Every database in a dbms consists of two separate data stores.

Formally, a "database" refers to a set of related data and the way it is organized. Access to this data is usually provided by a "database management system" (DBMS) consisting of an integrated set of computer software that allows users to interact with one or more databases and provides access to all of the data contained in the database (although restrictions may exist that limit access to particular data). The DBMS provides various functions that allow entry, storage and retrieval of large quantities of information and provides ways to manage how that information is organized.

Because of the close relationship between them, the term "database" is often used casually to refer to both a database and the DBMS used to manipulate it.

Outside the world of professional information technology, the term database is often used to refer to any collection of related data (such as a spreadsheet or a card index) as size and usage requirements typically necessitate use of a database management system.[1]

Existing DBMSs provide various functions that allow management of a database and its data which can be classified into four main functional groups:

• Data definition – Creation, modification and removal of definitions that define the organization of the data.
• Update – Insertion, modification, and deletion of the actual data.[2]
• Retrieval – Providing information in a form directly usable or for further processing by other applications. The retrieved data may be made available in a form basically the same as it is stored in the database or in a new form obtained by altering or combining existing data from the database.[3]
• Administration – Registering and monitoring users, enforcing data security, monitoring performance, maintaining data integrity, dealing with concurrency control, and recovering information that has been corrupted by some event such as an unexpected system failure.[4]

Both a database and its DBMS conform to the principles of a particular database model.[5] "Database system" refers collectively to the database model, database management system, and database.[6]

Physically, database servers are dedicated computers that hold the actual databases and run only the DBMS and related software. Database servers are usually multiprocessor computers, with generous memory and RAID disk arrays used for stable storage. Hardware database accelerators, connected to one or more servers via a high-speed channel, are also used in large volume transaction processing environments. DBMSs are found at the heart of most database applications. DBMSs may be built around a custom multitasking kernel with built-in networking support, but modern DBMSs typically rely on a standard operating system to provide these functions.[citation needed]

Since DBMSs comprise a significant market, computer and storage vendors often take into account DBMS requirements in their own development plans.[7]

Databases and DBMSs can be categorized according to the database model(s) that they support (such as relational or XML), the type(s) of computer they run on (from a server cluster to a mobile phone), the query language(s) used to access the database (such as SQL or XQuery), and their internal engineering, which affects performance, scalability, resilience, and security.

The sizes, capabilities, and performance of databases and their respective DBMSs have grown in orders of magnitude. These performance increases were enabled by the technology progress in the areas of processors, computer memory, computer storage, and computer networks. The concept of a database was made possible by the emergence of direct access storage media such as magnetic disks, which became widely available in the mid-1960s; earlier systems relied on sequential storage of data on magnetic tape. The subsequent development of database technology can be divided into three eras based on data model or structure: navigational,[8] SQL/relational, and post-relational.

The two main early navigational data models were the hierarchical model and the CODASYL model (network model). These were characterized by the use of pointers (often physical disk addresses) to follow relationships from one record to another.

The relational model, first proposed in 1970 by Edgar F. Codd, departed from this tradition by insisting that applications should search for data by content, rather than by following links. The relational model employs sets of ledger-style tables, each used for a different type of entity. Only in the mid-1980s did computing hardware become powerful enough to allow the wide deployment of relational systems (DBMSs plus applications). By the early 1990s, however, relational systems dominated in all large-scale data processing applications, and as of 2018[update] they remain dominant: IBM Db2, Oracle, MySQL, and Microsoft SQL Server are the most searched DBMS.[9] The dominant database language, standardized SQL for the relational model, has influenced database languages for other data models.[citation needed]

Object databases were developed in the 1980s to overcome the inconvenience of object–relational impedance mismatch, which led to the coining of the term "post-relational" and also the development of hybrid object–relational databases.

The next generation of post-relational databases in the late 2000s became known as NoSQL databases, introducing fast key–value stores and document-oriented databases. A competing "next generation" known as NewSQL databases attempted new implementations that retained the relational/SQL model while aiming to match the high performance of NoSQL compared to commercially available relational DBMSs.

1960s, navigational DBMS

Further information: Navigational database

The introduction of the term database coincided with the availability of direct-access storage (disks and drums) from the mid-1960s onwards. The term represented a contrast with the tape-based systems of the past, allowing shared interactive use rather than daily batch processing. The Oxford English Dictionary cites a 1962 report by the System Development Corporation of California as the first to use the term "data-base" in a specific technical sense.[10]

As computers grew in speed and capability, a number of general-purpose database systems emerged; by the mid-1960s a number of such systems had come into commercial use. Interest in a standard began to grow, and Charles Bachman, author of one such product, the Integrated Data Store (IDS), founded the Database Task Group within CODASYL, the group responsible for the creation and standardization of COBOL. In 1971, the Database Task Group delivered their standard, which generally became known as the CODASYL approach, and soon a number of commercial products based on this approach entered the market.

The CODASYL approach offered applications the ability to navigate around a linked data set which was formed into a large network. Applications could find records by one of three methods:

1. Use of a primary key (known as a CALC key, typically implemented by hashing)
2. Navigating relationships (called sets) from one record to another
3. Scanning all the records in a sequential order

Later systems added B-trees to provide alternate access paths. Many CODASYL databases also added a declarative query language for end users (as distinct from the navigational API). However, CODASYL databases were complex and required significant training and effort to produce useful applications.

IBM also had its own DBMS in 1966, known as Information Management System (IMS). IMS was a development of software written for the Apollo program on the System/360. IMS was generally similar in concept to CODASYL, but used a strict hierarchy for its model of data navigation instead of CODASYL's network model. Both concepts later became known as navigational databases due to the way data was accessed: the term was popularized by Bachman's 1973 Turing Award presentation The Programmer as Navigator. IMS is classified by IBM as a hierarchical database. IDMS and Cincom Systems' TOTAL databases are classified as network databases. IMS remains in use as of 2014[update].[11]

1970s, relational DBMS

Edgar F. Codd worked at IBM in San Jose, California, in one of their offshoot offices that were primarily involved in the development of hard disk systems. He was unhappy with the navigational model of the CODASYL approach, notably the lack of a "search" facility. In 1970, he wrote a number of papers that outlined a new approach to database construction that eventually culminated in the groundbreaking A Relational Model of Data for Large Shared Data Banks.[12]

In this paper, he described a new system for storing and working with large databases. Instead of records being stored in some sort of linked list of free-form records as in CODASYL, Codd's idea was to organize the data as a number of "tables", each table being used for a different type of entity. Each table would contain a fixed number of columns containing the attributes of the entity. One or more columns of each table were designated as a primary key by which the rows of the table could be uniquely identified; cross-references between tables always used these primary keys, rather than disk addresses, and queries would join tables based on these key relationships, using a set of operations based on the mathematical system of relational calculus (from which the model takes its name). Splitting the data into a set of normalized tables (or relations) aimed to ensure that each "fact" was only stored once, thus simplifying update operations. Virtual tables called views could present the data in different ways for different users, but views could not be directly updated.

Codd used mathematical terms to define the model: relations, tuples, and domains rather than tables, rows, and columns. The terminology that is now familiar came from early implementations. Codd would later criticize the tendency for practical implementations to depart from the mathematical foundations on which the model was based.

