Metrics export pipeline + metrics stdout exporter (open-telemetry#341)

Initial implementation of the end-to-end metrics pipeline.

docs/opentelemetry.sdk.metrics.export.aggregate.rst

@@ -0,0 +1,7 @@
+opentelemetry.sdk.metrics.export.aggregate
+==========================================
+
+.. automodule:: opentelemetry.sdk.metrics.export.aggregate
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/opentelemetry.sdk.metrics.export.batcher.rst

@@ -0,0 +1,11 @@
+opentelemetry.sdk.metrics.export.batcher
+==========================================
+
+.. toctree::
+
+   opentelemetry.sdk.metrics.export
+
+.. automodule:: opentelemetry.sdk.metrics.export.batcher
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/opentelemetry.sdk.metrics.export.rst

@@ -0,0 +1,7 @@
+opentelemetry.sdk.metrics.export
+==========================================
+
+.. automodule:: opentelemetry.sdk.metrics.export
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/opentelemetry.sdk.metrics.rst

@@ -1,6 +1,14 @@
 opentelemetry.sdk.metrics package
 ==========================================
 
+Submodules
+----------
+
+.. toctree::
+
+   opentelemetry.sdk.metrics.export.aggregate
+   opentelemetry.sdk.metrics.export.batcher
+
 .. automodule:: opentelemetry.sdk.metrics
     :members:
     :undoc-members:

examples/metrics/record.py

@@ -0,0 +1,69 @@
+# 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 demonstrates the different ways you can record metrics via the meter.
+"""
+import time
+
+from opentelemetry import metrics
+from opentelemetry.sdk.metrics import Counter, Meter
+from opentelemetry.sdk.metrics.export import ConsoleMetricsExporter
+from opentelemetry.sdk.metrics.export.controller import PushController
+
+# Meter is responsible for creating and recording metrics
+metrics.set_preferred_meter_implementation(lambda _: Meter())
+meter = metrics.meter()
+# exporter to export metrics to the console
+exporter = ConsoleMetricsExporter()
+# controller collects metrics created from meter and exports it via the
+# exporter every interval
+controller = PushController(meter, exporter, 5)
+
+# Example to show how to record using the meter
+counter = meter.create_metric(
+    "requests", "number of requests", 1, int, Counter, ("environment",)
+)
+
+counter2 = meter.create_metric(
+    "clicks", "number of clicks", 1, int, Counter, ("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
+
+# The meter takes a dictionary of key value pairs
+label_set = meter.get_label_set({"environment": "staging"})
+
+# Handle usage
+# You can record metrics with metric handles. Handles are created by passing in
+# a labelset. A handle is essentially metric data that corresponds to a specific
+# set of labels. Therefore, getting a handle using the same set of labels will
+# yield the same metric handle.
+counter_handle = counter.get_handle(label_set)
+counter_handle.add(100)
+
+# Direct metric usage
+# You can record metrics directly using the metric instrument. You pass in a
+# labelset that you would like to record for.
+counter.add(25, label_set)
+
+# Record batch usage
+# You can record metrics in a batch by passing in a labelset and a sequence of
+# (metric, value) pairs. The value would be recorded for each metric using the
+# specified labelset for each.
+meter.record_batch(label_set, [(counter, 50), (counter2, 70)])
+time.sleep(100)

examples/metrics/stateful.py

@@ -0,0 +1,72 @@
+# 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
+Examples show how to recording affects the collection of metrics to be exported
+"""
+import time
+
+from opentelemetry import metrics
+from opentelemetry.sdk.metrics import Counter, 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 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(True)
+# If a batcher is not provded, 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()
+# controller collects metrics created from meter and exports it via the
+# exporter every interval
+controller = PushController(meter, exporter, 5)
+
+counter = meter.create_metric(
+    "requests", "number of requests", 1, int, Counter, ("environment",)
+)
+
+counter2 = meter.create_metric(
+    "clicks", "number of clicks", 1, int, Counter, ("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
+label_set = meter.get_label_set({"environment": "staging"})
+label_set2 = meter.get_label_set({"environment": "testing"})
+
+counter.add(25, label_set)
+# We sleep for 5 seconds, exported value should be 25
+time.sleep(5)
+
+counter.add(50, label_set)
+# exported value should be 75
+time.sleep(5)
+
+counter.add(35, label_set2)
+# should be two exported values 75 and 35, one for each labelset
+time.sleep(5)
+
+counter2.add(5, label_set)
+# should be three exported values, labelsets can be reused for different
+# metrics but will be recorded seperately, 75, 35 and 5
+time.sleep(5)

