Sampling Rate Optimizer

Head-based vs. tail-based sampling: Head-based sampling decides whether to collect a trace at the very start, before any spans exist. It is simple and lightweight but cannot distinguish between interesting and routine traces. Tail-based sampling waits until the trace is complete, then decides based on the outcome -- keeping all errors and slow requests while sampling routine successes. Tail-based provides better signal but requires a collector component to buffer traces before deciding.

Why tiered sampling works: Not all services need the same level of trace visibility. Payment and authentication services handle critical transactions where every failure matters -- these deserve high sampling rates. Standard API and web services can tolerate moderate sampling because individual request failures are less impactful. Background jobs like cron tasks and batch processing generate high volume but low-value traces that can be aggressively sampled without losing meaningful observability.

Common sampling mistakes: The most frequent mistake is applying a uniform sampling rate across all services. This either over-spends on low-value traces or under-samples critical ones. Another common issue is setting sampling too aggressively during initial rollout -- start with higher rates and reduce gradually while monitoring that your debugging workflow is not impacted. Always ensure error traces are captured at 100% regardless of your sampling configuration.