112 lines
3.5 KiB
Python
112 lines
3.5 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright 2015 OpenMarket Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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class BaseMetric(object):
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def __init__(self, name, keys=[]):
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self.name = name
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self.keys = keys # OK not to clone as we never write it
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def dimension(self):
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return len(self.keys)
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def is_scalar(self):
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return not len(self.keys)
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def _render_key(self, values):
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# TODO: some kind of value escape
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return ",".join(["%s=%s" % kv for kv in zip(self.keys, values)])
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class CounterMetric(BaseMetric):
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"""The simplest kind of metric; one that stores a monotonically-increasing
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integer that counts events."""
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def __init__(self, *args, **kwargs):
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super(CounterMetric, self).__init__(*args, **kwargs)
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self.counts = {}
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# Scalar metrics are never empty
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if self.is_scalar():
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self.counts[()] = 0
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def inc(self, *values):
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if len(values) != self.dimension():
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raise ValueError("Expected as many values to inc() as keys (%d)" %
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(self.dimension())
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)
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# TODO: should assert that the tag values are all strings
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if values not in self.counts:
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self.counts[values] = 1
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else:
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self.counts[values] += 1
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def fetch(self):
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return dict(self.counts)
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def render(self):
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if self.is_scalar():
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return ["%s %d" % (self.name, self.counts[()])]
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return ["%s{%s} %d" % (self.name, self._render_key(k), self.counts[k])
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for k in sorted(self.counts.keys())]
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class CallbackMetric(BaseMetric):
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"""A metric that returns the numeric value returned by a callback whenever
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it is rendered. Typically this is used to implement gauges that yield the
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size or other state of some in-memory object by actively querying it."""
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def __init__(self, name, callback, keys=[]):
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super(CallbackMetric, self).__init__(name, keys=keys)
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self.callback = callback
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def render(self):
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# TODO(paul): work out something we can do with keys and vectors
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return ["%s %d" % (self.name, self.callback())]
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class CacheMetric(object):
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"""A combination of two CounterMetrics, one to count cache hits and one to
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count misses, and a callback metric to yield the current size.
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This metric generates standard metric name pairs, so that monitoring rules
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can easily be applied to measure hit ratio."""
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def __init__(self, name, size_callback, keys=[]):
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self.name = name
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self.hits = CounterMetric(name + ":hits", keys=keys)
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self.misses = CounterMetric(name + ":misses", keys=keys)
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self.size = CallbackMetric(name + ":size",
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callback=size_callback,
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keys=keys,
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)
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def inc_hits(self, *values):
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self.hits.inc(*values)
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def inc_misses(self, *values):
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self.misses.inc(*values)
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def render(self):
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return self.hits.render() + self.misses.render() + self.size.render()
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