Chapter 2: A Brief History of SQL
SQL was born from the idea that data could be managed with a mathematical model rather than through custom file formats or procedural code. In the early 1970s, researchers at IBM applied the relational model (first proposed by Dr. Edgar F. Codd) to practical database systems. They created a language called SEQUEL (Structured English Query Language) to express operations on relational data. It soon evolved into SQL after a trademark issue forced a name change, but the core concept stayed the same: describe what you want, not how to get it.
SQL’s success was abstraction. It hides physical storage and indexing details so that users can think in terms of relations, rows, and columns rather than file structures.
Over the following decades, SQL became the foundation of nearly every relational database system. It went through several standardization phases and vendor interpretations, but its declarative nature and strong theoretical base made it remarkably durable. Even with the rise of NoSQL and newer paradigms, SQL remains the most widely used database language in the world.
From SEQUEL to SQL-92/99/2003/2016/2023
The evolution of SQL can be traced through its major standard releases, each adding features to reflect the changing needs of applications and hardware over time.
Early development (1970s–1980s)
IBM’s first prototypes, System R and SQL/DS, demonstrated that the relational model could outperform file-based systems in flexibility and data integrity. In 1979, Oracle released the first commercial implementation of SQL, followed by IBM’s DB2 and other systems throughout the 1980s. During this period, the core commands (SELECT, INSERT, UPDATE, DELETE, and CREATE) were firmly established.
SQL-86 and SQL-89
The first official standard, SQL-86, was published by ANSI and ISO to provide a common foundation. SQL-89 refined syntax and clarified behaviors but added little new functionality. These versions unified the core vocabulary and helped different vendors align basic behavior.
SQL-92 (SQL2)
SQL-92 marked a huge expansion. It added set operations, CASE expressions, more join types, schema definitions, and stricter data typing rules. Compliance levels (Entry, Intermediate, and Full) were introduced so vendors could claim partial compatibility while adding proprietary features.
SQL:1999 (SQL3)
This version introduced procedural and object-oriented extensions. Features such as recursive queries (WITH RECURSIVE), triggers, user-defined types, and large objects (BLOB, CLOB) made SQL capable of expressing more complex business logic directly in the database. It also introduced BOOLEAN types and regular expression-like pattern matching.
SQL:2003 to SQL:2016
SQL:2003 added XML integration, MERGE statements, and the window function framework. Later standards (SQL:2008, SQL:2011, SQL:2016) refined analytic functions, introduced temporal tables for tracking changes over time, and standardized JSON operations.
SQL:2023 and beyond
The most recent updates include further JSON handling, improvements to polymorphic table functions, and clarifications for modern distributed and cloud databases. Despite decades of growth, the core philosophy remains intact: maintain data integrity, express queries declaratively, and ensure forward compatibility.
ANSI and ISO standards
SQL is governed by two standard bodies. The American National Standards Institute (ANSI) maintains the U.S. standard, and the International Organization for Standardization (ISO) manages the global equivalent. In practice, the two collaborate, and the published standard documents are nearly identical. Each revision is identified by the release year, for example, SQL:2016 or SQL:2023.
Structure of the standard
The standard is divided into numbered parts, each covering an area such as core language, data definition, triggers, or external bindings. The complete text runs to thousands of pages and defines precise behavior for every keyword and clause.
Levels of conformance
Vendors can claim conformance to different levels of the standard. Core conformance covers basic SELECT, INSERT, UPDATE, and DELETE. Higher levels add support for advanced types, recursive queries, and temporal data. This layered structure encourages adoption without forcing every feature at once.
Vendor dialects and cross-compatibility
Although all major relational databases implement SQL, each adds its own syntax extensions, data types, and functions. These variations (often called dialects) arose from competition and innovation. For example, LIMIT in MySQL and PostgreSQL corresponds to TOP in SQL Server, while Oracle uses FETCH FIRST n ROWS ONLY.
Common dialect families
| Vendor | Common Extension Example |
| MySQL | AUTO_INCREMENT columns |
| PostgreSQL | SERIAL and RETURNING clauses |
| SQL Server | TOP n in SELECT |
| Oracle | DUAL table and ROWNUM filtering |
| SQLite | Dynamic typing with relaxed constraints |
Portable practices
To keep your code portable, rely on standard features unless a vendor-specific function is essential. Always consult documentation for type names, string concatenation operators, and date functions, which often differ between engines.
Why dialects persist
Vendors compete by offering extra capabilities such as procedural extensions, optimized data types, integration with cloud services, and special indexes. These differences drive innovation, and many features later migrate into future SQL standards. The result is a rich but sometimes inconsistent ecosystem.
Understanding the shared foundations and differences among dialects helps you write clear, portable queries while still taking advantage of each system’s strengths.