opencensus-ext-azure 1.1.9

OpenCensus Azure Monitor Exporter

Installation

pip install opencensus-ext-azure

Prerequisites

• Create an Azure Monitor resource and get the instrumentation key, more information can be found in the official docs.

• Place your instrumentation key in a connection string and directly into your code.

• Alternatively, you can specify your connection string in an environment variable APPLICATIONINSIGHTS_CONNECTION_STRING.

Usage

Log

The Azure Monitor Log Handler allows you to export Python logs to Azure Monitor.

This example shows how to send a warning level log to Azure Monitor.

import logging

from opencensus.ext.azure.log_exporter import AzureLogHandler

logger = logging.getLogger(__name__)
logger.addHandler(AzureLogHandler(connection_string='InstrumentationKey=<your-instrumentation_key-here>'))
logger.warning('Hello, World!')

Correlation

You can enrich the logs with trace IDs and span IDs by using the logging integration.

• Install the logging integration package using pip install opencensus-ext-logging.

import logging

from opencensus.ext.azure.log_exporter import AzureLogHandler
from opencensus.ext.azure.trace_exporter import AzureExporter
from opencensus.trace import config_integration
from opencensus.trace.samplers import ProbabilitySampler
from opencensus.trace.tracer import Tracer

config_integration.trace_integrations(['logging'])

logger = logging.getLogger(__name__)

handler = AzureLogHandler(connection_string='InstrumentationKey=<your-instrumentation_key-here>')
handler.setFormatter(logging.Formatter('%(traceId)s %(spanId)s %(message)s'))
logger.addHandler(handler)

tracer = Tracer(
    exporter=AzureExporter(connection_string='InstrumentationKey=<your-instrumentation_key-here>'),
    sampler=ProbabilitySampler(1.0)
)

logger.warning('Before the span')
with tracer.span(name='test'):
    logger.warning('In the span')
logger.warning('After the span')

Custom Properties

You can also add custom properties to your log messages in the extra keyword argument using the custom_dimensions field.

WARNING: For this feature to work, you need to pass a dictionary to the custom_dimensions field. If you pass arguments of any other type, the logger will ignore them.

import logging

from opencensus.ext.azure.log_exporter import AzureLogHandler

logger = logging.getLogger(__name__)
logger.addHandler(AzureLogHandler(connection_string='InstrumentationKey=<your-instrumentation_key-here>'))

properties = {'custom_dimensions': {'key_1': 'value_1', 'key_2': 'value_2'}}
logger.warning('action', extra=properties)

Modifying Logs

• You can pass a callback function to the exporter to process telemetry before it is exported.

• Your callback function can return False if you do not want this envelope exported.

• Your callback function must accept an envelope data type as its parameter.

• You can see the schema for Azure Monitor data types in the envelopes here.

• The AzureLogHandler handles ExceptionData and MessageData data types.

import logging

from opencensus.ext.azure.log_exporter import AzureLogHandler

logger = logging.getLogger(__name__)

# Callback function to append '_hello' to each log message telemetry
def callback_function(envelope):
    envelope.data.baseData.message += '_hello'
    return True

handler = AzureLogHandler(connection_string='InstrumentationKey=<your-instrumentation_key-here>')
handler.add_telemetry_processor(callback_function)
logger.addHandler(handler)
logger.warning('Hello, World!')

Events

You can send customEvent telemetry in exactly the same way you would send trace telemetry except using the AzureEventHandler instead.

import logging

from opencensus.ext.azure.log_exporter import AzureEventHandler

logger = logging.getLogger(__name__)
logger.addHandler(AzureEventHandler(connection_string='InstrumentationKey=<your-instrumentation_key-here>'))
logger.setLevel(logging.INFO)
logger.info('Hello, World!')

Metrics

The Azure Monitor Metrics Exporter allows you to export metrics to Azure Monitor.

from opencensus.ext.azure import metrics_exporter
from opencensus.stats import aggregation as aggregation_module
from opencensus.stats import measure as measure_module
from opencensus.stats import stats as stats_module
from opencensus.stats import view as view_module
from opencensus.tags import tag_map as tag_map_module

stats = stats_module.stats
view_manager = stats.view_manager
stats_recorder = stats.stats_recorder

CARROTS_MEASURE = measure_module.MeasureInt("carrots",
                                            "number of carrots",
                                            "carrots")
CARROTS_VIEW = view_module.View("carrots_view",
                                "number of carrots",
                                [],
                                CARROTS_MEASURE,
                                aggregation_module.CountAggregation())

def main():
    # Enable metrics
    # Set the interval in seconds to 60s, which is the time interval application insights
    # aggregates your metrics
    exporter = metrics_exporter.new_metrics_exporter(
        connection_string='InstrumentationKey=<your-instrumentation-key-here>'
    )
    view_manager.register_exporter(exporter)

    view_manager.register_view(CARROTS_VIEW)
    mmap = stats_recorder.new_measurement_map()
    tmap = tag_map_module.TagMap()

    mmap.measure_int_put(CARROTS_MEASURE, 1000)
    mmap.record(tmap)

    print("Done recording metrics")

if __name__ == "__main__":
    main()

Performance counters

The exporter also includes a set of performance counters that are exported to Azure Monitor by default.

