Introduction to GL Observability
GL 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 Library
Our observability library provides a streamlined way to add observability to a Python code with minimal configuration and easy to use. This library uses OpenTelemetry to ensures the telemetry data is standardized and vendor-neutral. Currently, the library supports traces and logs, with more observability components planned for future releases.
Benefit of using this library:
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.
Next Steps
Check out the Prerequisites page if you want to learn how to use GL Observability library or Debugging guide if you want to learn how to use observability tools for debugging.
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