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In 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 .
ClickHouse: C++ Released in 2016 to analyze data that is updated in real time CrateDB: Java C-Store: C++ The last release of the original code was in 2006; Vertica a commercial fork, lives on. DuckDB: C++ An embeddable, in-process, column-oriented SQL OLAP RDBMS Databend Rust An elastic and reliable Serverless Data Warehouse InfluxDB: Rust Time ...
ClickHouse is an open-source column-oriented DBMS (columnar database management system) for online analytical processing (OLAP) that allows users to generate analytical reports using SQL queries in real-time. ClickHouse Inc. is headquartered in the San Francisco Bay Area with the subsidiary, ClickHouse B.V., based in Amsterdam, Netherlands.
Materialized views were introduced by Oracle Database, while IBM Db2 provides so-called "materialized query tables" (MQTs) for the same purpose. Microsoft SQL Server introduced in its 2000 version indexed views which only store a separate index from the table, but not the entire data. PostgreSQL implemented materialized views in its 9.3 release.
Each column in an SQL table declares the type(s) that column may contain. ANSI SQL includes the following data types. [14] Character strings and national character strings. CHARACTER(n) (or CHAR(n)): fixed-width n-character string, padded with spaces as needed; CHARACTER VARYING(n) (or VARCHAR(n)): variable-width string with a maximum size of n ...
A column may contain text values, numbers, or even pointers to files in the operating system. [2] Columns typically contain simple types, though some relational database systems allow columns to contain more complex data types, such as whole documents, images, or even video clips. [3] [better source needed] A column can also be called an attribute.
Data orientation is the representation of tabular data in a linear memory model such as in-disk or in-memory. The two most common representations are column-oriented (columnar format) and row-oriented (row format). [1] [2] The choice of data orientation is a trade-off and an architectural decision in databases, query engines, and numerical ...
In an EAV data model, each attribute–value pair is a fact describing an entity, and a row in an EAV table stores a single fact. EAV tables are often described as "long and skinny": "long" refers to the number of rows, "skinny" to the few columns. Data is recorded as three columns: The entity: the item being described.