory goes below the ``target`` threshold"""
assert self.memory_limit
total_spilled = 0
# Implement hysteresis cycle where spilling starts at the spill threshold and
# stops at the target threshold. Normally that here the target threshold defines
# process memory, whereas normally it defines reported managed memory (e.g.
# output of sizeof() ). If target=False, disable hysteresis.
target = self.memory_limit * (
self.memory_target_fraction or self.memory_spill_fraction
)
count = 0
need = memory - target
last_checked_for_pause = last_yielded = monotonic()
data = cast(ManualEvictProto, self.data)
while memory > target:
if not data.fast:
worker_logger.warning(
"Unmanaged memory use is high. This may indicate a memory leak "
"or the memory may not be released to the OS; see "
"https://distributed.dask.org/en/latest/worker-memory.html#memory-not-released-back-to-the-os "
"for more information. "
"-- Unmanaged memory: %s -- Worker memory limit: %s",
format_bytes(memory),
format_bytes(self.memory_limit),
)
break
weight = data.evict()
if weight == -1:
# Failed to evict:
# disk full, spill size limit exceeded, or pickle error
break
total_spilled += weight
count += 1
memory = worker.monitor.get_process_memory()
if total_spilled > need and memory > target:
# Issue a GC to ensure that the evicted data is actually
# freed from memory and taken into account by the monitor
# before trying to evict even more data.
self._throttled_gc.collect()
memory = worker.monitor.get_process_memory()
now = monotonic()
# Spilling may potentially take multiple seconds; we may pass the pause
# threshold in the meantime.
if now - last_checked_for_pause > self.memory_monitor_interval:
self._maybe_pause_or_unpause(worker, memory)
last_checked_for_pause = now
# Increase spilling aggressiveness when the fast buffer is filled with a lot
# of small values. This artificially chokes the rest of the event loop -
# namely, the reception of new data from other workers. While this is
# somewhat of an ugly hack, DO NOT tweak this without a thorough cycle of
# stress testing. See: https://github.com/dask/distributed/issues/6110.
if now - last_yielded > 0.5:
await asyncio.sleep(0)
last_yielded = monotonic()
if count:
worker_logger.debug(
"Moved %d tasks worth %s to disk",
count,
format_bytes(total_spilled),
)
def _to_dict(self, *, exclude: Container[str] = ()) -> dict:
info = {k: v for k, v in self.__dict__.items() if not k.startswith("_")}
info["data"] = dict.fromkeys(self.data)
return info
class NannyMemoryManager:
memory_limit: int | None
memory_terminate_fraction: float | Literal[False]
memory_monitor_interval: float | None
_last_terminated_pid: int
def __init__(
self,
nanny: Nanny,
*,
memory_limit: str | float = "auto",
):
self.memory_limit = parse_memory_limit(
memory_limit, nanny.nthreads, logger=nanny_logger
)
self.memory_terminate_fraction = dask.config.get(
"distributed.worker.memory.terminate"
)
self.memory_monitor_interval = parse_timedelta(
dask.config.get("distributed.worker.memory.monitor-interval"),
default=False,
)
assert isinstance(self.memory_monitor_interval, (int, float))
self._last_terminated_pid = -1
if self.memory_limit and self.memory_terminate_fraction is not False:
pc = PeriodicCallback(
partial(self.memory_monitor, nanny),
self.memory_monitor_interval * 1000,
)
nanny.periodic_callbacks["memory_monitor"] = pc
def memory_monitor(self, nanny: Nanny) -> None:
"""Track worker's memory. Restart if it goes above terminate fraction."""
if (
nanny.status != Status.running
or nanny.process is None
or nanny.process.process is None
or nanny.process.process.pid is None
):
return # pragma: nocover
process = nanny.process.process
try:
memory = psutil.Process(process.pid).memory_info().rss
except (ProcessLookupError, psutil.NoSuchProcess, psutil.AccessDenied):
return # pragma: nocover
if memory / self.memory_limit <= self.memory_terminate_fraction:
return
if self._last_terminated_pid != process.pid:
msg = (
f"Worker {nanny.worker_address} (pid={process.pid}) exceeded "
f"{self.memory_terminate_fraction * 100:.0f}% memory budget. "
f"Restarting..."
