Collecting and Interpreting Performance Data — Forge College

Want to turn Solana performance metrics into reliable, comparable measurements you can trust? This lesson walks you through finding representative observations, extracting the right fields, and building a compact comparison table that supports clear interpretation of TPS, block time, and validator behavior. What you'll learn: You will identify at least three practical sources of Solana performance data — public explorers and dashboards, RPC and node telemetry endpoints, and Prometheus-style exporters — and explain what each source contributes to coverage. You'll extract timestamped raw fields that map to TPS, block time, and validator efficiency, then normalize them using per-second scaling, rolling-window averages, and unit alignment so different sources are directly comparable. The lesson shows how to assemble rows of time-stamped snapshots into a comparison table, add concise interpretation notes for each row, and apply basic validation checks to detect sampling errors, outliers, and inconsistent units. By the end you'll have a repeatable workflow to collect, validate, and interpret performance observations. Who this is for: Beginner learners who already understand the definitions of TPS, validator efficiency, and block time and want hands-on practice collecting and organizing real measurements. Key topics covered: - Sources of performance data: explorers, RPC/telemetry endpoints, Prometheus exporters - Extracting timestamped fields for TPS, block time, and validator metrics - Normalization strategies: per-second scaling, rolling-window averages, unit alignment - Building a comparison table with normalized columns and interpretation notes - Basic validation checks: outlier detection, sampling error identification, unit consistency Ready to practice these techniques and build reliable measurement artifacts? Learn more and continue your studies at https://www.forge.college/