observability-toolkit

Observability patterns for python applications.

No dependencies outside of standard python libraries

Table of Contents

• Installation
• Examples
• License

Installation

pip install observability-toolkit

License

observability-toolkit is distributed under the terms of the MIT license.

Future Scope

The following is an excerpt from a conversation with an LLM around what the features of this library should be:

Core Observability Components

1. Metrics collection and reporting
   • Ability to collect and expose application metrics
   • Integration with metrics systems like Prometheus
2. Logging capabilities
   • Standardized logging implementation
   • Support for different log levels (e.g. debug, info, warning, error)
   • Ability to configure log output destinations
3. Tracing and distributed tracing
   • Support for collecting and analyzing trace data
   • Integration with OpenTelemetry or other tracing standards

Data Collection and Instrumentation

1. Automatic instrumentation
   • Ability to automatically instrument common Python frameworks and libraries
2. Manual instrumentation APIs
   • Easy-to-use APIs for manually adding observability to specific parts of the application
3. Context propagation
   • Ability to propagate context across different components and services

Configuration and Customization

1. Flexible configuration options
   • Ability to customize various aspects of observability collection and reporting
2. Pluggable architecture
   • Support for custom plugins/extensions to integrate with different systems
3. Sampling and filtering
   • Options for sampling and filtering telemetry data to manage volume and performance impact

Developer Experience

1. Easy integration
   • Simple setup process for integrating the toolkit into existing applications
2. Documentation and examples
   • Clear guides on usage and best practices
   • Sample implementations for reference