Implement h_count in Histogram directly

Rather than relying on MultiProcessCollector to accummulate it

Signed-off-by: Stefano Rivera <stefano@rivera.za.net>

Author: stefanor

prometheus_client/metrics.py

@@ -652,6 +652,7 @@ def _metric_init(self) -> None:
         self._created = time.time()
         bucket_labelnames = self._labelnames + ('le',)
         self._sum = values.ValueClass(self._type, self._name, self._name + '_sum', self._labelnames, self._labelvalues, self._documentation)
+        self._count = values.ValueClass(self._type, self._name, self._name + '_count', self._labelnames, self._labelvalues, self._documentation)
         for b in self._upper_bounds:
             self._buckets.append(values.ValueClass(
                 self._type,
@@ -674,6 +675,7 @@ def observe(self, amount: float, exemplar: Optional[Dict[str, str]] = None) -> N
         """
         self._raise_if_not_observable()
         self._sum.inc(amount)
+        self._count.inc(1)
         for i, bound in enumerate(self._upper_bounds):
             if amount <= bound:
                 self._buckets[i].inc(1)

prometheus_client/multiprocess.py

@@ -156,8 +156,6 @@ def _accumulate_metrics(metrics, accumulate):
                             samples[labels][sample_key] = acc
                         else:
                             samples[labels][sample_key] = value
-                    if accumulate:
-                        samples[labels][(metric.name + '_count', labels)] = acc
 
             # Convert to correct sample format.
             metric.samples = []

tests/test_multiprocess.py

@@ -278,6 +278,7 @@ def add_label(key, value):
 
         expected_histogram = [
             Sample('h_sum', labels, 6.0),
+            Sample('h_count', labels, 2.0),
             Sample('h_bucket', add_label('le', '0.005'), 0.0),
             Sample('h_bucket', add_label('le', '0.01'), 0.0),
             Sample('h_bucket', add_label('le', '0.025'), 0.0),
@@ -293,7 +294,6 @@ def add_label(key, value):
             Sample('h_bucket', add_label('le', '7.5'), 2.0),
             Sample('h_bucket', add_label('le', '10.0'), 2.0),
             Sample('h_bucket', add_label('le', '+Inf'), 2.0),
-            Sample('h_count', labels, 2.0),
         ]
 
         self.assertEqual(metrics['h'].samples, expected_histogram)
@@ -321,6 +321,7 @@ def add_label(key, value):
 
         expected_histogram = [
             Sample('h_sum', {'view': 'view1'}, 6.0),
+            Sample('h_count', {'view': 'view1'}, 2.0),
             Sample('h_bucket', {'view': 'view1', 'le': '0.005'}, 0.0),
             Sample('h_bucket', {'view': 'view1', 'le': '0.01'}, 0.0),
             Sample('h_bucket', {'view': 'view1', 'le': '0.025'}, 0.0),
@@ -336,8 +337,8 @@ def add_label(key, value):
             Sample('h_bucket', {'view': 'view1', 'le': '7.5'}, 2.0),
             Sample('h_bucket', {'view': 'view1', 'le': '10.0'}, 2.0),
             Sample('h_bucket', {'view': 'view1', 'le': '+Inf'}, 2.0),
-            Sample('h_count', {'view': 'view1'}, 2.0),
             Sample('h_sum', {'view': 'view2'}, 1.0),
+            Sample('h_count', {'view': 'view2'}, 1.0),
             Sample('h_bucket', {'view': 'view2', 'le': '0.005'}, 0.0),
             Sample('h_bucket', {'view': 'view2', 'le': '0.01'}, 0.0),
             Sample('h_bucket', {'view': 'view2', 'le': '0.025'}, 0.0),
@@ -353,7 +354,6 @@ def add_label(key, value):
             Sample('h_bucket', {'view': 'view2', 'le': '7.5'}, 1.0),
             Sample('h_bucket', {'view': 'view2', 'le': '10.0'}, 1.0),
             Sample('h_bucket', {'view': 'view2', 'le': '+Inf'}, 1.0),
-            Sample('h_count', {'view': 'view2'}, 1.0),
         ]
 
         self.assertEqual(metrics['h'].samples, expected_histogram)
@@ -435,6 +435,7 @@ def add_label(key, value):
 
         expected_histogram = [
             Sample('h_sum', labels, 6.0),
+            Sample('h_count', labels, 2.0),
             Sample('h_bucket', add_label('le', '0.005'), 0.0),
             Sample('h_bucket', add_label('le', '0.01'), 0.0),
             Sample('h_bucket', add_label('le', '0.025'), 0.0),