The use of primary keys (user-oriented identifiers) to represent cross-table relationships, rather than disk addresses, had two primary motivations. From an engineering perspective, it enabled tables to be relocated and resized without expensive database reorganization. But Codd was more interested in the difference in semantics: the use of explicit identifiers made it easier to define update operations with clean mathematical definitions, and it also enabled query operations to be defined in terms of the established discipline of first-order predicate calculus; because these operations have clean mathematical properties, it becomes possible to rewrite queries in provably correct ways, which is the basis of query optimization. There is no loss of expressiveness compared with the hierarchic or network models, though the connections between tables are no longer so explicit.

In the hierarchic and network models, records were allowed to have a complex internal structure. For example, the salary history of an employee might be represented as a "repeating group" within the employee record. In the relational model, the process of normalization led to such internal structures being replaced by data held in multiple tables, connected only by logical keys.

For instance, a common use of a database system is to track information about users, their name, login information, various addresses and phone numbers. In the navigational approach, all of this data would be placed in a single variable-length record. In the relational approach, the data would be normalized into a user table, an address table and a phone number table (for instance). Records would be created in these optional tables only if the address or phone numbers were actually provided.

As well as identifying rows/records using logical identifiers rather than disk addresses, Codd changed the way in which applications assembled data from multiple records. Rather than requiring applications to gather data one record at a time by navigating the links, they would use a declarative query language that expressed what data was required, rather than the access path by which it should be found. Finding an efficient access path to the data became the responsibility of the database management system, rather than the application programmer. This process, called query optimization, depended on the fact that queries were expressed in terms of mathematical logic.

Codd's paper was picked up by two people at Berkeley, Eugene Wong and Michael Stonebraker. They started a project known as INGRES using funding that had already been allocated for a geographical database project and student programmers to produce code. Beginning in 1973, INGRES delivered its first test products which were generally ready for widespread use in 1979. INGRES was similar to System R in a number of ways, including the use of a "language" for data access, known as QUEL. Over time, INGRES moved to the emerging SQL standard.

IBM itself did one test implementation of the relational model, PRTV, and a production one, Business System 12, both now discontinued. Honeywell wrote MRDS for Multics, and now there are two new implementations: Alphora Dataphor and Rel. Most other DBMS implementations usually called relational are actually SQL DBMSs.

In 1970, the University of Michigan began development of the MICRO Information Management System[13] based on D.L. Childs' Set-Theoretic Data model.[14][15][16] MICRO was used to manage very large data sets by the US Department of Labor, the U.S. Environmental Protection Agency, and researchers from the University of Alberta, the University of Michigan, and Wayne State University. It ran on IBM mainframe computers using the Michigan Terminal System.[17] The system remained in production until 1998.

Integrated approach

Main article: Database machine

In the 1970s and 1980s, attempts were made to build database systems with integrated hardware and software. The underlying philosophy was that such integration would provide higher performance at a lower cost. Examples were IBM System/38, the early offering of Teradata, and the Britton Lee, Inc. database machine.

Another approach to hardware support for database management was ICL's CAFS accelerator, a hardware disk controller with programmable search capabilities. In the long term, these efforts were generally unsuccessful because specialized database machines could not keep pace with the rapid development and progress of general-purpose computers. Thus most database systems nowadays are software systems running on general-purpose hardware, using general-purpose computer data storage. However, this idea is still pursued in certain applications by some companies like Netezza and Oracle (Exadata).

Late 1970s, SQL DBMS

IBM started working on a prototype system loosely based on Codd's concepts as System R in the early 1970s. The first version was ready in 1974/5, and work then started on multi-table systems in which the data could be split so that all of the data for a record (some of which is optional) did not have to be stored in a single large "chunk". Subsequent multi-user versions were tested by customers in 1978 and 1979, by which time a standardized query language – SQL[citation needed] – had been added. Codd's ideas were establishing themselves as both workable and superior to CODASYL, pushing IBM to develop a true production version of System R, known as SQL/DS, and, later, Database 2 (IBM Db2).

Larry Ellison's Oracle Database (or more simply, Oracle) started from a different chain, based on IBM's papers on System R. Though Oracle V1 implementations were completed in 1978, it wasn't until Oracle Version 2 when Ellison beat IBM to market in 1979.[18]

Stonebraker went on to apply the lessons from INGRES to develop a new database, Postgres, which is now known as PostgreSQL. PostgreSQL is often used for global mission-critical applications (the .org and .info domain name registries use it as their primary data store, as do many large companies and financial institutions).

In Sweden, Codd's paper was also read and Mimer SQL was developed in the mid-1970s at Uppsala University. In 1984, this project was consolidated into an independent enterprise.

Another data model, the entity–relationship model, emerged in 1976 and gained popularity for database design as it emphasized a more familiar description than the earlier relational model. Later on, entity–relationship constructs were retrofitted as a data modeling construct for the relational model, and the difference between the two has become irrelevant.[citation needed]

1980s, on the desktop

The 1980s ushered in the age of desktop computing. The new computers empowered their users with spreadsheets like Lotus 1-2-3 and database software like dBASE. The dBASE product was lightweight and easy for any computer user to understand out of the box. C. Wayne Ratliff, the creator of dBASE, stated: "dBASE was different from programs like BASIC, C, FORTRAN, and COBOL in that a lot of the dirty work had already been done. The data manipulation is done by dBASE instead of by the user, so the user can concentrate on what he is doing, rather than having to mess with the dirty details of opening, reading, and closing files, and managing space allocation."[19] dBASE was one of the top selling software titles in the 1980s and early 1990s.

1990s, object-oriented

The 1990s, along with a rise in object-oriented programming, saw a growth in how data in various databases were handled. Programmers and designers began to treat the data in their databases as objects. That is to say that if a person's data were in a database, that person's attributes, such as their address, phone number, and age, were now considered to belong to that person instead of being extraneous data. This allows for relations between data to be related to objects and their attributes and not to individual fields.[20] The term "object–relational impedance mismatch" described the inconvenience of translating between programmed objects and database tables. Object databases and object–relational databases attempt to solve this problem by providing an object-oriented language (sometimes as extensions to SQL) that programmers can use as alternative to purely relational SQL. On the programming side, libraries known as object–relational mappings (ORMs) attempt to solve the same problem.

2000s, NoSQL and NewSQL

Main articles: NoSQL and NewSQL

XML databases are a type of structured document-oriented database that allows querying based on XML document attributes. XML databases are mostly used in applications where the data is conveniently viewed as a collection of documents, with a structure that can vary from the very flexible to the highly rigid: examples include scientific articles, patents, tax filings, and personnel records.

NoSQL databases are often very fast, do not require fixed table schemas, avoid join operations by storing denormalized data, and are designed to scale horizontally.

In recent years, there has been a strong demand for massively distributed databases with high partition tolerance, but according to the CAP theorem, it is impossible for a distributed system to simultaneously provide consistency, availability, and partition tolerance guarantees. A distributed system can satisfy any two of these guarantees at the same time, but not all three. For that reason, many NoSQL databases are using what is called eventual consistency to provide both availability and partition tolerance guarantees with a reduced level of data consistency.