examples/metrics/stateless.py

@@ -0,0 +1,57 @@
+# 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
+Examples show how to recording affects the collection of metrics to be exported
+"""
+import time
+
+from opentelemetry import metrics
+from opentelemetry.sdk.metrics import Counter, 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 used to collect all created metrics from meter ready for exporting
+# Pass in false for non-stateful batcher. Indicates the batcher computes
+# checkpoints which describe the updates of a single collection period (deltas)
+batcher = UngroupedBatcher(False)
+# 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()
+# controller collects metrics created from meter and exports it via the
+# exporter every interval
+controller = PushController(meter, exporter, 5)
+
+counter = meter.create_metric(
+    "requests", "number of requests", 1, int, Counter, ("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
+label_set = meter.get_label_set({"environment": "staging"})
+
+counter.add(25, label_set)
+# We sleep for 5 seconds, exported value should be 25
+time.sleep(5)
+
+counter.add(50, label_set)
+# exported value should be 50 due to non-stateful batcher
+time.sleep(20)
+
+# Following exported values would be 0

examples/opentelemetry-example-app/src/opentelemetry_example_app/metrics_example.py

@@ -18,8 +18,12 @@
 
 from opentelemetry import metrics
 from opentelemetry.sdk.metrics import Counter, Meter
+from opentelemetry.sdk.metrics.export import ConsoleMetricsExporter
+from opentelemetry.sdk.metrics.export.batcher import UngroupedBatcher
+from opentelemetry.sdk.metrics.export.controller import PushController
 
-metrics.set_preferred_meter_implementation(lambda _: Meter())
+batcher = UngroupedBatcher(True)
+metrics.set_preferred_meter_implementation(lambda _: Meter(batcher))
 meter = metrics.meter()
 counter = meter.create_metric(
     "available memory",
@@ -33,14 +37,14 @@
 label_set = meter.get_label_set({"environment": "staging"})
 
 # Direct metric usage
-counter.add(label_set, 25)
+counter.add(25, label_set)
 
 # Handle usage
 counter_handle = counter.get_handle(label_set)
 counter_handle.add(100)
 
 # Record batch usage
 meter.record_batch(label_set, [(counter, 50)])
-print(counter_handle.data)
 
-# TODO: exporters
+exporter = ConsoleMetricsExporter()
+controller = PushController(meter, exporter, 5)

ext/opentelemetry-ext-jaeger/tests/test_jaeger_exporter.py

@@ -163,7 +163,7 @@ def test_translate_to_jaeger(self):
                 vLong=StatusCanonicalCode.OK.value,
             ),
             jaeger.Tag(
-                key="status.message", vType=jaeger.TagType.STRING, vStr=None,
+                key="status.message", vType=jaeger.TagType.STRING, vStr=None
             ),
             jaeger.Tag(
                 key="span.kind",
@@ -246,7 +246,7 @@ def test_translate_to_jaeger(self):
                         vStr=trace_api.SpanKind.CLIENT.name,
                     ),
                     jaeger.Tag(
-                        key="error", vType=jaeger.TagType.BOOL, vBool=True,
+                        key="error", vType=jaeger.TagType.BOOL, vBool=True
                     ),
                 ],
                 references=[

ext/opentelemetry-ext-zipkin/src/opentelemetry/ext/zipkin/__init__.py

@@ -101,10 +101,7 @@ def export(self, spans: Sequence[Span]) -> SpanExportResult:
 
     def _translate_to_zipkin(self, spans: Sequence[Span]):
 
-        local_endpoint = {
-            "serviceName": self.service_name,
-            "port": self.port,
-        }
+        local_endpoint = {"serviceName": self.service_name, "port": self.port}
 
         if self.ipv4 is not None:
             local_endpoint["ipv4"] = self.ipv4

ext/opentelemetry-ext-zipkin/tests/test_zipkin_exporter.py

@@ -114,9 +114,7 @@ def test_export(self):
         )
 
         span_context = trace_api.SpanContext(
-            trace_id,
-            span_id,
-            trace_options=TraceOptions(TraceOptions.SAMPLED),
+            trace_id, span_id, trace_options=TraceOptions(TraceOptions.SAMPLED)
         )
         parent_context = trace_api.SpanContext(trace_id, parent_id)
         other_context = trace_api.SpanContext(trace_id, other_id)
@@ -168,10 +166,7 @@ def test_export(self):
         otel_spans[2].end(end_time=end_times[2])
 
         service_name = "test-service"
-        local_endpoint = {
-            "serviceName": service_name,
-            "port": 9411,
-        }
+        local_endpoint = {"serviceName": service_name, "port": 9411}
 
         exporter = ZipkinSpanExporter(service_name)
         expected = [