tahoe-lafs/src/allmydata/stats.py

104 lines
3.5 KiB
Python
Raw Normal View History

2020-12-29 14:28:14 +00:00
"""
Ported to Python 3.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
2020-12-29 14:28:14 +00:00
from __future__ import unicode_literals
stats: add a simple stats gathering system We have a desire to collect runtime statistics from multiple nodes primarily for server monitoring purposes. This implements a simple implementation of such a system, as a skeleton to build more sophistication upon. Each client now looks for a 'stats_gatherer.furl' config file. If it has been configured to use a stats gatherer, then it instantiates internally a StatsProvider. This is a central place for code which wishes to offer stats up for monitoring to report them to, either by calling stats_provider.count('stat.name', value) to increment a counter, or by registering a class as a stats producer with sp.register_producer(obj). The StatsProvider connects to the StatsGatherer server and provides its provider upon startup. The StatsGatherer is then responsible for polling the attached providers periodically to retrieve the data provided. The provider queries each registered producer when the gatherer queries the provider. Both the internal 'counters' and the queried 'stats' are then reported to the gatherer. This provides a simple gatherer app, (c.f. make stats-gatherer-run) which prints its furl and listens for incoming connections. Once a minute, the gatherer polls all connected providers, and writes the retrieved data into a pickle file. Also included is a munin plugin which knows how to read the gatherer's stats.pickle and output data munin can interpret. this plugin, tahoe-stats.py can be symlinked as multiple different names within munin's 'plugins' directory, and inspects argv to determine which data to display, doing a lookup in a table within that file. It looks in the environment for 'statsfile' to determine the path to the gatherer's stats.pickle. An example plugins-conf.d file is provided.
2008-01-31 03:11:07 +00:00
from future.utils import PY2
if PY2:
2020-12-29 14:28:14 +00:00
from future.builtins import filter, map, zip, ascii, chr, hex, input, next, oct, open, pow, round, super, bytes, dict, list, object, range, str, max, min # noqa: F401
from time import clock as process_time
else:
from time import process_time
2020-12-29 14:28:14 +00:00
import time
feat(py3): Convert unicode-only modules to str Modules that reference `unicode` but do *not* reference `str` can safely be converted to use `str` in a way that's closest to the way it should be done under Python 3 but that is still Python 2 compatible [per `python-future`](https://python-future.org/compatible_idioms.html?highlight=unicode#unicode). This change results in 4 additional tests passing under Python 3 that weren't before, one previous test error is now a failure, and more coverage in a few modules. Here's the diff of the output from running all tests under Python 3 before these changes and after. I've elided the irrelevant changes (time stamps, object ids, etc.): ```diff --- .tox/make-test-py3-all-old.log 2020-09-27 20:56:55.761691130 -0700 +++ .tox/make-test-py3-all-new.log 2020-09-27 20:58:16.242075678 -0700 @@ -1,6 +1,6 @@ ... @@ -4218,7 +4218,7 @@ [ERROR] (#.### secs) allmydata.test.mutable.test_version.Version.test_download_version ... Traceback (most recent call last): - File "/home/rpatterson/src/work/sfu/tahoe-lafs/src/allmydata/test/mutable/test_version.py", line 274, in test_download_version + File "/home/rpatterson/src/work/sfu/tahoe-lafs/src/allmydata/test/mutable/test_version.py", line 279, in test_download_version d = self.publish_multiple() File "/home/rpatterson/src/work/sfu/tahoe-lafs/src/allmydata/test/mutable/util.py", line 372, in publish_multiple self._nodemaker = make_nodemaker(self._storage) @@ -4438,40 +4438,26 @@ allmydata.test.test_abbreviate.Abbreviate.test_time ... [OK] (#.