import psutil
import time

from opencensus.ext.azure import metrics_exporter

def main():
    # Performance counters are sent by default. You can disable performance counters by
    # passing in enable_standard_metrics=False into the constructor of
    # new_metrics_exporter()
    _exporter = metrics_exporter.new_metrics_exporter(
        connection_string='InstrumentationKey=<your-instrumentation-key-here>',
        export_interval=60,
    )

    for i in range(100):
        print(psutil.virtual_memory())
        time.sleep(5)

    print("Done recording metrics")

if __name__ == "__main__":
    main()

Below is a list of performance counters that are currently available:

• Available Memory (bytes)

• CPU Processor Time (percentage)

• Incoming Request Rate (per second)

• Incoming Request Average Execution Time (milliseconds)

• Process CPU Usage (percentage)

• Process Private Bytes (bytes)

Modifying Metrics

• You can pass a callback function to the exporter to process telemetry before it is exported.

• Your callback function can return False if you do not want this envelope exported.

• Your callback function must accept an envelope data type as its parameter.

• You can see the schema for Azure Monitor data types in the envelopes here.

• The MetricsExporter handles MetricData data types.

from opencensus.ext.azure import metrics_exporter
from opencensus.stats import aggregation as aggregation_module
from opencensus.stats import measure as measure_module
from opencensus.stats import stats as stats_module
from opencensus.stats import view as view_module
from opencensus.tags import tag_map as tag_map_module

stats = stats_module.stats
view_manager = stats.view_manager
stats_recorder = stats.stats_recorder

CARROTS_MEASURE = measure_module.MeasureInt("carrots",
                                            "number of carrots",
                                            "carrots")
CARROTS_VIEW = view_module.View("carrots_view",
                                "number of carrots",
                                [],
                                CARROTS_MEASURE,
                                aggregation_module.CountAggregation())

# Callback function to only export the metric if value is greater than 0
def callback_function(envelope):
    return envelope.data.baseData.metrics[0].value > 0

def main():
    # Enable metrics
    # Set the interval in seconds to 60s, which is the time interval application insights
    # aggregates your metrics
    exporter = metrics_exporter.new_metrics_exporter(
        connection_string='InstrumentationKey=<your-instrumentation-key-here>',
        export_interval=60,
    )
    exporter.add_telemetry_processor(callback_function)
    view_manager.register_exporter(exporter)

    view_manager.register_view(CARROTS_VIEW)
    mmap = stats_recorder.new_measurement_map()
    tmap = tag_map_module.TagMap()

    mmap.measure_int_put(CARROTS_MEASURE, 1000)
    mmap.record(tmap)

    print("Done recording metrics")

if __name__ == "__main__":
    main()

Trace

The Azure Monitor Trace Exporter allows you to export OpenCensus traces to Azure Monitor.

This example shows how to send a span “hello” to Azure Monitor.

from opencensus.ext.azure.trace_exporter import AzureExporter
from opencensus.trace.samplers import ProbabilitySampler
from opencensus.trace.tracer import Tracer

tracer = Tracer(
    exporter=AzureExporter(
        connection_string='InstrumentationKey=<your-instrumentation-key-here>'
    ),
    sampler=ProbabilitySampler(1.0)
)

with tracer.span(name='hello'):
    print('Hello, World!')

Integrations

OpenCensus also supports several integrations which allows OpenCensus to integrate with third party libraries.

This example shows how to integrate with the requests library.

• Install the requests integration package using pip install opencensus-ext-requests.

import requests

from opencensus.ext.azure.trace_exporter import AzureExporter
from opencensus.trace import config_integration
from opencensus.trace.samplers import ProbabilitySampler
from opencensus.trace.tracer import Tracer

config_integration.trace_integrations(['requests'])
tracer = Tracer(
    exporter=AzureExporter(
        connection_string='InstrumentationKey=<your-instrumentation-key-here>',
    ),
    sampler=ProbabilitySampler(1.0),
)
with tracer.span(name='parent'):
    response = requests.get(url='https://www.wikipedia.org/wiki/Rabbit')

Modifying Traces

• You can pass a callback function to the exporter to process telemetry before it is exported.

• Your callback function can return False if you do not want this envelope exported.

• Your callback function must accept an envelope data type as its parameter.

• You can see the schema for Azure Monitor data types in the envelopes here.

• The AzureExporter handles Data data types.

import requests

from opencensus.ext.azure.trace_exporter import AzureExporter
from opencensus.trace import config_integration
from opencensus.trace.samplers import ProbabilitySampler
from opencensus.trace.tracer import Tracer

config_integration.trace_integrations(['requests'])

# Callback function to add os_type: linux to span properties
def callback_function(envelope):
    envelope.data.baseData.properties['os_type'] = 'linux'
    return True

exporter = AzureExporter(
    connection_string='InstrumentationKey=<your-instrumentation-key-here>'
)
exporter.add_telemetry_processor(callback_function)
tracer = Tracer(exporter=exporter, sampler=ProbabilitySampler(1.0))
with tracer.span(name='parent'):
    response = requests.get(url='https://www.wikipedia.org/wiki/Rabbit')

Integrate with Azure Functions

Users who want to capture custom telemetry in Azure Functions environments are encouraged to used the OpenCensus Python Azure Functions extension. More details can be found in this document.

References

• Azure Monitor

• Examples

• OpenCensus Project