summary refs log tree commit diff
path: root/synapse/metrics/__init__.py
blob: f237b8a2369e720e6026d1aa1b93e38efba80261 (plain) (blame)
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
# Copyright 2015, 2016 OpenMarket Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import functools
import gc
import itertools
import logging
import os
import platform
import threading
import time
from typing import Callable, Dict, Iterable, Optional, Tuple, Union

import attr
from prometheus_client import Counter, Gauge, Histogram
from prometheus_client.core import (
    REGISTRY,
    CounterMetricFamily,
    GaugeHistogramMetricFamily,
    GaugeMetricFamily,
)

from twisted.internet import reactor

import synapse
from synapse.metrics._exposition import (
    MetricsResource,
    generate_latest,
    start_http_server,
)
from synapse.util.versionstring import get_version_string

logger = logging.getLogger(__name__)

METRICS_PREFIX = "/_synapse/metrics"

running_on_pypy = platform.python_implementation() == "PyPy"
all_gauges: "Dict[str, Union[LaterGauge, InFlightGauge]]" = {}

HAVE_PROC_SELF_STAT = os.path.exists("/proc/self/stat")


class RegistryProxy:
    @staticmethod
    def collect():
        for metric in REGISTRY.collect():
            if not metric.name.startswith("__"):
                yield metric


@attr.s(slots=True, hash=True)
class LaterGauge:

    name = attr.ib(type=str)
    desc = attr.ib(type=str)
    labels = attr.ib(hash=False, type=Optional[Iterable[str]])
    # callback: should either return a value (if there are no labels for this metric),
    # or dict mapping from a label tuple to a value
    caller = attr.ib(type=Callable[[], Union[Dict[Tuple[str, ...], float], float]])

    def collect(self):

        g = GaugeMetricFamily(self.name, self.desc, labels=self.labels)

        try:
            calls = self.caller()
        except Exception:
            logger.exception("Exception running callback for LaterGauge(%s)", self.name)
            yield g
            return

        if isinstance(calls, dict):
            for k, v in calls.items():
                g.add_metric(k, v)
        else:
            g.add_metric([], calls)

        yield g

    def __attrs_post_init__(self):
        self._register()

    def _register(self):
        if self.name in all_gauges.keys():
            logger.warning("%s already registered, reregistering" % (self.name,))
            REGISTRY.unregister(all_gauges.pop(self.name))

        REGISTRY.register(self)
        all_gauges[self.name] = self


class InFlightGauge:
    """Tracks number of things (e.g. requests, Measure blocks, etc) in flight
    at any given time.

    Each InFlightGauge will create a metric called `<name>_total` that counts
    the number of in flight blocks, as well as a metrics for each item in the
    given `sub_metrics` as `<name>_<sub_metric>` which will get updated by the
    callbacks.

    Args:
        name (str)
        desc (str)
        labels (list[str])
        sub_metrics (list[str]): A list of sub metrics that the callbacks
            will update.
    """

    def __init__(self, name, desc, labels, sub_metrics):
        self.name = name
        self.desc = desc
        self.labels = labels
        self.sub_metrics = sub_metrics

        # Create a class which have the sub_metrics values as attributes, which
        # default to 0 on initialization. Used to pass to registered callbacks.
        self._metrics_class = attr.make_class(
            "_MetricsEntry", attrs={x: attr.ib(0) for x in sub_metrics}, slots=True
        )

        # Counts number of in flight blocks for a given set of label values
        self._registrations: Dict = {}

        # Protects access to _registrations
        self._lock = threading.Lock()

        self._register_with_collector()

    def register(self, key, callback):
        """Registers that we've entered a new block with labels `key`.

        `callback` gets called each time the metrics are collected. The same
        value must also be given to `unregister`.

        `callback` gets called with an object that has an attribute per
        sub_metric, which should be updated with the necessary values. Note that
        the metrics object is shared between all callbacks registered with the
        same key.

        Note that `callback` may be called on a separate thread.
        """
        with self._lock:
            self._registrations.setdefault(key, set()).add(callback)

    def unregister(self, key, callback):
        """Registers that we've exited a block with labels `key`."""

        with self._lock:
            self._registrations.setdefault(key, set()).discard(callback)

    def collect(self):
        """Called by prometheus client when it reads metrics.