### secs) allmydata.test.test_auth.AccountFileCheckerKeyTests.test_authenticated ... Traceback (most recent call last): - File "/home/rpatterson/src/work/sfu/tahoe-lafs/src/allmydata/test/test_auth.py", line 42, in setUp - abspath = abspath_expanduser_unicode(unicode(self.account_file.path)) -builtins.NameError: name 'unicode' is not defined +Failure: twisted.cred.error.UnauthorizedLogin: [ERROR] (#.### secs) allmydata.test.test_auth.AccountFileCheckerKeyTests.test_missing_signature ... Traceback (most recent call last): - File "/home/rpatterson/src/work/sfu/tahoe-lafs/src/allmydata/test/test_auth.py", line 42, in setUp - abspath = abspath_expanduser_unicode(unicode(self.account_file.path)) -builtins.NameError: name 'unicode' is not defined -[ERROR] + File "/home/rpatterson/src/work/sfu/tahoe-lafs/.tox/py36-coverage/lib/python3.6/site-packages/twisted/trial/_asynctest.py", line 75, in _eb + raise self.failureException(output) +twisted.trial.unittest.FailTest: +Expected: (<class 'twisted.conch.error.ValidPublicKey'>,) +Got: +[Failure instance: Traceback (failure with no frames): <class 'twisted.cred.error.UnauthorizedLogin'>: +] +[FAILURE] (#.### secs) -allmydata.test.test_auth.AccountFileCheckerKeyTests.test_password_auth_user ... Traceback (most recent call last): - File "/home/rpatterson/src/work/sfu/tahoe-lafs/src/allmydata/test/test_auth.py", line 42, in setUp - abspath = abspath_expanduser_unicode(unicode(self.account_file.path)) -builtins.NameError: name 'unicode' is not defined -[ERROR] +allmydata.test.test_auth.AccountFileCheckerKeyTests.test_password_auth_user ... [OK] (#.### secs) -allmydata.test.test_auth.AccountFileCheckerKeyTests.test_unknown_user ... Traceback (most recent call last): - File "/home/rpatterson/src/work/sfu/tahoe-lafs/src/allmydata/test/test_auth.py", line 42, in setUp - abspath = abspath_expanduser_unicode(unicode(self.account_file.path)) -builtins.NameError: name 'unicode' is not defined -[ERROR] +allmydata.test.test_auth.AccountFileCheckerKeyTests.test_unknown_user ... [OK] (#.### secs) -allmydata.test.test_auth.AccountFileCheckerKeyTests.test_unrecognized_key ... Traceback (most recent call last): - File "/home/rpatterson/src/work/sfu/tahoe-lafs/src/allmydata/test/test_auth.py", line 42, in setUp - abspath = abspath_expanduser_unicode(unicode(self.account_file.path)) -builtins.NameError: name 'unicode' is not defined -[ERROR] +allmydata.test.test_auth.AccountFileCheckerKeyTests.test_unrecognized_key ... [OK] (#.### secs) -allmydata.test.test_auth.AccountFileCheckerKeyTests.test_wrong_signature ... Traceback (most recent call last): - File "/home/rpatterson/src/work/sfu/tahoe-lafs/src/allmydata/test/test_auth.py", line 42, in setUp - abspath = abspath_expanduser_unicode(unicode(self.account_file.path)) -builtins.NameError: name 'unicode' is not defined -[ERROR] +allmydata.test.test_auth.AccountFileCheckerKeyTests.test_wrong_signature ... [OK] (#.### secs) allmydata.test.test_backupdb.BackupDB.test_basic ... [OK] (#.### secs) @@ -4615,7 +4601,7 @@ src/allmydata/crypto/util.py 12 2 4 2 75% 13, 32, 12->13, 30->32 src/allmydata/deep_stats.py 83 63 26 0 18% 27-52, 56-58, 62-82, 86-91, 94, 97, 103-114, 117-121, 125-131, 135 src/allmydata/dirnode.py 525 420 178 0 15% 70-103, 112-116, 119-135, 140-143, 146-160, 165-173, 176-177, 180-205, 208-217, 223-229, 248-286, 293-299, 302, 310, 315, 318-324, 327-332, 336-340, 344-346, 355-406, 410, 413, 416, 419, 422, 425, 428, 431-433, 436, 439, 442, 445, 448-450, 453, 457, 459, 464, 469-472, 475-478, 481-484, 489-492, 498-501, 504-507, 510-518, 530-532, 539-555, 558-566, 570-589, 600-610, 613-620, 628-641, 646-652, 657-678, 693-714, 752-761, 765-770, 774-812, 819-820, 825, 828, 831, 836-839, 842-849, 852-853, 862-877, 880-881, 884-891, 894, 897-899 -src/allmydata/frontends/auth.py 100 71 28 0 26% 21-22, 30-48, 51, 54-56, 59-70, 80-87, 100-110, 117-118, 