SlideShare a Scribd company logo
1 of 19
Index ing and Fast   Search engine  NBITSearch parameters www.nbitsearch.com Novosib-BIT LLC version  1.03.3
NBITSearch System NBITSearch is a search engine with an open   API . ---------------------------   NBITSearch  is  a  programme kernel   for ―   Database Management Systems ,  - ―  Warehouses   of Large Data,  - ―  Search Systems applied to any Objects . .
The System is Designed for ,[object Object],high-speed   exact   and   fuzzy   search  for objects   with   minimum   use   of RAM . for
Exact   and   Fuzzy Search Interval queries  provide    fuzzy   ( inexact )   search .     Precise   ( exact )   search  is   a particular case of   fuzzy   search .
Indexable   Objects Objects  S of any types  T
The system   indexes objects  S of any   types  T simultaneously by  a  set any functions   F (S) . Multifunctionality
Sizes of Indexable Arrays The most tangible effect in the speed of search is shown for such arrays of   objects , which support ≈ 50  ÷  100  million and more objects   for one index.  A size of arrays of   indexable objects   can be   1 0  ÷  100  terabyte and larger .
Indexing Limitations One index supports ≈ 2  billion of its   objects . Limitations of   number of   indexes   are   artificial .
What is a Billion? 1  billion   seconds   is ≈ 32  years . 1  billion pages for   a laser   printer   is     a pile with a   height   of  ≈ 100  km .
Indexing Speed Estimator : T  ~  ( N )  * LOG (N) T   –  time of forming one index , N  – number of indexable objects .
Compactness of Indexes A size of one index can vary within the range of   0 . 1 %  ÷  5 . 0 % of the size of indexable objects .
Search Speed Time estimation of defining the   address   of the first potential   block of data :   T  ~  LOG (N)     T  –  time of   “logic   probing” , N  – number of   indexed objects .
Search Speed A speed of fetching the result of interval queries from a hard disk   can be 10  ÷  100  times higher than   (for the large data array) , the speed of   similar   operation   in a standard relational   DBMS .
Search Speed A speed of fetching the result of interval queries from a hard disk   can be   1000  times  ( and more )  higher than (for the large data array) ,   the   speed   of similar   operation when solving the problems with the use of brute force method .
Search Speed A time of fetching    the result of interval queries from a hard disk depends   linearly   on objects number in result set .
Search Memory Due to compactness of indexes   the system loads each of them   in RAM entirely   before queries are made .
Search Memory A size of memory buffers to fetch the   data   depends on   user’s   needs . This size is often infinitesimal (~10 megabyte) .
Reading of Result Set Reading the result set from a hard disk   to RAM   is   optimum : magnetic head does not oscillate .
THANK YOU ! www.nbitsearch.com Technology developed with support from  FASIE formed by the Government of Russian Federation Novosib-BIT LLC  ©  2004 - 201 1 Patented

More Related Content

What's hot

Temporal Pattern Mining
Temporal Pattern MiningTemporal Pattern Mining
Temporal Pattern MiningPrakhar Dhama
 
FTS middleware doc.
FTS middleware doc.FTS middleware doc.
FTS middleware doc.chopkins19
 
Time Series Data with Apache Cassandra (ApacheCon EU 2014)
Time Series Data with Apache Cassandra (ApacheCon EU 2014)Time Series Data with Apache Cassandra (ApacheCon EU 2014)
Time Series Data with Apache Cassandra (ApacheCon EU 2014)Eric Evans
 
ДЕНИС КЛЕПIКОВ «Long Term storage for Prometheus» Lviv DevOps Conference 2019
ДЕНИС КЛЕПIКОВ «Long Term storage for Prometheus» Lviv DevOps Conference 2019ДЕНИС КЛЕПIКОВ «Long Term storage for Prometheus» Lviv DevOps Conference 2019
ДЕНИС КЛЕПIКОВ «Long Term storage for Prometheus» Lviv DevOps Conference 2019UA DevOps Conference
 
