Discuss How the Class Scheduler Can Be Limited to Access a Specific Database or Table

Organized drove of data

This commodity is most the computing concept. For instances of the general concept, see Lists of databases.

An SQL select statement and its result

In computing, a database is an organized collection of data stored and accessed electronically. Small databases can be stored on a file organization, while large databases are hosted on calculator clusters or deject storage. The design of databases spans formal techniques and applied considerations including data modeling, efficient data representation and storage, query languages, security and privacy of sensitive data, and distributed calculating issues including supporting concurrent access and fault tolerance.

A database management organization (DBMS) is the software that interacts with terminate users, applications, and the database itself to capture and clarify the data. The DBMS software additionally encompasses the cadre facilities provided to administer the database. The sum total of the database, the DBMS and the associated applications tin can be referred to equally a database system. Often the term "database" is as well used loosely to refer to any of the DBMS, the database system or an application associated with the database.

Computer scientists may classify database management systems according to the database models that they support. Relational databases became dominant in the 1980s. These model information every bit rows and columns in a serial of tables, and the vast majority utilize SQL for writing and querying data. In the 2000s, non-relational databases became popular, collectively referred to as NoSQL considering they use different query languages.

Terminology and overview

Formally, a "database" refers to a set of related information and the way it is organized. Access to this data is commonly provided by a "database direction system" (DBMS) consisting of an integrated set of computer software that allows users to interact with ane or more databases and provides access to all of the information independent in the database (although restrictions may be that limit admission 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 shut human relationship between them, the term "database" is often used casually to refer to both a database and the DBMS used to dispense it.

Outside the earth of professional person information technology, the term database is often used to refer to whatever collection of related data (such as a spreadsheet or a menu index) equally size and usage requirements typically necessitate employ of a database management system.^[1]

Existing DBMSs provide diverse functions that allow direction of a database and its data which can be classified into iv main functional groups:

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

Both a database and its DBMS conform to the principles of a particular database model.^[5] "Database organization" refers collectively to the database model, database management arrangement, 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 deejay arrays used for stable storage. Hardware database accelerators, continued to one or more than servers via a high-speed channel, are as well used in large volume transaction processing environments. DBMSs are found at the heart of well-nigh database applications. DBMSs may be built effectually a custom multitasking kernel with built-in networking support, only mod DBMSs typically rely on a standard operating system to provide these functions.^{[ citation needed ]}

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

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

History

The sizes, capabilities, and operation 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, reckoner storage, and calculator networks. The concept of a database was made possible past the emergence of straight access storage media such as magnetic disks, which became widely available in the mid 1960s; before systems relied on sequential storage of data on magnetic record. The subsequent development of database technology can be divided into 3 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 employ of pointers (often physical disk addresses) to follow relationships from one record to some other.

The relational model, commencement proposed in 1970 past 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-fashion tables, each used for a unlike type of entity. Just in the mid-1980s did calculating hardware become powerful enough to allow the wide deployment of relational systems (DBMSs plus applications). By the early 1990s, withal, relational systems dominated in all large-scale information processing applications, and as of 2018^[update] they remain dominant: IBM DB2, Oracle, MySQL, and Microsoft SQL Server are the most searched DBMS.^[nine] The dominant database language, standardised SQL for the relational model, has influenced database languages for other data models.^{[ citation needed ]}

Object databases were adult 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 loftier performance of NoSQL compared to commercially available relational DBMSs.

1960s, navigational DBMS

Basic structure of navigational CODASYL database model

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

Equally computers grew in speed and adequacy, 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 inside 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 arroyo entered the market.

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

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

Later systems added B-copse to provide alternating admission paths. Many CODASYL databases also added a declarative query language for finish users (as distinct from the navigational API). However CODASYL databases were circuitous and required pregnant grooming and attempt to produce useful applications.