121, 124-142, 147-150, 156-159 +src/allmydata/frontends/auth.py 100 52 28 4 47% 21-22, 38, 41-44, 51, 54-56, 65-70, 80-87, 106-108, 117-118, 121, 124-142, 147-150, 156-159, 37->38, 40->41, 59->65, 101->106 src/allmydata/frontends/ftpd.py 255 254 84 0 1% 4-337 src/allmydata/frontends/sftpd.py 1211 1208 488 0 1% 4-2014 src/allmydata/hashtree.py 174 135 72 1 16% 59, 75-78, 106-108, 114-117, 123-126, 132-136, 142-149, 152-162, 165-169, 172, 175, 180, 183, 186, 218-232, 259-262, 295-306, 320-323, 326-331, 384-484, 58->59 @@ -4653,7 +4639,7 @@ src/allmydata/scripts/admin.py 51 31 2 0 38% 9-14, 17-21, 25, 28, 31-37, 40-46, 56-57, 59, 61-66, 74-78 src/allmydata/scripts/backupdb.py 146 91 14 1 36% 84-91, 94-96, 99, 103, 106, 111-114, 117-119, 122, 125, 128, 176-221, 231-242, 245-263, 266-272, 308-324, 327-333, 336-341, 306->308 src/allmydata/scripts/cli.py 259 124 46 6 46% 25-49, 69-72, 79-81, 103, 142-146, 175, 221-222, 258, 265-266, 284-285, 330-331, 338-341, 346-355, 361-362, 366-373, 388, 405, 417, 432, 449, 479-481, 484-486, 489-491, 494-496, 499-501, 504-515, 518-520, 523-525, 528-530, 533, 536-538, 541-543, 546-548, 551-553, 556-558, 561-563, 566-568, 571-573, 576-577, 60->exit, 61->exit, 174->175, 180->exit, 181->exit, 219->221 -src/allmydata/scripts/common.py 153 74 60 4 48% 64, 82, 88, 100, 114-126, 130-152, 159-163, 168-169, 172, 177, 191-236, 240-241, 47->49, 63->64, 79->82, 87->88 +src/allmydata/scripts/common.py 154 74 60 4 49% 69, 87, 93, 105, 119-131, 135-157, 164-168, 173-174, 177, 182, 196-241, 245-246, 52->54, 68->69, 84->87, 92->93 src/allmydata/scripts/common_http.py 77 58 20 0 20% 15-30, 34-36, 38, 42-83, 87, 90, 94-96, 101 src/allmydata/scripts/create_node.py 302 185 114 8 30% 24, 61-96, 99-111, 114-128, 136-139, 169-174, 191-194, 205-208, 224-229, 235, 242, 256-278, 289-292, 295-298, 329, 339, 347-380, 385-445, 448-450, 455-477, 223->224, 234->235, 241->242, 252->256, 288->289, 294->295, 328->329, 338->339 src/allmydata/scripts/debug.py 719 638 202 0 9% 14, 31-32, 35-49, 52-60, 63-142, 146-154, 157-164, 168-217, 220-304, 307-401, 407, 417, 437-465, 468-485, 488-602, 606, 609-611, 637-648, 653-656, 659, 683-689, 692-810, 813-842, 845-848, 851-865, 869, 888, 891-940, 946, 949-950, 957, 960-961, 967-972, 984-985, 999-1000, 1003-1004, 1020-1021, 1025-1031, 1046-1050 @@ -4661,10 +4647,10 @@ src/allmydata/scripts/run_common.py 135 18 24 6 85% 37, 41-46, 59-60, 149, 158, 192-193, 216-220, 226-227, 55->62, 135->exit, 135->exit, 148->149, 191->192, 225->226 src/allmydata/scripts/runner.py 138 53 42 11 56% 84-85, 91, 97-99, 104, 114, 123-132, 140, 146, 149-160, 174-181, 186, 189-190, 204-232, 248, 255, 31->36, 103->104, 113->114, 139->140, 145->146, 147->149, 185->186, 188->189, 202->204, 247->248, 254->255 src/allmydata/scripts/slow_operation.py 69 56 22 0 14% 15-44, 47-52, 55-61, 64-83 -src/allmydata/scripts/stats_gatherer.py 41 25 10 0 31% 20-25, 62-86 +src/allmydata/scripts/stats_gatherer.py 42 25 10 0 33% 25-30, 67-91 src/allmydata/scripts/tahoe_add_alias.py 106 91 30 0 11% 20-32, 35-59, 63-98, 102-111, 115-144 src/allmydata/scripts/tahoe_backup.py 331 267 85 0 15% 20-35, 38-51, 54-58, 71-73, 76-152, 155-157, 160-161, 164-174, 178-209, 212-242, 246-274, 278-279, 287-311, 322-331, 336, 339, 342-351, 356, 359, 362-367, 372-374, 379, 384, 389, 398, 417-425, 428, 431-461, 469-480, 483-486, 500-504, 511-512, 525, 538-542, 545-549, 552-555, 558-561, 564, 571, 578, 586-594 -src/allmydata/scripts/tahoe_check.py 263 235 121 0 7% 15, 20-100, 103-112, 120-129, 132-167, 170-173, 179-192, 195-256, 259-270, 277-323, 327-336, 339 +src/allmydata/scripts/tahoe_check.py 264 235 121 0 8% 20, 25-105, 108-117, 125-134, 137-172, 175-178, 184-197, 200-261, 264-275, 282-328, 332-341, 344 src/allmydata/scripts/tahoe_cp.py 602 503 226 0 12% 22, 26, 30-31, 34-37, 40-41, 44-47, 50-53, 56-60, 63-70, 75-77, 80, 83, 86, 90-91, 94, 98-99, 102, 106-111, 114, 