A Parallel Algorithm for Approximate Frequent Itemset Mining using MapReduce
A Parallel Algorithm for Approximate Frequent Itemset Mining using MapReduce A Parallel Algorithm for Approximate Frequent Itemset Mining using MapReduce
A Parallel Algorithm for Approximate Frequent Itemset Mining using MapReduce Fabio Fumarola
 
Lightning Talk: MongoDB Migration Strategies
Lightning Talk: MongoDB Migration StrategiesLightning Talk: MongoDB Migration Strategies
Lightning Talk: MongoDB Migration StrategiesMongoDB
 
MongoDB-Migration-Strategies
MongoDB-Migration-StrategiesMongoDB-Migration-Strategies
MongoDB-Migration-Strategiesandyjwoodard
 
Frequent Itemset Mining(FIM) on BigData
Frequent Itemset Mining(FIM) on BigDataFrequent Itemset Mining(FIM) on BigData
Frequent Itemset Mining(FIM) on BigDataRaju Gupta
 
Time Series Data with Apache Cassandra
Time Series Data with Apache CassandraTime Series Data with Apache Cassandra
Time Series Data with Apache CassandraEric Evans
 
Research Papers Recommender based on Digital Repositories Metadata
Research Papers Recommender based on Digital Repositories MetadataResearch Papers Recommender based on Digital Repositories Metadata
Research Papers Recommender based on Digital Repositories MetadataRicard de la Vega
 
Ch 5: Introduction to heap overflows
Ch 5: Introduction to heap overflowsCh 5: Introduction to heap overflows
Ch 5: Introduction to heap overflowsSam Bowne
 
Multi pattern searching
Multi pattern searchingMulti pattern searching
Multi pattern searching小蜜 許
 
Mining top k frequent closed itemsets
Mining top k frequent closed itemsetsMining top k frequent closed itemsets
Mining top k frequent closed itemsetsyuanchung
 
Time Series Data with Apache Cassandra
Time Series Data with Apache CassandraTime Series Data with Apache Cassandra
Time Series Data with Apache CassandraEric Evans
 
Extended memory access in PHP
Extended memory access in PHPExtended memory access in PHP
Extended memory access in PHPAndrew Goodwin
 
Big Data DC - Analytics at Clearspring
Big Data DC - Analytics at ClearspringBig Data DC - Analytics at Clearspring
Big Data DC - Analytics at Clearspringabramsm
 
Geo Package and OWS Context at FOSS4G PDX
Geo Package and OWS Context at FOSS4G PDXGeo Package and OWS Context at FOSS4G PDX
Geo Package and OWS Context at FOSS4G PDXLuis Bermudez
 
Text Mining with Node.js - Philipp Burckhardt, Carnegie Mellon University
Text Mining with Node.js - Philipp Burckhardt, Carnegie Mellon UniversityText Mining with Node.js - Philipp Burckhardt, Carnegie Mellon University
Text Mining with Node.js - Philipp Burckhardt, Carnegie Mellon UniversityNodejsFoundation
 
An Overview of Bionimbus (March 2010)
An Overview of Bionimbus (March 2010)An Overview of Bionimbus (March 2010)
An Overview of Bionimbus (March 2010)Robert Grossman
 

What's hot (19)

Temporal Pattern Mining
Temporal Pattern MiningTemporal Pattern Mining
Temporal Pattern Mining
 
FTS middleware doc.
FTS middleware doc.FTS middleware doc.
FTS middleware doc.
 
