What is SQL? In­tro­duc­tion to Struc­tured Query Language

SQL can be used to create re­la­tion­al databases and perform an array of op­er­a­tions on existing databases, including data queries. The language is part of the standard reper­toire of web de­vel­op­ers, data analysts and re­searchers. But compared to other pro­gram­ming languages, SQL is special. We’ll explain the features of the language.

What exactly is SQL?

SQL is the “Struc­tured Query Language”. It can be used to make queries to databases that contain struc­tured or re­la­tion­al data. The language is based on “re­la­tion­al algebra”. This is a math­e­mat­i­cal theory for struc­tur­ing data and cal­cu­lat­ing the results of queries. Many of the special features of SQL as a pro­gram­ming language are derived from this math­e­mat­i­cal basis. Developed in the mid-1970s, SQL is now con­sid­ered the standard pro­gram­ming language for database ap­pli­ca­tions.

An important detail about SQL is that it is a pure query or pro­gram­ming language, not a com­pre­hen­sive database man­age­ment system (DBMS). Some popular DBMSs that implement SQL are MySQL, Oracle SQL and SQLite. However, these DBMSs mostly use dialects of SQL, which may have ad­di­tion­al and/or different commands.

SQL as a domain-specific and de­clar­a­tive language

Compared to most es­tab­lished pro­gram­ming languages, SQL is special because it is a domain-specific language (DSL). In contrast to the General Purpose Languages (GPLs), which are suitable for use in many different ap­pli­ca­tions, SQL can only be used for one thing, databases.

SQL is also a de­clar­a­tive pro­gram­ming language. This means that the pro­gram­mer enters a desired result as a command and the system ensures that this result is achieved. This is in contrast to im­per­a­tive pro­gram­ming, in which the in­di­vid­ual steps to achieve the goals are ex­plic­it­ly defined in the code.

What is SQL used for?

SQL serves as an interface for in­ter­act­ing with re­la­tion­al database man­age­ment systems (RDBMS). A re­la­tion­al database can be thought of as a table in which each row entry has a pre­de­ter­mined set of at­trib­ut­es populated with values. The SQL code can either be entered by humans via a text-based interface or in­te­grat­ed into API accesses.

Ad­van­tages and dis­ad­van­tages of SQL

Ad­van­tages of SQL

The biggest advantage of SQL lies in the high profile and wide­spread use of the tech­nol­o­gy. Since its origin in the 1970s, SQL has been the industry standard for database ap­pli­ca­tions. This makes it rel­a­tive­ly easy to find ex­pe­ri­enced SQL pro­gram­mers, as well as in­ter­faces with other common tech­nolo­gies and languages.

Moreover, SQL has become the industry standard for a reason. The language is based on a robust math­e­mat­i­cal foun­da­tion that allows for optimal data storage. However, re­la­tion­al databases require a solid un­der­stand­ing of the tech­nol­o­gy and theory, as well as skill and planning in modeling. But a well-designed database schema makes it possible to gain new insights from the data through ap­pro­pri­ate queries.

Dis­ad­van­tages of SQL

One dis­ad­van­tage of SQL and re­la­tion­al databases in general is the high com­plex­i­ty of the tech­nol­o­gy. SQL comprises hundreds of commands and clauses, which represent a major challenge for newcomers. Many of these are im­ple­men­ta­tion-specific, making things even more chal­leng­ing again.

In addition, the structure of a re­la­tion­al database requires a number of as­sump­tions about the data to be stored. These serve to ensure the quality of the stored data, but also entail a number of lim­i­ta­tions that can cause permanent problems if the schema is poorly designed. Changes to the schema during operation can pose a serious challenge. In addition to this lack of flex­i­bil­i­ty, it is usually very chal­leng­ing to dis­trib­ute an SQL database ge­o­graph­i­cal­ly. Op­ti­miz­ing the per­for­mance of a database through de­cen­tral­iza­tion is, therefore, anything but simple.

A final dis­ad­van­tage of SQL is its in­com­pat­i­bil­i­ty with the widely used object-oriented pro­gram­ming, which is becoming in­creas­ing­ly relevant. In object-oriented pro­gram­ming, data and “behavior” (methods) are en­cap­su­lat­ed in objects. Data and methods are inherited through class hi­er­ar­chies. The re­la­tion­al approach is fun­da­men­tal­ly different, as data can be dis­trib­uted across several tables. In addition, it is im­pos­si­ble to model the behavior of an object. For this reason, objects cannot be trans­ferred 1:1 to re­la­tion­al database struc­tures.

Al­ter­na­tives to SQL

As SQL was invented at the beginning of the digital rev­o­lu­tion, the language has not lost its relevance. However, some al­ter­na­tive schemas have since emerged that may be more suitable for some ap­pli­ca­tions.

Object-re­la­tion­al database man­age­ment systems

Object-re­la­tion­al database man­age­ment systems (ORDBMS) such as Post­greSQL use SQL as a query language, but also support core concepts of object-oriented pro­gram­ming. Object hi­er­ar­chies, in­her­i­tance and object behavior can be used without the use of object-re­la­tion­al mapping (ORM). User-defined and composite data types in par­tic­u­lar reduce the com­plex­i­ty of schemas and queries.

NoSQL

SQL-based DBMSs are primarily intended for storing struc­tured data, but not all data follows a fixed schema. This is where NoSQL databases come into play. The term NoSQL refers to a family of non-re­la­tion­al DBMS. Instead of modeling data as fields in a table, various other ap­proach­es are used.

A popular approach is the document-based storage of data. This works by storing data in in­di­vid­ual documents rather than storing it in a table. One advantage of the document-based approach is that the data can be self-writing. This means that the schema of the data is de­ter­mined by the in­di­vid­ual document, not by the database, meaning that data entries can follow different schemas.

NoSQL solutions are usually less complex and offer ad­van­tages in terms of scaling and per­for­mance op­ti­miza­tion. Fur­ther­more, it is usually easier to change the schema during operation or to store data flexibly. On the other hand, there may be fewer guar­an­tees with regard to the quality of the data.