Merge pull request #2785 from matrix-org/rav/reorganise_metrics_again

Reorganise request and block metrics

1 files changed, 67 insertions, 14 deletions

diff --git a/synapse/metrics/metric.py b/synapse/metrics/metric.py
index 1d054dd557..f480aae614 100644
--- a/synapse/metrics/metric.py
+++ b/synapse/metrics/metric.py
@@ -17,16 +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=[]):
-        self.name = name
+    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._names = [name] + list(alternative_names)
         self.labels = labels  # OK not to clone as we never write it
 
     def dimension(self):
@@ -36,7 +53,7 @@ class BaseMetric(object):
         return not len(self.labels)
 
     def _render_labelvalue(self, value):
-        # TODO: some kind of value escape
+        # TODO: escape backslashes, quotes and newlines
         return '"%s"' % (value)
 
     def _render_key(self, values):
@@ -47,6 +64,36 @@ 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
+
+        Each metric is rendered as:
+
+            name{label1="val1",label2="val2"} value
+
+        https://prometheus.io/docs/instrumenting/exposition_formats/#text-format-details
+
+        Returns:
+            iterable[str]: rendered metrics
+        """
+        raise NotImplementedError()
+
 
 class CounterMetric(BaseMetric):
     """The simplest kind of metric; one that stores a monotonically-increasing
@@ -62,6 +109,10 @@ class CounterMetric(BaseMetric):
     def __init__(self, *args, **kwargs):
         super(CounterMetric, self).__init__(*args, **kwargs)
 
+        # dict[list[str]]: value for each set of label values. the keys are the
+        # label values, in the same order as the labels in self.labels.
+        #
+        # (if the metric is a scalar, the (single) key is the empty list).
         self.counts = {}
 
         # Scalar metrics are never empty
@@ -84,11 +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):
@@ -105,10 +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):