"""
This file is originally based on code from https://github.com/nylas/nylas-perftools,
which is published under the following license:
The MIT License (MIT)
Copyright (c) 2014 Nylas
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
import atexit
import os
import platform
import random
import sys
import threading
import time
import uuid
from abc import ABC, abstractmethod
from collections import deque
import sentry_sdk
from sentry_sdk._compat import PY311
from sentry_sdk._lru_cache import LRUCache
from sentry_sdk._types import TYPE_CHECKING
from sentry_sdk.utils import (
capture_internal_exception,
filename_for_module,
get_current_thread_meta,
is_gevent,
is_valid_sample_rate,
logger,
nanosecond_time,
set_in_app_in_frames,
)
if TYPE_CHECKING:
from types import FrameType
from typing import Any
from typing import Callable
from typing import Deque
from typing import Dict
from typing import List
from typing import Optional
from typing import Set
from typing import Sequence
from typing import Tuple
from typing_extensions import TypedDict
import sentry_sdk.tracing
from sentry_sdk._types import Event, SamplingContext, ProfilerMode
ThreadId = str
ProcessedSample = TypedDict(
"ProcessedSample",
{
"elapsed_since_start_ns": str,
"thread_id": ThreadId,
"stack_id": int,
},
)
ProcessedStack = List[int]
ProcessedFrame = TypedDict(
"ProcessedFrame",
{
"abs_path": str,
"filename": Optional[str],
"function": str,
"lineno": int,
"module": Optional[str],
},
)
ProcessedThreadMetadata = TypedDict(
"ProcessedThreadMetadata",
{"name": str},
)
ProcessedProfile = TypedDict(
"ProcessedProfile",
{
"frames": List[ProcessedFrame],
"stacks": List[ProcessedStack],
"samples": List[ProcessedSample],
"thread_metadata": Dict[ThreadId, ProcessedThreadMetadata],
},
)
ProfileContext = TypedDict(
"ProfileContext",
{"profile_id": str},
)
FrameId = Tuple[
str, # abs_path
int, # lineno
str, # function
]
FrameIds = Tuple[FrameId, ...]
# The exact value of this id is not very meaningful. The purpose
# of this id is to give us a compact and unique identifier for a
# raw stack that can be used as a key to a dictionary so that it
# can be used during the sampled format generation.
StackId = Tuple[int, int]
ExtractedStack = Tuple[StackId, FrameIds, List[ProcessedFrame]]
ExtractedSample = Sequence[Tuple[ThreadId, ExtractedStack]]
try:
from gevent.monkey import get_original # type: ignore
from gevent.threadpool import ThreadPool # type: ignore
thread_sleep = get_original("time", "sleep")
except ImportError:
thread_sleep = time.sleep
ThreadPool = None
_scheduler = None # type: Optional[Scheduler]
# The default sampling frequency to use. This is set at 101 in order to
# mitigate the effects of lockstep sampling.
DEFAULT_SAMPLING_FREQUENCY = 101
# The minimum number of unique samples that must exist in a profile to be
# considered valid.
PROFILE_MINIMUM_SAMPLES = 2
def has_profiling_enabled(options):
# type: (Dict[str, Any]) -> bool
profiles_sampler = options["profiles_sampler"]
if profiles_sampler is not None:
return True
profiles_sample_rate = options["profiles_sample_rate"]
if profiles_sample_rate is not None and profiles_sample_rate > 0:
return True
profiles_sample_rate = options["_experiments"].get("profiles_sample_rate")
if profiles_sample_rate is not None:
logger.warning(
"_experiments['profiles_sample_rate'] is deprecated. "
"Please use the non-experimental profiles_sample_rate option "
"directly."
)
if profiles_sample_rate > 0:
return True
return False
def setup_profiler(options):
# type: (Dict[str, Any]) -> bool
global _scheduler
if _scheduler is not None:
logger.debug("[Profiling] Profiler is already setup")
return False
frequency = DEFAULT_SAMPLING_FREQUENCY
if is_gevent():
# If gevent has patched the threading modules then we cannot rely on
# them to spawn a native thread for sampling.
# Instead we default to the GeventScheduler which is capable of
# spawning native threads within gevent.
default_profiler_mode = GeventScheduler.mode
else:
default_profiler_mode = ThreadScheduler.mode
if options.get("profiler_mode") is not None:
profiler_mode = options["profiler_mode"]
else:
profiler_mode = options.get("_experiments", {}).get("profiler_mode")
if profiler_mode is not None:
logger.warning(
"_experiments['profiler_mode'] is deprecated. Please use the "
"non-experimental profiler_mode option directly."
