/
profiler.py
745 lines (597 loc) · 24.7 KB
/
profiler.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
"""
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
from contextlib import contextmanager
import sentry_sdk
from sentry_sdk._compat import PY33
from sentry_sdk._types import MYPY
from sentry_sdk.utils import (
filename_for_module,
handle_in_app_impl,
logger,
nanosecond_time,
)
if MYPY:
from types import FrameType
from typing import Any
from typing import Callable
from typing import Deque
from typing import Dict
from typing import Generator
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.scope
import sentry_sdk.tracing
ThreadId = str
# 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.
RawStackId = Tuple[int, int]
RawFrame = Tuple[
str, # abs_path
Optional[str], # module
Optional[str], # filename
str, # function
int, # lineno
]
RawStack = Tuple[RawFrame, ...]
RawSample = Sequence[Tuple[str, Tuple[RawStackId, RawStack]]]
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],
},
)
try:
from gevent.monkey import is_module_patched # type: ignore
except ImportError:
def is_module_patched(*args, **kwargs):
# type: (*Any, **Any) -> bool
# unable to import from gevent means no modules have been patched
return False
_scheduler = None # type: Optional[Scheduler]
def setup_profiler(options):
# type: (Dict[str, Any]) -> None
"""
`buffer_secs` determines the max time a sample will be buffered for
`frequency` determines the number of samples to take per second (Hz)
"""
global _scheduler
if _scheduler is not None:
logger.debug("profiling is already setup")
return
if not PY33:
logger.warn("profiling is only supported on Python >= 3.3")
return
frequency = 101
if is_module_patched("threading") or is_module_patched("_thread"):
# 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
profiler_mode = options["_experiments"].get("profiler_mode", 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:
try:
_scheduler = GeventScheduler(frequency=frequency)
except ImportError:
raise ValueError("Profiler mode: {} is not available".format(profiler_mode))
else:
raise ValueError("Unknown profiler mode: {}".format(profiler_mode))
_scheduler.setup()
atexit.register(teardown_profiler)
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(
frame, # type: Optional[FrameType]
cwd, # type: str
prev_cache=None, # type: Optional[Tuple[RawStackId, RawStack, Deque[FrameType]]]
max_stack_depth=MAX_STACK_DEPTH, # type: int
):
# type: (...) -> Tuple[RawStackId, RawStack, Deque[FrameType]]
"""
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.
"""
frames = deque(maxlen=max_stack_depth) # type: Deque[FrameType]
while frame is not None:
frames.append(frame)
frame = frame.f_back
if prev_cache is None:
stack = tuple(extract_frame(frame, cwd) for frame in frames)
else:
_, prev_stack, prev_frames = prev_cache
prev_depth = len(prev_frames)
depth = len(frames)
# We want to match the frame found in this sample to the frames found in the
# previous sample. If they are the same (using the `is` operator), we can
# skip the expensive work of extracting the frame information and reuse what
# we extracted during the last sample.
#
# Make sure to keep in mind that the stack is ordered from the inner most
# from to the outer most frame so be careful with the indexing.
stack = tuple(
prev_stack[i]
if i >= 0 and frame is prev_frames[i]
else extract_frame(frame, cwd)
for i, frame in zip(range(prev_depth - depth, prev_depth), frames)
)
# 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(stack), hash(stack)
return stack_id, stack, frames
def extract_frame(frame, cwd):
# type: (FrameType, str) -> RawFrame
abs_path = frame.f_code.co_filename
try:
module = 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.
os.path.join(cwd, abs_path),
module,
filename_for_module(module, abs_path) or None,
get_frame_name(frame),
frame.f_lineno,
)
def get_frame_name(frame):
# type: (FrameType) -> str
# in 3.11+, there is a frame.f_code.co_qualname that
# we should consider using instead where possible
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
class Profile(object):
def __init__(
self,
scheduler, # type: Scheduler
transaction, # type: sentry_sdk.tracing.Transaction
):
# type: (...) -> None
self.scheduler = scheduler
self.transaction = transaction
self.start_ns = 0 # type: int
self.stop_ns = 0 # type: int
self.active = False # type: bool
self.indexed_frames = {} # type: Dict[RawFrame, int]
self.indexed_stacks = {} # type: Dict[RawStackId, int]
self.frames = [] # type: List[ProcessedFrame]
self.stacks = [] # type: List[ProcessedStack]
self.samples = [] # type: List[ProcessedSample]
transaction._profile = self
def __enter__(self):
# type: () -> None
self.start_ns = nanosecond_time()
self.scheduler.start_profiling(self)
def __exit__(self, ty, value, tb):
# type: (Optional[Any], Optional[Any], Optional[Any]) -> None
self.scheduler.stop_profiling(self)
self.stop_ns = nanosecond_time()
def write(self, ts, sample):
# type: (int, RawSample) -> None
if ts < self.start_ns:
return
offset = ts - self.start_ns
if offset > MAX_PROFILE_DURATION_NS:
return
elapsed_since_start_ns = str(offset)
for tid, (stack_id, stack) in sample:
# 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 frame in stack:
if frame not in self.indexed_frames:
