Structured Query Language

Structured Query Language
Name	Structured Query Language
Alt	SQL
Developed by	Donald D. Chamberlin, Raymond F. Boyce
Initial release	1974
Paradigm	Declarative, domain-specific
Typing	Strong (implementation-dependent)
Influenced by	Relational Model (database), SEQUEL
Influenced	PL/SQL, T-SQL, MySQL
License	Varies by implementation

Structured Query Language Structured Query Language is a standardized, declarative programming language designed for managing and querying data held in relational database management systems. Originating from early work on the relational model at IBM, the language became central to commercial and academic database systems, influencing enterprise deployments at organizations such as Oracle Corporation, Microsoft, IBM, and SAP. It drives data operations in sectors serviced by Amazon Web Services, Google Cloud Platform, Oracle Cloud, and infrastructure projects like Apache Hadoop where SQL-like interfaces are layered atop distributed storage.

History

Early concepts leading to Structured Query Language trace to the relational model developed by Edgar F. Codd at IBM Research and the prototype language SEQUEL at IBM San Jose Research Laboratory. Key implementers included Donald D. Chamberlin and Raymond F. Boyce who formalized a data sublanguage that evolved into commercial products at IBM and competitors such as Ingres from University of California, Berkeley. Standardization efforts appeared through organizations like American National Standards Institute and International Organization for Standardization producing ISO/IEC specifications that guided vendors including Oracle Corporation, Microsoft Corporation, Sybase, and Ingres Corporation. Legal and market events, for example the United States v. Microsoft Corp. litigation, and the growth of open-source projects such as MySQL and PostgreSQL, shaped dialect proliferation and interoperability concerns addressed by consortia like ANSI and the SQL Standards Committee.

Language Overview

The language uses declarative statements to express desired results rather than explicit procedural steps, aligning it with the relational algebra and relational calculus formalizations. Core statements include data definition and data manipulation constructs that map to relational concepts introduced by Edgar F. Codd and implemented in systems produced by vendors like IBM's Db2, Oracle Database and Microsoft SQL Server. Standardized syntax members (SELECT, INSERT, UPDATE, DELETE) enable querying across tables and views; these constructs are implemented by projects such as PostgreSQL, MySQL, SQLite, and commercial stacks used by SAP SE and Teradata. SQL integrates with programming environments including Java (programming language), Python (programming language), C#, and frameworks like Hibernate (framework) and Entity Framework.

Core Components

- Data Definition Language (DDL): Statements such as CREATE and ALTER allow schema operations referenced in implementations from Oracle Corporation and IBM Db2. - Data Manipulation Language (DML): INSERT, UPDATE, DELETE, and SELECT form the operational backbone used in systems including Microsoft SQL Server and MySQL AB. - Transaction Control: Commands like COMMIT and ROLLBACK support atomicity and durability principles central to ACID properties discussed in literature by Jim Gray. - Data Control Language (DCL): GRANT and REVOKE enable permissioning found in enterprise products by Oracle and cloud IAM services such as AWS Identity and Access Management. - Query Optimization and Execution: Optimizers in engines like PostgreSQL's planner, Oracle's optimizer, and SQL Server's Query Processor translate declarative statements into execution plans, leveraging algorithms studied by researchers at institutions like MIT and Stanford University.

Standards and Dialects

Multiple ISO/ANSI standards have specified schemas and language elements; vendors implemented extensions resulting in dialects like Transact-SQL from Sybase and Microsoft Corporation and PL/SQL from Oracle Corporation. Open-source projects such as PostgreSQL Global Development Group and MariaDB Corporation implement substantial subsets while adding proprietary features; embedded systems like SQLite provide lightweight compliance suited to applications from Mozilla and Android (operating system). Industry standards evolved through committees including ISO/IEC JTC 1/SC 32 and working groups influenced by corporate and academic stakeholders such as IBM Research, Oracle Labs, and Microsoft Research.

Implementation and Use

Implementations vary from enterprise-class systems—Oracle Database, IBM Db2, Microsoft SQL Server—to open-source servers—PostgreSQL, MySQL, MariaDB—and embedded engines like SQLite. Cloud providers integrate SQL into managed offerings such as Amazon Aurora, Google BigQuery, and Azure SQL Database. Use cases span transactional systems in corporations like Walmart and Bank of America, analytical warehouses in projects by Snowflake Inc. and Teradata Corporation, and OLAP cubes in SAP BW. Interoperability is facilitated by connectors and standards including ODBC, JDBC, and drivers developed by communities around PHP, Ruby on Rails, and Node.js.

Extensions and Advanced Features

Vendors and projects introduced procedural extensions—PL/SQL for Oracle Corporation, T-SQL for Microsoft Corporation—enabling control flow, exception handling, and stored procedures used in enterprise applications at Cisco Systems and Siemens. Analytical and temporal features appear in products like Greenplum, Vertica, and ClickHouse supporting window functions, common table expressions, and time-series extensions used in organizations such as Netflix and LinkedIn. Integration with big data ecosystems is exemplified by SQL-on-Hadoop engines like Apache Hive, Presto, and Apache Impala, while analytical platforms such as Snowflake and BigQuery add SQL-compatible dialects for scalable warehousing. Security and governance features intersect with regulatory frameworks enforced at institutions like European Union agencies and compliance programs at HIPAA-bound healthcare providers.

Category:Database query languages