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SQL support. ClickHouse supports an extended SQL-like language that includes arrays and nested data structures, approximate and URI functions, and the availability to connect an external key-value store. High performance. [6] Vector calculations are used. Data is not only stored by columns, but is processed by vectors (parts of columns).
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 .
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.
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 ...
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.
A result set is the set of results returned by a query, usually in the same format as the database the query is called on. [1] For example, in SQL, which is used in conjunction with relational databases, it is the result of a SELECT query on a table or view and is itself a non-permanent table of rows, and could include metadata about the query such as the column names, and the types and sizes ...
EAV, as stated earlier, is specifically (and only) applicable to the sparse-attribute scenario. When such a scenario holds, the use of datatype-specific attribute–value tables that can be indexed by entity, by attribute, and by value and manipulated through simple SQL statements is vastly more scalable than the use of an XML tree structure.