Actian Vector

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"{{cite journal| last = Żukowski| first = Marcin | title = Balancing vectorized query execution with bandwidth-optimized storage| publisher = Universiteit van Amsterdam| date = 11 September 2009| url = http://oai.cwi.nl/oai/asset/14075/14075B.pdf| access-date = 7 February 2016}} and Sandor Héman: "Updating Compressed Column Stores",{{cite journal| last = Héman| first = Sandor | title = Updating Compressed Column Stores | publisher = Vrije Universiteit Amsterdam| date = 2015| url = http://dare.ubvu.vu.nl/bitstream/handle/1871/53448/complete_dissertation.pdf | access-date = 7 February 2016}} 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.

{{cite journal

| last1 = Inkster

| first1 = Doug

| last2 = Żukowski

| first2 = Marcin

| last3 = Boncz

| first3 = Peter

| title = Integration of VectorWise with Ingres

| journal = SIGMOD Record

| volume = 40

| issue = 3

| pages = 45–53

| date = September 2011

| url = http://www.sigmod.org/publications/sigmod-record/1109/pdfs/08.industry.inkster.pdf

| access-date = 7 February 2016

| doi = 10.1145/2070736.2070747

| hdl = 1871/33100

| s2cid = 6372175

}}

The query execution architecture makes use of "Vectorized Query Execution" {{mdash}} processing in chunks of cache-fitting vectors of data. This allows to involve the principles of vector processing and single instruction, multiple data (SIMD){{mdash}} 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,

{{cite journal

| last1 = Zukowski

| first1 = Marcin

| last2 = Boncz

| first2 = Peter

| title = Vectorwise: Beyond Column Stores

| journal = IEEE Data Engineering Bulletin

| volume = 35

| issue = 1

| pages = 21–27

| date = March 2012

| url = http://sites.computer.org/debull/A12mar/vectorwise.pdf

| access-date = 4 May 2012}}

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

{{cite patent

| country = US

| number = 20100235335

| status = application

| title = Column-store database architecture utilizing positional delta tree update system and methods

| pubdate = 2010-09-16

| fdate = 2010-03-08

| pridate = 2009-03-11

| inventor = Sandor ABC Heman, Peter A. Boncz, Marcin Zukowski, Nicolaas J. Nes

}}

Positional Delta Trees (PDTs)

{{cite journal

| last1 = Héman

| first1 = Sándor

| last2 = Żukowski

| first2 = Marcin

| last3 = Nes

| first3 = Niels

| last4 = Sidirourgos

| first4 = Lefteris

| last5 = Boncz

| first5 = Peter

| title = Positional update handling in column stores

| journal = SIGMOD Conference 2010

| pages = 543–554

| url = http://event.cwi.nl/SIGMOD-RWE/2010/22-7f15a1/paper.pdf

}}

{{mdash}} 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".{{cite web| url = http://homepages.cwi.nl/~boncz/ | title = Homepage of Peter Boncz | access-date = 7 February 2016}}{{cite web| url = http://www.cwi.nl/news/2009/1025/Faster-database-technology-with-MonetDB-X100| title = Faster database technology with MonetDB/X100 |access-date = 4 May 2012| publisher = CWI Amsterdam}}

The X100 project team won the 2007 DaMoN Best Paper Award for the paper "Vectorized Data Processing on the Cell Broadband Engine"

{{cite journal| author1 = Héman, S.| author2 = Nes, N.J.| author3 = Zukowski, M.| author4 = Boncz, P.A.| title = Vectorized Data Processing on the Cell Broadband Engine| publisher = Universiteit van Amsterdam| year = 2007| url = http://repository.cwi.nl/search/fullrecord.php?publnr=12426| access-date = 4 May 2012}}{{cite web| url = https://www.cs.cmu.edu/~damon2007/index.html| title = Third International Workshop on Data Management on New Hardware (DaMoN 2007)| access-date = 4 May 2012| publisher = Carnegie Mellon’s School of Computer Science (SCS)}} as well as the 2008 DaMoN Best Paper Award for the paper "DSM vs. NSM: CPU Performance Tradeoffs in Block-Oriented Query Processing".

{{cite book

| doi= 10.1145/1457150.1457160

| isbn = 9781605581842

| chapter = DSM vs. NSM

| title = Proceedings of the 4th international workshop on Data management on new hardware - DaMoN '08

| pages = 47

| year = 2008

| last1 = Zukowski

| first1 = Marcin

| last2 = Nes

| first2 = Niels

| last3 = Boncz

| first3 = Peter

| s2cid = 11946467

}}

{{cite web

| url = https://www.cs.cmu.edu/~damon2008/index.html

| title = Fourth International Workshop on Data Management on New Hardware (DaMoN 2008)

| access-date = 4 May 2012

| publisher = Carnegie Mellon School of Computer Science }}

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.

{{cite web

| url = http://vldb2009.org/?q=node/50

| title = 10-year Best Paper Award – VLDB 2009

| access-date = 4 May 2012

| publisher = International Conference on Very Large Data Bases }}

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.