IBM besides had their own DBMS in 1966, known as Data Management System (IMS). IMS was a evolution of software written for the Apollo program on the System/360. IMS was mostly similar in concept to CODASYL, only 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 mode data was accessed: the term was popularized by Bachman's 1973 Turing Accolade presentation The Programmer every bit Navigator. IMS is classified past IBM equally a hierarchical database. IDMS and Cincom Systems' TOTAL database are classified equally network databases. IMS remains in employ as of 2014^[update].^[11]

1970s, relational DBMS

Edgar F. Codd worked at IBM in San Jose, California, in 1 of their offshoot offices that was primarily involved in the development of hd 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 existence stored in some sort of linked list of costless-course records as in CODASYL, Codd's thought was to organize the information equally a number of "tables", each tabular array beingness used for a unlike type of entity. Each tabular array would contain a fixed number of columns containing the attributes of the entity. One or more columns of each table were designated as a principal primal by which the rows of the table could be uniquely identified; cantankerous-references between tables always used these primary keys, rather than deejay addresses, and queries would join tables based on these fundamental 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 ready 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 dissimilar ways for different users, but views could non 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.

In the relational model, records are "linked" using virtual keys not stored in the database merely divers as needed between the data contained in the records.

The use of primary keys (user-oriented identifiers) to represent cantankerous-table relationships, rather than deejay addresses, had two primary motivations. From an applied science perspective, it enabled tables to be relocated and resized without expensive database reorganization. Only 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 bailiwick of first-order predicate calculus; because these operations have clean mathematical backdrop, it becomes possible to rewrite queries in provably correct ways, which is the basis of query optimization. In that location is no loss of expressiveness compared with the hierarchic or network models, though the connections between tables are no longer and so explicit.

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

For example, a mutual utilise of a database system is to track information about users, their name, login information, diverse addresses and phone numbers. In the navigational approach, all of this data would exist placed in a single variable-length tape. In the relational approach, the data would be normalized into a user table, an accost tabular array and a telephone number tabular array (for example). 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 information from multiple records. Rather than requiring applications to gather data one record at a time by navigating the links, they would utilize a declarative query language that expressed what information was required, rather than the access path by which it should be found. Finding an efficient access path to the data became the responsibleness of the database management arrangement, rather than the application developer. This process, chosen query optimization, depended on the fact that queries were expressed in terms of mathematical logic.

Codd's paper was picked up by 2 people at Berkeley, Eugene Wong and Michael Stonebraker. They started a projection known every bit INGRES using funding that had already been allocated for a geographical database project and student programmers to produce lawmaking. Beginning in 1973, INGRES delivered its first examination products which were generally prepare for widespread use in 1979. INGRES was similar to Organization R in a number of ways, including the use of a "linguistic communication" for data admission, known as QUEL. Over time, INGRES moved to the emerging SQL standard.

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

In 1970, the University of Michigan began development of the MICRO Information Management Arrangement^[13] based on D.L. Childs' Set-Theoretic Data model.^{[xiv] [15] [xvi]} MICRO was used to manage very large data sets by the US Department of Labor, the U.Southward. Ecology Protection Agency, and researchers from the Academy of Alberta, the University of Michigan, and Wayne Country University. It ran on IBM mainframe computers using the Michigan Concluding Organization.^[17] The organization remained in product until 1998.

Integrated approach

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 on offer of Teradata, and the Britton Lee, Inc. database machine.

Another approach to hardware support for database direction was ICL's CAFS accelerator, a hardware deejay controller with programmable search capabilities. In the long term, these efforts were more often than not unsuccessful because specialized database machines could not keep pace with the rapid development and progress of general-purpose computers. Thus most database systems present are software systems running on general-purpose hardware, using general-purpose computer information storage. Notwithstanding, this idea is all the same pursued for sure 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 Organization 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 "clamper". 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, afterwards, Database 2 (DB2).

Larry Ellison'south Oracle Database (or more simply, Oracle) started from a different concatenation, based on IBM'southward papers on Organisation R. Though Oracle V1 implementations were completed in 1978, it wasn't until Oracle Version 2 when Ellison beat out IBM to market in 1979.^[eighteen]

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

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

Another information model, the entity–relationship model, emerged in 1976 and gained popularity for database design as information technology emphasized a more familiar description than the earlier relational model. Later on, entity–human relationship constructs were retrofitted equally a data modeling construct for the relational model, and the difference between the 2 have become irrelevant.^{[ citation needed ]}

1980s, on the desktop

The 1980s ushered in the age of desktop calculating. The new computers empowered their users with spreadsheets similar Lotus 1-ii-3 and database software similar dBASE. The dBASE product was lightweight and easy for any computer user to empathise 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 washed. The data manipulation is done past dBASE instead of past the user, so the user tin 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 on 1990s.