NewSQL is a class of modern relational databases that aims to provide the same scalable performance of NoSQL systems for online transaction processing (read-write) workloads while still using SQL and maintaining the ACID guarantees of a traditional database system.

Databases are used to support internal operations of organizations and to underpin online interactions with customers and suppliers (see Enterprise software).

Databases are used to hold administrative information and more specialized data, such as engineering data or economic models. Examples include computerized library systems, flight reservation systems, computerized parts inventory systems, and many content management systems that store websites as collections of webpages in a database.

One way to classify databases involves the type of their contents, for example: bibliographic, document-text, statistical, or multimedia objects. Another way is by their application area, for example: accounting, music compositions, movies, banking, manufacturing, or insurance. A third way is by some technical aspect, such as the database structure or interface type. This section lists a few of the adjectives used to characterize different kinds of databases.

• An in-memory database is a database that primarily resides in main memory, but is typically backed-up by non-volatile computer data storage. Main memory databases are faster than disk databases, and so are often used where response time is critical, such as in telecommunications network equipment.
• An active database includes an event-driven architecture which can respond to conditions both inside and outside the database. Possible uses include security monitoring, alerting, statistics gathering and authorization. Many databases provide active database features in the form of database triggers.
• A cloud database relies on cloud technology. Both the database and most of its DBMS reside remotely, "in the cloud", while its applications are both developed by programmers and later maintained and used by end-users through a web browser and Open APIs.
• Data warehouses archive data from operational databases and often from external sources such as market research firms. The warehouse becomes the central source of data for use by managers and other end-users who may not have access to operational data. For example, sales data might be aggregated to weekly totals and converted from internal product codes to use UPCs so that they can be compared with ACNielsen data. Some basic and essential components of data warehousing include extracting, analyzing, and mining data, transforming, loading, and managing data so as to make them available for further use.
• A deductive database combines logic programming with a relational database.
• A distributed database is one in which both the data and the DBMS span multiple computers.
• A document-oriented database is designed for storing, retrieving, and managing document-oriented, or semi structured, information. Document-oriented databases are one of the main categories of NoSQL databases.
• An embedded database system is a DBMS which is tightly integrated with an application software that requires access to stored data in such a way that the DBMS is hidden from the application's end-users and requires little or no ongoing maintenance.[21]
• End-user databases consist of data developed by individual end-users. Examples of these are collections of documents, spreadsheets, presentations, multimedia, and other files. Several products exist to support such databases. Some of them are much simpler than full-fledged DBMSs, with more elementary DBMS functionality.
• A federated database system comprises several distinct databases, each with its own DBMS. It is handled as a single database by a federated database management system (FDBMS), which transparently integrates multiple autonomous DBMSs, possibly of different types (in which case it would also be a heterogeneous database system), and provides them with an integrated conceptual view.
• Sometimes the term multi-database is used as a synonym for federated database, though it may refer to a less integrated (e.g., without an FDBMS and a managed integrated schema) group of databases that cooperate in a single application. In this case, typically middleware is used for distribution, which typically includes an atomic commit protocol (ACP), e.g., the two-phase commit protocol, to allow distributed (global) transactions across the participating databases.
• A graph database is a kind of NoSQL database that uses graph structures with nodes, edges, and properties to represent and store information. General graph databases that can store any graph are distinct from specialized graph databases such as triplestores and network databases.
• An array DBMS is a kind of NoSQL DBMS that allows modeling, storage, and retrieval of (usually large) multi-dimensional arrays such as satellite images and climate simulation output.
• In a hypertext or hypermedia database, any word or a piece of text representing an object, e.g., another piece of text, an article, a picture, or a film, can be hyperlinked to that object. Hypertext databases are particularly useful for organizing large amounts of disparate information. For example, they are useful for organizing online encyclopedias, where users can conveniently jump around the text. The World Wide Web is thus a large distributed hypertext database.
• A knowledge base (abbreviated KB, kb or Δ[22][23]) is a special kind of database for knowledge management, providing the means for the computerized collection, organization, and retrieval of knowledge. Also a collection of data representing problems with their solutions and related experiences.

• A mobile database can be carried on or synchronized from a mobile computing device.
• Operational databases store detailed data about the operations of an organization. They typically process relatively high volumes of updates using transactions. Examples include customer databases that record contact, credit, and demographic information about a business's customers, personnel databases that hold information such as salary, benefits, skills data about employees, enterprise resource planning systems that record details about product components, parts inventory, and financial databases that keep track of the organization's money, accounting and financial dealings.
• A parallel database seeks to improve performance through parallelization for tasks such as loading data, building indexes and evaluating queries.

• Shared memory architecture, where multiple processors share the main memory space, as well as other data storage.
• Shared disk architecture, where each processing unit (typically consisting of multiple processors) has its own main memory, but all units share the other storage.
• Shared-nothing architecture, where each processing unit has its own main memory and other storage.

• Probabilistic databases employ fuzzy logic to draw inferences from imprecise data.
• Real-time databases process transactions fast enough for the result to come back and be acted on right away.
• A spatial database can store the data with multidimensional features. The queries on such data include location-based queries, like "Where is the closest hotel in my area?".
• A temporal database has built-in time aspects, for example a temporal data model and a temporal version of SQL. More specifically the temporal aspects usually include valid-time and transaction-time.
• A terminology-oriented database builds upon an object-oriented database, often customized for a specific field.
• An unstructured data database is intended to store in a manageable and protected way diverse objects that do not fit naturally and conveniently in common databases. It may include email messages, documents, journals, multimedia objects, etc. The name may be misleading since some objects can be highly structured. However, the entire possible object collection does not fit into a predefined structured framework. Most established DBMSs now support unstructured data in various ways, and new dedicated DBMSs are emerging.

Connolly and Begg define database management system (DBMS) as a "software system that enables users to define, create, maintain and control access to the database".[24] Examples of DBMS's include MySQL, PostgreSQL, Microsoft SQL Server, Oracle Database, and Microsoft Access.

The DBMS acronym is sometimes extended to indicate the underlying database model, with RDBMS for the relational, OODBMS for the object (oriented) and ORDBMS for the object–relational model. Other extensions can indicate some other characteristics, such as DDBMS for a distributed database management systems.

The functionality provided by a DBMS can vary enormously. The core functionality is the storage, retrieval and update of data. Codd proposed the following functions and services a fully-fledged general purpose DBMS should provide:[25]

• Data storage, retrieval and update
• User accessible catalog or data dictionary describing the metadata
• Support for transactions and concurrency
• Facilities for recovering the database should it become damaged
• Support for authorization of access and update of data
• Access support from remote locations
• Enforcing constraints to ensure data in the database abides by certain rules

It is also generally to be expected the DBMS will provide a set of utilities for such purposes as may be necessary to administer the database effectively, including import, export, monitoring, defragmentation and analysis utilities.[26] The core part of the DBMS interacting between the database and the application interface sometimes referred to as the database engine.

Often DBMSs will have configuration parameters that can be statically and dynamically tuned, for example the maximum amount of main memory on a server the database can use. The trend is to minimize the amount of manual configuration, and for cases such as embedded databases the need to target zero-administration is paramount.