Time Series Data with Apache Cassandra (ApacheCon EU 2014)
Time Series Data with Apache Cassandra (ApacheCon EU 2014)Time Series Data with Apache Cassandra (ApacheCon EU 2014)
Time Series Data with Apache Cassandra (ApacheCon EU 2014)
 
ДЕНИС КЛЕПIКОВ «Long Term storage for Prometheus» Lviv DevOps Conference 2019
ДЕНИС КЛЕПIКОВ «Long Term storage for Prometheus» Lviv DevOps Conference 2019ДЕНИС КЛЕПIКОВ «Long Term storage for Prometheus» Lviv DevOps Conference 2019
ДЕНИС КЛЕПIКОВ «Long Term storage for Prometheus» Lviv DevOps Conference 2019
 
A Parallel Algorithm for Approximate Frequent Itemset Mining using MapReduce
A Parallel Algorithm for Approximate Frequent Itemset Mining using MapReduce A Parallel Algorithm for Approximate Frequent Itemset Mining using MapReduce
A Parallel Algorithm for Approximate Frequent Itemset Mining using MapReduce
 
Lightning Talk: MongoDB Migration Strategies
Lightning Talk: MongoDB Migration StrategiesLightning Talk: MongoDB Migration Strategies
Lightning Talk: MongoDB Migration Strategies
 
MongoDB-Migration-Strategies
MongoDB-Migration-StrategiesMongoDB-Migration-Strategies
MongoDB-Migration-Strategies
 
Frequent Itemset Mining(FIM) on BigData
Frequent Itemset Mining(FIM) on BigDataFrequent Itemset Mining(FIM) on BigData
Frequent Itemset Mining(FIM) on BigData
 
Time Series Data with Apache Cassandra
Time Series Data with Apache CassandraTime Series Data with Apache Cassandra
Time Series Data with Apache Cassandra
 
Research Papers Recommender based on Digital Repositories Metadata
Research Papers Recommender based on Digital Repositories MetadataResearch Papers Recommender based on Digital Repositories Metadata
Research Papers Recommender based on Digital Repositories Metadata
 
Ch 5: Introduction to heap overflows
Ch 5: Introduction to heap overflowsCh 5: Introduction to heap overflows
Ch 5: Introduction to heap overflows
 
Multi pattern searching
Multi pattern searchingMulti pattern searching
Multi pattern searching
 
Mining top k frequent closed itemsets
Mining top k frequent closed itemsetsMining top k frequent closed itemsets
Mining top k frequent closed itemsets
 
Time Series Data with Apache Cassandra
Time Series Data with Apache CassandraTime Series Data with Apache Cassandra
Time Series Data with Apache Cassandra
 
Extended memory access in PHP
Extended memory access in PHPExtended memory access in PHP
Extended memory access in PHP
 
Big Data DC - Analytics at Clearspring
Big Data DC - Analytics at ClearspringBig Data DC - Analytics at Clearspring
Big Data DC - Analytics at Clearspring
 
Geo Package and OWS Context at FOSS4G PDX
Geo Package and OWS Context at FOSS4G PDXGeo Package and OWS Context at FOSS4G PDX
Geo Package and OWS Context at FOSS4G PDX
 
Text Mining with Node.js - Philipp Burckhardt, Carnegie Mellon University
Text Mining with Node.js - Philipp Burckhardt, Carnegie Mellon UniversityText Mining with Node.js - Philipp Burckhardt, Carnegie Mellon University
Text Mining with Node.js - Philipp Burckhardt, Carnegie Mellon University
 
An Overview of Bionimbus (March 2010)
An Overview of Bionimbus (March 2010)An Overview of Bionimbus (March 2010)
An Overview of Bionimbus (March 2010)
 

Viewers also liked

Bioinformatics Meets Information Retrieval: State of the Art and a Case Study
Bioinformatics Meets Information Retrieval: State of the Art and a Case StudyBioinformatics Meets Information Retrieval: State of the Art and a Case Study
Bioinformatics Meets Information Retrieval: State of the Art and a Case StudyEloisa Vargiu
 
Windows Azure Casestudy on Document Search & Retrieval
Windows Azure Casestudy on Document Search & RetrievalWindows Azure Casestudy on Document Search & Retrieval
Windows Azure Casestudy on Document Search & RetrievalSaviant Consulting
 