)
profiler_mode = profiler_mode or default_profiler_mode
if (
profiler_mode == ThreadScheduler.mode
# for legacy reasons, we'll keep supporting sleep mode for this scheduler
or profiler_mode == "sleep"
):
_scheduler = ThreadScheduler(frequency=frequency)
elif profiler_mode == GeventScheduler.mode:
_scheduler = GeventScheduler(frequency=frequency)
else:
raise ValueError("Unknown profiler mode: {}".format(profiler_mode))
logger.debug(
"[Profiling] Setting up profiler in {mode} mode".format(mode=_scheduler.mode)
)
_scheduler.setup()
atexit.register(teardown_profiler)
return True
def teardown_profiler():
# type: () -> None
global _scheduler
if _scheduler is not None:
_scheduler.teardown()
_scheduler = None
# We want to impose a stack depth limit so that samples aren't too large.
MAX_STACK_DEPTH = 128
def extract_stack(
raw_frame, # type: Optional[FrameType]
cache, # type: LRUCache
cwd, # type: str
max_stack_depth=MAX_STACK_DEPTH, # type: int
):
# type: (...) -> ExtractedStack
"""
Extracts the stack starting the specified frame. The extracted stack
assumes the specified frame is the top of the stack, and works back
to the bottom of the stack.
In the event that the stack is more than `MAX_STACK_DEPTH` frames deep,
only the first `MAX_STACK_DEPTH` frames will be returned.
"""
raw_frames = deque(maxlen=max_stack_depth) # type: Deque[FrameType]
while raw_frame is not None:
f_back = raw_frame.f_back
raw_frames.append(raw_frame)
raw_frame = f_back
frame_ids = tuple(frame_id(raw_frame) for raw_frame in raw_frames)
frames = []
for i, fid in enumerate(frame_ids):
frame = cache.get(fid)
if frame is None:
frame = extract_frame(fid, raw_frames[i], cwd)
cache.set(fid, frame)
frames.append(frame)
# Instead of mapping the stack into frame ids and hashing
# that as a tuple, we can directly hash the stack.
# This saves us from having to generate yet another list.
# Additionally, using the stack as the key directly is
# costly because the stack can be large, so we pre-hash
# the stack, and use the hash as the key as this will be
# needed a few times to improve performance.
#
# To Reduce the likelihood of hash collisions, we include
# the stack depth. This means that only stacks of the same
# depth can suffer from hash collisions.
stack_id = len(raw_frames), hash(frame_ids)
return stack_id, frame_ids, frames
def frame_id(raw_frame):
# type: (FrameType) -> FrameId
return (raw_frame.f_code.co_filename, raw_frame.f_lineno, get_frame_name(raw_frame))
def extract_frame(fid, raw_frame, cwd):
# type: (FrameId, FrameType, str) -> ProcessedFrame
abs_path = raw_frame.f_code.co_filename
try:
module = raw_frame.f_globals["__name__"]
except Exception:
