Observability

Observability is the ability to understand the internal state of a complex system by examining its outputs, primarily through traces, logs, and metrics. While monitoring tells you that something is wrong, observability empowers you to understand why.

Why is it Matters?

In modern distributed systems, complexity often hides the root cause of issues. Observability provides the visibility needed to:

• Accelerate Debugging: Pinpoint failures across service boundaries instantly.

• Optimize Performance: Identify latency bottlenecks in complex request flows.

• Build Confidence: Understand system behavior in real-time, even during peak loads.

GL Observability

The GL Observability provides a streamlined way to add observability to a Python code with minimal configuration and easy to use. This SDK uses OpenTelemetry to ensures the telemetry data is standardized and vendor-neutral. Currently, the SDK supports traces and logs, with more observability components planned for future releases.

Benefit of using this SDK:

• Simplified Setup: Initialize distributed tracing and logging with just a few lines of code.

• Backend Flexibility: Export telemetry data to OTLP-compatible backends (like Jaeger or Tempo) or Sentry.

• Security First: Built-in handlers for PII redaction to ensure compliance with GDPR, SOC2, and UU PDP.

Installation

Python 3.11-3.13 is required

GL Observability package is available on PyPI and can be installed via:

pip install gl-observability-binary

Debugging Guides

The GL Observability SDK handles the collection, and export the telemetry data, while the actual analysis and debugging take place in external dashboards. For guidance on how to use these tools check out the Debugging Guide tutorial.

Next Step?

Check out the tutorial on how to use GL Observability to process observability data.

1. Traces tutorial

2. Logs tutorial