Realtime search engine concept
Realtime search engine conceptRealtime search engine concept
Realtime search engine concept상욱 송
 
Developing Document Image Retrieval System
Developing Document Image Retrieval SystemDeveloping Document Image Retrieval System
Developing Document Image Retrieval SystemKonstantinos Zagoris
 
google search engine
google search enginegoogle search engine
google search engineway2go
 

Viewers also liked (6)

Bioinformatics Meets Information Retrieval: State of the Art and a Case Study
Bioinformatics Meets Information Retrieval: State of the Art and a Case StudyBioinformatics Meets Information Retrieval: State of the Art and a Case Study
Bioinformatics Meets Information Retrieval: State of the Art and a Case Study
 
Windows Azure Casestudy on Document Search & Retrieval
Windows Azure Casestudy on Document Search & RetrievalWindows Azure Casestudy on Document Search & Retrieval
Windows Azure Casestudy on Document Search & Retrieval
 
Text Indexing and Retrieval
Text Indexing and RetrievalText Indexing and Retrieval
Text Indexing and Retrieval
 
Realtime search engine concept
Realtime search engine conceptRealtime search engine concept
Realtime search engine concept
 
Developing Document Image Retrieval System
Developing Document Image Retrieval SystemDeveloping Document Image Retrieval System
Developing Document Image Retrieval System
 
google search engine
google search enginegoogle search engine
google search engine
 

Similar to NBITSearch. Features.

About elasticsearch
About elasticsearchAbout elasticsearch
About elasticsearchMinsoo Jun
 
Michigan Information Retrieval Enthusiasts Group Meetup - August 19, 2010
Michigan Information Retrieval Enthusiasts Group Meetup - August 19, 2010Michigan Information Retrieval Enthusiasts Group Meetup - August 19, 2010
Michigan Information Retrieval Enthusiasts Group Meetup - August 19, 2010ivan provalov
 
E Science As A Lens On The World Lazowska
E Science As A Lens On The World   LazowskaE Science As A Lens On The World   Lazowska
E Science As A Lens On The World Lazowskaguest43b4df3
 
E Science As A Lens On The World Lazowska
E Science As A Lens On The World   LazowskaE Science As A Lens On The World   Lazowska
E Science As A Lens On The World LazowskaWCET
 
large_scale_search.pdf
large_scale_search.pdflarge_scale_search.pdf
large_scale_search.pdfEmerald72
 
Roaring with elastic search sangam2018
Roaring with elastic search sangam2018Roaring with elastic search sangam2018
Roaring with elastic search sangam2018Vinay Kumar
 
How a search engine works slide
How a search engine works slideHow a search engine works slide
How a search engine works slideSovan Misra
 
OSDC 2016 - Chronix - A fast and efficient time series storage based on Apach...
OSDC 2016 - Chronix - A fast and efficient time series storage based on Apach...OSDC 2016 - Chronix - A fast and efficient time series storage based on Apach...
OSDC 2016 - Chronix - A fast and efficient time series storage based on Apach...NETWAYS
 
A Fast and Efficient Time Series Storage Based on Apache Solr
A Fast and Efficient Time Series Storage Based on Apache SolrA Fast and Efficient Time Series Storage Based on Apache Solr
A Fast and Efficient Time Series Storage Based on Apache SolrQAware GmbH
 
Chronix: A fast and efficient time series storage based on Apache Solr
Chronix: A fast and efficient time series storage based on Apache SolrChronix: A fast and efficient time series storage based on Apache Solr
Chronix: A fast and efficient time series storage based on Apache SolrFlorian Lautenschlager
 
Chronix Time Series Database - The New Time Series Kid on the Block
Chronix Time Series Database - The New Time Series Kid on the BlockChronix Time Series Database - The New Time Series Kid on the Block
Chronix Time Series Database - The New Time Series Kid on the BlockQAware GmbH
 