The large major enterprise DBMSs have tended to increase in size and functionality and can have involved thousands of human years of development effort throughout their lifetime.[a]

Early multi-user DBMS typically only allowed for the application to reside on the same computer with access via terminals or terminal emulation software. The client–server architecture was a development where the application resided on a client desktop and the database on a server allowing the processing to be distributed. This evolved into a multitier architecture incorporating application servers and web servers with the end user interface via a web browser with the database only directly connected to the adjacent tier.[27]

A general-purpose DBMS will provide public application programming interfaces (API) and optionally a processor for database languages such as SQL to allow applications to be written to interact with and manipulate the database. A special purpose DBMS may use a private API and be specifically customized and linked to a single application. For example, an email system performs many of the functions of a general-purpose DBMS such as message insertion, message deletion, attachment handling, blocklist lookup, associating messages an email address and so forth however these functions are limited to what is required to handle email.

Main article: Database application

External interaction with the database will be via an application program that interfaces with the DBMS.[28] This can range from a database tool that allows users to execute SQL queries textually or graphically, to a website that happens to use a database to store and search information.

Application program interface

A programmer will code interactions to the database (sometimes referred to as a datasource) via an application program interface (API) or via a database language. The particular API or language chosen will need to be supported by DBMS, possibly indirectly via a preprocessor or a bridging API. Some API's aim to be database independent, ODBC being a commonly known example. Other common API's include JDBC and ADO.NET.

Database languages are special-purpose languages, which allow one or more of the following tasks, sometimes distinguished as sublanguages:

• Data control language (DCL) – controls access to data;
• Data definition language (DDL) – defines data types such as creating, altering, or dropping tables and the relationships among them;
• Data manipulation language (DML) – performs tasks such as inserting, updating, or deleting data occurrences;
• Data query language (DQL) – allows searching for information and computing derived information.

Database languages are specific to a particular data model. Notable examples include:

• SQL combines the roles of data definition, data manipulation, and query in a single language. It was one of the first commercial languages for the relational model, although it departs in some respects from the relational model as described by Codd (for example, the rows and columns of a table can be ordered). SQL became a standard of the American National Standards Institute (ANSI) in 1986, and of the International Organization for Standardization (ISO) in 1987. The standards have been regularly enhanced since and are supported (with varying degrees of conformance) by all mainstream commercial relational DBMSs.[29][30]
• OQL is an object model language standard (from the Object Data Management Group). It has influenced the design of some of the newer query languages like JDOQL and EJB QL.
• XQuery is a standard XML query language implemented by XML database systems such as MarkLogic and eXist, by relational databases with XML capability such as Oracle and Db2, and also by in-memory XML processors such as Saxon.
• SQL/XML combines XQuery with SQL.[31]

A database language may also incorporate features like:

• DBMS-specific configuration and storage engine management
• Computations to modify query results, like counting, summing, averaging, sorting, grouping, and cross-referencing
• Constraint enforcement (e.g. in an automotive database, only allowing one engine type per car)
• Application programming interface version of the query language, for programmer convenience

Main articles: Computer data storage and Database engine

Database storage is the container of the physical materialization of a database. It comprises the internal (physical) level in the database architecture. It also contains all the information needed (e.g., metadata, "data about the data", and internal data structures) to reconstruct the conceptual level and external level from the internal level when needed. Databases as digital objects contain three layers of information which must be stored: the data, the structure, and the semantics. Proper storage of all three layers is needed for future preservation and longevity of the database.[32] Putting data into permanent storage is generally the responsibility of the database engine a.k.a. "storage engine". Though typically accessed by a DBMS through the underlying operating system (and often using the operating systems' file systems as intermediates for storage layout), storage properties and configuration settings are extremely important for the efficient operation of the DBMS, and thus are closely maintained by database administrators. A DBMS, while in operation, always has its database residing in several types of storage (e.g., memory and external storage). The database data and the additional needed information, possibly in very large amounts, are coded into bits. Data typically reside in the storage in structures that look completely different from the way the data look at the conceptual and external levels, but in ways that attempt to optimize (the best possible) these levels' reconstruction when needed by users and programs, as well as for computing additional types of needed information from the data (e.g., when querying the database).

Some DBMSs support specifying which character encoding was used to store data, so multiple encodings can be used in the same database.

Various low-level database storage structures are used by the storage engine to serialize the data model so it can be written to the medium of choice. Techniques such as indexing may be used to improve performance. Conventional storage is row-oriented, but there are also column-oriented and correlation databases.

Materialized views

Main article: Materialized view

Often storage redundancy is employed to increase performance. A common example is storing materialized views, which consist of frequently needed external views or query results. Storing such views saves the expensive computing them each time they are needed. The downsides of materialized views are the overhead incurred when updating them to keep them synchronized with their original updated database data, and the cost of storage redundancy.

Replication

See also: Replication (computing) § Database replication

Occasionally a database employs storage redundancy by database objects replication (with one or more copies) to increase data availability (both to improve performance of simultaneous multiple end-user accesses to the same database object, and to provide resiliency in a case of partial failure of a distributed database). Updates of a replicated object need to be synchronized across the object copies. In many cases, the entire database is replicated.

Main article: Database security

Database security deals with all various aspects of protecting the database content, its owners, and its users. It ranges from protection from intentional unauthorized database uses to unintentional database accesses by unauthorized entities (e.g., a person or a computer program).

Database access control deals with controlling who (a person or a certain computer program) are allowed to access what information in the database. The information may comprise specific database objects (e.g., record types, specific records, data structures), certain computations over certain objects (e.g., query types, or specific queries), or using specific access paths to the former (e.g., using specific indexes or other data structures to access information). Database access controls are set by special authorized (by the database owner) personnel that uses dedicated protected security DBMS interfaces.

This may be managed directly on an individual basis, or by the assignment of individuals and privileges to groups, or (in the most elaborate models) through the assignment of individuals and groups to roles which are then granted entitlements. Data security prevents unauthorized users from viewing or updating the database. Using passwords, users are allowed access to the entire database or subsets of it called "subschemas". For example, an employee database can contain all the data about an individual employee, but one group of users may be authorized to view only payroll data, while others are allowed access to only work history and medical data. If the DBMS provides a way to interactively enter and update the database, as well as interrogate it, this capability allows for managing personal databases.

Data security in general deals with protecting specific chunks of data, both physically (i.e., from corruption, or destruction, or removal; e.g., see physical security), or the interpretation of them, or parts of them to meaningful information (e.g., by looking at the strings of bits that they comprise, concluding specific valid credit-card numbers; e.g., see data encryption).

Change and access logging records who accessed which attributes, what was changed, and when it was changed. Logging services allow for a forensic database audit later by keeping a record of access occurrences and changes. Sometimes application-level code is used to record changes rather than leaving this in the database. Monitoring can be set up to attempt to detect security breaches. Therefore, organizations must take database security seriously because of the many benefits it provides. Organizations will be safeguarded from security breaches and hacking activities like firewall intrusion, virus spread, and ransom ware. This helps in protecting the company’s essential information, which cannot be shared with outsiders at any cause.[33]

Further information: Concurrency control

Database transactions can be used to introduce some level of fault tolerance and data integrity after recovery from a crash. A database transaction is a unit of work, typically encapsulating a number of operations over a database (e.g., reading a database object, writing, acquiring or releasing a lock, etc.), an abstraction supported in database and also other systems. Each transaction has well defined boundaries in terms of which program/code executions are included in that transaction (determined by the transaction's programmer via special transaction commands).

