Relational Database Management System
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| Relational Database Management System | |
|---|---|
| Name | Relational Database Management System |
| Developer | IBM, Oracle Corporation, Microsoft Corporation, MySQL AB, PostgreSQL Global Development Group, SAP SE, Amazon Web Services, Google LLC, MariaDB Corporation Ab |
| Released | 1970s |
| Operating system | Unix, Windows NT, Linux, macOS |
| Genre | Database management system |
Relational Database Management System A relational database management system is a software system that stores, retrieves, and manages data structured in tables. It originated from research at IBM Research and has influenced products from Oracle Corporation, Microsoft Corporation, and Amazon Web Services. Implementations like PostgreSQL, MySQL, SQLite, and MariaDB support standards set by organizations such as ISO and ANSI.
History and evolution
The conceptual foundation was proposed by Edgar F. Codd at IBM Research leading to the Relational model paper that shaped systems by IBM, Ingres Corporation, and University of California, Berkeley. Commercial adoption accelerated with Oracle Corporation releasing early products and with academic projects at Stanford University, University of California, Berkeley, and MIT informing InnoDB and Berkeley DB. Standards efforts were driven by ANSI and ISO SQL committees, influencing dialects used by Microsoft SQL Server, IBM Db2, SAP HANA, and cloud offerings from Google Cloud Platform and Amazon Web Services. Open source initiatives like PostgreSQL Global Development Group and MySQL AB broadened deployment across Linux Foundation ecosystems and motivated extensions from Apache Software Foundation projects.
Architecture and components
A typical system includes a storage engine, query processor, transaction manager, and access control modules developed by vendors like Oracle Corporation, Microsoft Corporation, IBM, and MariaDB Corporation Ab. Storage layers may use filesystems from Linux, Solaris, or Windows NT Server; indexing structures such as B-trees and hash indexes trace to algorithms by researchers at Bell Labs and AT&T, while buffer management techniques derive from studies at University of California, Berkeley and Carnegie Mellon University. Networked deployments integrate with middleware from Red Hat and VMware, Inc. and with orchestration by Kubernetes and Docker, Inc. in data centers run by Amazon Web Services, Google LLC, and Microsoft Azure.
Data model and principles
The model is based on relations formalized by Edgar F. Codd and uses set theory and predicate logic studied at Princeton University and University of Cambridge. Schema design leverages normalization forms articulated in academic work at IBM Research and course material from Massachusetts Institute of Technology and Stanford University, with denormalization strategies used in systems from Facebook, Twitter, Inc., and Netflix, Inc. for performance. Integrity constraints such as primary keys and foreign keys reflect practices codified by ANSI and ISO committees and applied in products by Oracle Corporation, IBM, and PostgreSQL.
Query languages and APIs
SQL was standardized by ANSI and later by ISO and implemented with vendor-specific extensions by Microsoft Corporation (Transact-SQL), Oracle Corporation (PL/SQL), and IBM (SQL PL). APIs and drivers such as ODBC from Microsoft Corporation, JDBC from Sun Microsystems and later Oracle Corporation, and native connectors from MySQL AB and PostgreSQL Global Development Group enable integration with application platforms like Java, .NET Framework, Python Software Foundation ecosystems, and frameworks from Spring Framework and Django Software Foundation. Newer interfaces include RESTful gateways offered by Amazon Web Services and Google Cloud Platform for web-scale services used by companies like Airbnb and Uber Technologies, Inc..
Transactions, concurrency, and recovery
Transaction models follow ACID principles articulated in research at IBM Research and formalized in standards from ANSI; implementations use locking protocols from studies at University of California, Berkeley and timestamp ordering variants used in Oracle Corporation systems. Recovery techniques like write-ahead logging are rooted in work at Princeton University and employed by PostgreSQL, IBM Db2, and Microsoft SQL Server; distributed transactions use two-phase commit popularized in systems by IBM and adopted in middleware from Oracle Corporation and Red Hat. Concurrency control mechanisms support isolation levels defined by ANSI and are tuned in cloud services from Amazon Web Services and Google Cloud Platform.
Performance, scalability, and optimization
Performance engineering draws on research from Stanford University and Carnegie Mellon University covering indexing, query optimization, and cost-based planners used in Oracle Database, PostgreSQL, and MySQL. Scaling strategies include sharding used by Facebook and Twitter, Inc. and replication deployed by MariaDB Corporation Ab and Percona LLC; cloud-native scaling is implemented by Amazon Aurora on Amazon Web Services and by Google Cloud Spanner at Google LLC. Optimization techniques reference query planners from Ingres Corporation and heuristics tested at Bell Labs, while hardware acceleration uses NVMe from Intel Corporation and GPU offload prototypes from NVIDIA Corporation.
Uses, implementations, and standards
Relational systems underpin financial platforms at Goldman Sachs, healthcare records in systems certified by Health Level Seven International, and government registries in jurisdictions using standards from European Union bodies. Major implementations include Oracle Database, Microsoft SQL Server, IBM Db2, PostgreSQL, MySQL, SQLite, MariaDB, and cloud variants from Amazon Web Services and Google Cloud Platform. Standards work is ongoing in ISO and ANSI committees, and governance is influenced by consortia like Linux Foundation and policy groups in European Commission initiatives.