Евгений Бобров "Powered by OSS. Масштабируемая потоковая обработка и анализ б...
Евгений Бобров "Powered by OSS. Масштабируемая потоковая обработка и анализ б...Евгений Бобров "Powered by OSS. Масштабируемая потоковая обработка и анализ б...
Евгений Бобров "Powered by OSS. Масштабируемая потоковая обработка и анализ б...Fwdays
 
Redis Modules - Redis India Tour - 2017
Redis Modules - Redis India Tour - 2017Redis Modules - Redis India Tour - 2017
Redis Modules - Redis India Tour - 2017HashedIn Technologies
 
A tour of Amazon Redshift
A tour of Amazon RedshiftA tour of Amazon Redshift
A tour of Amazon RedshiftKel Graham
 
Making Machine Learning Scale: Single Machine and Distributed
Making Machine Learning Scale: Single Machine and DistributedMaking Machine Learning Scale: Single Machine and Distributed
Making Machine Learning Scale: Single Machine and DistributedTuri, Inc.
 

Similar to NBITSearch. Features. (20)

About elasticsearch
About elasticsearchAbout elasticsearch
About elasticsearch
 
Oz search
Oz search Oz search
Oz search
 
Elasticsearch
ElasticsearchElasticsearch
Elasticsearch
 
Michigan Information Retrieval Enthusiasts Group Meetup - August 19, 2010
Michigan Information Retrieval Enthusiasts Group Meetup - August 19, 2010Michigan Information Retrieval Enthusiasts Group Meetup - August 19, 2010
Michigan Information Retrieval Enthusiasts Group Meetup - August 19, 2010
 
E Science As A Lens On The World Lazowska
E Science As A Lens On The World   LazowskaE Science As A Lens On The World   Lazowska
E Science As A Lens On The World Lazowska
 
E Science As A Lens On The World Lazowska
E Science As A Lens On The World   LazowskaE Science As A Lens On The World   Lazowska
E Science As A Lens On The World Lazowska
 
large_scale_search.pdf
large_scale_search.pdflarge_scale_search.pdf
large_scale_search.pdf
 
Roaring with elastic search sangam2018
Roaring with elastic search sangam2018Roaring with elastic search sangam2018
Roaring with elastic search sangam2018
 
UNIT V.pdf
UNIT V.pdfUNIT V.pdf
UNIT V.pdf
 
How a search engine works slide
How a search engine works slideHow a search engine works slide
How a search engine works slide
 
OSDC 2016 - Chronix - A fast and efficient time series storage based on Apach...
OSDC 2016 - Chronix - A fast and efficient time series storage based on Apach...OSDC 2016 - Chronix - A fast and efficient time series storage based on Apach...
OSDC 2016 - Chronix - A fast and efficient time series storage based on Apach...
 
A Fast and Efficient Time Series Storage Based on Apache Solr
A Fast and Efficient Time Series Storage Based on Apache SolrA Fast and Efficient Time Series Storage Based on Apache Solr
A Fast and Efficient Time Series Storage Based on Apache Solr
 
Chronix: A fast and efficient time series storage based on Apache Solr
Chronix: A fast and efficient time series storage based on Apache SolrChronix: A fast and efficient time series storage based on Apache Solr
Chronix: A fast and efficient time series storage based on Apache Solr
 
The new time series kid on the block
The new time series kid on the blockThe new time series kid on the block
The new time series kid on the block
 
Chronix Time Series Database - The New Time Series Kid on the Block
Chronix Time Series Database - The New Time Series Kid on the BlockChronix Time Series Database - The New Time Series Kid on the Block
Chronix Time Series Database - The New Time Series Kid on the Block
 
Евгений Бобров "Powered by OSS. Масштабируемая потоковая обработка и анализ б...
Евгений Бобров "Powered by OSS. Масштабируемая потоковая обработка и анализ б...Евгений Бобров "Powered by OSS. Масштабируемая потоковая обработка и анализ б...
Евгений Бобров "Powered by OSS. Масштабируемая потоковая обработка и анализ б...
 