self.indexed_frames[frame] = len(self.indexed_frames)
self.frames.append(
{
"abs_path": frame[0],
"module": frame[1],
"filename": frame[2],
"function": frame[3],
"lineno": frame[4],
}
)
self.indexed_stacks[stack_id] = len(self.indexed_stacks)
self.stacks.append([self.indexed_frames[frame] for frame in stack])
self.samples.append(
{
"elapsed_since_start_ns": elapsed_since_start_ns,
"thread_id": tid,
"stack_id": self.indexed_stacks[stack_id],
}
)
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, scope):
# type: (Any, Dict[str, Any], Optional[sentry_sdk.scope.Scope]) -> Dict[str, Any]
profile = self.process()
handle_in_app_impl(
profile["frames"], options["in_app_exclude"], options["in_app_include"]
)
# the active thread id from the scope always take priorty if it exists
active_thread_id = None if scope is None else scope.active_thread_id
return {
"environment": event_opt.get("environment"),
"event_id": uuid.uuid4().hex,
"platform": "python",
"profile": profile,
"release": event_opt.get("release", ""),
"timestamp": event_opt["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": self.transaction.name,
# 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": self.transaction.trace_id,
"active_thread_id": str(
self.transaction._active_thread_id
if active_thread_id is None
else active_thread_id
),
}
],
}
class Scheduler(object):
mode = "unknown"
def __init__(self, frequency):
# type: (int) -> None
self.interval = 1.0 / frequency
self.sampler = self.make_sampler()
self.new_profiles = deque() # 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 start_profiling(self, profile):
# type: (Profile) -> None
profile.active = True
self.new_profiles.append(profile)
def stop_profiling(self, profile):
# type: (Profile) -> None
profile.active = False
def make_sampler(self):
# type: () -> Callable[..., None]
cwd = os.getcwd()
# In Python3+, we can use the `nonlocal` keyword to rebind the value,
# but this is not possible in Python2. To get around this, we wrap
# the value in a list to allow updating this value each sample.
last_sample = [
{}
] # type: List[Dict[int, Tuple[RawStackId, RawStack, Deque[FrameType]]]]
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
last_sample[0] = {}
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()
raw_sample = {
tid: extract_stack(frame, cwd, last_sample[0].get(tid))
for tid, frame in sys._current_frames().items()
}
# make sure to update the last sample so the cache has
# the most recent stack for better cache hits
last_sample[0] = raw_sample
sample = [
(str(tid), (stack_id, stack))
for tid, (stack_id, stack, _) in raw_sample.items()
]
# 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"
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.event = threading.Event()
# 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)
def setup(self):
# type: () -> None
self.thread.start()
def teardown(self):
# type: () -> None
self.event.set()
self.thread.join()
def run(self):
# type: () -> None
last = time.perf_counter()
while True:
if self.event.is_set():
break
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:
time.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"
name = "sentry.profiler.GeventScheduler"
def __init__(self, frequency):
# type: (int) -> None
# This can throw an ImportError that must be caught if `gevent` is
# not installed.
from gevent.threadpool import ThreadPool # type: ignore
super(GeventScheduler, self).__init__(frequency=frequency)
# used to signal to the thread that it should stop
self.event = threading.Event()
# Using gevent's ThreadPool allows us to bypass greenlets and spawn
# native threads.
self.pool = ThreadPool(1)
def setup(self):
# type: () -> None
self.pool.spawn(self.run)
def teardown(self):
# type: () -> None
self.event.set()
self.pool.join()
def run(self):
# type: () -> None
last = time.perf_counter()
while True:
if self.event.is_set():
break
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:
time.sleep(self.interval - elapsed)
# after sleeping, make sure to take the current
# timestamp so we can use it next iteration
last = time.perf_counter()
def _should_profile(transaction, hub):
# type: (sentry_sdk.tracing.Transaction, sentry_sdk.Hub) -> bool
# The corresponding transaction was not sampled,
# so don't generate a profile for it.
if not transaction.sampled:
return False
# The profiler hasn't been properly initialized.
if _scheduler is None:
return False
client = hub.client
# The client is None, so we can't get the sample rate.
if client is None:
return False
options = client.options
profiles_sample_rate = options["_experiments"].get("profiles_sample_rate")
# The profiles_sample_rate option was not set, so profiling
# was never enabled.
if profiles_sample_rate is None:
return False
return random.random() < float(profiles_sample_rate)
@contextmanager
def start_profiling(transaction, hub=None):
# type: (sentry_sdk.tracing.Transaction, Optional[sentry_sdk.Hub]) -> Generator[None, None, None]
hub = hub or sentry_sdk.Hub.current
# if profiling was not enabled, this should be a noop
if _should_profile(transaction, hub):
assert _scheduler is not None
with Profile(_scheduler, transaction):
yield
else:
yield