1990s, object-oriented

The 1990s, along with a rise in object-oriented programming, saw a growth in how data in diverse 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, telephone number, and age, were now considered to belong to that person instead of beingness extraneous information. This allows for relations between data to be relations to objects and their attributes and not to individual fields.^[twenty] 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 linguistic communication (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

XML databases are a blazon 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 frequently very fast, do non require fixed table schemas, avoid join operations by storing denormalized data, and are designed to calibration horizontally.

In recent years, there has been a strong demand for massively distributed databases with loftier partition tolerance, just co-ordinate to the CAP theorem it is impossible for a distributed system to simultaneously provide consistency, availability, and sectionalisation tolerance guarantees. A distributed system can satisfy whatever two of these guarantees at the same time, only 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 grade of mod relational databases that aims to provide the aforementioned scalable performance of NoSQL systems for online transaction processing (read-write) workloads while still using SQL and maintaining the Acrid guarantees of a traditional database arrangement.

Utilise cases

Databases are used to back up internal operations of organizations and to underpin online interactions with customers and suppliers (come across Enterprise software).

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

Classification

One way to classify databases involves the blazon of their contents, for instance: bibliographic, certificate-text, statistical, or multimedia objects. Another way is by their application expanse, for example: bookkeeping, music compositions, movies, banking, manufacturing, or insurance. A third fashion is by some technical aspect, such equally the database structure or interface type. This section lists a few of the adjectives used to characterize dissimilar kinds of databases.

• An in-memory database is a database that primarily resides in main memory, only is typically backed-up by non-volatile estimator data storage. Primary retention databases are faster than disk databases, and and then are often used where response time is critical, such as in telecommunications network equipment.
• An active database includes an result-driven architecture which can respond to conditions both inside and exterior the database. Possible uses include security monitoring, alerting, statistics gathering and authorization. Many databases provide active database features in the class of database triggers.
• A cloud database relies on deject 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 stop-users through a web browser and Open APIs.
• Information warehouses archive information from operational databases and often from external sources such as marketplace research firms. The warehouse becomes the cardinal 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 apply UPCs and so that they can be compared with ACNielsen information. Some basic and essential components of data warehousing include extracting, analyzing, and mining data, transforming, loading, and managing information then every bit to brand them bachelor for further utilise.
• A deductive database combines logic programming with a relational database.
• A distributed database is i in which both the information and the DBMS bridge multiple computers.
• A document-oriented database is designed for storing, retrieving, and managing certificate-oriented, or semi structured, information. Certificate-oriented databases are one of the primary 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 fashion that the DBMS is hidden from the application's end-users and requires niggling or no ongoing maintenance.^[21]
• Finish-user databases consist of data developed by individual cease-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 organization comprises several distinct databases, each with its own DBMS. Information technology is handled equally a single database past a federated database direction organisation (FDBMS), which transparently integrates multiple autonomous DBMSs, maybe of unlike types (in which instance information technology would besides exist a heterogeneous database system), and provides them with an integrated conceptual view.
• Sometimes the term multi-database is used as a synonym to federated database, though it may refer to a less integrated (e.one thousand., without an FDBMS and a managed integrated schema) grouping of databases that cooperate in a single awarding. In this case, typically middleware is used for distribution, which typically includes an atomic commit protocol (ACP), due east.k., the 2-stage commit protocol, to permit 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 tin can shop 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 equally satellite images and climate simulation output.
• In a hypertext or hypermedia database, any word or a piece of text representing an object, east.g., another slice of text, an article, a flick, or a film, can be hyperlinked to that object. Hypertext databases are particularly useful for organizing large amounts of disparate information. For instance, they are useful for organizing online encyclopedias, where users can conveniently spring 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, system, and retrieval of knowledge. Besides a collection of data representing problems with their solutions and related experiences.

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

The major parallel DBMS architectures which are induced past the underlying hardware compages are:

• Shared retention architecture, where multiple processors share the master memory space, besides equally other information storage.
• Shared deejay architecture, where each processing unit (typically consisting of multiple processors) has its own main memory, only all units share the other storage.
• Shared-nothing architecture, where each processing unit has its ain primary memory and other storage.

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

Database management system

Connolly and Begg ascertain 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'southward 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 bespeak some other characteristic, such as DDBMS for a distributed database management systems.

