Implement MinMaxSumCount aggregator

examples/metrics/simple_example.py

@@ -0,0 +1,106 @@
+# Copyright 2019, OpenTelemetry Authors
+#
+# 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.
+#
+"""
+This module serves as an example for a simple application using metrics
+It shows:
+- How to configure a meter passing a sateful or stateless.
+- How to configure an exporter and how to create a controller.
+- How to create some metrics intruments and how to capture data with them.
+"""
+import sys
+import time
+
+from opentelemetry import metrics
+from opentelemetry.sdk.metrics import Counter, Measure, Meter
+from opentelemetry.sdk.metrics.export import ConsoleMetricsExporter
+from opentelemetry.sdk.metrics.export.batcher import UngroupedBatcher
+from opentelemetry.sdk.metrics.export.controller import PushController
+
+batcher_mode = "stateful"
+
+
+def usage(argv):
+ print("usage:")
+ print("{} [mode]".format(argv[0]))
+ print("mode: stateful (default) or stateless")
+
+
+if len(sys.argv) >= 2:
+ batcher_mode = sys.argv[1]
+ if batcher_mode not in ("stateful", "stateless"):
+ print("bad mode specified.")
+ usage(sys.argv)
+ sys.exit(1)
+
+# Batcher used to collect all created metrics from meter ready for exporting
+# Pass in True/False to indicate whether the batcher is stateful.
+# True indicates the batcher computes checkpoints from over the process
+# lifetime.
+# False indicates the batcher computes checkpoints which describe the updates
+# of a single collection period (deltas)
+batcher = UngroupedBatcher(batcher_mode == "stateful")
+
+# If a batcher is not provided, a default batcher is used
+# Meter is responsible for creating and recording metrics
+metrics.set_preferred_meter_implementation(lambda _: Meter(batcher))
+meter = metrics.meter()
+
+# Exporter to export metrics to the console
+exporter = ConsoleMetricsExporter()
+
+# A PushController collects metrics created from meter and exports it via the
+# exporter every interval
+controller = PushController(meter, exporter, 5)
+
+# Metric instruments allow to capture measurements
+requests_counter = meter.create_metric(
+ "requests", "number of requests", 1, int, Counter, ("environment",)
+)
+
+clicks_counter = meter.create_metric(
+ "clicks", "number of clicks", 1, int, Counter, ("environment",)
+)
+
+requests_size = meter.create_metric(
+ "requests_size", "size of requests", 1, int, Measure, ("environment",)
+)
+
+# Labelsets are used to identify key-values that are associated with a specific
+# metric that you want to record. These are useful for pre-aggregation and can
+# be used to store custom dimensions pertaining to a metric
+staging_label_set = meter.get_label_set({"environment": "staging"})
+testing_label_set = meter.get_label_set({"environment": "testing"})
+
+# Update the metric instruments using the direct calling convention
+requests_size.record(100, staging_label_set)
+requests_counter.add(25, staging_label_set)
+# Sleep for 5 seconds, exported value should be 25
+time.sleep(5)
+
+requests_size.record(5000, staging_label_set)
+requests_counter.add(50, staging_label_set)
+# Exported value should be 75
+time.sleep(5)
+
+requests_size.record(2, testing_label_set)
+requests_counter.add(35, testing_label_set)
+# There should be two exported values 75 and 35, one for each labelset
+time.sleep(5)
+
+clicks_counter.add(5, staging_label_set)
+# There should be three exported values, labelsets can be reused for different
+# metrics but will be recorded seperately, 75, 35 and 5
+
+time.sleep(5)

opentelemetry-sdk/src/opentelemetry/sdk/metrics/export/aggregate.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import abc
+from collections import namedtuple
 
 
 class Aggregator(abc.ABC):
@@ -56,3 +57,52 @@ def take_checkpoint(self):
 
  def merge(self, other):
  self.checkpoint += other.checkpoint
+
+
+class MinMaxSumCountAggregator(Aggregator):
+ """Agregator for Measure metrics that keeps min, max, sum and count."""
+
+ _TYPE = namedtuple("minmaxsumcount", "min max sum count")
+
+ @classmethod
+ def _min(cls, val1, val2):
+ if val1 is None and val2 is None:
+ return None
+ return min(val1 or val2, val2 or val1)
+
+ @classmethod
+ def _max(cls, val1, val2):
+ if val1 is None and val2 is None:
+ return None
+ return max(val1 or val2, val2 or val1)
+
+ @classmethod
+ def _sum(cls, val1, val2):
+ if val1 is None and val2 is None:
+ return None
+ return (val1 or 0) + (val2 or 0)
+
+ def __init__(self):
+ super().__init__()
+ self.current = self._TYPE(None, None, None, 0)
+ self.checkpoint = self._TYPE(None, None, None, 0)
+
+ def update(self, value):
+ self.current = self._TYPE(
+ self._min(self.current.min, value),
+ self._max(self.current.max, value),
+ self._sum(self.current.sum, value),
+ self.current.count + 1,
+ )
+
+ def take_checkpoint(self):
+ self.checkpoint = self.current
+ self.current = self._TYPE(None, None, None, 0)
+
+ def merge(self, other):
+ self.checkpoint = self._TYPE(
+ self._min(self.checkpoint.min, other.checkpoint.min),
+ self._max(self.checkpoint.max, other.checkpoint.max),
+ self._sum(self.checkpoint.sum, other.checkpoint.sum),
+ self.checkpoint.count + other.checkpoint.count,
+ )

opentelemetry-sdk/src/opentelemetry/sdk/metrics/export/batcher.py

@@ -15,11 +15,12 @@
 import abc
 from typing import Sequence, Type
 
-from opentelemetry.metrics import Counter, MetricT
+from opentelemetry.metrics import Counter, Measure, MetricT
 from opentelemetry.sdk.metrics.export import MetricRecord
 from opentelemetry.sdk.metrics.export.aggregate import (
  Aggregator,
  CounterAggregator,
+ MinMaxSumCountAggregator,
 )
 
 
@@ -45,8 +46,10 @@ def aggregator_for(self, metric_type: Type[MetricT]) -> Aggregator:
  Aggregators keep track of and updates values when metrics get updated.
  """
  # pylint:disable=R0201
- if metric_type == Counter:
+ if issubclass(metric_type, Counter):
  return CounterAggregator()
+ if issubclass(metric_type, Measure):
+ return MinMaxSumCountAggregator()
  # TODO: Add other aggregators
  return CounterAggregator()