Redis Modules - Redis India Tour - 2017
Redis Modules - Redis India Tour - 2017Redis Modules - Redis India Tour - 2017
Redis Modules - Redis India Tour - 2017
 
A tour of Amazon Redshift
A tour of Amazon RedshiftA tour of Amazon Redshift
A tour of Amazon Redshift
 
Making Machine Learning Scale: Single Machine and Distributed
Making Machine Learning Scale: Single Machine and DistributedMaking Machine Learning Scale: Single Machine and Distributed
Making Machine Learning Scale: Single Machine and Distributed
 
Lucece Indexing
Lucece IndexingLucece Indexing
Lucece Indexing
 

Recently uploaded

Long journey of Ruby Standard library at RubyKaigi 2024
Long journey of Ruby Standard library at RubyKaigi 2024Long journey of Ruby Standard library at RubyKaigi 2024
Long journey of Ruby Standard library at RubyKaigi 2024Hiroshi SHIBATA
 
(Explainable) Data-Centric AI: what are you explaininhg, and to whom?
(Explainable) Data-Centric AI: what are you explaininhg, and to whom?(Explainable) Data-Centric AI: what are you explaininhg, and to whom?
(Explainable) Data-Centric AI: what are you explaininhg, and to whom?Paolo Missier
 
Event-Driven Architecture Masterclass: Challenges in Stream Processing
Event-Driven Architecture Masterclass: Challenges in Stream ProcessingEvent-Driven Architecture Masterclass: Challenges in Stream Processing
Event-Driven Architecture Masterclass: Challenges in Stream ProcessingScyllaDB
 
2024 May Patch Tuesday
2024 May Patch Tuesday2024 May Patch Tuesday
2024 May Patch TuesdayIvanti
 
Continuing Bonds Through AI: A Hermeneutic Reflection on Thanabots
Continuing Bonds Through AI: A Hermeneutic Reflection on ThanabotsContinuing Bonds Through AI: A Hermeneutic Reflection on Thanabots
Continuing Bonds Through AI: A Hermeneutic Reflection on ThanabotsLeah Henrickson
 
Using IESVE for Room Loads Analysis - UK & Ireland
Using IESVE for Room Loads Analysis - UK & IrelandUsing IESVE for Room Loads Analysis - UK & Ireland
Using IESVE for Room Loads Analysis - UK & IrelandIES VE
 
Introduction to FIDO Authentication and Passkeys.pptx
Introduction to FIDO Authentication and Passkeys.pptxIntroduction to FIDO Authentication and Passkeys.pptx
Introduction to FIDO Authentication and Passkeys.pptxFIDO Alliance
 
Oauth 2.0 Introduction and Flows with MuleSoft
Oauth 2.0 Introduction and Flows with MuleSoftOauth 2.0 Introduction and Flows with MuleSoft
Oauth 2.0 Introduction and Flows with MuleSoftshyamraj55
 
Where to Learn More About FDO _ Richard at FIDO Alliance.pdf
Where to Learn More About FDO _ Richard at FIDO Alliance.pdfWhere to Learn More About FDO _ Richard at FIDO Alliance.pdf
Where to Learn More About FDO _ Richard at FIDO Alliance.pdfFIDO Alliance
 
ERP Contender Series: Acumatica vs. Sage Intacct
ERP Contender Series: Acumatica vs. Sage IntacctERP Contender Series: Acumatica vs. Sage Intacct
ERP Contender Series: Acumatica vs. Sage IntacctBrainSell Technologies
 
Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...
Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...
Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...panagenda
 
Intro to Passkeys and the State of Passwordless.pptx
Intro to Passkeys and the State of Passwordless.pptxIntro to Passkeys and the State of Passwordless.pptx
Intro to Passkeys and the State of Passwordless.pptxFIDO Alliance
 