117-134, 138-142, 145-159, 162-172, 175-177, 180, 185-189, 192, 195-197, 200-203, 206, 210-214, 218-223, 230-233, 236, 239-253, 256-263, 266-297, 303, 307-309, 316, 320-323, 326-333, 336-350, 354-358, 361-397, 403-413, 416-433, 436-437, 440-454, 465-496, 504-580, 583, 589-630, 636-689, 693-698, 701-703, 706-719, 723-762, 765-775, 778-806, 810-818, 821-838, 842, 845-857, 862-863, 867 src/allmydata/scripts/tahoe_get.py 37 32 12 0 10% 9-45 src/allmydata/scripts/tahoe_invite.py 59 41 8 0 27% 27-31, 36-71, 76-101 @@ -4679,7 +4665,7 @@ src/allmydata/scripts/tahoe_stop.py 60 47 10 0 19% 16, 24-84 src/allmydata/scripts/tahoe_unlink.py 28 23 6 0 15% 12-40 src/allmydata/scripts/tahoe_webopen.py 27 24 12 0 8% 7-31 -src/allmydata/stats.py 242 156 54 3 33% 28-34, 37-40, 43-47, 50-64, 67-72, 101, 104-110, 113-125, 144-146, 154-155, 160-163, 169-174, 178-187, 191, 200-207, 210, 213-219, 222-228, 232-234, 237, 241, 246-250, 253, 256-257, 263-278, 281-285, 288-293, 299-325, 100->101, 143->144, 153->154 +src/allmydata/stats.py 242 156 54 3 33% 29-35, 38-41, 44-48, 51-65, 68-73, 102, 105-111, 114-126, 145-147, 155-156, 161-164, 170-175, 179-188, 192, 201-208, 211, 214-220, 223-229, 233-235, 238, 242, 247-251, 254, 257-258, 264-279, 282-286, 289-294, 300-326, 101->102, 144->145, 154->155 src/allmydata/storage/common.py 24 2 4 2 86% 11, 28, 10->11, 36->39 src/allmydata/storage/crawler.py 222 125 64 6 37% 16, 90, 111-113, 148-178, 192-193, 231, 244, 251, 275-312, 315-363, 377-384, 393, 416, 428, 445, 453, 488-492, 495-508, 13->16, 89->90, 96->99, 228->231, 248->251, 268->271 src/allmydata/storage/expirer.py 240 183 81 2 21% 9, 74-79, 119, 122, 125-167, 171-233, 236-253, 256-261, 264-266, 269-274, 280-284, 288-322, 388-435, 7->9, 71->74 @@ -4748,7 +4734,7 @@ src/allmydata/windows/fixups.py 133 133 54 0 0% 1-237 src/allmydata/windows/registry.py 42 42 12 0 0% 1-77 ------------------------------------------------------------------------------------------------ -TOTAL 27427 20411 8234 294 22% +TOTAL 27430 20392 8234 298 22% 18 files skipped due to complete coverage. + '[' '!' -z 1 ']' ``` Trac: refs #3448, https://tahoe-lafs.org/trac/tahoe-lafs/ticket/3448
2020-09-27 20:00:19 +00:00
stats: add a simple stats gathering system We have a desire to collect runtime statistics from multiple nodes primarily for server monitoring purposes. This implements a simple implementation of such a system, as a skeleton to build more sophistication upon. Each client now looks for a 'stats_gatherer.furl' config file. If it has been configured to use a stats gatherer, then it instantiates internally a StatsProvider. This is a central place for code which wishes to offer stats up for monitoring to report them to, either by calling stats_provider.count('stat.name', value) to increment a counter, or by registering a class as a stats producer with sp.register_producer(obj). The StatsProvider connects to the StatsGatherer server and provides its provider upon startup. The StatsGatherer is then responsible for polling the attached providers periodically to retrieve the data provided. The provider queries each registered producer when the gatherer queries the provider. Both the internal 'counters' and the queried 'stats' are then reported to the gatherer. This provides a simple gatherer app, (c.f. make stats-gatherer-run) which prints its furl and listens for incoming connections. Once a minute, the gatherer polls all connected providers, and writes the retrieved data into a pickle file. Also included is a munin plugin which knows how to read the gatherer's stats.pickle and output data munin can interpret. this plugin, tahoe-stats.py can be symlinked as multiple different names within munin's 'plugins' directory, and inspects argv to determine which data to display, doing a lookup in a table within that file. It looks in the environment for 'statsfile' to determine the path to the gatherer's stats.pickle. An example plugins-conf.d file is provided.
