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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.
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
For example, a table of 128 rows with a Boolean column requires 128 bytes a row-oriented format (one byte per Boolean) but 128 bits (16 bytes) in a column-oriented format (via a bitmap). Another example is the use of run-length encoding to encode a column.
In relational databases, the information schema (information_schema) is an ANSI-standard set of read-only views that provide information about all of the tables, views, columns, and procedures in a database. [1]
In the context of Oracle Databases, a schema object is a logical data storage structure. [4] An Oracle database associates a separate schema with each database user. [5] A schema comprises a collection of schema objects. Examples of schema objects include: tables; views; sequences; synonyms; indexes; clusters; database links; snapshots ...
In situations where the number of unique values of a column is far less than the number of rows in the table, column-oriented storage allow significant savings in space through data compression. Columnar storage also allows fast execution of range queries (e.g., show all records where a particular column is between X and Y, or less than X.)
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 ...
ClickHouse: Apache License 2.0 Clustrix: Proprietary CockroachDB: Proprietary CSQL: Proprietary CUBRID: Apache, BSD DataEase: Proprietary DataFlex: Proprietary Dataphor: Proprietary dBase: Proprietary Derby (aka Java DB) Apache License 2.0 Empress Embedded Database: Proprietary EnterpriseDB: Proprietary eXtremeDB: Proprietary Exasol ...