Relational database management system
A relational database management system is a database management system based on the relational model of data. Most databases in widespread use today are based on this model. RDBMSs have been a common option for the storage of information in databases used for financial records and logistical information, personnel data, other applications since the 1980s. Relational databases have replaced legacy hierarchical databases and network databases, because RDBMS were easier to implement and administer. Nonetheless, relational databases received continued, unsuccessful challenges by object database management systems in the 1980s and 1990s, as well as by XML database management systems in the 1990s. However, due to the expanse of technologies, such as horizontal scaling of computer clusters, NoSQL databases have become popular as an alternative to RDBMS databases. According to DB-Engines, in June 2018, the most used systems were Oracle, MySQL, Microsoft SQL Server, PostgreSQL, IBM DB2, Microsoft Access, SQLite.
According to research company Gartner, in 2011, the five leading Proprietary software relational database vendors by revenue were Oracle, IBM, Microsoft, SAP including Sybase, Teradata. In 1974, IBM began developing System R, a research project to develop a prototype RDBMS. However, the first commercially available RDBMS was Oracle, released in 1979 by Relational Software, now Oracle Corporation. Other examples of an RDBMS include DB2, SAP Sybase ASE, Informix. In 1984, the first RDBMS for Macintosh began being developed, code-named Silver Surfer, it was released in 1987 as 4th Dimension and known today as 4D; the term "relational database" was invented by E. F. Codd at IBM in 1970. Codd introduced the term in his research paper "A Relational Model of Data for Large Shared Data Banks". In this paper and papers, he defined what he meant by "relational". One well-known definition of what constitutes a relational database system is composed of Codd's 12 rules. However, no commercial implementations of the relational model conform to all of Codd's rules, so the term has come to describe a broader class of database systems, which at a minimum: Present the data to the user as relations.
The first systems that were faithful implementations of the relational model were from: University of Michigan -- Micro DBMS Massachusetts Institute of Technology IBM UK Scientific Centre at Peterlee -- IS1 and its successor, PRTV The first system sold as an RDBMS was Multics Relational Data Store. Ingres and IBM BS12 followed; the most definition of an RDBMS is a product that presents a view of data as a collection of rows and columns if it is not based upon relational theory. By this definition, RDBMS products implement some but not all of Codd's 12 rules. A second school of thought argues that if a database does not implement all of Codd's rules, it is not relational; this view, shared by many theorists and other strict adherents to Codd's principles, would disqualify most DBMSs as not relational. For clarification, they refer to some RDBMSs as truly-relational database management systems, naming others pseudo-relational database management systems; as of 2009, most commercial relational DBMSs employ SQL as their query language.
Alternative query languages have been proposed and implemented, notably the pre-1996 implementation of Ingres QUEL. SQL Object database Online analytical processing and ROLAP Data warehouse Star schema Snowflake schema List of relational database management systems Comparison of relational database management systems
International Business Machines Corporation is an American multinational information technology company headquartered in Armonk, New York, with operations in over 170 countries. The company began in 1911, founded in Endicott, New York, as the Computing-Tabulating-Recording Company and was renamed "International Business Machines" in 1924. IBM produces and sells computer hardware and software, provides hosting and consulting services in areas ranging from mainframe computers to nanotechnology. IBM is a major research organization, holding the record for most U. S. patents generated by a business for 26 consecutive years. Inventions by IBM include the automated teller machine, the floppy disk, the hard disk drive, the magnetic stripe card, the relational database, the SQL programming language, the UPC barcode, dynamic random-access memory; the IBM mainframe, exemplified by the System/360, was the dominant computing platform during the 1960s and 1970s. IBM has continually shifted business operations by focusing on higher-value, more profitable markets.