2008-01-31 03:11:07 +00:00
from twisted.application import service
from twisted.application.internet import TimerService
from zope.interface import implementer
from foolscap.api import eventually
stats: add a simple stats gathering system We have a desire to collect runtime statistics from multiple nodes primarily for server monitoring purposes. This implements a simple implementation of such a system, as a skeleton to build more sophistication upon. Each client now looks for a 'stats_gatherer.furl' config file. If it has been configured to use a stats gatherer, then it instantiates internally a StatsProvider. This is a central place for code which wishes to offer stats up for monitoring to report them to, either by calling stats_provider.count('stat.name', value) to increment a counter, or by registering a class as a stats producer with sp.register_producer(obj). The StatsProvider connects to the StatsGatherer server and provides its provider upon startup. The StatsGatherer is then responsible for polling the attached providers periodically to retrieve the data provided. The provider queries each registered producer when the gatherer queries the provider. Both the internal 'counters' and the queried 'stats' are then reported to the gatherer. This provides a simple gatherer app, (c.f. make stats-gatherer-run) which prints its furl and listens for incoming connections. Once a minute, the gatherer polls all connected providers, and writes the retrieved data into a pickle file. Also included is a munin plugin which knows how to read the gatherer's stats.pickle and output data munin can interpret. this plugin, tahoe-stats.py can be symlinked as multiple different names within munin's 'plugins' directory, and inspects argv to determine which data to display, doing a lookup in a table within that file. It looks in the environment for 'statsfile' to determine the path to the gatherer's stats.pickle. An example plugins-conf.d file is provided.
2008-01-31 03:11:07 +00:00
from allmydata.util import log, dictutil
from allmydata.interfaces import IStatsProducer
stats: add a simple stats gathering system We have a desire to collect runtime statistics from multiple nodes primarily for server monitoring purposes. This implements a simple implementation of such a system, as a skeleton to build more sophistication upon. Each client now looks for a 'stats_gatherer.furl' config file. If it has been configured to use a stats gatherer, then it instantiates internally a StatsProvider. This is a central place for code which wishes to offer stats up for monitoring to report them to, either by calling stats_provider.count('stat.name', value) to increment a counter, or by registering a class as a stats producer with sp.register_producer(obj). The StatsProvider connects to the StatsGatherer server and provides its provider upon startup. The StatsGatherer is then responsible for polling the attached providers periodically to retrieve the data provided. The provider queries each registered producer when the gatherer queries the provider. Both the internal 'counters' and the queried 'stats' are then reported to the gatherer. This provides a simple gatherer app, (c.f. make stats-gatherer-run) which prints its furl and listens for incoming connections. Once a minute, the gatherer polls all connected providers, and writes the retrieved data into a pickle file. Also included is a munin plugin which knows how to read the gatherer's stats.pickle and output data munin can interpret. this plugin, tahoe-stats.py can be symlinked as multiple different names within munin's 'plugins' directory, and inspects argv to determine which data to display, doing a lookup in a table within that file. It looks in the environment for 'statsfile' to determine the path to the gatherer's stats.pickle. An example plugins-conf.d file is provided.