module = None
# namedtuples can be many times slower when initialing
# and accessing attribute so we opt to use a tuple here instead
return {
# This originally was `os.path.abspath(abs_path)` but that had
# a large performance overhead.
#
# According to docs, this is equivalent to
# `os.path.normpath(os.path.join(os.getcwd(), path))`.
# The `os.getcwd()` call is slow here, so we precompute it.
#
# Additionally, since we are using normalized path already,
# we skip calling `os.path.normpath` entirely.
"abs_path": os.path.join(cwd, abs_path),
"module": module,
"filename": filename_for_module(module, abs_path) or None,
"function": fid[2],
"lineno": raw_frame.f_lineno,
}
if PY311:
def get_frame_name(frame):
# type: (FrameType) -> str
return frame.f_code.co_qualname
else:
def get_frame_name(frame):
# type: (FrameType) -> str
f_code = frame.f_code
co_varnames = f_code.co_varnames
# co_name only contains the frame name. If the frame was a method,
# the class name will NOT be included.
name = f_code.co_name
# if it was a method, we can get the class name by inspecting
# the f_locals for the `self` argument
try:
if (
# the co_varnames start with the frame's positional arguments
# and we expect the first to be `self` if its an instance method
co_varnames
and co_varnames[0] == "self"
and "self" in frame.f_locals
):
for cls in frame.f_locals["self"].__class__.__mro__:
if name in cls.__dict__:
return "{}.{}".format(cls.__name__, name)
except (AttributeError, ValueError):
pass
# if it was a class method, (decorated with `@classmethod`)
# we can get the class name by inspecting the f_locals for the `cls` argument
try:
if (
# the co_varnames start with the frame's positional arguments
# and we expect the first to be `cls` if its a class method
co_varnames
and co_varnames[0] == "cls"
and "cls" in frame.f_locals
):
for cls in frame.f_locals["cls"].__mro__:
if name in cls.__dict__:
return "{}.{}".format(cls.__name__, name)
except (AttributeError, ValueError):
pass
# nothing we can do if it is a staticmethod (decorated with @staticmethod)
# we've done all we can, time to give up and return what we have
return name
MAX_PROFILE_DURATION_NS = int(3e10) # 30 seconds
[docs]
class Profile:
def __init__(
self,
transaction, # type: sentry_sdk.tracing.Transaction
hub=None, # type: Optional[sentry_sdk.Hub]
scheduler=None, # type: Optional[Scheduler]
):
# type: (...) -> None
self.scheduler = _scheduler if scheduler is None else scheduler
self.hub = hub
self.event_id = uuid.uuid4().hex # type: str
# Here, we assume that the sampling decision on the transaction has been finalized.
#
# We cannot keep a reference to the transaction around here because it'll create
# a reference cycle. So we opt to pull out just the necessary attributes.
self.sampled = transaction.sampled # type: Optional[bool]
# Various framework integrations are capable of overwriting the active thread id.
# If it is set to `None` at the end of the profile, we fall back to the default.
self._default_active_thread_id = get_current_thread_meta()[0] or 0 # type: int
self.active_thread_id = None # type: Optional[int]
try:
self.start_ns = transaction._start_timestamp_monotonic_ns # type: int
except AttributeError:
self.start_ns = 0
self.stop_ns = 0 # type: int
self.active = False # type: bool
self.indexed_frames = {} # type: Dict[FrameId, int]
self.indexed_stacks = {} # type: Dict[StackId, int]
self.frames = [] # type: List[ProcessedFrame]
self.stacks = [] # type: List[ProcessedStack]
self.samples = [] # type: List[ProcessedSample]
self.unique_samples = 0
transaction._profile = self
def update_active_thread_id(self):
# type: () -> None
self.active_thread_id = get_current_thread_meta()[0]
logger.debug(
"[Profiling] updating active thread id to {tid}".format(
tid=self.active_thread_id
)
)
def _set_initial_sampling_decision(self, sampling_context):
# type: (SamplingContext) -> None
"""
Sets the profile's sampling decision according to the following
precedence rules:
1. If the transaction to be profiled is not sampled, that decision
will be used, regardless of anything else.
2. Use `profiles_sample_rate` to decide.
"""
# The corresponding transaction was not sampled,
# so don't generate a profile for it.
if not self.sampled:
logger.debug(
"[Profiling] Discarding profile because transaction is discarded."
)
self.sampled = False
return
# The profiler hasn't been properly initialized.
if self.scheduler is None:
logger.debug(
"[Profiling] Discarding profile because profiler was not started."
)
self.sampled = False
return
client = sentry_sdk.Scope.get_client()
if not client.is_active():
self.sampled = False
return
options = client.options
if callable(options.get("profiles_sampler")):
sample_rate = options["profiles_sampler"](sampling_context)
elif options["profiles_sample_rate"] is not None:
sample_rate = options["profiles_sample_rate"]
else:
sample_rate = options["_experiments"].get("profiles_sample_rate")
# The profiles_sample_rate option was not set, so profiling
# was never enabled.
if sample_rate is None:
logger.debug(
"[Profiling] Discarding profile because profiling was not enabled."