This includes spinning off printer manufacturer Lexmark in 1991 and the sale of personal computer and x86-based server businesses to Lenovo, acquiring companies such as PwC Consulting, SPSS, The Weather Company, Red Hat. In 2014, IBM announced that it would go "fabless", continuing to design semiconductors, but offloading manufacturing to GlobalFoundries. Nicknamed Big Blue, IBM is one of 30 companies included in the Dow Jones Industrial Average and one of the world's largest employers, with over 380,000 employees, known as "IBMers". At least 70% of IBMers are based outside the United States, the country with the largest number of IBMers is India. IBM employees have been awarded five Nobel Prizes, six Turing Awards, ten National Medals of Technology and five National Medals of Science. In the 1880s, technologies emerged that would form the core of International Business Machines. Julius E. Pitrap patented the computing scale in 1885. On June 16, 1911, their four companies were amalgamated in New York State by Charles Ranlett Flint forming a fifth company, the Computing-Tabulating-Recording Company based in Endicott, New York.
The five companies had offices and plants in Endicott and Binghamton, New York. C.. They manufactured machinery for sale and lease, ranging from commercial scales and industrial time recorders and cheese slicers, to tabulators and punched cards. Thomas J. Watson, Sr. fired from the National Cash Register Company by John Henry Patterson, called on Flint and, in 1914, was offered a position at CTR. Watson joined CTR as General Manager 11 months was made President when court cases relating to his time at NCR were resolved. Having learned Patterson's pioneering business practices, Watson proceeded to put the stamp of NCR onto CTR's companies, he implemented sales conventions, "generous sales incentives, a focus on customer service, an insistence on well-groomed, dark-suited salesmen and had an evangelical fervor for instilling company pride and loyalty in every worker". His favorite slogan, "THINK", became a mantra for each company's employees. During Watson's first four years, revenues reached $9 million and the company's operations expanded to Europe, South America and Australia.
Watson never liked the clumsy hyphenated name "Computing-Tabulating-Recording Company" and on February 14, 1924 chose to replace it with the more expansive title "International Business Machines". By 1933 most of the subsidiaries had been merged into one company, IBM. In 1937, IBM's tabulating equipment enabled organizations to process unprecedented amounts of data, its clients including the U. S. Government, during its first effort to maintain the employment records for 26 million people pursuant to the Social Security Act, the tracking of persecuted groups by Hitler's Third Reich through the German subsidiary Dehomag. In 1949, Thomas Watson, Sr. created IBM World Trade Corporation, a subsidiary of IBM focused on foreign operations. In 1952, he stepped down after 40 years at the company helm, his son Thomas Watson, Jr. was named president. In 1956, the company demonstrated the first practical example of artificial intelligence when Arthur L. Samuel of IBM's Poughkeepsie, New York, laboratory programmed an IBM 704 not to play checkers but "learn" from its own experience.
In 1957, the FORTRAN scientific programming language was developed. In 1961, IBM developed the SABRE reservation system for American Airlines and introduced the successful Selectric typewriter. In 1963, IBM employees and computers helped. A year it moved its corporate headquarters from New York City to Armonk, New York; the latter half of the 1960s saw IBM continue its support of space exploration, participating in the 1965 Gemini flights, 1966 Saturn flights and 1969 lunar mission. On April 7, 1964, IBM announced the first computer system family, the IBM System/360, it spanned the complete range of commercial and scientific applications from large to small, allowing companies for the first time to upgrade to models with greater computing capability without having to rewrite their applications. It was followed by the IBM System/370 in 1970. Together the
In computing, a graph database is a database that uses graph structures for semantic queries with nodes and properties to represent and store data. A key concept of the system is the graph, which directly relates data items in the store a collection of nodes of data and edges representing the relationships between the nodes; the relationships allow data in the store to be linked together directly, in many cases retrieved with one operation. Graph databases hold the relationships between data as a priority. Querying relationships within a graph database is fast because they are perpetually stored within the database itself. Relationships can be intuitively visualized using graph databases, making it useful for inter-connected data. Graph databases are part of the NoSQL databases created to address the limitations of the existing relational databases. While the graph model explicitly lays out the dependencies between nodes of data, the relational model and other NoSQL database models link the data by implicit connections.