The acronym ACID describes some ideal properties of a database transaction: atomicity, consistency, isolation, and durability.

See also: Data migration § Database migration

A database built with one DBMS is not portable to another DBMS (i.e., the other DBMS cannot run it). However, in some situations, it is desirable to migrate a database from one DBMS to another. The reasons are primarily economical (different DBMSs may have different total costs of ownership or TCOs), functional, and operational (different DBMSs may have different capabilities). The migration involves the database's transformation from one DBMS type to another. The transformation should maintain (if possible) the database related application (i.e., all related application programs) intact. Thus, the database's conceptual and external architectural levels should be maintained in the transformation. It may be desired that also some aspects of the architecture internal level are maintained. A complex or large database migration may be a complicated and costly (one-time) project by itself, which should be factored into the decision to migrate. This is in spite of the fact that tools may exist to help migration between specific DBMSs. Typically, a DBMS vendor provides tools to help import databases from other popular DBMSs.

Main article: Database tuning

After designing a database for an application, the next stage is building the database. Typically, an appropriate general-purpose DBMS can be selected to be used for this purpose. A DBMS provides the needed user interfaces to be used by database administrators to define the needed application's data structures within the DBMS's respective data model. Other user interfaces are used to select needed DBMS parameters (like security related, storage allocation parameters, etc.).

When the database is ready (all its data structures and other needed components are defined), it is typically populated with initial application's data (database initialization, which is typically a distinct project; in many cases using specialized DBMS interfaces that support bulk insertion) before making it operational. In some cases, the database becomes operational while empty of application data, and data are accumulated during its operation.

After the database is created, initialized and populated it needs to be maintained. Various database parameters may need changing and the database may need to be tuned (tuning) for better performance; application's data structures may be changed or added, new related application programs may be written to add to the application's functionality, etc.

Main article: Backup

Sometimes it is desired to bring a database back to a previous state (for many reasons, e.g., cases when the database is found corrupted due to a software error, or if it has been updated with erroneous data). To achieve this, a backup operation is done occasionally or continuously, where each desired database state (i.e., the values of its data and their embedding in database's data structures) is kept within dedicated backup files (many techniques exist to do this effectively). When it is decided by a database administrator to bring the database back to this state (e.g., by specifying this state by a desired point in time when the database was in this state), these files are used to restore that state.

Static analysis techniques for software verification can be applied also in the scenario of query languages. In particular, the *Abstract interpretation framework has been extended to the field of query languages for relational databases as a way to support sound approximation techniques.[34] The semantics of query languages can be tuned according to suitable abstractions of the concrete domain of data. The abstraction of relational database systems has many interesting applications, in particular, for security purposes, such as fine-grained access control, watermarking, etc.

Other DBMS features might include:

• Database logs – This helps in keeping a history of the executed functions.
• Graphics component for producing graphs and charts, especially in a data warehouse system.
• Query optimizer – Performs query optimization on every query to choose an efficient query plan (a partial order (tree) of operations) to be executed to compute the query result. May be specific to a particular storage engine.
• Tools or hooks for database design, application programming, application program maintenance, database performance analysis and monitoring, database configuration monitoring, DBMS hardware configuration (a DBMS and related database may span computers, networks, and storage units) and related database mapping (especially for a distributed DBMS), storage allocation and database layout monitoring, storage migration, etc.

Increasingly, there are calls for a single system that incorporates all of these core functionalities into the same build, test, and deployment framework for database management and source control. Borrowing from other developments in the software industry, some market such offerings as "DevOps for database".[35]

Main article: Database design

The first task of a database designer is to produce a conceptual data model that reflects the structure of the information to be held in the database. A common approach to this is to develop an entity–relationship model, often with the aid of drawing tools. Another popular approach is the Unified Modeling Language. A successful data model will accurately reflect the possible state of the external world being modeled: for example, if people can have more than one phone number, it will allow this information to be captured. Designing a good conceptual data model requires a good understanding of the application domain; it typically involves asking deep questions about the things of interest to an organization, like "can a customer also be a supplier?", or "if a product is sold with two different forms of packaging, are those the same product or different products?", or "if a plane flies from New York to Dubai via Frankfurt, is that one flight or two (or maybe even three)?". The answers to these questions establish definitions of the terminology used for entities (customers, products, flights, flight segments) and their relationships and attributes.

Producing the conceptual data model sometimes involves input from business processes, or the analysis of workflow in the organization. This can help to establish what information is needed in the database, and what can be left out. For example, it can help when deciding whether the database needs to hold historic data as well as current data.

Having produced a conceptual data model that users are happy with, the next stage is to translate this into a schema that implements the relevant data structures within the database. This process is often called logical database design, and the output is a logical data model expressed in the form of a schema. Whereas the conceptual data model is (in theory at least) independent of the choice of database technology, the logical data model will be expressed in terms of a particular database model supported by the chosen DBMS. (The terms data model and database model are often used interchangeably, but in this article we use data model for the design of a specific database, and database model for the modeling notation used to express that design).

The most popular database model for general-purpose databases is the relational model, or more precisely, the relational model as represented by the SQL language. The process of creating a logical database design using this model uses a methodical approach known as normalization. The goal of normalization is to ensure that each elementary "fact" is only recorded in one place, so that insertions, updates, and deletions automatically maintain consistency.

The final stage of database design is to make the decisions that affect performance, scalability, recovery, security, and the like, which depend on the particular DBMS. This is often called physical database design, and the output is the physical data model. A key goal during this stage is data independence, meaning that the decisions made for performance optimization purposes should be invisible to end-users and applications. There are two types of data independence: Physical data independence and logical data independence. Physical design is driven mainly by performance requirements, and requires a good knowledge of the expected workload and access patterns, and a deep understanding of the features offered by the chosen DBMS.

Another aspect of physical database design is security. It involves both defining access control to database objects as well as defining security levels and methods for the data itself.

Models

Main article: Database model

A database model is a type of data model that determines the logical structure of a database and fundamentally determines in which manner data can be stored, organized, and manipulated. The most popular example of a database model is the relational model (or the SQL approximation of relational), which uses a table-based format.

Common logical data models for databases include:

• Navigational databases
  • Hierarchical database model
  • Network model
  • Graph database
• Relational model
• Entity–relationship model
  • Enhanced entity–relationship model
• Object model
• Document model
• Entity–attribute–value model
• Star schema

An object–relational database combines the two related structures.