)
self.sampled = False
return
if not is_valid_sample_rate(sample_rate, source="Profiling"):
logger.warning(
"[Profiling] Discarding profile because of invalid sample rate."
)
self.sampled = False
return
# Now we roll the dice. random.random is inclusive of 0, but not of 1,
# so strict < is safe here. In case sample_rate is a boolean, cast it
# to a float (True becomes 1.0 and False becomes 0.0)
self.sampled = random.random() < float(sample_rate)
if self.sampled:
logger.debug("[Profiling] Initializing profile")
else:
logger.debug(
"[Profiling] Discarding profile because it's not included in the random sample (sample rate = {sample_rate})".format(
sample_rate=float(sample_rate)
)
)
def start(self):
# type: () -> None
if not self.sampled or self.active:
return
assert self.scheduler, "No scheduler specified"
logger.debug("[Profiling] Starting profile")
self.active = True
if not self.start_ns:
self.start_ns = nanosecond_time()
self.scheduler.start_profiling(self)
def stop(self):
# type: () -> None
if not self.sampled or not self.active:
return
assert self.scheduler, "No scheduler specified"
logger.debug("[Profiling] Stopping profile")
self.active = False
self.stop_ns = nanosecond_time()
def __enter__(self):
# type: () -> Profile
scope = sentry_sdk.scope.Scope.get_isolation_scope()
old_profile = scope.profile
scope.profile = self
self._context_manager_state = (scope, old_profile)
self.start()
return self
def __exit__(self, ty, value, tb):
# type: (Optional[Any], Optional[Any], Optional[Any]) -> None
self.stop()
scope, old_profile = self._context_manager_state
del self._context_manager_state
scope.profile = old_profile
def write(self, ts, sample):
# type: (int, ExtractedSample) -> None
if not self.active:
return
if ts < self.start_ns:
return
offset = ts - self.start_ns
if offset > MAX_PROFILE_DURATION_NS:
self.stop()
return
self.unique_samples += 1
elapsed_since_start_ns = str(offset)
for tid, (stack_id, frame_ids, frames) in sample:
try:
# Check if the stack is indexed first, this lets us skip
# indexing frames if it's not necessary
if stack_id not in self.indexed_stacks:
for i, frame_id in enumerate(frame_ids):
if frame_id not in self.indexed_frames:
self.indexed_frames[frame_id] = len(self.indexed_frames)
self.frames.append(frames[i])
self.indexed_stacks[stack_id] = len(self.indexed_stacks)
self.stacks.append(
[self.indexed_frames[frame_id] for frame_id in frame_ids]
)
self.samples.append(
{
"elapsed_since_start_ns": elapsed_since_start_ns,
"thread_id": tid,
"stack_id": self.indexed_stacks[stack_id],
}
)
except AttributeError:
# For some reason, the frame we get doesn't have certain attributes.
# When this happens, we abandon the current sample as it's bad.
capture_internal_exception(sys.exc_info())
def process(self):
