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Vectorwise

Actian Vector
Developer(s) Actian Corporation
Stable release
Vector 5.0 / July 1, 2016 (2016-07-01)
Operating system Cross-platform
Type RDBMS
License Proprietary
Website www.actian.com/products/analytics-platform/vector-smp-analytics-database/
Actian Vector in Hadoop
Developer(s) Actian Corporation
Stable release
Vector in Hadoop 4.2 Service Pack 3 / April 15, 2016 (2016-04-15)
Operating system Linux
Type RDBMS
License Proprietary
Website www.actian.com/products/analytics-platform/vortex-sql-hadoop-analytics/

Actian Vector (formerly known as 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 - clustered MPP version of Vector, working in Hadoop with storage in HDFS.

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 has been integrated with Ingres SQL front-end, making the database operatable using the Ingres SQL syntax, and Ingres set of client and DBA 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.


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