        Note: may be called by a separate thread.
        """
        in_flight = GaugeMetricFamily(
            self.name + "_total", self.desc, labels=self.labels
        )

        metrics_by_key = {}

        # We copy so that we don't mutate the list while iterating
        with self._lock:
            keys = list(self._registrations)

        for key in keys:
            with self._lock:
                callbacks = set(self._registrations[key])

            in_flight.add_metric(key, len(callbacks))

            metrics = self._metrics_class()
            metrics_by_key[key] = metrics
            for callback in callbacks:
                callback(metrics)

        yield in_flight

        for name in self.sub_metrics:
            gauge = GaugeMetricFamily(
                "_".join([self.name, name]), "", labels=self.labels
            )
            for key, metrics in metrics_by_key.items():
                gauge.add_metric(key, getattr(metrics, name))
            yield gauge

    def _register_with_collector(self):
        if self.name in all_gauges.keys():
            logger.warning("%s already registered, reregistering" % (self.name,))
            REGISTRY.unregister(all_gauges.pop(self.name))

        REGISTRY.register(self)
        all_gauges[self.name] = self


class GaugeBucketCollector:
    """Like a Histogram, but the buckets are Gauges which are updated atomically.

    The data is updated by calling `update_data` with an iterable of measurements.

    We assume that the data is updated less frequently than it is reported to
    Prometheus, and optimise for that case.
    """

    __slots__ = (
        "_name",
        "_documentation",
        "_bucket_bounds",
        "_metric",
    )

    def __init__(
        self,
        name: str,
        documentation: str,
        buckets: Iterable[float],
        registry=REGISTRY,
    ):
        """
        Args:
            name: base name of metric to be exported to Prometheus. (a _bucket suffix
               will be added.)
            documentation: help text for the metric
            buckets: The top bounds of the buckets to report
            registry: metric registry to register with
        """
        self._name = name
        self._documentation = documentation

        # the tops of the buckets
        self._bucket_bounds = [float(b) for b in buckets]
        if self._bucket_bounds != sorted(self._bucket_bounds):
            raise ValueError("Buckets not in sorted order")

        if self._bucket_bounds[-1] != float("inf"):
            self._bucket_bounds.append(float("inf"))

        # We initially set this to None. We won't report metrics until
        # this has been initialised after a successful data update
        self._metric: Optional[GaugeHistogramMetricFamily] = None

        registry.register(self)

    def collect(self):
        # Don't report metrics unless we've already collected some data
        if self._metric is not None:
            yield self._metric

    def update_data(self, values: Iterable[float]):
        """Update the data to be reported by the metric

        The existing data is cleared, and each measurement in the input is assigned
        to the relevant bucket.
        """
        self._metric = self._values_to_metric(values)

    def _values_to_metric(self, values: Iterable[float]) -> GaugeHistogramMetricFamily:
        total = 0.0
        bucket_values = [0 for _ in self._bucket_bounds]

        for v in values:
            # assign each value to a bucket
            for i, bound in enumerate(self._bucket_bounds):
                if v <= bound:
                    bucket_values[i] += 1
                    break

            # ... and increment the sum
            total += v

        # now, aggregate the bucket values so that they count the number of entries in
        # that bucket or below.
        accumulated_values = itertools.accumulate(bucket_values)

        return GaugeHistogramMetricFamily(
            self._name,
            self._documentation,
            buckets=list(
                zip((str(b) for b in self._bucket_bounds), accumulated_values)
            ),
            gsum_value=total,
        )


#
# Detailed CPU metrics
#


class CPUMetrics:
    def __init__(self):
        ticks_per_sec = 100
        try:
            # Try and get the system config
            ticks_per_sec = os.sysconf("SC_CLK_TCK")
        except (ValueError, TypeError, AttributeError):
            pass

        self.ticks_per_sec = ticks_per_sec

    def collect(self):
        if not HAVE_PROC_SELF_STAT:
            return

        with open("/proc/self/stat") as s:
            line = s.read()
            raw_stats = line.split(") ", 1)[1].split(" ")

            user = GaugeMetricFamily("process_cpu_user_seconds_total", "")
            user.add_metric([], float(raw_stats[11]) / self.ticks_per_sec)
            yield user

            sys = GaugeMetricFamily("process_cpu_system_seconds_total", "")
            sys.add_metric([], float(raw_stats[12]) / self.ticks_per_sec)
            yield sys


REGISTRY.register(CPUMetrics())

#
# Python GC metrics
#

gc_unreachable = Gauge("python_gc_unreachable_total", "Unreachable GC objects", ["gen"])
gc_time = Histogram(
    "python_gc_time",
    "Time taken to GC (sec)",
    ["gen"],
    buckets=[
        0.0025,
        0.005,
        0.01,
        0.025,
        0.05,
        0.10,
        0.25,
        0.50,
        1.00,
        2.50,
        5.00,
        7.50,
        15.00,
        30.00,
        45.00,
        60.00,
    ],
)


class GCCounts:
    def collect(self):
        cm = GaugeMetricFamily("python_gc_counts", "GC object counts", labels=["gen"])
        for n, m in enumerate(gc.get_count()):
            cm.add_metric([str(n)], m)

        yield cm


if not running_on_pypy:
    REGISTRY.register(GCCounts())


