More SQL Antipatterns: Avoid Common but Deadly Mistakes of Database Optimization by Bill Karwin

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Pages: 220 Published: March 2026 ISBN: 9798888652060 In Beta

More SQL Antipatterns

Avoid Common but Deadly Mistakes of Database Optimization

by Bill Karwin

Most of us taught ourselves SQL; we learn what works on the job. This book (and its companion) contain years of accumulated experience of what works and, more importantly, what doesn’t. This volume focusses on scaling and performance. See why the solutions you find online are often unhelpful (or just plain wrong). Learn how the database handles complex queries so you can better predict what works and what doesn’t. See when to denormalize for performance, and how to do it right. Learn when to use JSON columns. Along the way, discover a bunch of SQL tricks you never knew existed.

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This book is in Beta, final version expected Mar 2026

Beta Books: What do I get? It’s easy to get something working with SQL but getting it working well requires skill and experience. Rather than learning by trial and error, let renowned database expert Bill Karwin give you the benefit of his years of experience as he shows you the mistakes people make and how to fix them.

Dig into SQL language issues rarely covered in other books. Learn advanced queries such as relational division, efficient pagination, and pivoting. Explore tradeoffs in optimization, including denormalization, JSON, and choosing SQL versus NoSQL. Turn mistakes into successes by employing SQL in application design: transactions, polling, and object-relational mapping. Finally, take a deep dive into scaling out with sharded databases.

This book contains fourteen all-new topics, making it a valuable complement to SQL Antipatterns Volume 1. In each chapter, you’ll learn how to recognize one type of common pitfall, and how to solve it. Examples use the current popular combination of PostgreSQL and Python, but the principles apply to any modern database and language. Like the first volume, this book also includes a selection of mini-antipatterns, or brief tips you can use for quick wins.

Optimize SQL the right way and graduate from journeyman to senior database developer.

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Releases:

2026/05/19

B4.0

Into production

2026/01/14

B1.0

Contents & Extracts

Note: Contents and extracts of beta books will change as the book is developed.

Table of Contents

SQL Language Antipatterns
Fear of JOINs
Objective: Combining Matching Data in Two Tables
Antipattern: Fearing JOINs
How to Recognize the Antipattern
Legitimate Uses of the Antipattern
Solution: Preferring JOINs
Mini-Antipattern: Comma-Style Join Syntax
Mini-Antipattern: NATURAL JOIN
Relational Division
Objective: Matching a List of Values
Antipattern: Searching Multiple Rows in a WHERE Clause
How to Recognize the Antipattern
Legitimate Uses of the Antipattern
Solution: Using Relational Division
Mini-Antipattern: Aggregation Functions in the WHERE Clause
Dynamic Pivot
Objective: Turning the Table
Antipattern: Using a Single Query
How to Recognize the Antipattern
Legitimate Uses of the Antipattern
Solution: Using Code to Assist
Mini-Antipattern: Operand Must Contain One Column
Pagination: Take It from the Top
Objective: Presenting Query Results in Pages
Antipattern: Mistaking Offset for Optimization
How to Recognize the Antipattern
Legitimate Uses of the Antipattern
Solution: Changing the Rules
Mini-Antipattern: LIMIT without ORDER BY
Optimization Antipatterns
Non-Sargable Queries
Objective: Optimizing Complex Queries
Antipattern: Using Queries That Can’t Be Optimized
How to Recognize the Antipattern
Legitimate Uses of the Antipattern
Solution: Refactoring the Query
Mini-Antipattern: Redundant Indexes
Premature Denormalization
Objective: Breaking the Rules to Improve Performance
Antipattern: Breaking the Rules Recklessly
How to Recognize the Antipattern
Legitimate Uses of the Antipattern
Solution: Breaking the Rules Carefully
Mini-Antipattern: Query Snipers
NoSQL: The Grass Is Always Greener on the Other Side
Objective: Relief from the Challenges of SQL
Antipattern: Switching to NoSQL Is Easy
How to Recognize the Antipattern
Legitimate Uses of the Antipattern
Solution: Match Your Queries to Your NoSQL Models
Mini-Antipattern: N+1 Queries
JSON: Matryoshka Dolls
Objective: Unlimited Flexibility
Antipattern: Semi-Structured Data in a Relational Database
How to Recognize the Antipattern
Legitimate Uses of the Antipattern
Solution: Be Mindful and Moderate About Flexibility
Mini-Antipattern: Quotes for All Occasions
Cacheless Transactions
Objective: A Magic Trick to Improve Performance
Antipattern: The Performance Vanishing Act
How to Recognize the Antipattern
Legitimate Uses of the Antipattern
Solution: Use Sleight of Hand
Mini-Antipattern: Query Caching
Application Design Antipatterns
Polling: Are We There Yet?
Objective: Notify of Changes to Data
Antipattern: Polling for Changes
How to Recognize the Antipattern
Legitimate Uses of the Antipattern
Solution: Just Wake Me Up When We Get There
Mini-Antipattern: Enqueuing Before Committing
Transaction Encapsulation
Objective: Transactions for Multiple Models
Antipattern: Every Model Class Manages Its Transactions
How to Recognize the Antipattern
Legitimate Uses of the Antipattern
Solution: Simplify, Simplify
Mini-Antipattern: ORMs that Pluralize Table Names
Fear of Deadlocks
Objective: Don’t See Deadlock Errors
Antipattern: Trying to Prevent All Deadlocks
How to Recognize the Antipattern
Legitimate Uses of the Antipattern
Solution: Mitigate Deadlocks
Mini-Antipattern: Sharing Connections Between Threads
Data Hoarding
Objective: Cope with Data Bloat
Antipattern: The Belief That You Need All Data
How to Recognize the Antipattern
Legitimate Uses of the Antipattern
Solution: An Intervention to Manage Data Retention

Author

Bill Karwin has been a software engineer for more than 30 years, developing and supporting applications, libraries, and servers, and consulting for leading technology companies. Throughout his career, Bill has shared his knowledge to help other programmers achieve success and productivity. Bill has answered thousands of questions, giving him a unique perspective on SQL mistakes that most commonly cause problems.