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

• Data storage, retrieval and update
• User accessible itemize or data dictionary describing the metadata
• Support for transactions and concurrency
• Facilities for recovering the database should information technology become damaged
• Support for say-so of access and update of information
• Admission support from remote locations
• Enforcing constraints to ensure information in the database abides by sure rules

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

Often DBMSs will have configuration parameters that can be statically and dynamically tuned, for example the maximum amount of main retentivity on a server the database can utilize. The trend is to minimize the corporeality of manual configuration, and for cases such as embedded databases the need to target zilch-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 through their lifetime.^[a]

Early on multi-user DBMS typically only allowed for the application to reside on the same reckoner 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 compages 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 volition 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 the database. A special purpose DBMS may utilize a private API and exist specifically customized and linked to a unmarried awarding. For example, an email organisation performing many of the functions of a general-purpose DBMS such every bit message insertion, bulletin deletion, zipper handling, blocklist lookup, associating messages an email address and then forth all the same these functions are limited to what is required to handle electronic mail.

Application

External interaction with the database will be via an awarding plan 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 web site that happens to utilise 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 item API or linguistic communication chosen volition need to be supported by DBMS, possible indirectly via a preprocessor or a bridging API. Some API'south aim to be database independent, ODBC being a commonly known example. Other common API'southward include JDBC and ADO.NET.

Database languages

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

• Data control language (DCL) – controls admission to information;
• Data definition language (DDL) – defines data types such as creating, altering, or dropping tables and the relationships among them;
• Information 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 detail data model. Notable examples include:

• SQL combines the roles of data definition, data manipulation, and query in a single language. Information technology was ane of the outset 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 is supported (with varying degrees of conformance) past all mainstream commercial relational DBMSs.^{[29] [xxx]}
• 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 equally Oracle and DB2, and also by in-retentivity 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, similar counting, summing, averaging, sorting, grouping, and cross-referencing
• Constraint enforcement (e.g. in an automotive database, just assuasive 1 engine type per automobile)
• Application programming interface version of the query linguistic communication, for developer convenience

Storage

Database storage is the container of the physical materialization of a database. It comprises the internal (physical) level in the database architecture. It likewise contains all the information needed (e.g., metadata, "data about the data", and internal information structures) to reconstruct the conceptual level and external level from the internal level when needed. Databases equally digital objects contain three layers of data 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.m.a. "storage engine". Though typically accessed past a DBMS through the underlying operating system (and ofttimes using the operating systems' file systems as intermediates for storage layout), storage backdrop and configuration setting are extremely important for the efficient operation of the DBMS, and thus are closely maintained by database administrators. A DBMS, while in performance, always has its database residing in several types of storage (e.g., retentiveness and external storage). The database data and the additional needed information, maybe in very large amounts, are coded into $.25. Data typically reside in the storage in structures that look completely different from the way the data look in the conceptual and external levels, but in ways that try to optimize (the all-time possible) these levels' reconstruction when needed past users and programs, too as for computing additional types of needed information from the information (e.g., when querying the database).

Some DBMSs support specifying which character encoding was used to store information, 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 information model and then information technology can be written to the medium of choice. Techniques such as indexing may exist used to better performance. Conventional storage is row-oriented, but at that place are also column-oriented and correlation databases.

Materialized views

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 of 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 information, and the cost of storage back-up.

Replication

Occasionally a database employs storage redundancy past database objects replication (with one or more than copies) to increase data availability (both to ameliorate functioning of simultaneous multiple end-user accesses to a same database object, and to provide resiliency in a example of partial failure of a distributed database). Updates of a replicated object need to exist synchronized across the object copies. In many cases, the unabridged database is replicated.

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 past unauthorized entities (e.thousand., a person or a computer program).

Database admission command deals with controlling who (a person or a certain computer plan) is allowed to access what data in the database. The information may comprise specific database objects (e.g., record types, specific records, data structures), certain computations over sure objects (e.yard., query types, or specific queries), or using specific access paths to the erstwhile (eastward.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 and so granted entitlements. Data security prevents unauthorized users from viewing or updating the database. Using passwords, users are allowed admission to the entire database or subsets of information technology chosen "subschemas". For instance, an employee database tin comprise all the data about an individual employee, but ane 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, besides as interrogate it, this adequacy 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; east.g., see physical security), or the interpretation of them, or parts of them to meaningful data (e.k., by looking at the strings of $.25 that they comprise, concluding specific valid credit-carte du jour numbers; e.yard., see data encryption).

