OpenCensus Zenoss Exporter for Python

A Python library intended to be used in Python applications instrumented with OpenCensus to export stats to Zenoss.

Status

This library is in an alpha stage, and the API is subject to change.

Installation

The package can be installed with the following command.

pip install opencensus-ext-zenoss

Usage

The following example shows how to configure the exporter.

from opencensus.ext.zenoss import stats_exporter as zenoss
from opencensus.stats import stats as stats_module

stats = stats_module.stats
view_manager = stats.view_manager

exporter = zenoss.new_stats_exporter(
    zenoss.Options(api_key="YOUR-ZENOSS-API-KEY"))

view_manager.register_exporter(exporter)

Options

The following options are available when creating a stats exporter.

• address: Zenoss API address. Default is https://api.zenoss.io.
• api_key: Zenoss API key.
• source: Added as a tag to all sent metrics. Recommended.
• extra_tags: Map of additional tags to add to all sent metrics. Default is {}.
• insecure_tls: Set to True to disable server certification verification. Default is False.

Example Application

The following is a complete example of an application that will write to a measure once per second for 100 seconds. A distribution view is created for measure that will be exported to Zenoss once every 10 seconds.

import os
import random
import sys
import time

from opencensus.ext.zenoss import stats_exporter as zenoss
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

# Setup aliases to make working with OpenCensus easier.
stats = stats_module.stats
view_manager = stats.view_manager
stats_recorder = stats.stats_recorder

# Create a measure.
m_latency_ms = measure_module.MeasureFloat(
    "task_latency", "The task latency in milliseconds", "ms")

# Create a view using the measure.
latency_view = view_module.View(
    "task_latency_distribution",
    "The distribution of the task latencies",
    [],
    m_latency_ms,
    # Latency in buckets: [>=0ms, >=100ms, >=200ms, >=400ms, >=1s, >=2s, >=4s]
    aggregation_module.DistributionAggregation(
        [100.0, 200.0, 400.0, 1000.0, 2000.0, 4000.0]))


def main():
    address = os.environ.get("ZENOSS_ADDRESS", zenoss.DEFAULT_ADDRESS)
    api_key = os.environ.get("ZENOSS_API_KEY")
    if not api_key:
        sys.exit("ZENOSS_API_KEY must be set")

    # Create Zenoss exporter.
    exporter = zenoss.new_stats_exporter(
        options=zenoss.Options(
            address=address,
            api_key=api_key,
            source="app.example.com"),
        interval=10)

    # Register Zenoss exporter.
    view_manager.register_exporter(exporter)

    # Register our example view.
    view_manager.register_view(latency_view)

    # Prepare measurement map, and tag map we can reuse for each sample.
    measurement_map = stats_recorder.new_measurement_map()
    tag_map = tag_map_module.TagMap()

    # Record one random measurement each second for 100 seconds.
    print("Recording measurements:")
    for i in range(100):
        ms = random.random() * 5 * 1000
        print("  - latency {}:{}".format(i, ms))
        measurement_map.measure_float_put(m_latency_ms, ms)
        measurement_map.record(tag_map)
        time.sleep(1)


if __name__ == "__main__":
    import logging

    # Setup default logging configuration.
    logging.basicConfig()

    # Change level of Zenoss exporter's logging to debug.
    logging.getLogger("opencensus.ext.zenoss").setLevel(logging.DEBUG)

    main()

Useful Links

• For more information about Zenoss, visit: https://zenoss.com.
• For more information about OpenCensus, visit: https://opencensus.io.