# type: () -> ProcessedProfile
# This collects the thread metadata at the end of a profile. Doing it
# this way means that any threads that terminate before the profile ends
# will not have any metadata associated with it.
thread_metadata = {
str(thread.ident): {
"name": str(thread.name),
}
for thread in threading.enumerate()
} # type: Dict[str, ProcessedThreadMetadata]
return {
"frames": self.frames,
"stacks": self.stacks,
"samples": self.samples,
"thread_metadata": thread_metadata,
}
def to_json(self, event_opt, options):
# type: (Event, Dict[str, Any]) -> Dict[str, Any]
profile = self.process()
set_in_app_in_frames(
profile["frames"],
options["in_app_exclude"],
options["in_app_include"],
options["project_root"],
)
return {
"environment": event_opt.get("environment"),
"event_id": self.event_id,
"platform": "python",
"profile": profile,
"release": event_opt.get("release", ""),
"timestamp": event_opt["start_timestamp"],
"version": "1",
"device": {
"architecture": platform.machine(),
},
"os": {
"name": platform.system(),
"version": platform.release(),
},
"runtime": {
"name": platform.python_implementation(),
"version": platform.python_version(),
},
"transactions": [
{
"id": event_opt["event_id"],
"name": event_opt["transaction"],
# we start the transaction before the profile and this is
# the transaction start time relative to the profile, so we
# hardcode it to 0 until we can start the profile before
"relative_start_ns": "0",
# use the duration of the profile instead of the transaction
# because we end the transaction after the profile
"relative_end_ns": str(self.stop_ns - self.start_ns),
"trace_id": event_opt["contexts"]["trace"]["trace_id"],
"active_thread_id": str(
self._default_active_thread_id
if self.active_thread_id is None
else self.active_thread_id
),
}
],
}
def valid(self):
# type: () -> bool
client = sentry_sdk.Scope.get_client()
if not client.is_active():
return False
if not has_profiling_enabled(client.options):
return False
if self.sampled is None or not self.sampled:
if client.transport:
client.transport.record_lost_event(
"sample_rate", data_category="profile"
)
return False
if self.unique_samples < PROFILE_MINIMUM_SAMPLES:
if client.transport:
client.transport.record_lost_event(
"insufficient_data", data_category="profile"
)
logger.debug("[Profiling] Discarding profile because insufficient samples.")
return False
return True
class Scheduler(ABC):
mode = "unknown" # type: ProfilerMode
def __init__(self, frequency):
# type: (int) -> None
self.interval = 1.0 / frequency
self.sampler = self.make_sampler()
# cap the number of new profiles at any time so it does not grow infinitely
self.new_profiles = deque(maxlen=128) # type: Deque[Profile]
self.active_profiles = set() # type: Set[Profile]
def __enter__(self):
# type: () -> Scheduler
self.setup()
return self
def __exit__(self, ty, value, tb):
# type: (Optional[Any], Optional[Any], Optional[Any]) -> None
self.teardown()
@abstractmethod
def setup(self):
# type: () -> None
pass
@abstractmethod
def teardown(self):
# type: () -> None
pass
def ensure_running(self):
# type: () -> None
"""
Ensure the scheduler is running. By default, this method is a no-op.
The method should be overridden by any implementation for which it is
relevant.
"""
return None
def start_profiling(self, profile):
# type: (Profile) -> None
self.ensure_running()
self.new_profiles.append(profile)
def make_sampler(self):
# type: () -> Callable[..., None]
cwd = os.getcwd()
cache = LRUCache(max_size=256)
def _sample_stack(*args, **kwargs):
# type: (*Any, **Any) -> None
"""
Take a sample of the stack on all the threads in the process.
This should be called at a regular interval to collect samples.
"""
# no profiles taking place, so we can stop early
if not self.new_profiles and not self.active_profiles:
# make sure to clear the cache if we're not profiling so we dont
# keep a reference to the last stack of frames around
return
# This is the number of profiles we want to pop off.
# It's possible another thread adds a new profile to
# the list and we spend longer than we want inside
# the loop below.
#
# Also make sure to set this value before extracting
# frames so we do not write to any new profiles that
# were started after this point.
new_profiles = len(self.new_profiles)
now = nanosecond_time()
try:
sample = [
(str(tid), extract_stack(frame, cache, cwd))
for tid, frame in sys._current_frames().items()
]
except AttributeError:
# For some reason, the frame we get doesn't have certain attributes.
# When this happens, we abandon the current sample as it's bad.
capture_internal_exception(sys.exc_info())
return
# Move the new profiles into the active_profiles set.
#
# We cannot directly add the to active_profiles set
# in `start_profiling` because it is called from other
# threads which can cause a RuntimeError when it the
# set sizes changes during iteration without a lock.
#
# We also want to avoid using a lock here so threads
# that are starting profiles are not blocked until it
# can acquire the lock.
for _ in range(new_profiles):
self.active_profiles.add(self.new_profiles.popleft())
inactive_profiles = []
for profile in self.active_profiles:
if profile.active:
profile.write(now, sample)
else:
# If a thread is marked inactive, we buffer it
# to `inactive_profiles` so it can be removed.
# We cannot remove it here as it would result
# in a RuntimeError.
inactive_profiles.append(profile)
for profile in inactive_profiles:
self.active_profiles.remove(profile)
return _sample_stack
class ThreadScheduler(Scheduler):
"""
This scheduler is based on running a daemon thread that will call
the sampler at a regular interval.