)
nanny_logger.warning(msg)
self._last_terminated_pid = process.pid
event = {
"worker": nanny.worker_address,
"pid": process.pid,
"rss": memory,
}
nanny.log_event("worker-restart-memory", event)
process.terminate()
else:
# We already sent SIGTERM to the worker, but the process is still alive
# since the previous iteration of the memory_monitor - for example, some
# user code may have tampered with signal handlers.
# Send SIGKILL for immediate termination.
#
# Note that this should not be a disk-related issue. Unlike in a regular
# worker shutdown, where the worker cleans up its own spill directory, in
# case of SIGTERM no atexit or weakref.finalize callback is triggered
# whatsoever; instead, the nanny cleans up the spill directory *after* the
# worker has been shut down and before starting a new one.
# This is important, as spill directory cleanup may potentially take tens of
# seconds and, if the worker did it, any task that was running and leaking
# would continue to do so for the whole duration of the cleanup, increasing
# the risk of going beyond 100%.
nanny_logger.warning(
f"Worker {nanny.worker_address} (pid={process.pid}) is slow to %s",
# On Windows, kill() is an alias to terminate()
"terminate; trying again"
if WINDOWS
else "accept SIGTERM; sending SIGKILL",
)
process.kill()
def parse_memory_limit(
memory_limit: str | float | None,
nthreads: int,
total_cores: int = CPU_COUNT,
*,
logger: logging.Logger,
) -> int | None:
if memory_limit is None:
return None
orig = memory_limit
if memory_limit == "auto":
memory_limit = int(system.MEMORY_LIMIT * min(1, nthreads / total_cores))
with suppress(ValueError, TypeError):
memory_limit = float(memory_limit)
if isinstance(memory_limit, float) and memory_limit <= 1:
memory_limit = int(memory_limit * system.MEMORY_LIMIT)
if isinstance(memory_limit, str):
memory_limit = parse_bytes(memory_limit)
else:
memory_limit = int(memory_limit)
assert isinstance(memory_limit, int)
if memory_limit == 0:
return None
if system.MEMORY_LIMIT < memory_limit:
logger.warning(
"Ignoring provided memory limit %s due to system memory limit of %s",
orig,
format_bytes(system.MEMORY_LIMIT),
)
return system.MEMORY_LIMIT
else:
return memory_limit
def _parse_threshold(
config_key: str,
deprecated_param_name: str,
deprecated_param_value: float | Literal[False] | None,
) -> float | Literal[False]:
if deprecated_param_value is not None:
warnings.warn(
f"Parameter {deprecated_param_name} has been deprecated and will be "
f"removed in a future version; please use dask config key {config_key} "
"instead",
FutureWarning,
)
return deprecated_param_value
return dask.config.get(config_key)
def _warn_deprecated(w: Nanny | Worker, name: str) -> None:
warnings.warn(
f"The `{type(w).__name__}.{name}` attribute has been moved to "
f"`{type(w).__name__}.memory_manager.{name}",
FutureWarning,
)
class DeprecatedMemoryManagerAttribute:
name: str
def __set_name__(self, owner: type, name: str) -> None:
self.name = name
def __get__(self, instance: Nanny | Worker | None, owner: type) -> Any:
if instance is None:
# This is triggered by Sphinx
return None # pragma: nocover
_warn_deprecated(instance, self.name)
return getattr(instance.memory_manager, self.name)
def __set__(self, instance: Nanny | Worker, value: Any) -> None:
_warn_deprecated(instance, self.name)
setattr(instance.memory_manager, self.name, value)
class DeprecatedMemoryMonitor:
def __get__(self, instance: Nanny | Worker | None, owner: type) -> Any:
if instance is None:
# This is triggered by Sphinx
return None # pragma: nocover
_warn_deprecated(instance, "memory_monitor")
return partial(instance.memory_manager.memory_monitor, instance)