B-Trees: Fast Reads, Painful Writes — The Hidden Trade-Off

You add an index—a slow query becomes instant. But then your insert throughput plummets and write latency spikes. That’s the hidden cost of the B‑tree. This video opens up the data structure that powers nearly every relational database to reveal why it’s brilliant for reads and can silently destroy write performance. You’ll understand how B‑trees organize data on disk, why point lookups and range scans stay fast at scale, and why a single random insert can cascade into a storm of page splits. You’ll see a real‑world production incident and walk away with actionable strategies to regain write throughput. What you’ll learn: • How B‑tree nodes use sorted keys and high fan‑out for O(log N) search with minimal disk I/O • Why inserting into a full leaf triggers page splits that can climb to the root • How random key order (like UUIDv4) causes write amplification of 3×–5× • The hidden cost of updating indexed columns: delete + insert inside the index • How to use fill factor to trade space for stable write performance • When to consider BRIN, LSM‑trees, or simply dropping unused indexes • Real monitoring signals from index usage stats and disk‑write metrics Parts of this video (script, visuals, narration) were produced with AI assistance.