Physical data models include:

• Inverted index
• Flat file

Other models include:

• Multidimensional model
• Array model
• Multivalue model

Specialized models are optimized for particular types of data:

• XML database
• Semantic model
• Content store
• Event store
• Time series model

External, conceptual, and internal views

A database management system provides three views of the database data:

• The external level defines how each group of end-users sees the organization of data in the database. A single database can have any number of views at the external level.
• The conceptual level unifies the various external views into a compatible global view.[37] It provides the synthesis of all the external views. It is out of the scope of the various database end-users, and is rather of interest to database application developers and database administrators.
• The internal level (or physical level) is the internal organization of data inside a DBMS. It is concerned with cost, performance, scalability and other operational matters. It deals with storage layout of the data, using storage structures such as indexes to enhance performance. Occasionally it stores data of individual views (materialized views), computed from generic data, if performance justification exists for such redundancy. It balances all the external views' performance requirements, possibly conflicting, in an attempt to optimize overall performance across all activities.

While there is typically only one conceptual (or logical) and physical (or internal) view of the data, there can be any number of different external views. This allows users to see database information in a more business-related way rather than from a technical, processing viewpoint. For example, a financial department of a company needs the payment details of all employees as part of the company's expenses, but does not need details about employees that are in the interest of the human resources department. Thus different departments need different views of the company's database.

The three-level database architecture relates to the concept of data independence which was one of the major initial driving forces of the relational model. The idea is that changes made at a certain level do not affect the view at a higher level. For example, changes in the internal level do not affect application programs written using conceptual level interfaces, which reduces the impact of making physical changes to improve performance.

The conceptual view provides a level of indirection between internal and external. On one hand it provides a common view of the database, independent of different external view structures, and on the other hand it abstracts away details of how the data are stored or managed (internal level). In principle every level, and even every external view, can be presented by a different data model. In practice usually a given DBMS uses the same data model for both the external and the conceptual levels (e.g., relational model). The internal level, which is hidden inside the DBMS and depends on its implementation, requires a different level of detail and uses its own types of data structure types.

Separating the external, conceptual and internal levels was a major feature of the relational database model implementations that dominate 21st century databases.[37]

Database technology has been an active research topic since the 1960s, both in academia and in the research and development groups of companies (for example IBM Research). Research activity includes theory and development of prototypes. Notable research topics have included models, the atomic transaction concept, related concurrency control techniques, query languages and query optimization methods, RAID, and more.

The database research area has several dedicated academic journals (for example, ACM Transactions on Database Systems-TODS, Data and Knowledge Engineering-DKE) and annual conferences (e.g., ACM SIGMOD, ACM PODS, VLDB, IEEE ICDE).

For a topical guide, see Outline of databases.

• Comparison of database tools
• Comparison of object database management systems
• Comparison of object–relational database management systems
• Comparison of relational database management systems
• Data hierarchy
• Data bank
• Data store
• Database theory
• Database testing
• Database-centric architecture
• Flat-file database
• INP (database)
• Journal of Database Management
• Question-focused dataset

1. ^ This article quotes a development time of 5 years involving 750 people for DB2 release 9 alone.(Chong et al. 2007)

1. ^ Ullman & Widom 1997, p. 1.
2. ^ "Update – Definition of update by Merriam-Webster". merriam-webster.com.
3. ^ "Retrieval – Definition of retrieval by Merriam-Webster". merriam-webster.com.
4. ^ "Administration – Definition of administration by Merriam-Webster". merriam-webster.com.
5. ^ Tsitchizris & Lochovsky 1982.
6. ^ Beynon-Davies 2003.
7. ^ Nelson & Nelson 2001.
8. ^ Bachman 1973.
9. ^ "TOPDB Top Database index". pypl.github.io.
10. ^ "database, n". OED Online. Oxford University Press. June 2013. Retrieved July 12, 2013. (Subscription required.)
11. ^ IBM Corporation (October 2013). "IBM Information Management System (IMS) 13 Transaction and Database Servers delivers high performance and low total cost of ownership". Retrieved Feb 20, 2014.
12. ^ Codd 1970.
13. ^ Hershey & Easthope 1972.
14. ^ North 2010.
15. ^ Childs 1968a.
16. ^ Childs 1968b.
17. ^ M.A. Kahn; D.L. Rumelhart; B.L. Bronson (October 1977). MICRO Information Management System (Version 5.0) Reference Manual. Institute of Labor and Industrial Relations (ILIR), University of Michigan and Wayne State University.
18. ^ "Oracle 30th Anniversary Timeline" (PDF). Retrieved 23 August 2017.
19. ^ Interview with Wayne Ratliff. The FoxPro History. Retrieved on 2013-07-12.
20. ^ Development of an object-oriented DBMS; Portland, Oregon, United States; Pages: 472–482; 1986; ISBN 0-89791-204-7
21. ^ Graves, Steve. "COTS Databases For Embedded Systems" Archived 2007-11-14 at the Wayback Machine, Embedded Computing Design magazine, January 2007. Retrieved on August 13, 2008.
22. ^ Argumentation in Artificial Intelligence by Iyad Rahwan, Guillermo R. Simari
23. ^ "OWL DL Semantics". Retrieved 10 December 2010.
24. ^ Connolly & Begg 2014, p. 64.
25. ^ Connolly & Begg 2014, pp. 97–102.
26. ^ Connolly & Begg 2014, p. 102.
27. ^ Connolly & Begg 2014, pp. 106–113.
28. ^ Connolly & Begg 2014, p. 65.
29. ^ Chapple 2005.
30. ^ "Structured Query Language (SQL)". International Business Machines. October 27, 2006. Retrieved 2007-06-10.
31. ^ Wagner 2010.
32. ^ Ramalho, J.C.; Faria, L.; Helder, S.; Coutada, M. (31 December 2013). "Database Preservation Toolkit: A flexible tool to normalize and give access to databases". Biblioteca Nacional de Portugal (BNP). University of Minho.
33. ^ David Y. Chan; Victoria Chiu; Miklos A. Vasarhelyi (2018). Continuous auditing : theory and application (1st ed.). Bingley, UK. ISBN 978-1-78743-413-4. OCLC 1029759767.
34. ^ Halder & Cortesi 2011.
35. ^ Ben Linders (January 28, 2016). "How Database Administration Fits into DevOps". Retrieved April 15, 2017.
36. ^ itl.nist.gov (1993) Integration Definition for Information Modeling (IDEFIX) Archived 2013-12-03 at the Wayback Machine. 21 December 1993.
37. ^ a b Date 2003, pp. 31–32.

