You may know SQL basics, but are you taking advantage of its expressive power? This second edition applies a highly practical approach to Structured Query Language (SQL) so you can create and manipulate large stores of data.
Based on real-world examples, this updated cookbook provides a framework to help you construct solutions and executable examples in several flavors of SQL, including Oracle, DB2, SQL Server, MySQL, and PostgreSQL.
SQL programmers, analysts, data scientists, database administrators, and even relatively casual SQL users will find SQL Cookbook to be a valuable problem-solving guide for everyday issues.
No other resource offers recipes in this unique format to help you tackle nagging day-to-day conundrums with SQL.
The second edition includes:
- Fully revised recipes that recognize the greater adoption of window functions in SQL implementations
- Additional recipes that reflect the widespread adoption of common table expressions (CTEs) for more readable, easier-to-implement solutions
- New recipes to make SQL more useful for people who aren't database experts, including data scientists
- Expanded solutions for working with numbers and strings
- Up-to-date SQL recipes throughout the book to guide you through the basics
AI Reading Assistant
Whole-book reading guide from stratified index samples; jump to passages in the text
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AI guide
【One-Line Pitch】
A problem-first SQL reference that turns "I know the syntax but not the solution" into working, cross-platform queries. Best for analysts, data scientists, DBAs, and developers who already know basic SQL and want to solve real data problems faster.
【Book Arc】
- **Opening (~0%–10%)**: Frames the cookbook approach and the multi-vendor problem—every recipe must work across Oracle, DB2, SQL Server, MySQL, and PostgreSQL, so you learn to think in portable SQL rather than one dialect.
- **Early (~10%–30%)**: Covers retrieval fundamentals: sorting results (including sorting by substrings and controlling NULL placement), working with multiple tables via joins and set operations, and the NULL pitfalls of NOT IN versus EXCEPT/MINUS.
- **Early–Middle (~30%–45%)**: Moves into metadata queries, inserting/updating/deleting through views, and heavy string manipulation—concatenation, separating numeric from character data, extracting initials, alphabetizing strings, and parsing values like IP addresses.
- **Middle (~45%–60%)**: Expands into numeric and date work: date arithmetic, business-day calculations, generating and filling date ranges, and identifying overlapping ranges.
- **Late (~60%–85%)**: Advances to range handling and advanced searching—locating consecutive values, finding differences between rows in a partition, filling missing values, generating sequences, and paginating result sets.
- **Ending (~85%–100%)**: Consolidates with window functions and CTEs as the modern backbone of readable solutions, plus expanded recipes aimed at non-DBAs such as data scientists.
【Key Takeaways】
- **Portability is the organizing principle** (Opening): each recipe shows vendor-specific variants side by side, so you learn both the general technique and the dialect quirks that break it.
- **NULL handling is a recurring trap** (Early): NOT IN silently returns no rows when the subquery contains NULL, while EXCEPT/MINUS avoids the problem—an early lesson that shapes how you write later queries.
- **String manipulation is where SQL gets surprisingly powerful** (Middle): TRANSLATE, REPLACE, SUBSTR, and recursive CTEs let you split, reorder, and parse text that many users assume requires application code.
- **Dates and ranges need explicit scaffolding** (Middle–Late): business-day counts, missing-date filling, and consecutive-value detection require generating rows or auxiliary columns rather than relying on built-in functions alone.
- **Window functions are now the default tool** (Late): the second edition revises recipes around their wider adoption, replacing older self-join and subquery patterns with cleaner OVER-based solutions.
- **CTEs make complex queries readable** (Late): common table expressions are treated as a first-class technique for structuring multi-step logic instead of nesting subqueries.
- **Advanced searching is about controlling result sets** (Late): pagination, skipping rows, and row-difference comparisons are framed as practical reporting needs, not academic exercises.
- **The book is a reference, not a tutorial** (Throughout): recipes are indexed by problem, so the intended use is lookup-and-adapt rather than linear reading.
【Reading Tips】
- **Skim the vendor comparison blocks on first pass**: read the problem statement and the primary solution, then return to dialect-specific variants only when you hit a compatibility issue.
- **Deep-read the NULL and set-operation discussions early**: these concepts recur throughout the book and misunderstanding them causes silent wrong answers.
- **Treat string and date chapters as technique libraries**: the specific recipes matter less than the patterns—TRANSLATE/REPLACE tricks and row-generation scaffolding transfer to many problems.
- **Use the window function and CTE recipes as your modernization checklist**: if your existing queries use older self-join patterns, these sections show the cleaner rewrite.
- **Keep your own DBMS documentation open**: the book explicitly warns that view insertion and similar features have vendor rules too complex to fully cover.
【Coverage Limits】
The excerpts cover the table of contents, preface, and selected recipes through string, date, range, and advanced-searching chapters; later chapters on window functions and CTEs are referenced but their detailed recipes are not shown, so this guide describes their role rather than their specific content.
Page 11
f Business Days Between Two Dates 210 8.4 Determining the Number of Months or Years Between Two Dates 215 8.5 Determining the Number of Seconds, Minutes, or...
ist in the lower query (the query after the EXCEPT). Oracle The Oracle solution is identical to the solution using the EXCEPT operator; however, Oracle calls...
CT—you can delete the wrong data even in a simple situation. For example, in the previous case, a typo could lead to the employees in department 20 being del...
152 | Chapter 6: Working with Strings SQL Server Use the recursive WITH clause to simulate an iteration through the IP address while using SUBSTR to easily p...
. In other contexts, if there wasn’t a clear explanation of why the value differed so much, it could lead us to question whether that value was correct or wh...
e use of the RECURSIVE keyword to identify a recursive CTE. The final step is to use the TO_CHAR function to keep only the Fridays. MySQL To find all the Fri...
lap: 1 select a.empno,a.ename, 2 concat('project ',b.proj_id, 3 ' overlaps project ',a.proj_id) as msg 4 from emp_project a, 5 emp_project b 6 where a.empno...
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