#
# PyPy GC / memory metrics
#


class PyPyGCStats:
    def collect(self):

        # @stats is a pretty-printer object with __str__() returning a nice table,
        # plus some fields that contain data from that table.
        # unfortunately, fields are pretty-printed themselves (i. e. '4.5MB').
        stats = gc.get_stats(memory_pressure=False)  # type: ignore
        # @s contains same fields as @stats, but as actual integers.
        s = stats._s  # type: ignore

        # also note that field naming is completely braindead
        # and only vaguely correlates with the pretty-printed table.
        # >>>> gc.get_stats(False)
        # Total memory consumed:
        #     GC used:            8.7MB (peak: 39.0MB)        # s.total_gc_memory, s.peak_memory
        #        in arenas:            3.0MB                  # s.total_arena_memory
        #        rawmalloced:          1.7MB                  # s.total_rawmalloced_memory
        #        nursery:              4.0MB                  # s.nursery_size
        #     raw assembler used: 31.0kB                      # s.jit_backend_used
        #     -----------------------------
        #     Total:              8.8MB                       # stats.memory_used_sum
        #
        #     Total memory allocated:
        #     GC allocated:            38.7MB (peak: 41.1MB)  # s.total_allocated_memory, s.peak_allocated_memory
        #        in arenas:            30.9MB                 # s.peak_arena_memory
        #        rawmalloced:          4.1MB                  # s.peak_rawmalloced_memory
        #        nursery:              4.0MB                  # s.nursery_size
        #     raw assembler allocated: 1.0MB                  # s.jit_backend_allocated
        #     -----------------------------
        #     Total:                   39.7MB                 # stats.memory_allocated_sum
        #
        #     Total time spent in GC:  0.073                  # s.total_gc_time

        pypy_gc_time = CounterMetricFamily(
            "pypy_gc_time_seconds_total",
            "Total time spent in PyPy GC",
            labels=[],
        )
        pypy_gc_time.add_metric([], s.total_gc_time / 1000)
        yield pypy_gc_time

        pypy_mem = GaugeMetricFamily(
            "pypy_memory_bytes",
            "Memory tracked by PyPy allocator",
            labels=["state", "class", "kind"],
        )
        # memory used by JIT assembler
        pypy_mem.add_metric(["used", "", "jit"], s.jit_backend_used)
        pypy_mem.add_metric(["allocated", "", "jit"], s.jit_backend_allocated)
        # memory used by GCed objects
        pypy_mem.add_metric(["used", "", "arenas"], s.total_arena_memory)
        pypy_mem.add_metric(["allocated", "", "arenas"], s.peak_arena_memory)
        pypy_mem.add_metric(["used", "", "rawmalloced"], s.total_rawmalloced_memory)
        pypy_mem.add_metric(["allocated", "", "rawmalloced"], s.peak_rawmalloced_memory)
        pypy_mem.add_metric(["used", "", "nursery"], s.nursery_size)
        pypy_mem.add_metric(["allocated", "", "nursery"], s.nursery_size)
        # totals
        pypy_mem.add_metric(["used", "totals", "gc"], s.total_gc_memory)
        pypy_mem.add_metric(["allocated", "totals", "gc"], s.total_allocated_memory)
        pypy_mem.add_metric(["used", "totals", "gc_peak"], s.peak_memory)
        pypy_mem.add_metric(["allocated", "totals", "gc_peak"], s.peak_allocated_memory)
        yield pypy_mem


if running_on_pypy:
    REGISTRY.register(PyPyGCStats())


#
# Twisted reactor metrics
#

tick_time = Histogram(
    "python_twisted_reactor_tick_time",
    "Tick time of the Twisted reactor (sec)",
    buckets=[0.001, 0.002, 0.005, 0.01, 0.025, 0.05, 0.1, 0.2, 0.5, 1, 2, 5],
)
pending_calls_metric = Histogram(
    "python_twisted_reactor_pending_calls",
    "Pending calls",
    buckets=[1, 2, 5, 10, 25, 50, 100, 250, 500, 1000],
)

#
# Federation Metrics
#

sent_transactions_counter = Counter("synapse_federation_client_sent_transactions", "")

events_processed_counter = Counter("synapse_federation_client_events_processed", "")

event_processing_loop_counter = Counter(
    "synapse_event_processing_loop_count", "Event processing loop iterations", ["name"]
)

event_processing_loop_room_count = Counter(
    "synapse_event_processing_loop_room_count",
    "Rooms seen per event processing loop iteration",
    ["name"],
)


# Used to track where various components have processed in the event stream,
# e.g. federation sending, appservice sending, etc.
event_processing_positions = Gauge("synapse_event_processing_positions", "", ["name"])

# Used to track the current max events stream position
event_persisted_position = Gauge("synapse_event_persisted_position", "")

