diff --git a/synapse/metrics/metric.py b/synapse/metrics/metric.py
index c5f0bcbc15..f480aae614 100644
--- a/synapse/metrics/metric.py
+++ b/synapse/metrics/metric.py
@@ -17,24 +17,33 @@
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)))
+def flatten(items):
+ """Flatten a list of lists
+
+ Args:
+ items: iterable[iterable[X]]
+
+ Returns:
+ list[X]: flattened list
+ """
+ return list(chain.from_iterable(items))
class BaseMetric(object):
"""Base class for metrics which report a single value per label set
"""
- def __init__(self, name, labels=[]):
+ def __init__(self, name, labels=[], alternative_names=[]):
"""
Args:
name (str): principal name for this metric
labels (list(str)): names of the labels which will be reported
for this metric
+ alternative_names (iterable(str)): list of alternative names for
+ this metric. This can be useful to provide a migration path
+ when renaming metrics.
"""
- self.name = name
+ self._names = [name] + list(alternative_names)
self.labels = labels # OK not to clone as we never write it
def dimension(self):
@@ -55,6 +64,22 @@ class BaseMetric(object):
for k, v in zip(self.labels, values)])
)
+ def _render_for_labels(self, label_values, value):
+ """Render this metric for a single set of labels
+
+ Args:
+ label_values (list[str]): values for each of the labels
+ value: value of the metric at with these labels
+
+ Returns:
+ iterable[str]: rendered metric
+ """
+ rendered_labels = self._render_key(label_values)
+ return (
+ "%s%s %.12g" % (name, rendered_labels, value)
+ for name in self._names
+ )
+
def render(self):
"""Render this metric
@@ -110,11 +135,11 @@ class CounterMetric(BaseMetric):
def inc(self, *values):
self.inc_by(1, *values)
- def render_item(self, k):
- return ["%s%s %.12g" % (self.name, self._render_key(k), self.counts[k])]
-
def render(self):
- return map_concat(self.render_item, sorted(self.counts.keys()))
+ return flatten(
+ self._render_for_labels(k, self.counts[k])
+ for k in sorted(self.counts.keys())
+ )
class CallbackMetric(BaseMetric):
@@ -131,10 +156,12 @@ class CallbackMetric(BaseMetric):
value = self.callback()
if self.is_scalar():
- return ["%s %.12g" % (self.name, value)]
+ return list(self._render_for_labels([], value))
- return ["%s%s %.12g" % (self.name, self._render_key(k), value[k])
- for k in sorted(value.keys())]
+ return flatten(
+ self._render_for_labels(k, value[k])
+ for k in sorted(value.keys())
+ )
class DistributionMetric(object):
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