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Sphinx can be used either as a stand-alone server or as a storage engine ("SphinxSE") for the MySQL family of databases. When run as a standalone server Sphinx operates similar to a DBMS and can communicate with MySQL, MariaDB and PostgreSQL through their native protocols or with any ODBC-compliant DBMS via ODBC.
MySQL (/ ˌ m aɪ ˌ ɛ s ˌ k juː ˈ ɛ l /) [6] is an open-source relational database management system (RDBMS). [6] [7] Its name is a combination of "My", the name of co-founder Michael Widenius's daughter My, [1] and "SQL", the acronym for Structured Query Language.
To reduce such index size, some systems allow including non-key fields in the index. Non-key fields are not themselves part of the index ordering but only included at the leaf level, allowing for a covering index with less overall index size. This can be done in SQL with CREATE INDEX my_index ON my_table (id) INCLUDE (name);. [8] [9]
In SQL Server 2012, an in-memory technology called xVelocity column-store indexes targeted for data-warehouse workloads. Mimer SQL: Mimer Information Technology SQL, ODBC, JDBC, ADO.NET, Embedded SQL, C, C++, Python Proprietary Mimer SQL is a general purpose relational database server that can be configured to run fully in-memory.
Soundex is the most widely known of all phonetic algorithms (in part because it is a standard feature of popular database software such as IBM Db2, PostgreSQL, [2] MySQL, [3] SQLite, [4] Ingres, MS SQL Server, [5] Oracle, [6] ClickHouse, [7] Snowflake [8] and SAP ASE. [9]) Improvements to Soundex are the basis for many modern phonetic algorithms.
Varchar fields can be of any size up to a limit, which varies by databases: an Oracle 11g database has a limit of 4000 bytes, [1] a MySQL 5.7 database has a limit of 65,535 bytes (for the entire row) [2] and Microsoft SQL Server 2008 has a limit of 8000 bytes (unless varchar(max) is used, which has a maximum storage capacity of 2 gigabytes).
The same may not be true of B-tree: B-tree requires a tree node for every approximately N rows in the table, where N is the capacity of a single node, thus the index size is large. As BRIN only requires a tuple for each block (of many rows), the index becomes sufficiently small to make the difference between disk and memory.
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 ...