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Skeleton for Azure Metrics Exporter (#678)
Add skeleton metrics exporter to azure
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‎README.rst‎

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@@ -205,14 +205,15 @@ OpenCensus supports integration with popular web frameworks, client libraries an
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- `SQLAlchemy`_
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- `threading`_
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Trace Exporter
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--------------
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Log Exporter
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------------
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- `Azure`_
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Metrics Exporter
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----------------
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- `Azure`_
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- `Jaeger`_
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- `OCAgent`_
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- `Stackdriver`_
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- `Zipkin`_
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Stats Exporter
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--------------
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- `Prometheus`_
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- `Stackdriver`_
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Trace Exporter
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--------------
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- `Azure`_
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- `Jaeger`_
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- `OCAgent`_
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- `Stackdriver`_
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- `Zipkin`_
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.. _Azure: https://github.com/census-instrumentation/opencensus-python/tree/master/contrib/opencensus-ext-azure
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.. _Django: https://github.com/census-instrumentation/opencensus-python/tree/master/contrib/opencensus-ext-django
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.. _Flask: https://github.com/census-instrumentation/opencensus-python/tree/master/contrib/opencensus-ext-flask
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.. _threading: https://github.com/census-instrumentation/opencensus-python/tree/master/contrib/opencensus-ext-threading
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.. _Zipkin: https://github.com/census-instrumentation/opencensus-python/tree/master/contrib/opencensus-ext-zipkin
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Log Exporter
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--------------
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- `Azure`_
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------------
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Versioning
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------------

‎contrib/opencensus-ext-azure/CHANGELOG.md‎

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# Changelog
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## Unreleased
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- Added metrics exporter
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([#678](https://github.com/census-instrumentation/opencensus-python/pull/678)
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## 0.2.1
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Released 2019-06-13