Alter and admission logging records who accessed which attributes, what was inverse, and when it was changed. Logging services allow for a forensic database audit afterwards by keeping a record of access occurrences and changes. Sometimes application-level code is used to record changes rather than leaving this to the database. Monitoring can be gear up upward to endeavour to detect security breaches.

Transactions and concurrency

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 measurement 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 programme/lawmaking executions are included in that transaction (determined by the transaction'south developer via special transaction commands).

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

Migration

A database built with one DBMS is not portable to another DBMS (i.e., the other DBMS cannot run it). Still, in some situations, information technology is desirable to drift a database from one DBMS to another. The reasons are primarily economical (different DBMSs may have different full costs of ownership or TCOs), functional, and operational (unlike DBMSs may have different capabilities). The migration involves the database'due south transformation from ane DBMS type to some other. The transformation should maintain (if possible) the database related application (i.due east., all related application programs) intact. Thus, the database'southward conceptual and external architectural levels should be maintained in the transformation. Information technology may be desired that likewise some aspects of the compages internal level are maintained. A circuitous or big database migration may be a complicated and plush (one-time) project by itself, which should exist factored into the decision to migrate. This in spite of the fact that tools may exist to help migration betwixt specific DBMSs. Typically, a DBMS vendor provides tools to help importing databases from other popular DBMSs.

Building, maintaining, and tuning

After designing a database for an application, the adjacent phase is building the database. Typically, an advisable general-purpose DBMS can exist 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 corresponding data model. Other user interfaces are used to select needed DBMS parameters (similar security related, storage allotment parameters, etc.).

When the database is ready (all its data structures and other needed components are defined), it is typically populated with initial application'southward information (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 awarding information, and information are accumulated during its operation.

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

Backup and restore

Sometimes it is desired to bring a database dorsum to a previous land (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 performance is washed occasionally or continuously, where each desired database state (i.e., the values of its information and their embedding in database's data structures) is kept within dedicated fill-in files (many techniques be to practise this effectively). When it is decided past a database administrator to bring the database back to this land (e.grand., by specifying this land by a desired point in time when the database was in this land), these files are used to restore that state.

Static analysis

Static analysis techniques for software verification tin 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 every bit a way to back up sound approximation techniques.^[33] The semantics of query languages tin can be tuned according to suitable abstractions of the concrete domain of data. The abstraction of relational database system has many interesting applications, in detail, for security purposes, such equally fine grained access control, watermarking, etc.

Miscellaneous features

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 organization.
• Query optimizer – Performs query optimization on every query to choose an efficient query plan (a fractional order (tree) of operations) to be executed to compute the query event. May exist specific to a particular storage engine.
• Tools or hooks for database design, awarding programming, application program maintenance, database performance analysis and monitoring, database configuration monitoring, DBMS hardware configuration (a DBMS and related database may bridge computers, networks, and storage units) and related database mapping (particularly for a distributed DBMS), storage allocation and database layout monitoring, storage migration, etc.

Increasingly, there are calls for a unmarried system that incorporates all of these cadre functionalities into the aforementioned build, test, and deployment framework for database management and source control. Borrowing from other developments in the software industry, some market such offerings every bit "DevOps for database".^[34]

Design and modeling

The first chore 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 pop approach is the Unified Modeling Language. A successful information model will accurately reflect the possible state of the external globe beingness modeled: for example, if people can have more than one phone number, it will allow this information to be captured. Designing a good conceptual information model requires a good understanding of the application domain; it typically involves asking deep questions nearly the things of interest to an organization, like "tin a customer too be a supplier?", or "if a production is sold with two dissimilar forms of packaging, are those the same product or dissimilar products?", or "if a plane flies from New York to Dubai via Frankfurt, is that one flying or two (or mayhap even three)?". The answers to these questions institute 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 system. This can help to establish what information is needed in the database, and what can be left out. For example, information technology can aid when deciding whether the database needs to hold historic information also 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 information structures within the database. This process is often called logical database pattern, and the output is a logical data model expressed in the form of a schema. Whereas the conceptual information model is (in theory at least) independent of the choice of database applied science, the logical data model will be expressed in terms of a item database model supported by the chosen DBMS. (The terms data model and database model are often used interchangeably, but in this article nosotros use data model for the design of a specific database, and database model for the modeling note used to express that pattern).