# Used to track the received_ts of the last event processed by various
# components
event_processing_last_ts = Gauge("synapse_event_processing_last_ts", "", ["name"])

# Used to track the lag processing events. This is the time difference
# between the last processed event's received_ts and the time it was
# finished being processed.
event_processing_lag = Gauge("synapse_event_processing_lag", "", ["name"])

event_processing_lag_by_event = Histogram(
    "synapse_event_processing_lag_by_event",
    "Time between an event being persisted and it being queued up to be sent to the relevant remote servers",
    ["name"],
)

# Build info of the running server.
build_info = Gauge(
    "synapse_build_info", "Build information", ["pythonversion", "version", "osversion"]
)
build_info.labels(
    " ".join([platform.python_implementation(), platform.python_version()]),
    get_version_string(synapse),
    " ".join([platform.system(), platform.release()]),
).set(1)

last_ticked = time.time()

# 3PID send info
threepid_send_requests = Histogram(
    "synapse_threepid_send_requests_with_tries",
    documentation="Number of requests for a 3pid token by try count. Note if"
    " there is a request with try count of 4, then there would have been one"
    " each for 1, 2 and 3",
    buckets=(1, 2, 3, 4, 5, 10),
    labelnames=("type", "reason"),
)


class ReactorLastSeenMetric:
    def collect(self):
        cm = GaugeMetricFamily(
            "python_twisted_reactor_last_seen",
            "Seconds since the Twisted reactor was last seen",
        )
        cm.add_metric([], time.time() - last_ticked)
        yield cm


REGISTRY.register(ReactorLastSeenMetric())

# The minimum time in seconds between GCs for each generation, regardless of the current GC
# thresholds and counts.
MIN_TIME_BETWEEN_GCS = (1.0, 10.0, 30.0)

# The time (in seconds since the epoch) of the last time we did a GC for each generation.
_last_gc = [0.0, 0.0, 0.0]


def runUntilCurrentTimer(reactor, func):
    @functools.wraps(func)
    def f(*args, **kwargs):
        now = reactor.seconds()
        num_pending = 0

        # _newTimedCalls is one long list of *all* pending calls. Below loop
        # is based off of impl of reactor.runUntilCurrent
        for delayed_call in reactor._newTimedCalls:
            if delayed_call.time > now:
                break

            if delayed_call.delayed_time > 0:
                continue

            num_pending += 1

        num_pending += len(reactor.threadCallQueue)
        start = time.time()
        ret = func(*args, **kwargs)
        end = time.time()

        # record the amount of wallclock time spent running pending calls.
        # This is a proxy for the actual amount of time between reactor polls,
        # since about 25% of time is actually spent running things triggered by
        # I/O events, but that is harder to capture without rewriting half the
        # reactor.
        tick_time.observe(end - start)
        pending_calls_metric.observe(num_pending)

        # Update the time we last ticked, for the metric to test whether
        # Synapse's reactor has frozen
        global last_ticked
        last_ticked = end

        if running_on_pypy:
            return ret

        # Check if we need to do a manual GC (since its been disabled), and do
        # one if necessary. Note we go in reverse order as e.g. a gen 1 GC may
        # promote an object into gen 2, and we don't want to handle the same
        # object multiple times.
        threshold = gc.get_threshold()
        counts = gc.get_count()
        for i in (2, 1, 0):
            # We check if we need to do one based on a straightforward
            # comparison between the threshold and count. We also do an extra
            # check to make sure that we don't a GC too often.
            if threshold[i] < counts[i] and MIN_TIME_BETWEEN_GCS[i] < end - _last_gc[i]:
                if i == 0:
                    logger.debug("Collecting gc %d", i)
                else:
                    logger.info("Collecting gc %d", i)

                start = time.time()
                unreachable = gc.collect(i)
                end = time.time()

                _last_gc[i] = end

                gc_time.labels(i).observe(end - start)
                gc_unreachable.labels(i).set(unreachable)

        return ret

    return f


try:
    # Ensure the reactor has all the attributes we expect
    reactor.seconds  # type: ignore
    reactor.runUntilCurrent  # type: ignore
    reactor._newTimedCalls  # type: ignore
    reactor.threadCallQueue  # type: ignore

    # runUntilCurrent is called when we have pending calls. It is called once
    # per iteratation after fd polling.
    reactor.runUntilCurrent = runUntilCurrentTimer(reactor, reactor.runUntilCurrent)  # type: ignore

    # We manually run the GC each reactor tick so that we can get some metrics
    # about time spent doing GC,
    if not running_on_pypy:
        gc.disable()
except AttributeError:
    pass


__all__ = [
    "MetricsResource",
    "generate_latest",
    "start_http_server",
    "LaterGauge",
    "InFlightGauge",
    "BucketCollector",
]