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Www Search Engine But Not In Perl

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  • 1. Building a scalable distributed WWW search engine … NOT in Perl! Presented by Alex Chudnovsky (http://www.majectic12.co.uk) at Birmingham Perl Mongers User Group (http://birmingham.pm.org) V1.0 27/07/05
  • 2. Contents
    • History
    • Goals
    • Architecture
    • Implementation
    • Why not Perl?
    • Conclusions
    • Credits
    • Recommended reading
  • 3. History (of my work in area of information retrieval)
    • First primitive pathetic stone-age search engine: 1000 documents in the “index” (1997, Perl)
    • Second engine using proper inverted indexing for Jungle.com: 500,000 products indexed (Perl + Java, 2002)
    • Current: 50,000,000 pages indexed with a lot more to go (to be revealed, 2005)
  • 4. Goals
    • Build a distributed WWW search engine capable of dealing with at least 1 bln web pages based on principles of [email_address] and D.NET
    • See to it that the chosen language for implementation (more on this later) fits purpose or more likely learn how to make it work
    • Eventually make some money out of it
  • 5. Architecture
    • Data collection (crawling)
    • Indexing: turning text into numbers
    • Merging: turning indexed barrels into single searchable index
    • Searching: locating documents for given keywords
  • 6. Data collection (crawling) Base Issues URLs to crawl and receives compressed pages Distributed c rawlers – receive lists of URLs to crawl, crawl them and send back compressed data. In the future will do distributed indexing Note: this stage is optional if you already have data to index, ie list of products with their descriptions
  • 7. Crawler screenshot 1
  • 8. Crawler screenshot 2
  • 9. Crawler screenshot 3
  • 10. Crawler screenshot 4
  • 11. Crawler screenshot 5
  • 12. Current Stats Source: http://www.majestic12.co.uk/projects/dsearch/stats.php as of 27/07/05
  • 13. Indexing Indexing is a process of turning words into numbers and creating inverted index. Data barrel Doc #0: Birmingham Perl Mongers Doc #1: Birmingham City Doc #2: Perl City Lexicon (maps words to their numeric WordIDs) Birmingham – 0 Perl – 1 Mongers – 2 City – 3 Inverted Index (Each of the WordID has list of (ideally sorted) DocIDs) 0 -> 0, 1 1 -> 0, 2 2 -> 0, 3 -> 1, 2 Note: if you use database then it make sense to have clustered index on WordID
  • 14. Merging Individual indexed barrels Single searchable index Note: this stage is not necessary if just one barrel is used as there will be no need to remap all Ids from local to their global equivalents.
  • 15. Searching Searching is a process of finding documents that contain words from search query Doc #0: Birmingham Perl Mongers Doc #1: Birmingham City Doc #2: Perl City Lexicon (maps words to their numeric WordIDs) Birmingham – 0 Perl – 1 Mongers – 2 City – 3 Inverted Index (lists DocIDs for each of the WordID) 0 -> 0, 1 1 -> 0, 2 2 -> 0, 3 -> 1, 2 Note: if you use database then it make sense to cluster on WordID Search query: “Birmingham Perl” WordIDs: 0, 1 Intersection of DocIDs present in both lists (implementation of boolean AND logic): Not matched! 2 n/a Not matched! n/a 1 Matched! 0 0 Result 1 (Perl) 0 (Brum)
  • 16. Search engine screenshot 1
  • 17. Search engine screenshot 2
  • 18. Implementation
    • Microsoft .NET C# ported to Linux using Mono ( http://www.mono-project.com )
    • ~90k lines of code (minimal copy/paste) written from scratch
    • Low level of dependencies (SharpZipLib/SQLite/NPlot)
  • 19. Why not Perl? (using C # instead)
    • Not strong in GUI department
    • Hard to deal with Multi-Threading and Asyncronous sockets
    • OOP is more of a hack
    • Lax compile-time checks due to not being strictly typed
    • Fear of performance bottlenecks forcing to use C++
    • Hard to profile for performance analysis
    • Managed memory lacks support for pointers (?)
    • Poor exceptions handling
    • I wanted something new :)
  • 20. Conclusions
    • Still work in progress, but some conclusions can be made already:
    • Inverted indexing approach helps to achieve fast searches
    • Its tough to build one – don’t try if you ain’t going to see it through!
    • Crawler is one tough piece of code – 6 months vs 2 months on searching
    • .NET C# is a decent language suitable for heavy duty tasks like this
  • 21. Credits
    • R&D: Alex Chudnovsky <alexc@majestic12.co.uk>
    • Pioneers*: FiddleAbout, dazza12, lazytom, Mordac, linuxbren, Cyber911, www.vanginkel.info , Vari, ASB, SEOBy.org, arni, japonicus, webstek.info | Pimpel, DimPrawn, Zyron, partys-bei-uns.de, jake, bull at webmasterworld, nada, dodgy4, sri-heinz
    * Volunteers running crawler and who crawled at least 1 mln URLs as of 27/07/05
  • 22. Recommended reading
    • “ The Anatomy of a Large-Scale Hypertextual Web Search Engine” Sergey Brin and Lawrence Page of Google ( http://www-db.stanford.edu/~backrub/google.html )
    • “ Managing Gigabytes” Ian h. Witten et al ISBN 1-55860-570-3
  • 23. Join! Join the project (unmetered broadband required!): majestic12.co.uk Your name could be here!