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Editing: profiler.py
""" 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 collections import deque import sentry_sdk from sentry_sdk._compat import PY33, 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, 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 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 import get_hub as get_gevent_hub # type: ignore from gevent.monkey import get_original, is_module_patched # type: ignore from gevent.threadpool import ThreadPool # type: ignore thread_sleep = get_original("time", "sleep") except ImportError: def get_gevent_hub(): # type: () -> Any return None thread_sleep = time.sleep def is_module_patched(*args, **kwargs): # type: (*Any, **Any) -> bool # unable to import from gevent means no modules have been patched return False ThreadPool = None def is_gevent(): # type: () -> bool return is_module_patched("threading") or is_module_patched("_thread") _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 and 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 if not PY33: logger.warn("[Profiling] Profiler requires Python >= 3.3") 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") 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 CWD = os.getcwd() def extract_stack( raw_frame, # type: Optional[FrameType] cache, # type: LRUCache cwd=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: 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: 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 def get_current_thread_id(thread=None): # type: (Optional[threading.Thread]) -> Optional[int] """ Try to get the id of the current thread, with various fall backs. """ # if a thread is specified, that takes priority if thread is not None: try: thread_id = thread.ident if thread_id is not None: return thread_id except AttributeError: pass # if the app is using gevent, we should look at the gevent hub first # as the id there differs from what the threading module reports if is_gevent(): gevent_hub = get_gevent_hub() if gevent_hub is not None: try: # this is undocumented, so wrap it in try except to be safe return gevent_hub.thread_ident except AttributeError: pass # use the current thread's id if possible try: current_thread_id = threading.current_thread().ident if current_thread_id is not None: return current_thread_id except AttributeError: pass # if we can't get the current thread id, fall back to the main thread id try: main_thread_id = threading.main_thread().ident if main_thread_id is not None: return main_thread_id except AttributeError: pass # we've tried everything, time to give up return None class Profile(object): 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_id() 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_id() 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 precdence 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 hub = self.hub or sentry_sdk.Hub.current client = hub.client # The client is None, so we can't get the sample rate. if client is None: 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.scheduler.stop_profiling(self) self.stop_ns = nanosecond_time() def __enter__(self): # type: () -> Profile hub = self.hub or sentry_sdk.Hub.current _, scope = hub._stack[-1] old_profile = scope.profile scope.profile = self self._context_manager_state = (hub, 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: (Any, Dict[str, Any], 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 hub = self.hub or sentry_sdk.Hub.current client = hub.client if client is None: 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(object): 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() def setup(self): # type: () -> None raise NotImplementedError def teardown(self): # type: () -> None raise NotImplementedError def ensure_running(self): # type: () -> None raise NotImplementedError def start_profiling(self, profile): # type: (Profile) -> None self.ensure_running() self.new_profiles.append(profile) def stop_profiling(self, profile): # type: (Profile) -> None pass 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(ThreadScheduler, self).__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 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) self.thread.start() 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(GeventScheduler, self).__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) self.thread.spawn(self.run) 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()
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