Query optimizationQuery optimization is a feature of many relational database management systems and other databases such as NoSQL and graph databases. The query optimizer attempts to determine the most efficient way to execute a given query by considering the possible query plans. Generally, the query optimizer cannot be accessed directly by users: once queries are submitted to the database server, and parsed by the parser, they are then passed to the query optimizer where optimization occurs.
Materialized viewIn computing, a materialized view is a database object that contains the results of a query. For example, it may be a local copy of data located remotely, or may be a subset of the rows and/or columns of a table or join result, or may be a summary using an aggregate function. The process of setting up a materialized view is sometimes called materialization. This is a form of caching the results of a query, similar to memoization of the value of a function in functional languages, and it is sometimes described as a form of precomputation.
Data elementIn metadata, the term data element is an atomic unit of data that has precise meaning or precise semantics. A data element has: An identification such as a data element name A clear data element definition One or more representation terms Optional enumerated values Code (metadata) A list of synonyms to data elements in other metadata registries Synonym ring Data elements usage can be discovered by inspection of software applications or application s through a process of manual or automated Application Discovery and Understanding.
Data integrityData integrity is the maintenance of, and the assurance of, data accuracy and consistency over its entire life-cycle and is a critical aspect to the design, implementation, and usage of any system that stores, processes, or retrieves data. The term is broad in scope and may have widely different meanings depending on the specific context - even under the same general umbrella of computing. It is at times used as a proxy term for data quality, while data validation is a prerequisite for data integrity.
Query throughputIn computer science, query throughput (QthD) is a measurement used to determine the performance of a database system. The throughput metric is a classical throughput measure characterizing the ability of the system to support a multi-user workload in a balanced way. In the background there is an update stream that runs a series of insert/delete operations (one pair for each query user). The choice of the number of users is at the discretion of the test sponsor.
Referential integrityReferential integrity is a property of data stating that all its references are valid. In the context of relational databases, it requires that if a value of one attribute (column) of a relation (table) references a value of another attribute (either in the same or a different relation), then the referenced value must exist. For referential integrity to hold in a relational database, any column in a base table that is declared a foreign key can only contain either null values or values from a parent table's primary key or a candidate key.
Data definition languageIn the context of SQL, data definition or data description language (DDL) is a syntax for creating and modifying database objects such as tables, indices, and users. DDL statements are similar to a computer programming language for defining data structures, especially database schemas. Common examples of DDL statements include CREATE, ALTER, and DROP. The concept of the data definition language and its name was first introduced in relation to the Codasyl database model, where the schema of the database was written in a language syntax describing the records, fields, and sets of the user data model.
Expressive power (computer science)In computer science, the expressive power (also called expressiveness or expressivity) of a language is the breadth of ideas that can be represented and communicated in that language. The more expressive a language is, the greater the variety and quantity of ideas it can be used to represent. For example, the Web Ontology Language expression language profile (OWL2 EL) lacks ideas (such as negation) which can be expressed in OWL2 RL (rule language). OWL2 EL may therefore be said to have less expressive power than OWL2 RL.
Wide-column storeA wide-column store (or extensible record store) is a column-oriented DBMS and therefore a special type of NoSQL database. It uses tables, rows, and columns, but unlike a relational database, the names and format of the columns can vary from row to row in the same table. A wide-column store can be interpreted as a two-dimensional key–value store. Google's Bigtable is one of the prototypical examples of a wide-column store.
Query languageA query language, also known as data query language or database query language (DQL), is a computer language used to make queries in databases and information systems. A well known example is the Structured Query Language (SQL). Broadly, query languages can be classified according to whether they are database query languages or information retrieval query languages. The difference is that a database query language attempts to give factual answers to factual questions, while an information retrieval query language attempts to find documents containing information that is relevant to an area of inquiry.
