In June 1970, Dr. E.F. Codd published the seminal paper, “A Relational Model of Data for Large Shared Data Banks,” for the ACM. This paper laid the foundation of relational databases, and Codd’s model was accepted as the definitive model for relational database management systems across research institutes around the world.
In 1974, at IBM’s San Jose Research Center, Donald Chamberlin and Raymond Boyce invented Structured English Query Language (SEQUEL) to implement Codd’s model in a System/R project that was aimed at developing the first SQL implementation. It also seeded IBM’s relational database technology.
Over the next three years, SEQUEL became SQL (still pronounced “sequel,” but some people spell it out as S-Q-L). IBM conducted the beta testing of System/R at customer test sites and demonstrated the usefulness and practicality of the system. As a result, IBM developed and sold commercial products that implemented SQL based on their System R prototype, including SQL/DS, introduced in 1981, and DB2 in 1983.
Meanwhile, in 1979, Relational Software introduced the first commercially available implementation of SQL, called Oracle Version 2. The company later changed its name to match its flagship product. Also, several other vendors such as Ingres (based on the Ingres project led by Michael Stonebraker and Eugene Wong at the Univ. of California, Berkeley during late 1970s) and Sybase introduced database products using SQL.
For the past three decades, SQL has been accepted as the standard RDBMS language. With the veracity of Moore’s law and the continuous research and upgrades on SQL implementations by some of the top vendors in RDBMS arena, SQL has come a long way.
There are three key reasons for the longevity of SQL:
Simplicity. SQL is simple. It is simple to learn and implement. It can be used for data definition, data manipulation and data control. It is declarative. You tell it what you want using simple language constructs. While this was a handicap, programming extensions such as PL/SQL and Pro*C bridged the gap and provided the ability to build programming logic too.
Mathematical foundation. SQL is built on concepts such as relational algebra and tuple calculus. This enabled its success in all databases that supported relational models to store, retrieve and manipulate data. Even today, a vast majority of data of businesses across the world resides on relational databases that support SQL.