"""
mode = "thread" # type: ProfilerMode
name = "sentry.profiler.ThreadScheduler"
def __init__(self, frequency):
# type: (int) -> None
super().__init__(frequency=frequency)
# used to signal to the thread that it should stop
self.running = False
self.thread = None # type: Optional[threading.Thread]
self.pid = None # type: Optional[int]
self.lock = threading.Lock()
def setup(self):
# type: () -> None
pass
def teardown(self):
# type: () -> None
if self.running:
self.running = False
if self.thread is not None:
self.thread.join()
def ensure_running(self):
# type: () -> None
"""
Check that the profiler has an active thread to run in, and start one if
that's not the case.
Note that this might fail (e.g. in Python 3.12 it's not possible to
spawn new threads at interpreter shutdown). In that case self.running
will be False after running this function.
"""
pid = os.getpid()
# is running on the right process
if self.running and self.pid == pid:
return
with self.lock:
# another thread may have tried to acquire the lock
# at the same time so it may start another thread
# make sure to check again before proceeding
if self.running and self.pid == pid:
return
self.pid = pid
self.running = True
# make sure the thread is a daemon here otherwise this
# can keep the application running after other threads
# have exited
self.thread = threading.Thread(name=self.name, target=self.run, daemon=True)
try:
self.thread.start()
except RuntimeError:
# Unfortunately at this point the interpreter is in a state that no
# longer allows us to spawn a thread and we have to bail.
self.running = False
self.thread = None
return
def run(self):
# type: () -> None
last = time.perf_counter()
while self.running:
self.sampler()
# some time may have elapsed since the last time
# we sampled, so we need to account for that and
# not sleep for too long
elapsed = time.perf_counter() - last
if elapsed < self.interval:
thread_sleep(self.interval - elapsed)
# after sleeping, make sure to take the current
# timestamp so we can use it next iteration
last = time.perf_counter()
class GeventScheduler(Scheduler):
"""
This scheduler is based on the thread scheduler but adapted to work with
gevent. When using gevent, it may monkey patch the threading modules
(`threading` and `_thread`). This results in the use of greenlets instead
of native threads.
This is an issue because the sampler CANNOT run in a greenlet because
1. Other greenlets doing sync work will prevent the sampler from running
2. The greenlet runs in the same thread as other greenlets so when taking
a sample, other greenlets will have been evicted from the thread. This
results in a sample containing only the sampler's code.
"""
mode = "gevent" # type: ProfilerMode
name = "sentry.profiler.GeventScheduler"
def __init__(self, frequency):
# type: (int) -> None
if ThreadPool is None:
raise ValueError("Profiler mode: {} is not available".format(self.mode))
super().__init__(frequency=frequency)
# used to signal to the thread that it should stop
self.running = False
self.thread = None # type: Optional[ThreadPool]
self.pid = None # type: Optional[int]
# This intentionally uses the gevent patched threading.Lock.
# The lock will be required when first trying to start profiles
# as we need to spawn the profiler thread from the greenlets.
self.lock = threading.Lock()
def setup(self):
# type: () -> None
pass
def teardown(self):
# type: () -> None
if self.running:
self.running = False
if self.thread is not None:
self.thread.join()
def ensure_running(self):
# type: () -> None
pid = os.getpid()
# is running on the right process
if self.running and self.pid == pid:
return
with self.lock:
# another thread may have tried to acquire the lock
# at the same time so it may start another thread
# make sure to check again before proceeding
if self.running and self.pid == pid:
return
self.pid = pid
self.running = True
self.thread = ThreadPool(1)
try:
self.thread.spawn(self.run)
except RuntimeError:
# Unfortunately at this point the interpreter is in a state that no
# longer allows us to spawn a thread and we have to bail.
self.running = False
self.thread = None
return
def run(self):
# type: () -> None
last = time.perf_counter()
while self.running:
self.sampler()
# some time may have elapsed since the last time
# we sampled, so we need to account for that and
# not sleep for too long
elapsed = time.perf_counter() - last
if elapsed < self.interval:
thread_sleep(self.interval - elapsed)
# after sleeping, make sure to take the current
# timestamp so we can use it next iteration
last = time.perf_counter()