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Anatomy of google
 

Anatomy of google

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    Anatomy of google Anatomy of google Presentation Transcript

    • THE ANATOMY OF A LARGE SCALE-HYPER TEXTUAL WEB SEARCH ENGINE ASIM FROM UNIVERSITY PESAHAWAR. Author: Sergey Brin, Lawrence Page
    • ABSTRACT Google Search Engine as Prototype Anatomy Web Users: Queries (tens of millions) Academic research Building a large scale search engine Heavy use of hyper textual information (anchor links, hyperlinks)
    • INTRODUCTION Web (as a dynamic entity) Irrelevant Search Results Human maintained Indices, Table of Contents Too many low quality research Address many problems of users (Page Ranking)
    • CONT…Google: Scaling with the Web  Google’s Fast Crawling Technology  Storage space availability  Indexing system processing 100’s of Gigabytes Data  Minimized Queries Response Time
    • DESIGN GOALS Improved Search Quality. Indexing does not provide Relevant Search Results. Making the percentage of Junks Results as low as possible. Users show interest in top ranked results. Notion is to provide relevant results. Google make uses of Link structure & anchor text.
    • CONT… Academic search engine results. User Accessibility & Availability of the desired results. Supports Novel Research. All problem solving solutions to be given in a single place.
    • SYSTEM FEATURES Google search engine has two important features. Link structure of the web(page ranking). Utilization Links(anchor text) to improve search results.  <A href="http://www.yahoo.com/">Yahoo!</A> Besides the text of a hyperlink (anchor text) isassociated with the page that the link is on,it is also associated with the page the linkpoints to.
    • PAGE RANK Page Rank: bringing order to the web Academic citation literature is applied to calculate page rank PR(A) = (1-d) + d(PR(t1)/C(t1) + ... + PR(tn)/C(tn)) In the equation t1 - tn are pages linking to page A, C is the number of outbound links that a page has and d is a damping factor, usually set to 0.85.
    • PAGE RANK (INTUITIVE JUSTIFICATION) Many pages that point to a single page A page having high PageRank that points to another page Broken Links are not listed on Higher Page Ranked sites Text of the link provides more description, Google utilizes such information  Provides more accurate results for images, graphs, databases
    • SYSTEM ANATOMY
    • SYSTEM ANATOMY URL Server:  provides list of URLs to the Crawlers for fetching information from web Distributed Crawlers (Downloading WebPages) Store Server:  Compression and Storage in Repository  docID’s are used to distinguish WebPages Indexer  Indexing, Sorting, Uncompressing, Parsing  Hits  records word occurences, position, text formate information in documents  Hits are organized into barrels which creates partially sorted forward index
    • FORWARD INDEXDocument WordsDocument 1 the,cow,says,mooDocument 2 the,cat,and,the,hatDocument 3 the,dish,ran,away,with,the,spoon
    • INVERTED INDEX T0 = "it is what it is“ T1 = "what is it“ T2 = "it is a banana“ A term search for the terms "what", "is" and "it" would give the set. If we run a phrase search for "what is it" we get hits for all the words in both document 0 and 1. But the terms occur consecutively only in document 1. Inverted Index Words {(2, 2)} a {(2, 3)} banana {(0, 1), (0, 4), (1, 1), (2, 1)} is {(0, 0), (0, 3), (1, 2), (2, 0)} It {(0, 2), (1, 0)} What
    • CONT… Indexer:  Anchor files as a result of parsing possessing links information (in & out links) URL resolver:  Reads anchor files, converts relative to absolute URLs and inturn into docIDs  Puts anchor text in forward index  Database of links, necessary to compute PageRanks Sorter :  Takes the barrels which are sorted by docID and resorts them by wordID to generate inverted index.  It produces a list of wordIDs and offsets into the inverted index.
    • CONT… DumpLexicon  A program DumpLexicon takes this list together with the lexicon produced by the indexer and generates a new lexicon to be used by the searcher. Searcher:  The searcher is run by a web server and uses the lexicon built by DumpLexicon together with the inverted index and the PageRanks to answer queries.
    • CONT… Major data structures  Data is stored in BigFiles which are virtual files and it supports compression.  Half of the storage used by raw html repository.  Having compressed html of every page and its small header.  Document index keep information of each document.  The ISAM(Index sequential access mode) index is ordered by docID.  Each stored entry includes information of current status, pointer into the repository, document checksum, URL and title information.  They all are memory-based hash tables with varying values attached with each word.
    • CONT… Hit lits encoding  Uses compact encoding(a hand optimized)  It requires less space and less bit manipulation.  It uses two bytes for every hits.  For saving space the length of a hit list is combined with the wordID in the forward index and the docID in the inverted index.  Forward index is stored in the number of barrels(64).  Each barrels holds word IDs  Words falling in particular barrel, the DocIDs is recorded into the barrel followed by the List of WordIDs with Hitlists which corresponds to those words
    • CONT… The inverted index consist of the same barrels as the forward index. Inverted index is processed by the sorter Pointer is used for pointing to wordID in barrels. Pointer points to List of docIDs and Hit list, this is called docList
    • CRAWLING Web Crawling (downloading pages) Crawlers (3 to 4) Each crawler contains three hundred open connections Social issues Efficiency
    • ARCHITECTURE OF THE GOOGLE SEARCH ENGINE
    • DESCRIPTION OF THE PICTORIAL COMPONENTSComponents DescriptionCrawlers There are several distributed crawlers, they parse the pages and extract links and keywords.URL Server Provides to crawlers a list of URLs to scan. The crawlers sends collected data to a store server.Server Store It compresses the pages and places them in the repository. Each page is stored with an identifier, a docID.Repository Contains a copy of the pages and images, allowing comparisons and caching.Indexer It decompresses documents and converts them into sets of words called "hits". It distributes hits among a set of "barrels". This provides an index partially sorted. It also creates a list of URLs on each page. A hit contains the following information: the word, its position in the document, font size, capitalization.Barrels These "barrels" are databases that classify documents by docID. They are created by the indexer and used by the sorter.Anchors The bank of anchors created by the indexer contains internal links and text associated with each link.
    • CONT…Components DescriptionURL It takes the contents of anchors, converts relative URLs into absoluteResolver addresses and finds or creates a docID. It builds an index of documents and a database of links.Doc Index Contains the text relative to each URL. The database of links associates each one with a docID (and so to aLinks real document on the Web). The software uses the database of links to define the PageRank of eachPageRank page. It interacts with barrels. It includes documents classified by docID andSorter creates an inverted list sorted by wordID. A software called DumpLexicon takes the list provided by the sorter (classified by wordID), and also includes the lexicon created by theLexicon indexer (the sets of keywords in each page), and produces a new lexicon to the searcher. It runs on a web server in a datacenter, uses the lexicon built bySearcher DumpLexicon in combination with the index classified by wordID, taking into account the PageRank, and produces a results page.
    • RESULTS, PROBLEMS & CONCLUSION Most important issue is quality of search results Google performance is better compared to other commercial engines Need of Relevant and exact Query Results Up to date information processing Performing search queries Crawling technologies Google employs a number of techniques to improve search quality including page rank, anchor text, and proximity information. “The ultimate search engine would understand exactly what you mean and give back exactly what you want.” by Larry Page
    •  “The ultimate search engine would understand exactly what you mean and give back exactly what you want.” by Larry Page “The absolute search engine’s query generation would be based on information, not based on the repository records and query results will be real timed, and it will change the whole internet and web architecture.” by asim
    • Thanks!