[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_28":-5,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56,"_57":58},"routes/_store+/_readers+/email_author.$slug/route",{"_9":10,"_11":-5},"book",{"_12":13,"_14":15,"_16":17,"_18":19,"_20":21,"_22":23},"currentUser","id","Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvODE0Mjc","title","PostgreSQL Problem Solver: Advanced SQL Recipes for Ranking, Retention, Funnels, and Vector Similarity","state","published","contactAuthorMethod","email","aboutText","

It's 4:45 on a Friday. Your boss wants the top 3 products in each category by Monday. You know ORDER BY ... LIMIT 3 won't do it — and you don't want to Google \"top N per group SQL\" for the hundredth time.

This book is the cure for that. It's a problem-first SQL cookbook for data analysts who know the basics — SELECT, WHERE, JOIN, GROUP BY — but keep hitting a wall on the medium-hard problems that real stakeholders ask for.

No theory dumps. No cover-to-cover slog. Every one of the 17 chapters follows the same format:

- The problem, stated the way your boss actually says it

- The SQL, copy-paste-ready and tested against PostgreSQL 17

- The exact output, so you know what \"right\" looks like

- The mistakes everyone makes — and how to dodge them

- The adaptations, so one recipe solves a whole family of questions

What you'll be able to do

- Rank anything: top / bottom N per group with DENSERANK and PARTITION BY

- Build running totals and moving averages with window frames

- Compare this period vs. last — % change and year-over-year with LAG / LEAD

- Find each customer's first and last purchase

- Split customers into quartiles and spending tiers with NTILE

- Track cohort retention in a matrix — without a BI tool

- Map event funnels and find exactly where users drop off

- Run RFM segmentation — Champions, Loyalists, At-Risk, Hibernating

- Calculate reorder alerts, days of supply, and dead stock

- Fill gaps in time-series data with generateseries

- Build a product recommendation engine with pgvector similarity search

- Make the slow query fast — indexing and EXPLAIN, demystified

Who it's for

Data analysts, analytics engineers, and anyone who writes SQL to answer business questions and is tired of stitching together half-answers from forum threads.

Who it's not for

DBAs (nothing on replication or vacuum), app developers (no ORM or Python glue), and complete beginners (you should already know what a JOIN is).

What's inside

17 problem-first chapters + 5 appendices: a SQL cheat sheet for analysts, the three sample dataset schemas, a troubleshooting guide for the errors that actually bite, and a 15-minute setup walkthrough. Every example runs on three small, free datasets you can load and follow along with.

Every query in this book was tested against PostgreSQL 17 with pgvector 0.7+.

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Kick off your book project, get started with GhostAI, get better at marketing, or spend the day doing all three! Free live workshops on Zoom. Saturday, June 27, 2026.

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