opentelemetry-exporter-gcp-monitoring · Python Simple Repository Browser

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Author: Google

Requires Python: >=3.9

Classifiers

Development Status

4 - Beta

Intended Audience

Developers

License

OSI Approved :: Apache Software License

Programming Language

Python
Python :: 3
Python :: 3.9
Python :: 3.10
Python :: 3.11
Python :: 3.12
Python :: 3.13

This library provides support for exporting metrics to Google Cloud Monitoring.

To get started with instrumentation in Google Cloud, see Generate traces and metrics with Python.

To learn more about instrumentation and observability, including opinionated recommendations for Google Cloud Observability, visit Instrumentation and observability.

For resource detection and GCP trace context propagation, see opentelemetry-tools-google-cloud. For the Google Cloud Trace exporter, see opentelemetry-exporter-gcp-trace.

Installation

pip install opentelemetry-exporter-gcp-monitoring

Usage

import time

from opentelemetry import metrics
from opentelemetry.exporter.cloud_monitoring import (
    CloudMonitoringMetricsExporter,
)
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
from opentelemetry.sdk.resources import Resource

metrics.set_meter_provider(
    MeterProvider(
        metric_readers=[
            PeriodicExportingMetricReader(
                CloudMonitoringMetricsExporter(), export_interval_millis=5000
            )
        ],
        resource=Resource.create(
            {
                "service.name": "basic_metrics",
                "service.namespace": "examples",
                "service.instance.id": "instance123",
            }
        ),
    )
)
meter = metrics.get_meter(__name__)

# Creates metric workload.googleapis.com/request_counter with monitored resource generic_task
requests_counter = meter.create_counter(
    name="request_counter",
    description="number of requests",
    unit="1",
)

staging_labels = {"environment": "staging"}

for i in range(20):
    requests_counter.add(25, staging_labels)
    time.sleep(5)