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Metrics export pipeline + metrics stdout exporter (#341)
Initial implementation of the end-to-end metrics pipeline.
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Diff for: ‎docs/opentelemetry.sdk.metrics.export.aggregate.rst

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opentelemetry.sdk.metrics.export.aggregate
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==========================================
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.. automodule:: opentelemetry.sdk.metrics.export.aggregate
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:members:
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:undoc-members:
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:show-inheritance:

Diff for: ‎docs/opentelemetry.sdk.metrics.export.batcher.rst

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opentelemetry.sdk.metrics.export.batcher
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==========================================
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.. toctree::
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opentelemetry.sdk.metrics.export
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.. automodule:: opentelemetry.sdk.metrics.export.batcher
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:members:
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:undoc-members:
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:show-inheritance:

Diff for: ‎docs/opentelemetry.sdk.metrics.export.rst

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opentelemetry.sdk.metrics.export
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==========================================
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.. automodule:: opentelemetry.sdk.metrics.export
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:members:
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:undoc-members:
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:show-inheritance:

Diff for: ‎docs/opentelemetry.sdk.metrics.rst

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opentelemetry.sdk.metrics package
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==========================================
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Submodules
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----------
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.. toctree::
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opentelemetry.sdk.metrics.export.aggregate
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opentelemetry.sdk.metrics.export.batcher
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.. automodule:: opentelemetry.sdk.metrics
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:members:
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:undoc-members:

Diff for: ‎examples/metrics/record.py

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# Copyright 2019, OpenTelemetry Authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""
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This module serves as an example for a simple application using metrics.
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It demonstrates the different ways you can record metrics via the meter.
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"""
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import time
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from opentelemetry import metrics
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from opentelemetry.sdk.metrics import Counter, Meter
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from opentelemetry.sdk.metrics.export import ConsoleMetricsExporter
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from opentelemetry.sdk.metrics.export.controller import PushController
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# Meter is responsible for creating and recording metrics
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metrics.set_preferred_meter_implementation(lambda _: Meter())
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meter = metrics.meter()
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# exporter to export metrics to the console
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exporter = ConsoleMetricsExporter()
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# controller collects metrics created from meter and exports it via the
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# exporter every interval
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controller = PushController(meter, exporter, 5)
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# Example to show how to record using the meter
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counter = meter.create_metric(
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"requests", "number of requests", 1, int, Counter, ("environment",)
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)
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counter2 = meter.create_metric(
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"clicks", "number of clicks", 1, int, Counter, ("environment",)
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)
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# Labelsets are used to identify key-values that are associated with a specific
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# metric that you want to record. These are useful for pre-aggregation and can
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# be used to store custom dimensions pertaining to a metric
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# The meter takes a dictionary of key value pairs
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label_set = meter.get_label_set({"environment": "staging"})
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# Handle usage
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# You can record metrics with metric handles. Handles are created by passing in
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# a labelset. A handle is essentially metric data that corresponds to a specific
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# set of labels. Therefore, getting a handle using the same set of labels will
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# yield the same metric handle.
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counter_handle = counter.get_handle(label_set)
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counter_handle.add(100)
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# Direct metric usage
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# You can record metrics directly using the metric instrument. You pass in a
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# labelset that you would like to record for.
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counter.add(25, label_set)
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# Record batch usage
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# You can record metrics in a batch by passing in a labelset and a sequence of
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# (metric, value) pairs. The value would be recorded for each metric using the
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# specified labelset for each.
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meter.record_batch(label_set, [(counter, 50), (counter2, 70)])
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time.sleep(100)

Diff for: ‎examples/metrics/stateful.py

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# Copyright 2019, OpenTelemetry Authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""
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This module serves as an example for a simple application using metrics
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Examples show how to recording affects the collection of metrics to be exported
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"""
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import time
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from opentelemetry import metrics
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from opentelemetry.sdk.metrics import Counter, Meter
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from opentelemetry.sdk.metrics.export import ConsoleMetricsExporter
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from opentelemetry.sdk.metrics.export.batcher import UngroupedBatcher
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from opentelemetry.sdk.metrics.export.controller import PushController
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# Batcher used to collect all created metrics from meter ready for exporting
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# Pass in true/false to indicate whether the batcher is stateful. True
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# indicates the batcher computes checkpoints from over the process lifetime.
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# False indicates the batcher computes checkpoints which describe the updates
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# of a single collection period (deltas)
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batcher = UngroupedBatcher(True)
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# If a batcher is not provded, a default batcher is used
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# Meter is responsible for creating and recording metrics
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metrics.set_preferred_meter_implementation(lambda _: Meter(batcher))
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meter = metrics.meter()
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# exporter to export metrics to the console
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exporter = ConsoleMetricsExporter()
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# controller collects metrics created from meter and exports it via the
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# exporter every interval
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controller = PushController(meter, exporter, 5)
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43+
counter = meter.create_metric(
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"requests", "number of requests", 1, int, Counter, ("environment",)
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)
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counter2 = meter.create_metric(
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"clicks", "number of clicks", 1, int, Counter, ("environment",)
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)
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# Labelsets are used to identify key-values that are associated with a specific
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# metric that you want to record. These are useful for pre-aggregation and can
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# be used to store custom dimensions pertaining to a metric
54+
label_set = meter.get_label_set({"environment": "staging"})
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label_set2 = meter.get_label_set({"environment": "testing"})
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counter.add(25, label_set)
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# We sleep for 5 seconds, exported value should be 25
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time.sleep(5)
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counter.add(50, label_set)
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# exported value should be 75
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time.sleep(5)
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counter.add(35, label_set2)
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# should be two exported values 75 and 35, one for each labelset
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time.sleep(5)
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counter2.add(5, label_set)
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# should be three exported values, labelsets can be reused for different
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# metrics but will be recorded seperately, 75, 35 and 5
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time.sleep(5)