• Bachman, Charles W. (1973). "The Programmer as Navigator". Communications of the ACM. 16 (11): 653–658. doi:10.1145/355611.362534.
• Beynon-Davies, Paul (2003). Database Systems (3rd ed.). Palgrave Macmillan. ISBN 978-1403916013.
• Chapple, Mike (2005). "SQL Fundamentals". Databases. About.com. Archived from the original on 22 February 2009. Retrieved 28 January 2009.
• Childs, David L. (1968a). Description of a set-theoretic data structure (PDF) (Technical report). CONCOMP (Research in Conversational Use of Computers) Project. University of Michigan. Technical Report 3.
• Childs, David L. (1968b). Feasibility of a set-theoretic data structure: a general structure based on a reconstituted definition (PDF) (Technical report). CONCOMP (Research in Conversational Use of Computers) Project. University of Michigan. Technical Report 6.
• Chong, Raul F.; Wang, Xiaomei; Dang, Michael; Snow, Dwaine R. (2007). "Introduction to DB2". Understanding DB2: Learning Visually with Examples (2nd ed.). ISBN 978-0131580183. Retrieved 17 March 2013.
• Codd, Edgar F. (1970). "A Relational Model of Data for Large Shared Data Banks" (PDF). Communications of the ACM. 13 (6): 377–387. doi:10.1145/362384.362685. S2CID 207549016.
• Connolly, Thomas M.; Begg, Carolyn E. (2014). Database Systems – A Practical Approach to Design Implementation and Management (6th ed.). Pearson. ISBN 978-1292061184.
• Date, C. J. (2003). An Introduction to Database Systems (8th ed.). Pearson. ISBN 978-0321197849.
• Halder, Raju; Cortesi, Agostino (2011). "Abstract Interpretation of Database Query Languages" (PDF). Computer Languages, Systems & Structures. 38 (2): 123–157. doi:10.1016/j.cl.2011.10.004. ISSN 1477-8424.
• Hershey, William; Easthope, Carol (1972). A set theoretic data structure and retrieval language. Spring Joint Computer Conference, May 1972. ACM SIGIR Forum. Vol. 7, no. 4. pp. 45–55. doi:10.1145/1095495.1095500.
• Nelson, Anne Fulcher; Nelson, William Harris Morehead (2001). Building Electronic Commerce: With Web Database Constructions. Prentice Hall. ISBN 978-0201741308.
• North, Ken (10 March 2010). "Sets, Data Models and Data Independence". Dr. Dobb's. Archived from the original on 24 October 2010.
• Tsitchizris, Dionysios C.; Lochovsky, Fred H. (1982). Data Models. Prentice–Hall. ISBN 978-0131964280.
• Ullman, Jeffrey; Widom, Jennifer (1997). A First Course in Database Systems. Prentice–Hall. ISBN 978-0138613372.
• Wagner, Michael (2010), SQL/XML:2006 – Evaluierung der Standardkonformität ausgewählter Datenbanksysteme, Diplomica Verlag, ISBN 978-3836696098

• Ling Liu and Tamer M. Özsu (Eds.) (2009). "Encyclopedia of Database Systems, 4100 p. 60 illus. ISBN 978-0-387-49616-0.
• Gray, J. and Reuter, A. Transaction Processing: Concepts and Techniques, 1st edition, Morgan Kaufmann Publishers, 1992.
• Kroenke, David M. and David J. Auer. Database Concepts. 3rd ed. New York: Prentice, 2007.
• Raghu Ramakrishnan and Johannes Gehrke, Database Management Systems
• Abraham Silberschatz, Henry F. Korth, S. Sudarshan, Database System Concepts
• Lightstone, S.; Teorey, T.; Nadeau, T. (2007). Physical Database Design: the database professional's guide to exploiting indexes, views, storage, and more. Morgan Kaufmann Press. ISBN 978-0-12-369389-1.
• Teorey, T.; Lightstone, S. and Nadeau, T. Database Modeling & Design: Logical Design, 4th edition, Morgan Kaufmann Press, 2005. ISBN 0-12-685352-5

Database at Wikipedia's sister projects

•  Definitions from Wiktionary
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• DB File extension – information about files with the DB extension

The System/38 was introduced on October 24, 1978 and delivered in 1980. Developed under the code-name "Pacific", it was made commercially available in August 1979. The system offered a number of innovative features, and was designed by a number of engineers including Frank Soltis and Glenn Henry. The architecture shared many similarities with the design of the failed IBM Future Systems project, including the single-level store, the use of microcode to implement operating system functionality, and the Machine Interface abstraction.[5][6][7] It had been developed over eight years by IBM's laboratory in Rochester, Minnesota.[8] The president of IBM's General Systems Division (GSD) said at the time: "The System/38 is the largest program we've ever introduced in GSD and it is one of the top three or four largest programs ever introduced in IBM."[9]

The system was designed as a follow-on for the System/3,[9] but it is not compatible with those computers. The predecessors to the System/38 include the System/3 (1969), System/32 (1975), and System/34 (1977). In 1983 the System/36 was released as a low-end business computer for users who found the System/38 too expensive for their needs. The System/38 was succeeded by the IBM AS/400 midrange computer family in 1988, which originally used a processor architecture similar to the System/38, before adopting PowerPC-based processors in 1995.[5]

Overview of internals

Two hard disks

The IBM 5381 System Unit contains processor, main memory, disk storage, a diskette magazine drive, and a system console with keyboard and a display. 5381 was available in Model 100 and Model 200.

The IBM 5382 System Unit is physically identical to 5381, but with more powerful processors, more memory, and more disk storage. 5382 was available in Models 300, 400, 500, 600, and 700.

Users typically interacted with the system through IBM 5250 series terminals. In 1984, IBM added the ability to attach graphics-oriented terminals that previously required a mainframe.[10]

Processing unit

The system includes a central processing unit with 512K, 768K, 1024K, 1280K, or 1536K bytes of main storage. The processor is implemented across twenty-nine Schottky TTL LSI chips mounted on a 10x15" circuit board.[11] It includes a memory management unit supporting demand paging, used by the system software to implement a single-level store architecture.

The System/38 CPU features a 48-bit address space, which was selected as a compromise between 64-bit addressing, which certain IBM engineers wanted for the sake of future proofing, and 32-bit addressing, which other engineers wanted for cost saving purposes.[5]

System console keyboard/display

The System/38 console incorporates a keyboard and a display screen with 16 lines of 64 characters, inconsistent with the locally attached 5250 terminals, which are either 12x40 or 24x80, depending on model. The keyboard is available to the system operator to enter Control Language commands.

Diskette magazine drive

The diskette magazine drive is standard on all models.

The System/38 and its descendants use a machine interface architecture to isolate the application software and most of the operating system from hardware dependencies, including such details as address size and register size.

Compilers for System/38 and its successors generate code in a high-level instruction set known as the Machine Interface, or MI. MI is a virtual instruction set; it is not the instruction set of the underlying CPU. MI operates on objects instead of traditional memory addresses or registers.[12]

Unlike some other virtual-machine architectures in which the virtual instructions are interpreted at runtime (see P-code machine), MI instructions are never interpreted. They constitute an intermediate compile time step and are translated into the processor's instruction set as the final compilation step. The MI instructions are stored within the final program object, in addition to the executable machine instructions. If a program is moved from a processor with one native instruction set to a processor with another native instruction set, the MI instructions will be re-translated into the native instruction set of the new machine before the program is executed for the first time on the new machine.

