Advanced Observability: Why Logs and Metrics are Not Enough

In the monolithic era, checking logs was enough to debug an application. In the cloud-native era, where a single user request can touch dozens of microservices, you need more. This is where Advanced Observability comes in.

The Limits of Logs and Metrics

• Logs: Great for knowing what happened, but poor for understanding the context across multiple services.
• Metrics: Great for knowing that something is wrong (high CPU, error spike), but poor for finding the root cause.

The Next Level of Observability

1. Distributed Tracing: Track a single request as it flows through your entire architecture. Essential for identifying bottlenecks and high-latency hops.
2. Continuous Profiling: Understand exactly which lines of code or functions are consuming resources in production, with minimal overhead.
3. eBPF-based Observability: Get deep insights into networking and kernel-level events without modifying your application code (e.g., using tools like Cilium or Pixie).
4. Contextual Correlation: The ability to jump from a log line to a trace, and from a trace to a profile, within the same interface.

Why it's a Platform concern

Implementing distributed tracing or eBPF shouldn't be a burden for every application team. The platform can provide these capabilities out-of-the-box through service meshes, sidecars, or kernel-level instrumentation.

How to adopt advanced observability pragmatically

You don’t need “all the signals” on day one. A good sequence:

1. Standardize context: consistent service names, environment tags, and trace IDs in logs.
2. Trace the critical paths: instrument the top user journeys first (login, checkout, search).
3. Add profiling for hot services: focus on CPU-bound or latency-sensitive services.
4. Use eBPF for blind spots: network drops, DNS issues, kernel-level latency, sidecar overhead.

Common pitfalls

• Too much cardinality: uncontrolled labels/tags explode costs and storage.
• Sampling without strategy: low sampling hides rare but severe incidents; use tail-based sampling for errors.
• No correlation: if logs, traces, and profiles can’t be joined, engineers lose time.

What to measure

• % of services emitting traces with consistent metadata
• time to root cause for top incident classes
• tracing/profiling cost per service (so the platform can optimize defaults)

Conclusion

Advanced observability is about reducing the "Mean Time to Understanding". By providing deep, correlated insights into your systems, you empower your engineers to fix complex issues faster and build more performant applications.