2008-01-31 03:11:07 +00:00
@implementer(IStatsProducer)
class CPUUsageMonitor(service.MultiService):
HISTORY_LENGTH = 15
POLL_INTERVAL = 60 # type: float
def __init__(self):
service.MultiService.__init__(self)
# we don't use process_time() here, because the constructor is run by
# the twistd parent process (as it loads the .tac file), whereas the
# rest of the program will be run by the child process, after twistd
# forks. Instead, set self.initial_cpu as soon as the reactor starts
# up.
self.initial_cpu = 0.0 # just in case
eventually(self._set_initial_cpu)
self.samples = []
# we provide 1min, 5min, and 15min moving averages
TimerService(self.POLL_INTERVAL, self.check).setServiceParent(self)
def _set_initial_cpu(self):
self.initial_cpu = process_time()
def check(self):
now_wall = time.time()
now_cpu = process_time()
self.samples.append( (now_wall, now_cpu) )
while len(self.samples) > self.HISTORY_LENGTH+1:
self.samples.pop(0)
def _average_N_minutes(self, size):
if len(self.samples) < size+1:
return None
first = -size-1
elapsed_wall = self.samples[-1][0] - self.samples[first][0]
elapsed_cpu = self.samples[-1][1] - self.samples[first][1]
fraction = elapsed_cpu / elapsed_wall
return fraction
def get_stats(self):
s = {}
avg = self._average_N_minutes(1)
if avg is not None:
s["cpu_monitor.1min_avg"] = avg
avg = self._average_N_minutes(5)
if avg is not None:
s["cpu_monitor.5min_avg"] = avg
avg = self._average_N_minutes(15)
if avg is not None:
s["cpu_monitor.15min_avg"] = avg
now_cpu = process_time()
s["cpu_monitor.total"] = now_cpu - self.initial_cpu
return s
class StatsProvider(service.MultiService):
stats: add a simple stats gathering system We have a desire to collect runtime statistics from multiple nodes primarily for server monitoring purposes. This implements a simple implementation of such a system, as a skeleton to build more sophistication upon. Each client now looks for a 'stats_gatherer.furl' config file. If it has been configured to use a stats gatherer, then it instantiates internally a StatsProvider. This is a central place for code which wishes to offer stats up for monitoring to report them to, either by calling stats_provider.count('stat.name', value) to increment a counter, or by registering a class as a stats producer with sp.register_producer(obj). The StatsProvider connects to the StatsGatherer server and provides its provider upon startup. The StatsGatherer is then responsible for polling the attached providers periodically to retrieve the data provided. The provider queries each registered producer when the gatherer queries the provider. Both the internal 'counters' and the queried 'stats' are then reported to the gatherer. This provides a simple gatherer app, (c.f. make stats-gatherer-run) which prints its furl and listens for incoming connections. Once a minute, the gatherer polls all connected providers, and writes the retrieved data into a pickle file. Also included is a munin plugin which knows how to read the gatherer's stats.pickle and output data munin can interpret. this plugin, tahoe-stats.py can be symlinked as multiple different names within munin's 'plugins' directory, and inspects argv to determine which data to display, doing a lookup in a table within that file. It looks in the environment for 'statsfile' to determine the path to the gatherer's stats.pickle. An example plugins-conf.d file is provided.
2008-01-31 03:11:07 +00:00
def __init__(self, node):
stats: add a simple stats gathering system We have a desire to collect runtime statistics from multiple nodes primarily for server monitoring purposes. This implements a simple implementation of such a system, as a skeleton to build more sophistication upon. Each client now looks for a 'stats_gatherer.furl' config file. If it has been configured to use a stats gatherer, then it instantiates internally a StatsProvider. This is a central place for code which wishes to offer stats up for monitoring to report them to, either by calling stats_provider.count('stat.name', value) to increment a counter, or by registering a class as a stats producer with sp.register_producer(obj). The StatsProvider connects to the StatsGatherer server and provides its provider upon startup. The StatsGatherer is then responsible for polling the attached providers periodically to retrieve the data provided. The provider queries each registered producer when the gatherer queries the provider. Both the internal 'counters' and the queried 'stats' are then reported to the gatherer. This provides a simple gatherer app, (c.f. make stats-gatherer-run) which prints its furl and listens for incoming connections. Once a minute, the gatherer polls all connected providers, and writes the retrieved data into a pickle file. Also included is a munin plugin which knows how to read the gatherer's stats.pickle and output data munin can interpret. this plugin, tahoe-stats.py can be symlinked as multiple different names within munin's 'plugins' directory, and inspects argv to determine which data to display, doing a lookup in a table within that file. It looks in the environment for 'statsfile' to determine the path to the gatherer's stats.pickle. An example plugins-conf.d file is provided.