{{cite book

| author1= Boncz, Peter

| author2= Manegold, Stefan

| author3= Kersten, Martin L.

| title = Database architecture optimized for the new bottleneck: Memory access

| publisher = Universiteit van Amsterdam

| date= 15 June 1999 |isbn= 1-55860-615-7 |work= Proceedings of the 25th International Conference on Very Large Data Bases

| url = http://www.vldb.org/conf/1999/P5.pdf |pages= 54–65

| access-date = 11 December 2013 }}

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.{{Cite web |title= Goodbye VectorWise, farewell ParAccel? |work= DBMS2 |author= Curt Monash |url= http://www.dbms2.com/2013/04/25/goodbye-vectorwise-farewell-paraccel/ |date= 25 April 2013 |access-date= 11 December 2013 }}{{Cite web |title= Peter Boncz |work= Staff web page |publisher= CWI |url= http://www.cwi.nl/people/800 |access-date= 11 December 2013 }}

In June 2010, the VectorWise technology was officially announced by Ingres Corporation,

{{cite news

| last = Clark

| first = Don

| title = Database-Software Firm Tries 'Action Apps'

| newspaper = The Wall Street Journal

| date = 22 September 2011

| url = https://www.wsj.com/articles/SB10001424053111904563904576584951170212870}}

with the release of Ingres VectorWise 1.0.{{cite web | url = http://www.actian.com/about-us/blog/actian-vectorwise-1-0/ | title = Ingres Vectorwise 1.0 | access-date = 7 February 2016}}

In March 2011, VectorWise 1.5 was released,{{cite web | url = http://www.actian.com/about-us/blog/an-early-look-at-actian-vectorwise-1-5/ | title = An early look at Actian VectorWise 1.5}} publishing a record breaking result on TPC-H 100 GB benchmark.{{cite web | url = http://www.tpc.org/3260 | title = TPC-H SF100 Vectorwise 1.5}} 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, publishing record breaking results on TPC-H 100 GB,{{cite web | url = http://www.tpc.org/3273 | title = TPC-H SF100 Vectorwise 1.6}} 300 GB{{cite web | url = http://www.tpc.org/3271 | title = TPC-H SF300 Vectorwise 1.6}} and 1 TB{{cite web | url = http://www.tpc.org/3272 | title = TPC-H SF1000 Vectorwise 1.6}} non-clustered benchmark.

In December 2011, VectorWise 2.0 was released{{cite web | url = http://www.actian.com/about-us/blog/an-even-faster-vectorwise/ | title = An even faster VectorWise}} 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.{{cite web | url = http://www.actian.com/company/news-and-events/press-releases/actian-releases-vectorwise-2-5/ | title = Actian Releases Vectorwise 2.5 – Record-Breaking Database Is Now Even Faster}} 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

{{cite patent

| country = US

| number = 8825959 B1

| status = patent

| title = Method and apparatus for using data access time prediction for improving data buffering policies

| pubdate = 2014-09-02

| fdate = 2012-07-31

| inventor = Michal Switakowski, Peter Boncz, Marcin Zukowski

}}

Predictive Buffer Manager (PBM) was introduced.

{{cite journal

| author1 = Świtakowski, Michał

| author2 = Boncz, Peter

| author3 = Żukowski, Marcin

| title = From Cooperative Scans to Predictive Buffer Management

| publisher = VLDB 2012

| date= August 2012

| journal= Proceedings of the VLDB Endowment

| volume=5 |issue=12

| pages = 1759–1770

| doi = 10.14778/2367502.2367515

| url = http://vldb.org/pvldb/vol5/p1759_michalswitakowski_vldb2012.pdf

| access-date = 7 February 2016 | bibcode = 2012arXiv1208.4170S

| arxiv = 1208.4170

| s2cid = 17184937

}}

In March 2013, VectorWise 3.0 was released.{{cite web | url = http://www.actian.com/company/news-and-events/press-releases/vectorwise-3-with-hadoop/ | title = Actian Announces Availability of Vectorwise 3.0 for Getting Fast Answers from Big Data}} 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. New features included support for partitioned tables, improved disk spilling, online backup capabilities and improved SQL support - e.g. MERGE/UPSERT DML operations and FIRST_VALUE and LAST_VALUE window aggregation functions.

In June 2014, at Hadoop Summit 2014 in San Jose, Actian announced Actian Vortex {{mdash}} clustered MPP version of Vector, with same level of SQL support working in Hadoop with storage directly in HDFS.

Actian Vortex was later renamed to Actian Vector in Hadoop, and non-clustered Actian Vector releases are also updated to match. In March 2015 Actian Vector 4 was released, and Actian Vector in Hadoop 4 was released in December 2015.{{cite web | url = https://communities.actian.com/s/supportservices/lifecycle-dates/actian-vector-vector-in-hadoop-lifecycle-date | title = Lifecycle Dates - Actian Vector and Vector in Hadoop}}

In March 2019, Actian Avalanche was released as a cloud data platform, with Vector as the core engine for the Warehouse offering.{{cite web | url = https://www.techtarget.com/searchdatamanagement/news/252482313/Actian-Avalanche-Real-Time-Connected-Data-Warehouse-adds-integration#:~:text=Actian%20launched%20the%20Avalanche%20Cloud,warehouse%20capabilities%20to%20the%20cloud. | title = Actian Avalanche Real-Time Connected Data Warehouse adds integration}} In November 2023, Actian rebranded and relaunched Avalanche as Actian Data Platform, including new capabilities for Data Quality.{{cite web | url = https://www.actian.com/company/press-releases/actian-data-platform-relaunches-with-integration-as-a-service/ | title = Actian Data Platform Relaunches With Integration as a Service

}}

{{Version|l|show=111110}}

{{Version|l|show=111110}}

In 2024, Actian decided to withdraw End of Obsolescence Support for Actian Vector in Hadoop, after discontinuing the marketing of this product line, thus making 6.0 its last release and Actian Data Platform's Cloud Data Warehouse service the only MPP implementation of Vector available.

• Database management system
• Relational database
• MonetDB
• Ingres (database)

{{Reflist|30em}}

• [https://www.actian.com/analytic-database/vector-analytic-database/ Official website of Actian Vector]

Category:Products introduced in 2010

Category:Proprietary database management systems

Category:Relational database management systems