Graph databases, by design, allow simple and fast retrieval of complex hierarchical structures that are difficult to model in relational systems. Graph databases are similar to 1970s network model databases in that both represent general graphs, but network-model databases operate at a lower level of abstraction and lack easy traversal over a chain of edges; the underlying storage mechanism of graph databases can vary. Some depend on a relational engine and “store” the graph data in a table. Others use a key-value store or document-oriented database for storage, making them inherently NoSQL structures. Most graph databases based on non-relational storage engines add the concept of tags or properties, which are relationships having a pointer to another document; this allows data elements to be categorized for easy retrieval en masse. Retrieving data from a graph database requires a query language other than SQL, designed for the manipulation of data in a relational system and therefore cannot “elegantly” handle traversing a graph.
As of 2017, no single graph query language has been universally adopted in the same way as SQL was for relational databases, there are a wide variety of systems, most tightly tied to one product. Some standardization efforts have occurred, leading to multi-vendor query languages like Gremlin, SPARQL, Cypher. In addition to having query language interfaces, some graph databases are accessed through application programming interfaces. Graph databases differ from graph compute engines. Graph databases are technologies. On the other hand, graph compute engines are utilized in OLAP for bulk analysis. Graph databases have attracted considerable attention in the 2000s, due to the successes of major technology corporations in using proprietary graph databases, the introduction of open-source graph databases. Graph databases portray the data; this is accomplished by transferring the data into its relationships into edges. A graph within graph databases is based on graph theory, it is a node or an edge. Nodes represent entities or instances such as people, accounts, or any other item to be tracked.
They are the equivalent of the record, relation or row in a relational database, or the document in a document-store database. Edges termed graphs or relationships, are the lines that connect nodes to other nodes. Meaningful patterns emerge when examining the connections and interconnections of nodes and edges; the edges can either be undirected. In an undirected graph, an edge from a point to another has one meaning. In a directed graph, the edges connecting two different points have different meanings depending on their direction. Edges are the key concept in graph databases, representing an abstraction, not directly implemented in a relational model or a document-store model. Properties are germane information to nodes. For example, if Wikipedia were one of the nodes, it might be tied to properties such as website, reference material, or words that starts with the letter w, depending on which aspects of Wikipedia are germane to a given database. A labeled-property graph model is represented by a set of nodes, relationships and labels.
Both nodes of data and their relationships are named and can store properties represented by key/value pairs. Nodes can be labelled to be grouped; the edges representing the relationships have two qualities: they always have a start node and an end node, are directed. Relationships can have properties; this is useful in providing additional semantics to relationships of the nodes. Direct storage of relationships allows a constant-time traversal, it is the most popular form of graph model as of 2018, the model for the most popular graph database as of October 2018, Neo4j. In an RDF graph model, the addition of information is each represented with a separate node. For example, imagine a scenario where a user has to add a name property for a person represented as a distinct node in the graph. In a labeled-property graph model, this would be done with an addition of a name property into the node of the person. However, in an RDF, the user has to add a separate node called'hasName' connecting it to the original person node.
An RDF graph model is composed of nodes and arcs. An RDF graph notation or a stateme
In computing, a data warehouse known as an enterprise data warehouse, is a system used for reporting and data analysis, is considered a core component of business intelligence. DWs are central repositories of integrated data from one or more disparate sources, they store current and historical data in one single place that are used for creating analytical reports for workers throughout the enterprise. The data stored in the warehouse is uploaded from the operational systems; the data may pass through an operational data store and may require data cleansing for additional operations to ensure data quality before it is used in the DW for reporting. The typical extract, load -based data warehouse uses staging, data integration, access layers to house its key functions; the staging layer or staging database stores raw data extracted from each of the disparate source data systems. The integration layer integrates the disparate data sets by transforming the data from the staging layer storing this transformed data in an operational data store database.
