Top Main Memory DBs

•FastDB•MonetDB•eXtremeDB•ERDB•DataBlitz•Times Ten

MonetDB

• MonetDB is an open source high-performance database system developed at CWI, (the Institute for Mathematics and Computer Science Research of The Netherlands)

• MonetDB has been successfully applied in high-performance applications for data mining, OLAP, GIS, XML Query, text and multimedia retrieval. (Real Time App)

DBMS Architecture

Architecture

MonetDB Architecture

Architecture

MonetDB: architecture

Front-end/back-end:

• support multiple data models

• support multiple end-user languages

• support diverse application domains

• perform all operations in main memory • a modern CPU-tuned vectorized query execution

architecture that often gives MonetDB a more than 10-fold raw speed

advantage on the same algorithm over a typical interpreter-based RDBMS

• extensible algebra. • binary relation model • shared-memory parallelism • MonetDB is one of the first database systems to focus its

query optimization effort on exploiting CPU caches

MonetDB Properties

• MonetDB has played a pivotal role in the development of datamining applications at DataDistilleries (now SPSS).

Monet Ideas

• : دستورات از استفاده امکان اول ايدهBack-End ) براي را جوهايي و پرس توانيد مي شما که معني اين نام Back-Endبه به واسطي از استفاده با

Mapi Client . کند مي کار آن با واسط اين که زباني دارد MILبنويسيد ) نام• : مي کاري هر دودويي جداول با دوم ايده

کرد توان• : دوباره اختراع به نياز عدم سوم ايده

عامل سيستم• : و پرس اجراي سازي بهينه چهارم ايده

حافظه در جو

Monet Data Model

• Some simple values

• Int,chr,str,oid,nil,intint(nil),chrchr(nil),strstr(nil),oidoid(nil)

• Binary Association Tables (BATs)

BAT ستون دو با است که Head و Tail جدولييا و فوق ساده مقادير باشد BAT شامل مي

Monet Data Model

BAT[oid,str]

BAT[oid,int]

BAT[oid, BAT[oid,int]]

Storing Relations in MonetDB

Relational Mapping

Object-Oriented Mapping

Monet Performance

• Compare some well-known open-source DBMSs with Monet on the task to build a binary table with the head representing an ordered sequence and the tail a permutation over the values in the head

Response time is given in seconds while running on a dual Athlon 1400 with 1GB of RAM and 3.5GB of free space

-- DBtapestry Version=1.1 -- See http://monetdb.cwi.nl/DBtapestry/ -- (c) CWI 2004-2005 - rows=100K --columns=2 -- Produced Sat Feb 26 23:41:51 2005

start transaction; select now(); create table RKA( head int, tail int); create table RKB( head int, tail int); insert into RKA values(0,0); insert into RKA values(1,360); insert into RKA values(2,427); …… insert into RKB select head+0, tail+0 from RKA; insert into RKB select head+8192, tail+8192 from RKA; …… drop table RKA; update RKB set tail=(tail*47) % 102400; ……create table tapestry( attr0 int , attr1 int); insert into tapestry select R0.head, R0.tail from RKB R0; drop table RKB; select now(); commit; -- actions=1249 -- tuplesRead= 1.07M tuplesWritten= 2.07M -- volumeRead= 8.59M

volumeWritten=16.59M

Monet Interpreter Language

MIL Extensibility

What is MonetDB Good for?

• Query-intensive application

• very high performance demanding

• complex data models

• complex query primitives

Transactions (OLTP)OLTP (Online Transaction Processing) is a form of transaction processing conducted via computer network. Some applications of OLTP include electronic banking, order processing, employee time

clock systems, e-commerce, and eTrading.

OLAP, Data MiningOn Line Analytical Processing. (OLAP) It is an approach to quickly provide the answer to

complex analytical queries. The typical applications of OLAP are in business reporting for sales, marketing, management reporting, business performance management, budgeting and

forecasting, financial reporting and similar areas.

New Domains: GIS

• New data types (point, polygon, etc..)• New search accelerators (R-Tree,

etc..)• New primitives

boolean intersects(polygon,polygon)

• Complex topological structures stored in DCELs that are decomposed over BATS queries are efficient due to MonetDB high join speed

New Domains: Multimedia

• New data types (url, image, etc..)

• new search accelerators (color histograms)

• new primitives (similarity search)• complex data structures:

bayesian inference networks (information retrieval) again decomposed in BATs and efficient to query