‎contrib/opencensus-ext-azure/README.rst‎

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@@ -16,53 +16,6 @@ Installation
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Usage
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-----
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Trace
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~~~~~
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The **Azure Monitor Trace Exporter** allows you to export `OpenCensus`_ traces to `Azure Monitor`_.
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This example shows how to send a span "hello" to Azure Monitor.
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* Create an Azure Monitor resource and get the instrumentation key, more information can be found `here <https://docs.microsoft.com/azure/azure-monitor/app/create-new-resource>`_.
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* Put the instrumentation key in ``APPINSIGHTS_INSTRUMENTATIONKEY`` environment variable.
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.. code:: python
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from opencensus.ext.azure.trace_exporter import AzureExporter
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from opencensus.trace.samplers import ProbabilitySampler
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from opencensus.trace.tracer import Tracer
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tracer = Tracer(exporter=AzureExporter(), sampler=ProbabilitySampler(1.0))
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with tracer.span(name='hello'):
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print('Hello, World!')
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You can also specify the instrumentation key explicitly in the code.
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* Create an Azure Monitor resource and get the instrumentation key, more information can be found `here <https://docs.microsoft.com/azure/azure-monitor/app/create-new-resource>`_.
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* Install the `requests integration package <../opencensus-ext-requests>`_ using ``pip install opencensus-ext-requests``.
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* Put the instrumentation key in the following code.
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.. code:: python
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import requests
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from opencensus.ext.azure.trace_exporter import AzureExporter
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from opencensus.trace import config_integration
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from opencensus.trace.samplers import ProbabilitySampler
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from opencensus.trace.tracer import Tracer
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config_integration.trace_integrations(['requests'])
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tracer = Tracer(
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exporter=AzureExporter(
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# TODO: replace this with your own instrumentation key.
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instrumentation_key='00000000-0000-0000-0000-000000000000',
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),
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sampler=ProbabilitySampler(1.0),
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)
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with tracer.span(name='parent'):
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response = requests.get(url='https://www.wikipedia.org/wiki/Rabbit')
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Log
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~~~
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* Create an Azure Monitor resource and get the instrumentation key, more information can be found `here <https://docs.microsoft.com/azure/azure-monitor/app/create-new-resource>`_.
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* Put the instrumentation key in ``APPINSIGHTS_INSTRUMENTATIONKEY`` environment variable.
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* You can also specify the instrumentation key explicitly in the code, which will take priority over a set environment variable.
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.. code:: python
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@@ -88,6 +42,7 @@ You can enrich the logs with trace IDs and span IDs by using the `logging integr
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* Create an Azure Monitor resource and get the instrumentation key, more information can be found `here <https://docs.microsoft.com/azure/azure-monitor/app/create-new-resource>`_.
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* Install the `logging integration package <../opencensus-ext-logging>`_ using ``pip install opencensus-ext-logging``.
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* Put the instrumentation key in ``APPINSIGHTS_INSTRUMENTATIONKEY`` environment variable.
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* You can also specify the instrumentation key explicitly in the code, which will take priority over a set environment variable.
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.. code:: python
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@@ -114,6 +69,113 @@ You can enrich the logs with trace IDs and span IDs by using the `logging integr
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logger.warning('In the span')
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logger.warning('After the span')
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Metrics
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~~~~~~~
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The **OpenCensus Azure Monitor Metrics Exporter** allows you to export metrics to `Azure Monitor`_.
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* Create an Azure Monitor resource and get the instrumentation key, more information can be found `here <https://docs.microsoft.com/azure/azure-monitor/app/create-new-resource>`_.
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* Put the instrumentation key in ``APPINSIGHTS_INSTRUMENTATIONKEY`` environment variable.
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* You can also specify the instrumentation key explicitly in the code, which will take priority over a set environment variable.
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Using the Metrics exporter
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*****************************
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.. code:: python
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import time
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from opencensus.ext.azure import metrics_exporter
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from opencensus.stats import aggregation as aggregation_module
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from opencensus.stats import measure as measure_module
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from opencensus.stats import stats as stats_module
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from opencensus.stats import view as view_module
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from opencensus.tags import tag_map as tag_map_module
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stats = stats_module.stats
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view_manager = stats.view_manager
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stats_recorder = stats.stats_recorder
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CARROTS_MEASURE = measure_module.MeasureInt("carrots",
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"number of carrots",
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"carrots")
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CARROTS_VIEW = view_module.View("carrots_view",
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"number of carrots",
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[],
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CARROTS_MEASURE,
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aggregation_module.CountAggregation())
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def main():
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# Enable metrics
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# Set the interval in seconds in which you want to send metrics
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exporter = metrics_exporter.new_metrics_exporter(export_interval=2)
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view_manager.register_exporter(exporter)
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view_manager.register_view(CARROTS_VIEW)
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mmap = stats_recorder.new_measurement_map()
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tmap = tag_map_module.TagMap()
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mmap.measure_int_put(CARROTS_MEASURE, 1000)
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mmap.record(tmap)
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time.sleep(10)
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print("Done recording metrics")
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if __name__ == "__main__":
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main()
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Trace
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~~~~~
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The **Azure Monitor Trace Exporter** allows you to export `OpenCensus`_ traces to `Azure Monitor`_.
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This example shows how to send a span "hello" to Azure Monitor.
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* Create an Azure Monitor resource and get the instrumentation key, more information can be found `here <https://docs.microsoft.com/azure/azure-monitor/app/create-new-resource>`_.
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* Put the instrumentation key in ``APPINSIGHTS_INSTRUMENTATIONKEY`` environment variable.
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* You can also specify the instrumentation key explicitly in the code, which will take priority over a set environment variable.
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.. code:: python
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from opencensus.ext.azure.trace_exporter import AzureExporter
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from opencensus.trace.samplers import ProbabilitySampler
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from opencensus.trace.tracer import Tracer
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tracer = Tracer(exporter=AzureExporter(), sampler=ProbabilitySampler(1.0))
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with tracer.span(name='hello'):
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print('Hello, World!')
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You can also specify the instrumentation key explicitly in the code.
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* Create an Azure Monitor resource and get the instrumentation key, more information can be found `here <https://docs.microsoft.com/azure/azure-monitor/app/create-new-resource>`_.
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* Install the `requests integration package <../opencensus-ext-requests>`_ using ``pip install opencensus-ext-requests``.
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* Put the instrumentation key in ``APPINSIGHTS_INSTRUMENTATIONKEY`` environment variable.
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* You can also specify the instrumentation key explicitly in the code, which will take priority over a set environment variable.
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.. code:: python
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import requests
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from opencensus.ext.azure.trace_exporter import AzureExporter
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from opencensus.trace import config_integration
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from opencensus.trace.samplers import ProbabilitySampler
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from opencensus.trace.tracer import Tracer
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config_integration.trace_integrations(['requests'])
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tracer = Tracer(
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exporter=AzureExporter(
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# TODO: replace this with your own instrumentation key.
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instrumentation_key='00000000-0000-0000-0000-000000000000',
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),
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sampler=ProbabilitySampler(1.0),
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)
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with tracer.span(name='parent'):
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response = requests.get(url='https://www.wikipedia.org/wiki/Rabbit')
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References
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----------
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‎contrib/opencensus-ext-azure/examples/metrics/distribution.py‎

