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". In August 2009 the originators for the X100 project won the "Ten Year Best Paper Award" at the 35th International Conference on Very Large Data Bases (VLDB) for their 1999 paper "Database architecture Optimized for the new bottleneck: Memory access". It was recognised by the VLDB that the project team had made great progress in implementing the ideas contained in the paper over the previous 10 years. The central premise of the paper is that traditional relational database systems were designed in the late 1970s and early 1980s during a time when database performance was dictated by the time required to read from and write data to hard disk. At that time available
CPU was relatively slow and main memory was relatively small, so that very little data could be loaded into memory at a time. Over time hardware improved, with CPU speed and memory size doubling roughly every two years in accordance with
Moore’s law, but that the design of traditional relational database systems had not adapted. The CWI research team described improvements in database code and data structures to make best use of modern hardware. In 2008 the X100 project was spun off from MonetDB as a separate project, with its own company, and renamed "VectorWise". Co-founders included Peter A. Boncz and Marcin Żukowski. In June 2010, the VectorWise technology was officially announced by
Ingres Corporation, with the release of Ingres VectorWise 1.0. In March 2011, VectorWise 1.5 was released, publishing a record breaking result on TPC-H 100 GB benchmark. New features included parallel query execution (single query executed on multiple CPU cores), improved bulk loading and enhanced SQL support. In June 2011, VectorWise 1.6 was released, 300 GB and 1 TB non-clustered benchmark. In December 2011, VectorWise 2.0 was released with new SQL support for analytical functions such as rank and percentile and enhanced date, time and timestamp datatypes, and support for disk spilling in hash joins and aggregation. In June 2012, VectorWise 2.5 was released. In this release storage format was reorganized to allow storing the database in multiple location, the background update propagation mechanism from PDTs to stable storage was enhanced to allow rewriting only the changed blocks instead of full rewrites, and a new patented Predictive Buffer Manager (PBM) was introduced. In March 2013, VectorWise 3.0 was released. New features included more efficient storage engine, support for more data types and analytical SQL functions, enhanced DDL features, and improved monitoring and profiling accessibility. In March 2014, Actian Vector 3.5 was released, with a new rebranded and shortened name. In March 2019, Actian Avalanche was released as a cloud data platform, with Vector as the core engine for the Warehouse offering. In November 2023, Actian rebranded and relaunched Avalanche as Actian Data Platform, including new capabilities for
Data Quality. == Release history ==