The integrated data are moved to yet another database called the data warehouse database, where the data is arranged into hierarchical groups called dimensions, into facts and aggregate facts. The combination of facts and dimensions is sometimes called a star schema; the access layer helps users retrieve data. The main source of the data is cleansed, transformed and made available for use by managers and other business professionals for data mining, online analytical processing, market research and decision support. However, the means to retrieve and analyze data, to extract and load data, to manage the data dictionary are considered essential components of a data warehousing system. Many references to data warehousing use this broader context. Thus, an expanded definition for data warehousing includes business intelligence tools, tools to extract and load data into the repository, tools to manage and retrieve metadata. A data warehouse maintains a copy of information from the source transaction systems.
This architectural complexity provides the opportunity to: Integrate data from multiple sources into a single database and data model. More congregation of data to single database so a single query engine can be used to present data in an ODS. Mitigate the problem of database isolation level lock contention in transaction processing systems caused by attempts to run large, long-running, analysis queries in transaction processing databases. Maintain data history if the source transaction systems do not. Integrate data from multiple source systems, enabling a central view across the enterprise; this benefit is always valuable, but so when the organization has grown by merger. Improve data quality, by providing consistent codes and descriptions, flagging or fixing bad data. Present the organization's information consistently. Provide a single common data model for all data of interest regardless of the data's source. Restructure the data so that it makes sense to the business users. Restructure the data so that it delivers excellent query performance for complex analytic queries, without impacting the operational systems.
Add value to operational business applications, notably customer relationship management systems. Make decision–support queries easier to write. Organize and disambiguate repetitive data The environment for data warehouses and marts includes the following: Source systems that provide data to the warehouse or mart. In regards to source systems listed above, R. Kelly Rainer states, "A common source for the data in data warehouses is the company's operational databases, which can be relational databases". Regarding data integration, Rainer states, "It is necessary to extract data from source systems, transform them, load them into a data mart or warehouse". Rainer discusses storing data in an organization's data warehouse or data marts. Metadata are data about data. "IT personnel need information about data sources. Today, the most successful companies are those that can respond and flexibly to market changes and opportunities. A key to this response is the effective and efficient use of data and information by analysts and managers.
A "data warehouse" is a repository of historical data that are organized by subject to support decision makers in the organization. Once data are stored in a data mart or warehouse, they can be accessed. A data mart is a simple form of a data warehouse, focused on a single subject, hence they draw data from a limited number of sources such as sales, finance or marketing. Data marts are built and controlled by a single department within an organization; the sources could be a central data warehouse, or external data. Denormalization is the norm for data modeling techniques in this system. Given that data marts cover only a subset of the data contained in a data warehouse, they are easier and faster to implement. Types of data marts include dependent and hybrid data marts. Online analytical processing is characterized by a low volume of transactions. Queries are very complex and involve aggregations. For OLAP systems, response time is an effectiveness measure
In a relational database, a column is a set of data values of a particular simple type, one value for each row of the database. A column may contain text values, numbers, or pointers to files in the operating system; some relational database systems allow columns to contain more complex data types. A column can be called an attribute; each row would provide a data value for each column and would be understood as a single structured data value. For example, a database that represents company contact information might have the following columns: ID, Company Name, Address Line 1, Address Line 2, Postal Code. More formally, each row can be interpreted as a relvar, composed of a set of tuples, with each tuple consisting of the relevant column and its value, for example, the tuple; the word'field' is used interchangeably with'column'. However, database perfectionists tend to favor using'field' to signify a specific cell of a given row. Relational databases use row-based data storage, but column-based storage can be more useful for many business applications.
