In May 1974, Donald Chamberlin and Raymond Boyce published a paper on SEQUEL, a structured query language that could be used to manage and sort data. After a change in title due to another company’s copyright on the word SEQUEL, Structured Query Language (SQL) was taken up by database companies like Oracle alongside their new-fangled relational database products later in the 1970s. The rest, as they say, is history.
SQL is now 50 years old. SQL was designed and then adopted around databases, and it has continued to grow and develop as a way to manage and interact with data. According to Stack Overflow, it is the third most popular language used by professional programmers on a regular basis. In 2023, the IEEE noted that SQL was the most popular language for developers to know when it came to getting a job, due to how it could be combined with other programming languages.
[ Also on InfoWorld: Why SQL still rules ]
When you look at other older languages being used today, the likes of COBOL (launched in 1959) and FORTRAN (first compiled in 1958) are still going, too. While they can lead to well-paying roles, they are linked to existing legacy deployments rather than new and exciting projects. SQL, on the other hand, is still being used as part of work around AI, analytics, and software development. It continues to be the standard for how we interact with data on a daily basis.
Why is SQL still so important?
When you look at SQL, you may ask why it has survived—even thrived—for so long. It is certainly not easy to learn, as it has a peculiar syntax that is very much of its time. The user experience around SQL can be challenging for new developers to pick up. Alongside this, every database vendor has to support SQL, but each also will have their own quirks or nuances in how they implement this support. Consequently, your approach for one database may not translate to another database easily, leading to both more work and more support requirements.
To make matters worse, it is easy to make mistakes in SQL that can have real and potentially catastrophic consequences. For example, missing a WHERE clause in your instructions can cause you to delete an entire table rather than carrying out the transaction you want, leading to lost data and recovery work. Checking your logic and knowing how things work in practice is a necessary requirement.
So why is SQL still the leading way to work with data today, 50 years after it was first designed and released? SQL is based on strong mathematical theory, so it continues to perform effectively and support the use cases it was designed for. The truth is that when you combine SQL with relational databases, you can map the data that you create—and how you manage that data—to many business practices in a way that is reliable, effective, and scalable. Put simply, SQL works, and no replacement option has measured up in the same way.
As an example, SQL was the first programming language to return multiple rows per single request. This makes it easier to get data on what is taking place within a set of data—and consequently, within the business and its applications—and then turn it into something the business can use. Similarly, SQL made it easier to compartmentalize and segregate information into different tables, and then use the data in those tables for specific business tasks, such as putting customer data in one table and manufacturing data in another. The ability to perform transactions is the backbone of most processes today, and SQL made that possible at scale.
Another important reason for the success of SQL is that the language has always moved with the times. From its relational roots, SQL has added support for geographic information system (GIS) data, for JSON documents, and for XML and YAML over the years. This has kept SQL up to speed with how developers want to interact with data. Now, SQL can be combined with vector data, enabling developers to interact with data using SQL but carrying out vector searches for generative AI applications.
What is the future for SQL?
There have been attempts to replace SQL in the past. NoSQL (Not only SQL) databases were developed to replace relational databases and get away from the traditional models of working with and managing data at scale. However, rather than replacing SQL, these databases added their own SQL-like languages that replicated some of the methods and approaches that SQL has ingrained into how developers work.
In the past, natural language processing advocates have called for new methods that do away with SQL’s standardized and clunky approach. However, these attempts have ended up with methods that were just as clunky as what they tried to replace, which led to them being sidelined or ignored. Generative AI may take on more of the task of writing SQL for developers, as large language models have been exposed to large quantities of SQL code as part of their training. However, while this approach may develop and become more popular in time, it still relies on SQL for the actual interaction with those sets of data and to deliver the results back to the user. If anything, this will likely make SQL more important for the future, not less, even though it will be less visible to the developer.
Even if SQL ends up moving behind the curtain, it will continue to play a critical role in how we interact with and use data. With such a huge percentage of all our IT systems relying on data to function, SQL will not be going away any time soon. So, let’s celebrate SQL turning 50, and consider how we can continue to develop and use it in the future.
Charly Batista is PostgreSQL technical lead at Percona.
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