The TIMI (Technology Independent Machine Interface) of OS/400 is a backwards compatible extension of the System/38 MI. As a result, it is possible for a program originally developed on a System/38 to run on current IBM i hardware without ever being recompiled.[5]

Microcode

IBM referred to all code below the Machine Interface layer of the System/38 architecture as microcode, and treated it as part of the hardware.[11] The term microcode was used to cover a wide array of low-level code, ranging from traditional microcode, up to functionality typically associated with the kernels of other operating systems, as well as the implementation of the integrated database.[13] There were two levels of microcode in the System/38:

• Horizontal Microcode (HMC), which implemented a register-memory/memory-memory[14] instruction set architecture known as the internal microprogramming (IMP) instruction set[14] or the Internal Microprogrammed Interface (IMPI) using the native microcode of the System/38's processor.[15] Certain low-level and performance sensitive functionality such as task (i.e. process) scheduling and message passing were implemented directly in microcode as part of the Horizontal Microcode. The Horizontal Microcode resided in control store; it corresponded to traditional microcode.
• Vertical Microcode (VMC), which implemented the Machine Interface in terms of the IMPI architecture; this was implemented by translating MI code into IMPI code and executing it. It also implemented the integrated database, and other components of the operating system which could not be implemented in terms of MI instructions.[16] This was implemented using a mixture of PL/MP and IMPI assembly.[5] The Vertical Microcode resided in main memory.

The use of the term microcode stemmed from a 1969 antitrust case against IBM which resulted in IBM unbundling software from its hardware products (i.e. requiring software to be purchased separately from the hardware). By treating the low level code of the System/38 as part of the hardware, IBM was able to treat the MI as the native instruction set of the System/38, and thus have the freedom to change the IMPI and microcode as the underlying hardware evolved.[5] Early AS/400 systems inherited the System/38 microcode structure, but the term microcode was dropped, leading IBM to rename the layers to the Vertical Licensed Internal Code and Horizontal Licensed Internal Code.

Control Program Facility

Main article: Control Program Facility

The operating system of the System/38 is the Control Program Facility (CPF). CPF is not related to the System Support Program operating system of the IBM System/34 and System/36. CPF objects are files, programs, message queues, user profiles, and libraries. While CPF is considered to be the operating system of the System/38, it sits on top of the System/38 Machine Interface layer, and consequently much of the traditional operating system functionality of the platform is implemented in the Horizontal and Vertical Microcode.[5]

The System/38 also has the distinction of being the first commercially available IBM Midrange computer to have a database management system (DBMS) integrated into the operating system.

The operational control language of the System/38 is called CL, for Control Language. CL programs, similar in concept to shell scripts, can be compiled and executed natively.

The System/38 was designed with security built in as part of its architecture. Each object or library can have access controlled on a user-by-user basis. This has been continued and expanded throughout the AS/400 and iSeries computer lines.

Main article: IBM AS/400

The System/38 was superseded by the AS/400, which also provided compatibility with System/36 data and software.[17] S/38 programs with 'observability' intact, that is source code embedded within the compiled binary executive at the expense of larger compiled object sizes, can still run on the AS/400 and successor systems as the restore option incorporates a recompile for the then back-version source. However, most proprietary vendor application libraries of objects were compiled without such 'observability' and require original vendor replacement and consequent expense when upgrading to an AS/400. Pricing at the time was tiered, the same exact software, but priced based upon the model, its speed and capacity, of the system to be installed upon.

IBM sold an estimated 20,000 System/38s within the first five years of availability, according to articles published in industry magazines NEWS 34/38 and Midrange Computing. Although billed as a minicomputer, the S/38 was much more expensive than IBM's established best-selling System/34, and its replacement, the System/36. Of equal importance was the difficulty of upgrading from a System/34 to a S/38. IBM tacitly acknowledged this by bringing out the System/36 – an upgraded System/34 – after the launch of the S/38. Although the System/38 did not sell in large numbers, it commanded a higher profit margin than IBM's other midrange systems, and thus was a profitable product line for IBM.[6]

In the marketplace, IBM thus found itself with three overlapping, but incompatible, ranges. The System/34, the System/38 and the mainframe System/370 architecture. Digital Equipment Corporation, at that time one of IBM's main competitors, was able to exploit this by offering a wide range of products based on a single architecture - specifically the VAX architecture.[18] IBM's counter to this, the 9370, was a commercial failure, and at that time, The New York Times wrote, sales of the System/36 and System/38 were "lagging."[19]

1. ^ IBM System/38 Installation Manual - Physical Planning (PDF) (Seventh ed.). IBM. March 1983. GA21-9293-16.
2. ^ David E. Sanger (June 17, 1986). "I.B.M. Strengthens Office Line". The New York Times.
3. ^ Andrew Pollack (October 21, 1982). "I.B.M. Cuts Computer Prices". The New York Times.
4. ^ "IBM Archives: 1970s, year 1978". IBM. 23 January 2003.
5. ^ a b c d e f g h Frank G. Soltis (1997). Inside the AS/400, Second Edition. Duke Press. ISBN 978-1882419661.
6. ^ a b c Schleicher, David L. (2006-01-24). "An Interview with DAVID L. SCHLEICHER" (PDF). conservancy.umn.edu (Interview). Interviewed by Arthur L. Norberg. Charles Babbage Institute. Retrieved 2021-03-05.
7. ^ Henry, Glenn (2001-08-07). "An Interview with An Interview with GLENN HENRY" (PDF). conservancy.umn.edu (Interview). Interviewed by Philip L. Frana. Charles Babbage Institute. Retrieved 2021-03-21.
8. ^ "IBM Rochester @ 50" (PDF).
9. ^ a b "IBM System/38". IBM Archives. 23 January 2003.
10. ^ "Changes at I.B.M." The New York Times. June 20, 1984.
11. ^ a b IBM System/38 Technical Developments (PDF). IBM Product Design and Development, General Systems Division. 1980 [1978]. ISBN 0-933186-03-7. G580-0237-1.
12. ^ Levy, Henry M. (1984). "The IBM System/38" (PDF). Capability-Based Computer Systems. Digital Press. ISBN 0-932376-22-3.
13. ^ Soltis, Frank (September 1981). "Design of a Small Business Data Processing System". IEEE Computer. 14: 77–93. doi:10.1109/C-M.1981.220610. S2CID 398484.
14. ^ a b "IBM System/38 Internal Microprogramming Instructions, Formats, and Functions Reference Manual" (PDF) (fourth ed.). IBM. August 1985. SC21-9037-3.
15. ^ David McKenzie. "Notes for storage research". Archived from the original on October 8, 1999.
16. ^ "IBM System/38 Vertical Microcode Logic Overviews and Component Descriptions Manual" (PDF) (sixth ed.). IBM. September 1985. SY21-0889-5.
17. ^ John Enck; Michael Ryan (1998). Navigating the AS/400: A Hands-On Guide. ISBN 978-0-1386-2558-0.
18. ^ Roy A. Bauer; Emilio Collar; Victor Tang (1992). The Silverlake Project: Transformation at IBM. Oxford University Press. ISBN 9780195067545. Retrieved 2021-03-06.
19. ^ "A Broad Decentralization at I.B.M." The New York Times. January 29, 1988.

• Levy, Henry M. (1984). "The IBM System/38" (PDF). Capability-Based Computer Systems. Digital Press. ISBN 0-932376-22-3.
• Soltis, Frank G.; Hoffman, RL (Spring 1979). Design Considerations for the IBM System/38. Compcon. IEEE. pp. 132–37.
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  • Henry, GG (1980) [1978]. "Introduction to IBM System/38 Architecture". IBM System/38 Technical Developments (PDF). IBM Product Design and Development, General Systems Division. pp. 3–6. ISBN 0-933186-03-7. G580-0237-1.
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