Diff for: ‎examples/metrics/stateless.py

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# Copyright 2019, OpenTelemetry Authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""
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This module serves as an example for a simple application using metrics
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Examples show how to recording affects the collection of metrics to be exported
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"""
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import time
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from opentelemetry import metrics
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from opentelemetry.sdk.metrics import Counter, Meter
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from opentelemetry.sdk.metrics.export import ConsoleMetricsExporter
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from opentelemetry.sdk.metrics.export.batcher import UngroupedBatcher
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from opentelemetry.sdk.metrics.export.controller import PushController
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# Batcher used to collect all created metrics from meter ready for exporting
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# Pass in false for non-stateful batcher. Indicates the batcher computes
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# checkpoints which describe the updates of a single collection period (deltas)
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batcher = UngroupedBatcher(False)
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# Meter is responsible for creating and recording metrics
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metrics.set_preferred_meter_implementation(lambda _: Meter(batcher))
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meter = metrics.meter()
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# exporter to export metrics to the console
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exporter = ConsoleMetricsExporter()
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# controller collects metrics created from meter and exports it via the
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# exporter every interval
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controller = PushController(meter, exporter, 5)
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counter = meter.create_metric(
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"requests", "number of requests", 1, int, Counter, ("environment",)
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)
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# Labelsets are used to identify key-values that are associated with a specific
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# metric that you want to record. These are useful for pre-aggregation and can
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# be used to store custom dimensions pertaining to a metric
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label_set = meter.get_label_set({"environment": "staging"})
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counter.add(25, label_set)
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# We sleep for 5 seconds, exported value should be 25
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time.sleep(5)
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counter.add(50, label_set)
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# exported value should be 50 due to non-stateful batcher
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time.sleep(20)
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# Following exported values would be 0

Diff for: ‎examples/opentelemetry-example-app/src/opentelemetry_example_app/metrics_example.py

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from opentelemetry import metrics
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from opentelemetry.sdk.metrics import Counter, Meter
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from opentelemetry.sdk.metrics.export import ConsoleMetricsExporter
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from opentelemetry.sdk.metrics.export.batcher import UngroupedBatcher
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from opentelemetry.sdk.metrics.export.controller import PushController
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metrics.set_preferred_meter_implementation(lambda _: Meter())
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batcher = UngroupedBatcher(True)
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metrics.set_preferred_meter_implementation(lambda _: Meter(batcher))
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meter = metrics.meter()
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counter = meter.create_metric(
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"available memory",
@@ -33,14 +37,14 @@
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label_set = meter.get_label_set({"environment": "staging"})
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3539
# Direct metric usage
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counter.add(label_set, 25)
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counter.add(25, label_set)
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# Handle usage
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counter_handle = counter.get_handle(label_set)
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counter_handle.add(100)
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# Record batch usage
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meter.record_batch(label_set, [(counter, 50)])
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print(counter_handle.data)
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# TODO: exporters
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exporter = ConsoleMetricsExporter()
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controller = PushController(meter, exporter, 5)

Diff for: ‎ext/opentelemetry-ext-jaeger/tests/test_jaeger_exporter.py

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vLong=StatusCanonicalCode.OK.value,
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),
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jaeger.Tag(
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key="status.message", vType=jaeger.TagType.STRING, vStr=None,
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key="status.message", vType=jaeger.TagType.STRING, vStr=None
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),
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jaeger.Tag(
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key="span.kind",
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vStr=trace_api.SpanKind.CLIENT.name,
247247
),
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jaeger.Tag(
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key="error", vType=jaeger.TagType.BOOL, vBool=True,
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key="error", vType=jaeger.TagType.BOOL, vBool=True
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),
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],
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references=[

Diff for: ‎ext/opentelemetry-ext-zipkin/src/opentelemetry/ext/zipkin/__init__.py

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102102
def _translate_to_zipkin(self, spans: Sequence[Span]):
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local_endpoint = {
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"serviceName": self.service_name,
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"port": self.port,
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}
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local_endpoint = {"serviceName": self.service_name, "port": self.port}
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if self.ipv4 is not None:
110107
local_endpoint["ipv4"] = self.ipv4

Diff for: ‎ext/opentelemetry-ext-zipkin/tests/test_zipkin_exporter.py

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)
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116116
span_context = trace_api.SpanContext(
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trace_id,
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span_id,
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trace_options=TraceOptions(TraceOptions.SAMPLED),
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trace_id, span_id, trace_options=TraceOptions(TraceOptions.SAMPLED)
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)
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parent_context = trace_api.SpanContext(trace_id, parent_id)
122120
other_context = trace_api.SpanContext(trace_id, other_id)
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168166
otel_spans[2].end(end_time=end_times[2])
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170168
service_name = "test-service"
171-
local_endpoint = {
172-
"serviceName": service_name,
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"port": 9411,
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}
169+
local_endpoint = {"serviceName": service_name, "port": 9411}
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176171
exporter = ZipkinSpanExporter(service_name)
177172
expected = [

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