Google I/O Extended 2024 Warsaw
Google I/O Extended 2024 WarsawGoogle I/O Extended 2024 Warsaw
Google I/O Extended 2024 WarsawGDSC PJATK
 
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...ScyllaDB
 
Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...
Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...
Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...Skynet Technologies
 
TopCryptoSupers 12thReport OrionX May2024
TopCryptoSupers 12thReport OrionX May2024TopCryptoSupers 12thReport OrionX May2024
TopCryptoSupers 12thReport OrionX May2024Stephen Perrenod
 
Simplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdf
Simplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdfSimplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdf
Simplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdfFIDO Alliance
 
Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024Patrick Viafore
 
How we scaled to 80K users by doing nothing!.pdf
How we scaled to 80K users by doing nothing!.pdfHow we scaled to 80K users by doing nothing!.pdf
How we scaled to 80K users by doing nothing!.pdfSrushith Repakula
 
Design Guidelines for Passkeys 2024.pptx
Design Guidelines for Passkeys 2024.pptxDesign Guidelines for Passkeys 2024.pptx
Design Guidelines for Passkeys 2024.pptxFIDO Alliance
 

Recently uploaded (20)

Long journey of Ruby Standard library at RubyKaigi 2024
Long journey of Ruby Standard library at RubyKaigi 2024Long journey of Ruby Standard library at RubyKaigi 2024
Long journey of Ruby Standard library at RubyKaigi 2024
 
(Explainable) Data-Centric AI: what are you explaininhg, and to whom?
(Explainable) Data-Centric AI: what are you explaininhg, and to whom?(Explainable) Data-Centric AI: what are you explaininhg, and to whom?
(Explainable) Data-Centric AI: what are you explaininhg, and to whom?
 
Event-Driven Architecture Masterclass: Challenges in Stream Processing
Event-Driven Architecture Masterclass: Challenges in Stream ProcessingEvent-Driven Architecture Masterclass: Challenges in Stream Processing
Event-Driven Architecture Masterclass: Challenges in Stream Processing
 
2024 May Patch Tuesday
2024 May Patch Tuesday2024 May Patch Tuesday
2024 May Patch Tuesday
 
Continuing Bonds Through AI: A Hermeneutic Reflection on Thanabots
Continuing Bonds Through AI: A Hermeneutic Reflection on ThanabotsContinuing Bonds Through AI: A Hermeneutic Reflection on Thanabots
Continuing Bonds Through AI: A Hermeneutic Reflection on Thanabots
 
Using IESVE for Room Loads Analysis - UK & Ireland
Using IESVE for Room Loads Analysis - UK & IrelandUsing IESVE for Room Loads Analysis - UK & Ireland
Using IESVE for Room Loads Analysis - UK & Ireland
 
Introduction to FIDO Authentication and Passkeys.pptx
Introduction to FIDO Authentication and Passkeys.pptxIntroduction to FIDO Authentication and Passkeys.pptx
Introduction to FIDO Authentication and Passkeys.pptx
 
Oauth 2.0 Introduction and Flows with MuleSoft
Oauth 2.0 Introduction and Flows with MuleSoftOauth 2.0 Introduction and Flows with MuleSoft
Oauth 2.0 Introduction and Flows with MuleSoft
 
Where to Learn More About FDO _ Richard at FIDO Alliance.pdf
Where to Learn More About FDO _ Richard at FIDO Alliance.pdfWhere to Learn More About FDO _ Richard at FIDO Alliance.pdf
Where to Learn More About FDO _ Richard at FIDO Alliance.pdf
 
ERP Contender Series: Acumatica vs. Sage Intacct
ERP Contender Series: Acumatica vs. Sage IntacctERP Contender Series: Acumatica vs. Sage Intacct
ERP Contender Series: Acumatica vs. Sage Intacct
 
Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...
Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...
Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...
 
