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server.py: get_latencies now reports percentiles _only_ if there are sufficient observations for the interpretation of the percentile to be unambiguous.

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interfaces.py:  modified the return type of RIStatsProvider.get_stats to allow for None as a return value
NEWS.rst, stats.py: documentation of change to get_latencies
stats.rst: now documents percentile modification in get_latencies
test_storage.py:  test_latencies now expects None in output categories that contain too few samples for the associated percentile to be unambiguously reported.
fixes #1392
This commit is contained in:
wilcoxjg 2011-05-27 05:01:35 -07:00
parent d566e4632d
commit 67ad0175cd
5 changed files with 74 additions and 33 deletions
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12
NEWS.rst
View File

@ -1,7 +1,17 @@
==================================
==================================
User-Visible Changes in Tahoe-LAFS
==================================
Release 1.9.0 (2011-??-??)
--------------------------
- Nodes now emit "None" for percentiles with higher implied precision
than the number of observations can support. Older stats gatherers
will throw an exception if they gather stats from a new storage
server and it sends a "None" for a percentile. (`#1392`_)
Release 1.8.2 (2011-01-30)
--------------------------

14
docs/stats.rst
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@ -1,4 +1,4 @@
================
================
Tahoe Statistics
================
@ -44,7 +44,7 @@ The currently available stats (as of release 1.6.0 or so) are described here:
this group counts inbound storage-server operations. They are not provided
by client-only nodes which have been configured to not run a storage server
(with [storage]enabled=false in tahoe.cfg)
allocate, write, close, abort
these are for immutable file uploads. 'allocate' is incremented when a
client asks if it can upload a share to the server. 'write' is
@ -134,6 +134,14 @@ The currently available stats (as of release 1.6.0 or so) are described here:
999 out of the last 1000 operations were faster than the
given number, and is the same threshold used by Amazon's
internal SLA, according to the Dynamo paper).
Percentiles are only reported in the case of a sufficient
number of observations for unambiguous interpretation. For
example, the 99.9th percentile is (at the level of thousandths
precision) 9 thousandths greater than the 99th
percentile for sample sizes greater than or equal to 1000,
thus the 99.9th percentile is only reported for samples of 1000
or more observations.
**counters.uploader.files_uploaded**
@ -195,7 +203,7 @@ The currently available stats (as of release 1.6.0 or so) are described here:
active_uploads
how many files are currently being uploaded. 0 when idle.
incoming_count
how many cache files are present in the incoming/ directory,
which holds ciphertext files that are still being fetched

4
src/allmydata/interfaces.py
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@ -2390,12 +2390,12 @@ class RIStatsProvider(RemoteInterface):
def get_stats():
"""
returns a dictionary containing 'counters' and 'stats', each a
dictionary with string counter/stat name keys, and numeric values.
dictionary with string counter/stat name keys, and numeric or None values.
counters are monotonically increasing measures of work done, and
stats are instantaneous measures (potentially time averaged
internally)
"""
return DictOf(str, DictOf(str, ChoiceOf(float, int, long)))
return DictOf(str, DictOf(str, ChoiceOf(float, int, long, None)))
class RIStatsGatherer(RemoteInterface):
__remote_name__ = "RIStatsGatherer.tahoe.allmydata.com"

39
src/allmydata/storage/server.py
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@ -116,12 +116,15 @@ class StorageServer(service.MultiService, Referenceable):
def get_latencies(self):
"""Return a dict, indexed by category, that contains a dict of
latency numbers for each category. Each dict will contain the
latency numbers for each category. If there are sufficient samples
for unambiguous interpretation, each dict will contain the
following keys: mean, 01_0_percentile, 10_0_percentile,
50_0_percentile (median), 90_0_percentile, 95_0_percentile,
99_0_percentile, 99_9_percentile. If no samples have been collected
for the given category, then that category name will not be present
in the return value."""
99_0_percentile, 99_9_percentile. If there are insufficient
samples for a given percentile to be interpreted unambiguously
that percentile will be reported as None. If no samples have been
collected for the given category, then that category name will
not be present in the return value. """
# note that Amazon's Dynamo paper says they use 99.9% percentile.
output = {}
for category in self.latencies:
@ -129,16 +132,25 @@ class StorageServer(service.MultiService, Referenceable):
continue
stats = {}
samples = self.latencies[category][:]
samples.sort()
count = len(samples)
stats["mean"] = sum(samples) / count
stats["01_0_percentile"] = samples[int(0.01 * count)]
stats["10_0_percentile"] = samples[int(0.1 * count)]
stats["50_0_percentile"] = samples[int(0.5 * count)]
stats["90_0_percentile"] = samples[int(0.9 * count)]
stats["95_0_percentile"] = samples[int(0.95 * count)]
stats["99_0_percentile"] = samples[int(0.99 * count)]
stats["99_9_percentile"] = samples[int(0.999 * count)]
stats["samplesize"] = count
samples.sort()
if count > 1:
stats["mean"] = sum(samples) / count
else:
stats["mean"] = None
orderstatlist = [(0.01, "01_0_percentile", 100), (0.1, "10_0_percentile", 10),\
(0.50, "50_0_percentile", 10), (0.90, "90_0_percentile", 10),\
(0.95, "95_0_percentile", 20), (0.99, "99_0_percentile", 100),\
(0.999, "99_9_percentile", 1000)]
for percentile, percentilestring, minnumtoobserve in orderstatlist:
if count >= minnumtoobserve:
stats[percentilestring] = samples[int(percentile*count)]
else:
stats[percentilestring] = None
output[category] = stats
return output
@ -551,4 +563,3 @@ class StorageServer(service.MultiService, Referenceable):
share_type=share_type, si=si_s, shnum=shnum, reason=reason,
level=log.SCARY, umid="SGx2fA")
return None

