diff --git a/synapse/metrics/metric.py b/synapse/metrics/metric.py
deleted file mode 100644
index e87b2b80a7..0000000000
--- a/synapse/metrics/metric.py
+++ /dev/null
@@ -1,195 +0,0 @@
-# -*- coding: utf-8 -*-
-# 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.
-
-
-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)])
- )
-
-
-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])]
-
- def render(self):
- return map_concat(self.render_item, sorted(self.counts.keys()))
-
-
-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 %.12g" % (self.name, value)]
-
- return ["%s%s %.12g" % (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):
- __slots__ = ("name", "cache_name", "hits", "misses", "size_callback")
-
- def __init__(self, name, size_callback, cache_name):
- self.name = name
- self.cache_name = cache_name
-
- self.hits = 0
- self.misses = 0
-
- self.size_callback = size_callback
-
- def inc_hits(self):
- self.hits += 1
-
- def inc_misses(self):
- self.misses += 1
-
- def render(self):
- size = self.size_callback()
- hits = self.hits
- total = self.misses + self.hits
-
- return [
- """%s:hits{name="%s"} %d""" % (self.name, self.cache_name, hits),
- """%s:total{name="%s"} %d""" % (self.name, self.cache_name, total),
- """%s:size{name="%s"} %d""" % (self.name, self.cache_name, size),
- ]
-
-
-class MemoryUsageMetric(object):
- """Keeps track of the current memory usage, using psutil.
-
- The class will keep the current min/max/sum/counts of rss over the last
- WINDOW_SIZE_SEC, by polling UPDATE_HZ times per second
- """
-
- UPDATE_HZ = 2 # number of times to get memory per second
- WINDOW_SIZE_SEC = 30 # the size of the window in seconds
-
- def __init__(self, hs, psutil):
- clock = hs.get_clock()
- self.memory_snapshots = []
-
- self.process = psutil.Process()
-
- clock.looping_call(self._update_curr_values, 1000 / self.UPDATE_HZ)
-
- def _update_curr_values(self):
- max_size = self.UPDATE_HZ * self.WINDOW_SIZE_SEC
- self.memory_snapshots.append(self.process.memory_info().rss)
- self.memory_snapshots[:] = self.memory_snapshots[-max_size:]
-
- def render(self):
- if not self.memory_snapshots:
- return []
-
- max_rss = max(self.memory_snapshots)
- min_rss = min(self.memory_snapshots)
- sum_rss = sum(self.memory_snapshots)
- len_rss = len(self.memory_snapshots)
-
- return [
- "process_psutil_rss:max %d" % max_rss,
- "process_psutil_rss:min %d" % min_rss,
- "process_psutil_rss:total %d" % sum_rss,
- "process_psutil_rss:count %d" % len_rss,
- ]
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