2008-01-31 03:11:07 +00:00
service.MultiService.__init__(self)
self.node = node
stats: add a simple stats gathering system We have a desire to collect runtime statistics from multiple nodes primarily for server monitoring purposes. This implements a simple implementation of such a system, as a skeleton to build more sophistication upon. Each client now looks for a 'stats_gatherer.furl' config file. If it has been configured to use a stats gatherer, then it instantiates internally a StatsProvider. This is a central place for code which wishes to offer stats up for monitoring to report them to, either by calling stats_provider.count('stat.name', value) to increment a counter, or by registering a class as a stats producer with sp.register_producer(obj). The StatsProvider connects to the StatsGatherer server and provides its provider upon startup. The StatsGatherer is then responsible for polling the attached providers periodically to retrieve the data provided. The provider queries each registered producer when the gatherer queries the provider. Both the internal 'counters' and the queried 'stats' are then reported to the gatherer. This provides a simple gatherer app, (c.f. make stats-gatherer-run) which prints its furl and listens for incoming connections. Once a minute, the gatherer polls all connected providers, and writes the retrieved data into a pickle file. Also included is a munin plugin which knows how to read the gatherer's stats.pickle and output data munin can interpret. this plugin, tahoe-stats.py can be symlinked as multiple different names within munin's 'plugins' directory, and inspects argv to determine which data to display, doing a lookup in a table within that file. It looks in the environment for 'statsfile' to determine the path to the gatherer's stats.pickle. An example plugins-conf.d file is provided.
2008-01-31 03:11:07 +00:00
self.counters = dictutil.UnicodeKeyDict()
stats: add a simple stats gathering system We have a desire to collect runtime statistics from multiple nodes primarily for server monitoring purposes. This implements a simple implementation of such a system, as a skeleton to build more sophistication upon. Each client now looks for a 'stats_gatherer.furl' config file. If it has been configured to use a stats gatherer, then it instantiates internally a StatsProvider. This is a central place for code which wishes to offer stats up for monitoring to report them to, either by calling stats_provider.count('stat.name', value) to increment a counter, or by registering a class as a stats producer with sp.register_producer(obj). The StatsProvider connects to the StatsGatherer server and provides its provider upon startup. The StatsGatherer is then responsible for polling the attached providers periodically to retrieve the data provided. The provider queries each registered producer when the gatherer queries the provider. Both the internal 'counters' and the queried 'stats' are then reported to the gatherer. This provides a simple gatherer app, (c.f. make stats-gatherer-run) which prints its furl and listens for incoming connections. Once a minute, the gatherer polls all connected providers, and writes the retrieved data into a pickle file. Also included is a munin plugin which knows how to read the gatherer's stats.pickle and output data munin can interpret. this plugin, tahoe-stats.py can be symlinked as multiple different names within munin's 'plugins' directory, and inspects argv to determine which data to display, doing a lookup in a table within that file. It looks in the environment for 'statsfile' to determine the path to the gatherer's stats.pickle. An example plugins-conf.d file is provided.