38
src/allmydata/test/test_storage.py
View File

@ -1311,6 +1311,8 @@ class Stats(unittest.TestCase):
ss.add_latency("allocate", 1.0 * i)
for i in range(1000):
ss.add_latency("renew", 1.0 * i)
for i in range(20):
ss.add_latency("write", 1.0 * i)
for i in range(10):
ss.add_latency("cancel", 2.0 * i)
ss.add_latency("get", 5.0)
@ -1318,7 +1320,7 @@ class Stats(unittest.TestCase):
output = ss.get_latencies()
self.failUnlessEqual(sorted(output.keys()),
sorted(["allocate", "renew", "cancel", "get"]))
sorted(["allocate", "renew", "cancel", "write", "get"]))
self.failUnlessEqual(len(ss.latencies["allocate"]), 1000)
self.failUnless(abs(output["allocate"]["mean"] - 9500) < 1, output)
self.failUnless(abs(output["allocate"]["01_0_percentile"] - 9010) < 1, output)
@ -1339,25 +1341,35 @@ class Stats(unittest.TestCase):
self.failUnless(abs(output["renew"]["99_0_percentile"] - 990) < 1, output)
self.failUnless(abs(output["renew"]["99_9_percentile"] - 999) < 1, output)
self.failUnlessEqual(len(ss.latencies["write"]), 20)
self.failUnless(abs(output["write"]["mean"] - 9) < 1, output)
self.failUnless(output["write"]["01_0_percentile"] is None, output)
self.failUnless(abs(output["write"]["10_0_percentile"] - 2) < 1, output)
self.failUnless(abs(output["write"]["50_0_percentile"] - 10) < 1, output)
self.failUnless(abs(output["write"]["90_0_percentile"] - 18) < 1, output)
self.failUnless(abs(output["write"]["95_0_percentile"] - 19) < 1, output)
self.failUnless(output["write"]["99_0_percentile"] is None, output)
self.failUnless(output["write"]["99_9_percentile"] is None, output)
self.failUnlessEqual(len(ss.latencies["cancel"]), 10)
self.failUnless(abs(output["cancel"]["mean"] - 9) < 1, output)
self.failUnless(abs(output["cancel"]["01_0_percentile"] - 0) < 1, output)
self.failUnless(output["cancel"]["01_0_percentile"] is None, output)
self.failUnless(abs(output["cancel"]["10_0_percentile"] - 2) < 1, output)
self.failUnless(abs(output["cancel"]["50_0_percentile"] - 10) < 1, output)
self.failUnless(abs(output["cancel"]["90_0_percentile"] - 18) < 1, output)
self.failUnless(abs(output["cancel"]["95_0_percentile"] - 18) < 1, output)
self.failUnless(abs(output["cancel"]["99_0_percentile"] - 18) < 1, output)
self.failUnless(abs(output["cancel"]["99_9_percentile"] - 18) < 1, output)
self.failUnless(output["cancel"]["95_0_percentile"] is None, output)
self.failUnless(output["cancel"]["99_0_percentile"] is None, output)
self.failUnless(output["cancel"]["99_9_percentile"] is None, output)
self.failUnlessEqual(len(ss.latencies["get"]), 1)
self.failUnless(abs(output["get"]["mean"] - 5) < 1, output)
self.failUnless(abs(output["get"]["01_0_percentile"] - 5) < 1, output)
self.failUnless(abs(output["get"]["10_0_percentile"] - 5) < 1, output)
self.failUnless(abs(output["get"]["50_0_percentile"] - 5) < 1, output)
self.failUnless(abs(output["get"]["90_0_percentile"] - 5) < 1, output)
self.failUnless(abs(output["get"]["95_0_percentile"] - 5) < 1, output)
self.failUnless(abs(output["get"]["99_0_percentile"] - 5) < 1, output)
self.failUnless(abs(output["get"]["99_9_percentile"] - 5) < 1, output)
self.failUnless(output["get"]["mean"] is None, output)
self.failUnless(output["get"]["01_0_percentile"] is None, output)
self.failUnless(output["get"]["10_0_percentile"] is None, output)
self.failUnless(output["get"]["50_0_percentile"] is None, output)
self.failUnless(output["get"]["90_0_percentile"] is None, output)
self.failUnless(output["get"]["95_0_percentile"] is None, output)
self.failUnless(output["get"]["99_0_percentile"] is None, output)
self.failUnless(output["get"]["99_9_percentile"] is None, output)
def remove_tags(s):
s = re.sub(r'<[^>]*>', ' ', s)