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MonetDB
MonetDB is an open-source column-oriented relational database management system (RDBMS) originally developed at the Centrum Wiskunde & Informatica (CWI) in the Netherlands. It is designed to provide high performance on complex queries against large databases, such as combining tables with hundreds of columns and millions of rows. MonetDB has been applied in high-performance applications for online analytical processing, data mining, geographic information system (GIS), Resource Description Framework (RDF), text retrieval and sequence alignment processing.
Data mining projects in the 1990s required improved analytical database support. This resulted in a CWI spin-off called Data Distilleries, which used early MonetDB implementations in its analytical suite. Data Distilleries eventually became a subsidiary of SPSS in 2003, which in turn was acquired by IBM in 2009.
MonetDB in its current form was first created in 2002 by doctoral student Peter Boncz and professor Martin L. Kersten as part of the 1990s' MAGNUM research project at University of Amsterdam. It was initially called simply Monet, after the French impressionist painter Claude Monet. The first version under an open-source software license (a modified version of the Mozilla Public License) was released on September 30, 2004. When MonetDB version 4 was released into the open-source domain, many extensions to the code base were added by the MonetDB/CWI team, including a new SQL front end, supporting the SQL:2003 standard.
MonetDB introduced innovations in all layers of the DBMS: a storage model based on vertical fragmentation, a modern CPU-tuned query execution architecture that often gave MonetDB a speed advantage over the same algorithm over a typical interpreter-based RDBMS. It was one of the first database systems to tune query optimization for CPU caches. MonetDB includes automatic and self-tuning indexes, run-time query optimization, and a modular software architecture.
By 2008, a follow-on project called X100 (MonetDB/X100) started, which evolved into the VectorWise technology. VectorWise was acquired by Actian Corporation, integrated with the Ingres database and sold as a commercial product.
In 2011 a major effort to renovate the MonetDB codebase was started. As part of it, the code for the MonetDB 4 kernel and its XQuery components were frozen. In MonetDB 5, parts of the SQL layer were pushed into the kernel. The resulting changes created a difference in internal APIs, as it transitioned from MonetDB Instruction Language (MIL) to MonetDB Assembly Language (MAL). Older, no-longer maintained top-level query interfaces were also removed. First was XQuery, which relied on MonetDB 4 and was never ported to version 5. The experimental Jaql interface support was removed with the October 2014 release. With the July 2015 release, MonetDB gained support for read-only data sharding and persistent indices. In this release the deprecated streaming data module DataCell was also removed from the main codebase in an effort to streamline the code. In addition, the license has been changed into the Mozilla Public License, version 2.0.
MonetDB architecture is represented in three layers, each with its own set of optimizers. The front end is the top layer, providing query interface for SQL, with SciQL and SPARQL interfaces under development. Queries are parsed into domain-specific representations, like relational algebra for SQL, and optimized. The generated logical execution plans are then translated into MonetDB Assembly Language (MAL) instructions, which are passed to the next layer. The middle or back-end layer provides a number of cost-based optimizers for the MAL. The bottom layer is the database kernel, which provides access to the data stored in Binary Association Tables (BATs). Each BAT is a table consisting of an Object-identifier and value columns, representing a single column in the database.
MonetDB internal data representation also relies on the memory addressing ranges of contemporary CPUs using demand paging of memory mapped files, and thus departing from traditional DBMS designs involving complex management of large data stores in limited memory.
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MonetDB
MonetDB is an open-source column-oriented relational database management system (RDBMS) originally developed at the Centrum Wiskunde & Informatica (CWI) in the Netherlands. It is designed to provide high performance on complex queries against large databases, such as combining tables with hundreds of columns and millions of rows. MonetDB has been applied in high-performance applications for online analytical processing, data mining, geographic information system (GIS), Resource Description Framework (RDF), text retrieval and sequence alignment processing.
Data mining projects in the 1990s required improved analytical database support. This resulted in a CWI spin-off called Data Distilleries, which used early MonetDB implementations in its analytical suite. Data Distilleries eventually became a subsidiary of SPSS in 2003, which in turn was acquired by IBM in 2009.
MonetDB in its current form was first created in 2002 by doctoral student Peter Boncz and professor Martin L. Kersten as part of the 1990s' MAGNUM research project at University of Amsterdam. It was initially called simply Monet, after the French impressionist painter Claude Monet. The first version under an open-source software license (a modified version of the Mozilla Public License) was released on September 30, 2004. When MonetDB version 4 was released into the open-source domain, many extensions to the code base were added by the MonetDB/CWI team, including a new SQL front end, supporting the SQL:2003 standard.
MonetDB introduced innovations in all layers of the DBMS: a storage model based on vertical fragmentation, a modern CPU-tuned query execution architecture that often gave MonetDB a speed advantage over the same algorithm over a typical interpreter-based RDBMS. It was one of the first database systems to tune query optimization for CPU caches. MonetDB includes automatic and self-tuning indexes, run-time query optimization, and a modular software architecture.
By 2008, a follow-on project called X100 (MonetDB/X100) started, which evolved into the VectorWise technology. VectorWise was acquired by Actian Corporation, integrated with the Ingres database and sold as a commercial product.
In 2011 a major effort to renovate the MonetDB codebase was started. As part of it, the code for the MonetDB 4 kernel and its XQuery components were frozen. In MonetDB 5, parts of the SQL layer were pushed into the kernel. The resulting changes created a difference in internal APIs, as it transitioned from MonetDB Instruction Language (MIL) to MonetDB Assembly Language (MAL). Older, no-longer maintained top-level query interfaces were also removed. First was XQuery, which relied on MonetDB 4 and was never ported to version 5. The experimental Jaql interface support was removed with the October 2014 release. With the July 2015 release, MonetDB gained support for read-only data sharding and persistent indices. In this release the deprecated streaming data module DataCell was also removed from the main codebase in an effort to streamline the code. In addition, the license has been changed into the Mozilla Public License, version 2.0.
MonetDB architecture is represented in three layers, each with its own set of optimizers. The front end is the top layer, providing query interface for SQL, with SciQL and SPARQL interfaces under development. Queries are parsed into domain-specific representations, like relational algebra for SQL, and optimized. The generated logical execution plans are then translated into MonetDB Assembly Language (MAL) instructions, which are passed to the next layer. The middle or back-end layer provides a number of cost-based optimizers for the MAL. The bottom layer is the database kernel, which provides access to the data stored in Binary Association Tables (BATs). Each BAT is a table consisting of an Object-identifier and value columns, representing a single column in the database.
MonetDB internal data representation also relies on the memory addressing ranges of contemporary CPUs using demand paging of memory mapped files, and thus departing from traditional DBMS designs involving complex management of large data stores in limited memory.