When scaling a backend, small inefficiencies add up fast. One of the most impactful improvements in PostgreSQL is switching from row-by-row inserts to bulk inserts. This approach can reduce write time by over 70%, with clear benefits even at small batch sizes. In this post, I break down why it works — and back it up with real-world benchmarks.

Not all tech debt is worth paying down, and not every optimization is a win. This post shares a simple, 4-step framework for backend engineers to focus optimization efforts where they actually deliver value—fast, measurable, and real.

Efficient SQL is crucial when dealing with millions of rows. This benchmark compares correlated subqueries and LEFT JOINs—both return the same results, but the LEFT JOIN runs in 4.5 seconds versus 14.7 for the subquery. This post breaks down the setup, explains the performance gap, and helps you choose the right approach for faster SQL.

PostgreSQL updates can trigger costly index rewrites—even without changing indexed columns. This post explores how HOT (Heap-Only Tuple) updates and proper fillfactor settings can significantly boost performance, with benchmarks and practical tuning advice.

When storing dynamic data in PostgreSQL, JSONB and EAV are common options. This post benchmarks both at scale—10 million rows—and compares performance, complexity, and storage. While EAV offers indexing flexibility, JSONB proves simpler and faster for most use cases.