The well-nigh pop database model for full general-purpose databases is the relational model, or more precisely, the relational model equally represented by the SQL language. The procedure 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 identify, and so that insertions, updates, and deletions automatically maintain consistency.

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

Some other attribute of concrete database design is security. It involves both defining access command to database objects as well as defining security levels and methods for the information itself.

Models

Collage of 5 types of database models

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

Common logical data models for databases include:

• Navigational databases
  • Hierarchical database model
  • Network model
  • Graph database
• Relational model
• Entity–human 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 information models include:

• Inverted index
• Flat file

Other models include:

• Multidimensional model
• Assortment model
• Multivalue model

Specialized models are optimized for detail types of information:

• XML database
• Semantic model
• Content store
• Issue store
• Fourth dimension series model

External, conceptual, and internal views

Traditional view of data^[35]

A database direction arrangement provides three views of the database data:

• The external level defines how each group of finish-users sees the organization of data in the database. A single database tin have any number of views at the external level.
• The conceptual level unifies the diverse external views into a compatible global view.^[36] It provides the synthesis of all the external views. Information technology is out of the telescopic 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 within a DBMS. Information technology is concerned with cost, performance, scalability and other operational matters. It deals with storage layout of the information, using storage structures such as indexes to heighten performance. Occasionally information technology 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, mayhap conflicting, in an attempt to optimize overall performance beyond all activities.

While there is typically just one conceptual (or logical) and physical (or internal) view of the data, in that location can exist 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 section of a company needs the payment details of all employees every bit part of the visitor'south expenses, just does not need details nearly employees that are the interest of the human resources department. Thus different departments need dissimilar views of the visitor'southward database.

The three-level database compages relates to the concept of information independence which was one of the major initial driving forces of the relational model. The idea is that changes fabricated at a certain level do non bear upon the view at a college level. For example, changes in the internal level exercise not touch application programs written using conceptual level interfaces, which reduces the bear on of making physical changes to improve functioning.

The conceptual view provides a level of indirection between internal and external. On one hand information technology provides a common view of the database, independent of different external view structures, and on the other mitt it abstracts abroad details of how the data are stored or managed (internal level). In principle every level, and even every external view, tin be presented by a dissimilar information model. In practise ordinarily a given DBMS uses the same information 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 unlike 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.^[36]

Research

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

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

See also

• Comparison of database tools
• Comparison of object database direction systems
• Comparison of object–relational database direction systems
• Comparison of relational database management systems
• Information bureaucracy
• Data banking company
• Information store
• Database theory
• Database testing
• Database-axial architecture
• Apartment-file database
• Periodical of Database Management
• Question-focused dataset

Notes

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

References

1. ^ Ullman & Widom 1997, p. i.
2. ^ "Update – Definition of update by Merriam-Webster". merriam-webster.com.
3. ^ "Retrieval – Definition of retrieval past Merriam-Webster". merriam-webster.com.
4. ^ "Administration – Definition of assistants by Merriam-Webster". merriam-webster.com.
5. ^ Tsitchizris & Lochovsky 1982.
6. ^ Beynon-Davies 2003.
7. ^ Nelson & Nelson 2001.
8. ^ Bachman 1973.
9. ^ "TOPDB Height Database index". pypl.github.io.
10. ^ "database, due north". OED Online. Oxford University Press. June 2013. Retrieved July 12, 2013. (Subscription required.)
11. ^ IBM Corporation (October 2013). "IBM Data Direction Organization (IMS) 13 Transaction and Database Servers delivers high performance and depression full cost of ownership". Retrieved Feb xx, 2014.
12. ^ Codd 1970.
13. ^ Hershey & Easthope 1972.
14. ^ N 2010.
15. ^ Childs 1968a.
16. ^ Childs 1968b.
17. ^ MICRO Information Management Organization (Version 5.0) Reference Manual, One thousand.A. Kahn, D.L. Rumelhart, and B.50. Bronson, October 1977, Institute of Labor and Industrial Relations (ILIR), Academy 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 of america; Pages: 472–482; 1986; ISBN 0-89791-204-7
21. ^ Graves, Steve. "COTS Databases For Embedded Systems" Archived 2007-xi-14 at the Wayback Machine, Embedded Computing Design mag, Jan 2007. Retrieved on Baronial xiii, 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, Thousand. (2013, December 31). Database Preservation Toolkit: A flexible tool to normalize and give access to databases. University of Minho. https://cadre.ac.britain/brandish/55635702?source=1&algorithmId=15&similarToDoc=55614406&similarToDocKey=CORE&recSetID=f3ffea4d-1504-45e9-bfd6-a0495f5c8f9c&position=two&recommendation_type=same_repo&otherRecs=55614407,55635702,55607961,55613627,2255664
33. ^ Halder & Cortesi 2011.
34. ^ Ben Linders (January 28, 2016). "How Database Administration Fits into DevOps". Retrieved April xv, 2017.
35. ^ itl.nist.gov (1993) Integration Definition for Information Modeling (IDEFIX) Archived 2013-12-03 at the Wayback Automobile. 21 December 1993.
36. ^ ^{a b} Date 2003, pp. 31–32.