For example, a column database has faster access to which columns can read throughout the ranging process of a query. Any of the columns are known to serve as an index. Alternatively, row-based applications process only one record at one time and need to access a complete record or two. Column databases have better compression as the data storage permits effective compression since the majority of the columns cover only a few distinct values compared to the number of rows. Furthermore, in a column store, data is vertically divided; this vertical organization allows operations on different columns to be processed in parallel. If multiple items need to be searched or aggregated, each of these operations can be assigned to a different processor core. In a row-based database table, rows are read through and checked when retrieving data representing the desired columns. Therefore, requests on a large amount of data can take a lot of time, whereas, in column database tables, this information is kept physically next to each other, knowingly increasing the speed of certain data queries.
The main benefit of keeping data in a column database is that some queries can come quickly. For instance, if you want to know the average age of all users, you can jump to the area where the'age' data is stored and read just the data needed instead of searching up the age for each record row by row. During querying, columnar storage avoids going over non-relevant data. Therefore, aggregation queries where one only needs to look up subsets of total data develop more compared to row-oriented databases; as the data type of each column is alike, better compression occurs when running compression algorithms on each column, which will help queries churn results more quickly. There are many situations. Column databases are not the best option for these types of queries; the more fields that need reading per record, the fewer benefits there are in storing data in a column-oriented fashion. If queries are looking for user-specific values only, row-oriented databases perform those queries faster. Secondly, writing new data could take more time in columnar storage.
For instance, if you're inserting a new record into a row-oriented database, you can write that in one process. However, if you're inserting a new record into a column database, you need to write to each column one by one; this results as it will take longer time when loading new data or updating many values in a columnar database. Some examples of popular databases include: Sybase DB2 MySQL SQL Server Access Oracle PostgreSQL Column-oriented DBMS, optimization for column-centric queries Column, a similar object used in distributed data stores Row SQL Query language Structured Query Language
IBM Z is a family name used by IBM for all of its non-POWER mainframe computers from the Z900 on. In July 2017, with another generation of products, the official family was changed to IBM Z from IBM z Systems; the zSeries, zEnterprise, System z and IBM Z families were named for their availability – z stands for zero downtime. The systems are built with spare components capable of hot failovers to ensure continuous operations; the IBM Z family maintains full backward compatibility. In effect, current systems are the direct, lineal descendants of System/360, announced in 1964, the System/370 from the 1970s. Many applications written for these systems can still run unmodified on the newest IBM Z system over five decades later. Virtualization is required by default on IBM Z systems. First layer virtualization is provided by the Processor Resource and System Manager to deploy one or more Logical Partitions; each LPAR supports a variety of operating systems. A hypervisor called z/VM can be run as the second layer virtualization in LPARs to create as many virtual machines as there are resources assigned to the LPARs to support them.
The first layer of IBM Z virtualization allows a z machine to run a limited number of LPARs. These can be considered virtual "bare metal" servers because PR/SM allows CPUs to be dedicated to individual LPARs. Z/VM LPARs allocated within PR/SM LPARs can run a large number of virtual machines as long as there are adequate CPU, I/O resources configured with the system for the desired performance and throughput. IBM Z's PR/SM and hardware attributes allow compute resources to be dynamically changed to meet workload demands. CPU and memory resources can be non-disruptively added to the system and dynamically assigned and used by LPARs. I/O resources such as IP and SAN ports can be added dynamically, they are shared across all LPARs. The hardware component that provides this capability is called the Channel Subsystem; each LPAR can be configured to either "see" or "not see" the virtualized I/O ports to establish desired "shareness" or isolation. This virtualization capability allows significant reduction in I/O resources because of its ability to share them and drive up utilization.
