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docs/topics/observability.md
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> >
> — H. James Harrington > — H. James Harrington
REST framework doesn't ship with built-in observability features, but there are many widely-used tools, standards and third party packages to choose from that work well with it. If your needs are simple, you can also implement your own. 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 ## Custom request logging
You can implement a custom middleware that logs relevant information about handled API requests and responses using Python's builtin `logging` module. You can implement a custom middleware that logs relevant information about handled API requests and responses using Python's built-in `logging` module.
```python ```python
import logging import logging
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[Prometheus](https://prometheus.io/) 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. [Prometheus](https://prometheus.io/) 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, response codes, error rates, and latency. The [django-prometheus](https://pypi.org/project/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. For a REST framework project, Prometheus can be used to track metrics such as request counts, error rates, and latency. The [django-prometheus](https://pypi.org/project/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](https://grafana.com/) to visualize metrics with interactive charts and dashboards. Prometheus can be paired with [Grafana](https://grafana.com/) to visualize metrics with interactive charts and dashboards.
@ -49,7 +49,7 @@ Prometheus can be paired with [Grafana](https://grafana.com/) to visualize metri
In Django applications, OpenTelemetry can be used to trace requests through views, middleware, and database operations. The [opentelemetry-instrumentation-django](https://pypi.org/project/opentelemetry-instrumentation-django/) package automatically instruments Django and integrates with the wider OpenTelemetry ecosystem. In Django applications, OpenTelemetry can be used to trace requests through views, middleware, and database operations. The [opentelemetry-instrumentation-django](https://pypi.org/project/opentelemetry-instrumentation-django/) package automatically instruments Django and integrates with the wider OpenTelemetry ecosystem.
The collected data can be exported to and visualized with any tool that supports OpenTelemetry, such as: The collected data can be exported to any OpenTelemetry-compatible backend for visualization and analysis. Examples include:
- Jaeger - Jaeger
- Grafana Tempo - Grafana Tempo
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[django-silk](https://pypi.org/project/django-silk/) is a profiling tool designed specifically for Django applications. It provides detailed insights into request performance, database queries, and custom code blocks or functions through context managers and decorators. [django-silk](https://pypi.org/project/django-silk/) is a profiling tool designed specifically for Django applications. It provides detailed insights into request performance, database queries, and custom code blocks or functions through context managers and decorators.
The package offers a web-based interface for analyzing requests, database queries, and profiling your code. It's particularly useful during development to identify performance bottlenecks before they reach production. The package offers a web-based interface for analyzing requests, database queries, and profiling code. It's primarily intended for local development to identify performance bottlenecks before they reach production.
### Sentry ### Sentry