Observability

Measurement is the first step that leads to control and eventually to improvement. If you can't measure something, you can't understand it. If you can't understand it, you can't control it. If you can't control it, you can't improve it.

— H. James Harrington

REST framework doesn’t include built-in observability features, but it works well with popular tools, standards, and third party packages. For simpler needs, you can also implement custom logging or metrics collection using Django middleware.

Custom request logging

You can implement a custom middleware that logs relevant information about handled API requests and responses using Python's built-in logging module.

import logging
import time

logger = logging.getLogger("your_app.requests")


def request_logging_middleware(get_response):
    def middleware(request):
        start_time = time.time()
        response = get_response(request)
        duration = time.time() - start_time
        logger.info(
            f"{request.method} {request.path} - {response.status_code} {response.reason_phrase} - {int(duration*1000)}ms"
        )
        return response

    return middleware

Then, add the middleware to your Django settings.

MIDDLEWARE = [
    "your_app.middleware.request_logging_middleware",
    # ... other middleware
]

OpenTelemetry

OpenTelemetry is an open-source framework for collecting distributed traces and metrics from applications. It provides a vendor-neutral standard for instrumenting code and exporting telemetry data.

In Django applications, OpenTelemetry can be used to trace requests through views, middleware, and database operations. The opentelemetry-instrumentation-django package automatically instruments Django and integrates with the wider OpenTelemetry ecosystem.

The collected data can be exported to any OpenTelemetry-compatible backend for visualization and analysis. Examples include:

Jaeger
Grafana Tempo
Datadog
Elastic APM

Prometheus

Prometheus is an open-source monitoring system that collects metrics by scraping HTTP endpoints exposed by applications. It stores the data in a time series database and supports flexible querying and alerting.

For a REST framework project, Prometheus can be used to track metrics such as request counts, error rates, and latency. The django-prometheus package adds the necessary instrumentation and exposes a /metrics endpoint that Prometheus can scrape. You can also add your own application-specific metrics.

Prometheus can be paired with Grafana to visualize metrics with interactive charts and dashboards.

Other third party packages

Apitally

Apitally is a simple API monitoring, analytics, and request logging tool with an integration for Django REST framework. It provides intuitive dashboards with metrics and insights into API requests, errors, performance and consumers.

The apitally package integrates with Django applications through middleware, which automatically captures metrics for API requests and responses, and synchronizes with Apitally in the background. See DRF-specific setup guide here.

django-health-check

django-health-check provides a set of health check endpoints for Django applications. It allows you to monitor the status of various application components including database connections, cache backends, and custom services.

The library is helpful for production deployments as it enables load balancers, orchestration systems, and monitoring tools to determine application health. It supports both simple health checks and detailed status reporting for different application components.

Sentry

Sentry is an error monitoring platform that provides real-time error tracking and performance monitoring for applications. It automatically captures exceptions, tracks performance issues, and provides detailed context to help debug problems in production.

For Django REST framework applications, Sentry can monitor API errors, slow endpoints, and database query performance. The sentry-sdk package provides automatic instrumentation for Django views, middleware, and database operations.

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