Intro to Passkeys and the State of Passwordless.pptx
Intro to Passkeys and the State of Passwordless.pptxIntro to Passkeys and the State of Passwordless.pptx
Intro to Passkeys and the State of Passwordless.pptx
 
Google I/O Extended 2024 Warsaw
Google I/O Extended 2024 WarsawGoogle I/O Extended 2024 Warsaw
Google I/O Extended 2024 Warsaw
 
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...
 
Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...
Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...
Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...
 
TopCryptoSupers 12thReport OrionX May2024
TopCryptoSupers 12thReport OrionX May2024TopCryptoSupers 12thReport OrionX May2024
TopCryptoSupers 12thReport OrionX May2024
 
Simplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdf
Simplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdfSimplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdf
Simplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdf
 
Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024
 
How we scaled to 80K users by doing nothing!.pdf
How we scaled to 80K users by doing nothing!.pdfHow we scaled to 80K users by doing nothing!.pdf
How we scaled to 80K users by doing nothing!.pdf
 
Design Guidelines for Passkeys 2024.pptx
Design Guidelines for Passkeys 2024.pptxDesign Guidelines for Passkeys 2024.pptx
Design Guidelines for Passkeys 2024.pptx
 

NBITSearch. Features.

  • 1. Index ing and Fast Search engine NBITSearch parameters www.nbitsearch.com Novosib-BIT LLC version 1.03.3
  • 2. NBITSearch System NBITSearch is a search engine with an open API . --------------------------- NBITSearch is a programme kernel for ― Database Management Systems , - ― Warehouses of Large Data, - ― Search Systems applied to any Objects . .
  • 3.
  • 4. Exact and Fuzzy Search Interval queries provide fuzzy ( inexact ) search . Precise ( exact ) search is a particular case of fuzzy search .
  • 5. Indexable Objects Objects S of any types T
  • 6. The system indexes objects S of any types T simultaneously by a set any functions F (S) . Multifunctionality
  • 7. Sizes of Indexable Arrays The most tangible effect in the speed of search is shown for such arrays of objects , which support ≈ 50 ÷ 100 million and more objects for one index. A size of arrays of indexable objects can be 1 0 ÷ 100 terabyte and larger .
  • 8. Indexing Limitations One index supports ≈ 2 billion of its objects . Limitations of number of indexes are artificial .
  • 9. What is a Billion? 1 billion seconds is ≈ 32 years . 1 billion pages for a laser printer is a pile with a height of ≈ 100 km .
  • 10. Indexing Speed Estimator : T ~ ( N ) * LOG (N) T – time of forming one index , N – number of indexable objects .
  • 11. Compactness of Indexes A size of one index can vary within the range of 0 . 1 % ÷ 5 . 0 % of the size of indexable objects .
  • 12. Search Speed Time estimation of defining the address of the first potential block of data : T ~ LOG (N) T – time of “logic probing” , N – number of indexed objects .
  • 13. Search Speed A speed of fetching the result of interval queries from a hard disk can be 10 ÷ 100 times higher than (for the large data array) , the speed of similar operation in a standard relational DBMS .
  • 14. Search Speed A speed of fetching the result of interval queries from a hard disk can be 1000 times ( and more ) higher than (for the large data array) , the speed of similar operation when solving the problems with the use of brute force method .
  • 15. Search Speed A time of fetching the result of interval queries from a hard disk depends linearly on objects number in result set .
  • 16. Search Memory Due to compactness of indexes the system loads each of them in RAM entirely before queries are made .
  • 17. Search Memory A size of memory buffers to fetch the data depends on user’s needs . This size is often infinitesimal (~10 megabyte) .
  • 18. Reading of Result Set Reading the result set from a hard disk to RAM is optimum : magnetic head does not oscillate .
  • 19. THANK YOU ! www.nbitsearch.com Technology developed with support from FASIE formed by the Government of Russian Federation Novosib-BIT LLC © 2004 - 201 1 Patented