Sources

• Bachman, Charles W. (1973). "The Developer as Navigator". Communications of the ACM. 16 (xi): 653–658. doi:10.1145/355611.362534.
• Beynon-Davies, Paul (2003). Database Systems (3rd ed.). Palgrave Macmillan. ISBN978-1403916013.
• Chapple, Mike (2005). "SQL Fundamentals". Databases. About.com. Archived from the original on 22 February 2009. Retrieved 28 January 2009.
• Childs, David L. (1968a). "Clarification of a set-theoretic information structure" (PDF). CONCOMP (Research in Conversational Employ of Computers) Project. Vol. Technical Report iii. Academy of Michigan.
• Childs, David L. (1968b). "Feasibility of a ready-theoretic data structure: a full general construction based on a reconstituted definition" (PDF). CONCOMP (Research in Conversational Apply of Computers) Projection. Vol. Technical Study 6. University of Michigan.
• Chong, Raul F.; Wang, Xiaomei; Dang, Michael; Snow, Dwaine R. (2007). "Introduction to DB2". Understanding DB2: Learning Visually with Examples (2nd ed.). ISBN978-0131580183 . Retrieved 17 March 2013.
• Codd, Edgar F. (1970). "A Relational Model of Information for Large Shared Data Banks" (PDF). Communications of the ACM. thirteen (half dozen): 377–387. doi:10.1145/362384.362685. S2CID 207549016.
• Connolly, Thomas Thousand.; Begg, Carolyn E. (2014). Database Systems – A Applied Arroyo to Design Implementation and Management (6th ed.). Pearson. ISBN978-1292061184.
• Date, C. J. (2003). An Introduction to Database Systems (8th ed.). Pearson. ISBN978-0321197849.
• 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 Articulation Estimator Briefing, May 1972. ACM SIGIR Forum. Vol. vii, no. iv. 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. ISBN978-0201741308.
• North, Ken (10 March 2010). "Sets, Data Models and Information Independence". Dr. Dobb's. Archived from the original on 24 October 2010.
• Tsitchizris, Dionysios C.; Lochovsky, Fred H. (1982). Data Models. Prentice–Hall. ISBN978-0131964280.
• Ullman, Jeffrey; Widom, Jennifer (1997). A Kickoff Course in Database Systems. Prentice–Hall. ISBN978-0138613372.
• Wagner, Michael (2010), SQL/XML:2006 – Evaluierung der Standardkonformität ausgewählter Datenbanksysteme, Diplomica Verlag, ISBN978-3836696098

Farther reading

• Ling Liu and Tamer M. Özsu (Eds.) (2009). "Encyclopedia of Database Systems, 4100 p. sixty 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 Yard. and David J. Auer. Database Concepts. third ed. New York: Prentice, 2007.
• Raghu Ramakrishnan and Johannes Gehrke, Database Management Systems
• Abraham Silberschatz, Henry F. Korth, South. Sudarshan, Database Organisation Concepts
• Lightstone, S.; Teorey, T.; Nadeau, T. (2007). Physical Database Design: the database professional person's guide to exploiting indexes, views, storage, and more. Morgan Kaufmann Press. ISBN978-0-12-369389-i.
• Teorey, T.; Lightstone, S. and Nadeau, T. Database Modeling & Design: Logical Pattern, quaternary edition, Morgan Kaufmann Printing, 2005. ISBN 0-12-685352-5

External links

• DB File extension – information about files with the DB extension

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Source: https://en.wikipedia.org/wiki/Database

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