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Actian Vector
Actian Analytics Engine (formerly known as Vector and VectorWise) is an SQL relational database management system designed for high performance in analytical database applications. It published record breaking results on the Transaction Processing Performance Council's TPC-H benchmark for database sizes of 100 GB, 300 GB, 1 TB and 3 TB on non-clustered hardware.
Vectorwise originated from the X100 research project carried out within the Centrum Wiskunde & Informatica (CWI, the Dutch National Research Institute for Mathematics and Computer Science) between 2003 and 2008. It was spun off as a start-up company in 2008, and acquired by Ingres Corporation in 2011. It was released as a commercial product in June, 2010, initially for 64-bit Linux platform, and later also for Windows. Starting from 3.5 release in April 2014, the product name was shortened to "Vector". In June 2014, Actian Vortex was announced as a clustered massive parallel processing version of Vector, in Hadoop with storage in HDFS. Actian Vortex was later renamed to Actian Vector in Hadoop.
The basic architecture and design principles of the X100 engine of the VectorWise database were well described in two Phd theses of VectorWise founders Marcin Żukowski: "Balancing Vectorized Query Execution with Bandwidth-Optimized Storage" and Sandor Héman: "Updating Compressed Column Stores", under supervision of another founder, professor Peter Boncz. The X100 engine was integrated with Ingres SQL front-end, allowing the database to use the Ingres SQL syntax, and Ingres set of client and database administration tools.
The query execution architecture makes use of "Vectorized Query Execution" — processing in chunks of cache-fitting vectors of data. This allows to involve the principles of vector processing and single instruction, multiple data (SIMD)— to perform the same operation on multiple data simultaneously and exploit data level parallelism on modern hardware. It also reduces overheads found in traditional "row-at-a-time processing" found in most RDBMSes.
The database storage is in a compressed column-oriented format, with scan-optimised buffer manager. In Actian Vortex in HDFS the same proprietary format is used.
Loading big amounts of data is supported through direct appends to stable storage, while small transactional updates are supported through patent-pending Positional Delta Trees (PDTs) — specialized B-tree-like structures of indexed differences on top of stable storage, which are seamlessly patched during scans, and which are transparently propagated to stable storage in a background process. The method of storing differences in patch-like structures and rewriting the stable storage in bulk made it possible to work in a filesystem like HDFS, in which files are append-only.
A comparative Transaction Processing Performance Council TPC-H performance test of MonetDB carried out by its original creator at Centrum Wiskunde & Informatica (CWI) in 2003 showed room for improvement in its performance as an analytical database. As a result, CWI researchers proposed a new architecture using pipelined query processing ("vectorised processing") to improve the performance of analytical queries. This led to the creation of the "X100" project, with the intention of designing a new kernel for MonetDB, to be called "MonetDB/X100".
The X100 project team won the 2007 DaMoN Best Paper Award for the paper "Vectorized Data Processing on the Cell Broadband Engine" as well as the 2008 DaMoN Best Paper Award for the paper "DSM vs. NSM: CPU Performance Tradeoffs in Block-Oriented Query Processing".
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Actian Vector
Actian Analytics Engine (formerly known as Vector and VectorWise) is an SQL relational database management system designed for high performance in analytical database applications. It published record breaking results on the Transaction Processing Performance Council's TPC-H benchmark for database sizes of 100 GB, 300 GB, 1 TB and 3 TB on non-clustered hardware.
Vectorwise originated from the X100 research project carried out within the Centrum Wiskunde & Informatica (CWI, the Dutch National Research Institute for Mathematics and Computer Science) between 2003 and 2008. It was spun off as a start-up company in 2008, and acquired by Ingres Corporation in 2011. It was released as a commercial product in June, 2010, initially for 64-bit Linux platform, and later also for Windows. Starting from 3.5 release in April 2014, the product name was shortened to "Vector". In June 2014, Actian Vortex was announced as a clustered massive parallel processing version of Vector, in Hadoop with storage in HDFS. Actian Vortex was later renamed to Actian Vector in Hadoop.
The basic architecture and design principles of the X100 engine of the VectorWise database were well described in two Phd theses of VectorWise founders Marcin Żukowski: "Balancing Vectorized Query Execution with Bandwidth-Optimized Storage" and Sandor Héman: "Updating Compressed Column Stores", under supervision of another founder, professor Peter Boncz. The X100 engine was integrated with Ingres SQL front-end, allowing the database to use the Ingres SQL syntax, and Ingres set of client and database administration tools.
The query execution architecture makes use of "Vectorized Query Execution" — processing in chunks of cache-fitting vectors of data. This allows to involve the principles of vector processing and single instruction, multiple data (SIMD)— to perform the same operation on multiple data simultaneously and exploit data level parallelism on modern hardware. It also reduces overheads found in traditional "row-at-a-time processing" found in most RDBMSes.
The database storage is in a compressed column-oriented format, with scan-optimised buffer manager. In Actian Vortex in HDFS the same proprietary format is used.
Loading big amounts of data is supported through direct appends to stable storage, while small transactional updates are supported through patent-pending Positional Delta Trees (PDTs) — specialized B-tree-like structures of indexed differences on top of stable storage, which are seamlessly patched during scans, and which are transparently propagated to stable storage in a background process. The method of storing differences in patch-like structures and rewriting the stable storage in bulk made it possible to work in a filesystem like HDFS, in which files are append-only.
A comparative Transaction Processing Performance Council TPC-H performance test of MonetDB carried out by its original creator at Centrum Wiskunde & Informatica (CWI) in 2003 showed room for improvement in its performance as an analytical database. As a result, CWI researchers proposed a new architecture using pipelined query processing ("vectorised processing") to improve the performance of analytical queries. This led to the creation of the "X100" project, with the intention of designing a new kernel for MonetDB, to be called "MonetDB/X100".
The X100 project team won the 2007 DaMoN Best Paper Award for the paper "Vectorized Data Processing on the Cell Broadband Engine" as well as the 2008 DaMoN Best Paper Award for the paper "DSM vs. NSM: CPU Performance Tradeoffs in Block-Oriented Query Processing".
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