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Database scalability is the ability of a database to handle changing demands by adding/removing resources. Databases use a host of techniques to cope. [ 1 ] According to Marc Brooker: "a system is scalable in the range where marginal cost of additional workload is nearly constant."
Horizontal partitioning splits one or more tables by row, usually within a single instance of a schema and a database server. It may offer an advantage by reducing index size (and thus search effort) provided that there is some obvious, robust, implicit way to identify in which partition a particular row will be found, without first needing to search the index, e.g., the classic example of the ...
Toggle Horizontal (scale out) and vertical scaling (scale up) subsection. 4.1 Horizontal or scale out. 4.2 Vertical or scale up. 5 Network scalability. 6 Database ...
Vertical scaling, also known as scaling up, is the process of replacing a component with a device that is generally more powerful or improved. For example, replacing a processor with a faster one. Horizontal scaling, also known as scaling out is setting up another server for example to run in parallel with the original so they share the workload.
Due to increasing requirements for horizontal scalability and fault tolerance, NoSQL databases became prominent after 2009. NoSQL databases use a variety of data models, with document, graph, and key–value models being popular. [2] A multi-model database is a database that can store, index and query data in more than one model.
An entity–attribute–value model (EAV) is a data model optimized for the space-efficient storage of sparse—or ad-hoc—property or data values, intended for situations where runtime usage patterns are arbitrary, subject to user variation, or otherwise unforeseeable using a fixed design.
Database partitioning refers to intentionally breaking a large database into smaller ones for scalability purposes, distinct from network partitions which are a type of network fault between nodes. [1] In a partitioned database, each piece of data belongs to exactly one partition, effectively making each partition a small database of its own. [1]
In computing, denormalization is the process of trying to improve the read performance of a database, at the expense of losing some write performance, by adding redundant copies of data or by grouping data. [1] [2] It is often motivated by performance or scalability in relational database software needing to carry out very large numbers of read ...