GROUP BY, HAVING, and Aggregations — The Complete Mental Model
Build the mental model of how SQL groups, aggregates, and filters — then GROUPING SETS, ROLLUP, FILTER, and DISTINCT aggregates all fall into place.
Anupam Sinha
CTEs vs Subqueries vs Temp Tables — When to Use Which (With Benchmarks)
Three ways to break up a complex query — subqueries, CTEs, and temp tables — with PostgreSQL benchmarks so you pick the right tool instead of guessing.
SQL Anti-Patterns — 10 Mistakes That Will Get You Rejected in Code Review
The 10 SQL habits senior reviewers flag on sight — from SELECT * to NOT IN with NULLs — and the correct patterns to replace them.
Read Replicas, Connection Pooling, and Scaling SQL to Millions of Users
How streaming replication, replica lag, read/write splitting, and PgBouncer connection pooling scale SQL from thousands to millions of users.
PostgreSQL vs MySQL in 2026 — An Honest Comparison
After a decade shipping both to production — where PostgreSQL wins, where MySQL wins, where the gap closed, and my default recommendation for a new backend in 2026.
JSON in SQL — Using PostgreSQL JSONB Like a Pro
Store, query, index, and mutate semi-structured data inside PostgreSQL — operators, GIN indexes, JSONPath, mutation, and when not to use JSONB.
I Optimized a Query from 45 Seconds to 200ms — Here's How
A real optimization story: an analytics query that timed out in production, the step-by-step diagnosis with every EXPLAIN plan, and the five changes that took it from 45 seconds to 200 milliseconds.
The N+1 Query Problem — And 4 Ways to Kill It
The most common performance bug in ORM-backed applications, explained from the SQL up — what N+1 is, how to detect it, and four concrete fixes with JPA/Hibernate examples.
Database Sharding Strategies — Range, Hash, and Directory-Based
A practical deep dive into range, hash, and directory-based sharding — routing, rebalancing, cross-shard queries, and global IDs.
How I Design Database Schemas That Scale — Normalization, Denormalization, and When to Break the Rules
Normalize until it hurts, denormalize until it works — an opinionated, code-heavy field guide to schema design that stays an asset instead of a liability.