Merge branch 'release-v0.8.1' of github.com:matrix-org/synapse v0.8.1

1 files changed, 155 insertions, 0 deletions

diff --git a/synapse/metrics/metric.py b/synapse/metrics/metric.py
new file mode 100644
index 0000000000..21b37748f6
--- /dev/null
+++ b/synapse/metrics/metric.py
@@ -0,0 +1,155 @@
+# -*- coding: utf-8 -*-
+# Copyright 2015 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.
+
+
+from itertools import chain
+
+
+# TODO(paul): I can't believe Python doesn't have one of these
+def map_concat(func, items):
+    # flatten a list-of-lists
+    return list(chain.from_iterable(map(func, items)))
+
+
+class BaseMetric(object):
+
+    def __init__(self, name, labels=[]):
+        self.name = name
+        self.labels = labels  # OK not to clone as we never write it
+
+    def dimension(self):
+        return len(self.labels)
+
+    def is_scalar(self):
+        return not len(self.labels)
+
+    def _render_labelvalue(self, value):
+        # TODO: some kind of value escape
+        return '"%s"' % (value)
+
+    def _render_key(self, values):
+        if self.is_scalar():
+            return ""
+        return "{%s}" % (
+            ",".join(["%s=%s" % (k, self._render_labelvalue(v))
+                      for k, v in zip(self.labels, values)])
+        )
+
+    def render(self):
+        return map_concat(self.render_item, sorted(self.counts.keys()))
+
+
+class CounterMetric(BaseMetric):
+    """The simplest kind of metric; one that stores a monotonically-increasing
+    integer that counts events."""
+
+    def __init__(self, *args, **kwargs):
+        super(CounterMetric, self).__init__(*args, **kwargs)
+
+        self.counts = {}
+
+        # Scalar metrics are never empty
+        if self.is_scalar():
+            self.counts[()] = 0
+
+    def inc_by(self, incr, *values):
+        if len(values) != self.dimension():
+            raise ValueError(
+                "Expected as many values to inc() as labels (%d)" % (self.dimension())
+            )
+
+        # TODO: should assert that the tag values are all strings
+
+        if values not in self.counts:
+            self.counts[values] = incr
+        else:
+            self.counts[values] += incr
+
+    def inc(self, *values):
+        self.inc_by(1, *values)
+
+    def render_item(self, k):
+        return ["%s%s %d" % (self.name, self._render_key(k), self.counts[k])]
+
+
+class CallbackMetric(BaseMetric):
+    """A metric that returns the numeric value returned by a callback whenever
+    it is rendered. Typically this is used to implement gauges that yield the
+    size or other state of some in-memory object by actively querying it."""
+
+    def __init__(self, name, callback, labels=[]):
+        super(CallbackMetric, self).__init__(name, labels=labels)
+
+        self.callback = callback
+
+    def render(self):
+        value = self.callback()
+
+        if self.is_scalar():
+            return ["%s %d" % (self.name, value)]
+
+        return ["%s%s %d" % (self.name, self._render_key(k), value[k])
+                for k in sorted(value.keys())]
+
+
+class DistributionMetric(object):
+    """A combination of an event counter and an accumulator, which counts
+    both the number of events and accumulates the total value. Typically this
+    could be used to keep track of method-running times, or other distributions
+    of values that occur in discrete occurances.
+
+    TODO(paul): Try to export some heatmap-style stats?
+    """
+
+    def __init__(self, name, *args, **kwargs):
+        self.counts = CounterMetric(name + ":count", **kwargs)
+        self.totals = CounterMetric(name + ":total", **kwargs)
+
+    def inc_by(self, inc, *values):
+        self.counts.inc(*values)
+        self.totals.inc_by(inc, *values)
+
+    def render(self):
+        return self.counts.render() + self.totals.render()
+
+
+class CacheMetric(object):
+    """A combination of two CounterMetrics, one to count cache hits and one to
+    count a total, and a callback metric to yield the current size.
+
+    This metric generates standard metric name pairs, so that monitoring rules
+    can easily be applied to measure hit ratio."""
+
+    def __init__(self, name, size_callback, labels=[]):
+        self.name = name
+
+        self.hits = CounterMetric(name + ":hits", labels=labels)
+        self.total = CounterMetric(name + ":total", labels=labels)
+
+        self.size = CallbackMetric(
+            name + ":size",
+            callback=size_callback,
+            labels=labels,
+        )
+
+    def inc_hits(self, *values):
+        self.hits.inc(*values)
+        self.total.inc(*values)
+
+    def inc_misses(self, *values):
+        self.total.inc(*values)
+
+    def render(self):
+        return self.hits.render() + self.total.render() + self.size.render()