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# Copyright 2019, OpenCensus 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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import random
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import time
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from opencensus.ext.azure import metrics_exporter
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from opencensus.stats import aggregation as aggregation_module
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from opencensus.stats import measure as measure_module
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from opencensus.stats import stats as stats_module
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from opencensus.stats import view as view_module
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from opencensus.tags import tag_map as tag_map_module
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stats = stats_module.stats
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view_manager = stats.view_manager
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stats_recorder = stats.stats_recorder
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# Create the measures
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# The latency in milliseconds
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m_latency_ms = measure_module.MeasureFloat(
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"task_latency", "The task latency in milliseconds", "ms")
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# Create a view in which defines an aggregation and tag keys
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latency_view = view_module.View(
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"task_latency_distribution",
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"The distribution of the task latencies",
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[],
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m_latency_ms,
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# Latency in buckets: [>=0ms, >=100ms, >=200ms, >=400ms, >=1s, >=2s, >=4s]
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aggregation_module.DistributionAggregation(
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[100.0, 200.0, 400.0, 1000.0, 2000.0, 4000.0]))
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def main():
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# Enable metrics
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# Set the interval in seconds in which you want to send metrics
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exporter = metrics_exporter.new_metrics_exporter(export_interval=5)
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view_manager.register_exporter(exporter)
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view_manager.register_view(latency_view)
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mmap = stats_recorder.new_measurement_map()
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tmap = tag_map_module.TagMap()
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for i in range(100):
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ms = random.random() * 5 * 1000
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print("Latency {0}:{1}".format(i, ms))
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mmap.measure_float_put(m_latency_ms, ms)
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mmap.record(tmap)
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time.sleep(1)
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print("Done recording metrics")
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if __name__ == "__main__":
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main()

‎contrib/opencensus-ext-azure/examples/metrics/simple.py‎

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# Copyright 2019, OpenCensus 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
13+
# limitations under the License.
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import time
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from opencensus.ext.azure import metrics_exporter
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from opencensus.stats import aggregation as aggregation_module
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from opencensus.stats import measure as measure_module
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from opencensus.stats import stats as stats_module
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from opencensus.stats import view as view_module
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from opencensus.tags import tag_map as tag_map_module
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stats = stats_module.stats
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view_manager = stats.view_manager
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stats_recorder = stats.stats_recorder
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CARROTS_MEASURE = measure_module.MeasureInt("carrots",
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"number of carrots",
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"carrots")
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CARROTS_VIEW = view_module.View("carrots_view",
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"number of carrots",
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[],
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CARROTS_MEASURE,
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aggregation_module.CountAggregation())
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def main():
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# Enable metrics
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# Set the interval in seconds in which you want to send metrics
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exporter = metrics_exporter.new_metrics_exporter(export_interval=2)
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view_manager.register_exporter(exporter)
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view_manager.register_view(CARROTS_VIEW)
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mmap = stats_recorder.new_measurement_map()
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tmap = tag_map_module.TagMap()
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mmap.measure_int_put(CARROTS_MEASURE, 1000)
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mmap.record(tmap)
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time.sleep(10)
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print("Done recording metrics")
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if __name__ == "__main__":
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main()

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