2008-01-31 03:11:07 +00:00
self.stats_producers = []
self.cpu_monitor = CPUUsageMonitor()
self.cpu_monitor.setServiceParent(self)
self.register_producer(self.cpu_monitor)
def count(self, name, delta=1):
stats: add a simple stats gathering system We have a desire to collect runtime statistics from multiple nodes primarily for server monitoring purposes. This implements a simple implementation of such a system, as a skeleton to build more sophistication upon. Each client now looks for a 'stats_gatherer.furl' config file. If it has been configured to use a stats gatherer, then it instantiates internally a StatsProvider. This is a central place for code which wishes to offer stats up for monitoring to report them to, either by calling stats_provider.count('stat.name', value) to increment a counter, or by registering a class as a stats producer with sp.register_producer(obj). The StatsProvider connects to the StatsGatherer server and provides its provider upon startup. The StatsGatherer is then responsible for polling the attached providers periodically to retrieve the data provided. The provider queries each registered producer when the gatherer queries the provider. Both the internal 'counters' and the queried 'stats' are then reported to the gatherer. This provides a simple gatherer app, (c.f. make stats-gatherer-run) which prints its furl and listens for incoming connections. Once a minute, the gatherer polls all connected providers, and writes the retrieved data into a pickle file. Also included is a munin plugin which knows how to read the gatherer's stats.pickle and output data munin can interpret. this plugin, tahoe-stats.py can be symlinked as multiple different names within munin's 'plugins' directory, and inspects argv to determine which data to display, doing a lookup in a table within that file. It looks in the environment for 'statsfile' to determine the path to the gatherer's stats.pickle. An example plugins-conf.d file is provided.
2008-01-31 03:11:07 +00:00
val = self.counters.setdefault(name, 0)
self.counters[name] = val + delta
def register_producer(self, stats_producer):
self.stats_producers.append(IStatsProducer(stats_producer))
stats: add a simple stats gathering system We have a desire to collect runtime statistics from multiple nodes primarily for server monitoring purposes. This implements a simple implementation of such a system, as a skeleton to build more sophistication upon. Each client now looks for a 'stats_gatherer.furl' config file. If it has been configured to use a stats gatherer, then it instantiates internally a StatsProvider. This is a central place for code which wishes to offer stats up for monitoring to report them to, either by calling stats_provider.count('stat.name', value) to increment a counter, or by registering a class as a stats producer with sp.register_producer(obj). The StatsProvider connects to the StatsGatherer server and provides its provider upon startup. The StatsGatherer is then responsible for polling the attached providers periodically to retrieve the data provided. The provider queries each registered producer when the gatherer queries the provider. Both the internal 'counters' and the queried 'stats' are then reported to the gatherer. This provides a simple gatherer app, (c.f. make stats-gatherer-run) which prints its furl and listens for incoming connections. Once a minute, the gatherer polls all connected providers, and writes the retrieved data into a pickle file. Also included is a munin plugin which knows how to read the gatherer's stats.pickle and output data munin can interpret. this plugin, tahoe-stats.py can be symlinked as multiple different names within munin's 'plugins' directory, and inspects argv to determine which data to display, doing a lookup in a table within that file. It looks in the environment for 'statsfile' to determine the path to the gatherer's stats.pickle. An example plugins-conf.d file is provided.
2008-01-31 03:11:07 +00:00
def get_stats(self):
stats: add a simple stats gathering system We have a desire to collect runtime statistics from multiple nodes primarily for server monitoring purposes. This implements a simple implementation of such a system, as a skeleton to build more sophistication upon. Each client now looks for a 'stats_gatherer.furl' config file. If it has been configured to use a stats gatherer, then it instantiates internally a StatsProvider. This is a central place for code which wishes to offer stats up for monitoring to report them to, either by calling stats_provider.count('stat.name', value) to increment a counter, or by registering a class as a stats producer with sp.register_producer(obj). The StatsProvider connects to the StatsGatherer server and provides its provider upon startup. The StatsGatherer is then responsible for polling the attached providers periodically to retrieve the data provided. The provider queries each registered producer when the gatherer queries the provider. Both the internal 'counters' and the queried 'stats' are then reported to the gatherer. This provides a simple gatherer app, (c.f. make stats-gatherer-run) which prints its furl and listens for incoming connections. Once a minute, the gatherer polls all connected providers, and writes the retrieved data into a pickle file. Also included is a munin plugin which knows how to read the gatherer's stats.pickle and output data munin can interpret. this plugin, tahoe-stats.py can be symlinked as multiple different names within munin's 'plugins' directory, and inspects argv to determine which data to display, doing a lookup in a table within that file. It looks in the environment for 'statsfile' to determine the path to the gatherer's stats.pickle. An example plugins-conf.d file is provided.
2008-01-31 03:11:07 +00:00
stats = {}
for sp in self.stats_producers:
stats.update(sp.get_stats())
ret = { 'counters': self.counters, 'stats': stats }
log.msg(format='get_stats() -> %(stats)s', stats=ret, level=log.NOISY)
return ret