Modern software systems produce a relentless flood of logs, metrics, and traces that can be as expensive as it is overwhelming. The key is not to collect everything — it's to collect the right things through intelligent sampling strategies.
What You'll Learn
- Signal vs. Noise: Identifying high-value data (errors, latency spikes) vs. low-value noise (routine health checks)
- The Danger of Simple Sampling: Why a random 10% sample can leave you blind to critical system failures
- The Prospector's Toolkit:
- Stratified Sampling — ensuring each service and error class is represented
- The Inversion Mindset — sampling more where failures are rare but costly
- Probability Tracking — maintaining statistical accuracy with weighted samples
- Handling Data Floods — graceful degradation when volume spikes
- Continuous Calibration — adapting sampling rates as your system evolves