PR/SM on IBM Z has earned Common Criteria Evaluation Assurance Level 5+ security certification, z/VM has earned Common Criteria EAL4+ certification. The KVM hypervisor from Linux has been ported. Since the move away from the System/390 name, a number of IBM Z models have been released; these can be grouped into families with similar architectural characteristics. IBM z14 ZR1 single-frame mainframe introduced on April 10, 2018 IBM z14 mainframe introduced on July 17, 2017 Official IBM z14 mainframe product page IBM Redbooks z14 technical guide z Systems z13s, introduced on February 17 2016 z Systems z13, introduced on January 13, 2015 The IBM zEnterprise System, announced in July 2010, with the z196 model, is designed to offer both mainframe and distributed server technologies in an integrated system; the zEnterprise System consists of three components. First is a System z server. Second is the IBM zEnterprise BladeCenter Extension. Last is the management layer, IBM zEnterprise Unified Resource Manager, which provides a single management view of zEnterprise resources.
The zEnterprise is designed to extend mainframe capabilities – management efficiency, dynamic resource allocation, serviceability – to other systems and workloads running on AIX on POWER7, Microsoft Windows or Linux on x86. The zEnterprise BladeCenter Extension is an infrastructure component that hosts both general purpose blade servers and appliance-like workload optimizers which can all be managed as if they were a single mainframe; the zBX supports a private high speed internal network that connects it to the central processing complex, which reduces the need for networking hardware and provides inherently high security. The IBM zEnterprise Unified Resource Manager integrates the System z and zBX resources as a single virtualized system and provides unified and integrated management across the zEnterprise System, it can identify system bottlenecks or failures among disparate systems and if a failure occurs it can dynamically reallocate system resources to prevent or reduce application problems.
The Unified Resource Manager provides energy monitoring and management, resource management, increased security, virtual networking, information management from a single user interface. Highlights of the original zEnterprise z196 include: BladeCenter Extension and Unified Resource Manager Up to 80 central processors 60% higher capacity than the z10 Twice the memory capacity 5.2 GHz quad-core chipsThe newest zEnterprise, the EC12, was announced in August 2012, included: Up to 101 central processors 50% higher capacity than the z196 Transactional Execution 5.5 GHz hex-core chips Flash Express – integrated SSDs which improve paging and certain other I/O performanceOn April 8, 2014, in honor of the 50th anniversary of the System/360 mainframe, IBM announced the release of its first converged infrastructure solution based on mainframe technology. Dubbed the IBM Enterprise Cloud System, this new offering combines IBM mainframe hardwa
Each replica set member may act in the role of secondary replica at any time. All writes and reads are done on the primary replica by default. Secondary replicas maintain a copy of the data of the primary using built-in replication; when a primary replica fails, the replica set automatically conducts an election process to determine which secondary should become the primary. Secondaries can optionally serve read operations, but that data is only consistent by default. MongoDB scales horizontally using sharding; the user chooses a shard key. The data is split into ranges and distributed across multiple shards.. Alternatively, the shard key can be hashed to map to a shard – enabling an data distribution. MongoDB can run over multiple servers, balancing the load or duplicating data to keep the system up and running in case of hardware failure. MongoDB can be used as a file system, called GridFS, with load balancing and data replication features over multiple machines for storing files; this function, called grid file system, is included with MongoDB drivers.
There are products and third-party projects that offer user interfaces for administration and data viewing. As of October 2018, MongoDB is released under the Server Side Public License, a license developed by the project, it replaces the GNU Affero General Public License, is nearly identical to the GNU General Public License version 3, but requires that those making the software publicly available as part of a "service" must make the service's entire source code available under this license. The SSPL was submitted for certification to the Open Source Initiative but withdrawn; the language drivers are available under an Apache License. In addition, MongoDB Inc. offers proprietary licenses for MongoDB. The last versions licensed as AGPL version 3 are 4.0.3 and 4.1.4. MongoDB has been dropped from the Debian and Red Hat Enterprise Linux distributions due to the licensing change. Fedora determined that the SSPL version 1 is not a free software license because it is "intentionally crafted to be aggressively discriminatory" towards commercial users.
Due to the default security configuration of MongoDB, allowing anyone to have full access to the database, data from tens of thousands of MongoDB installations has been stolen. Furthermore, many MongoDB servers have been held for ransom. From the MongoDB 2.6 release onwards, the binaries from