anatomy of a large-scale hyperbuid textual web search engine

7/28/2019 Anatomy of a Large-Scale HyperBuid Textual Web Search Engine

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Presented By:Ganesh C. Yadav.

(Roll No : 09141)


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Presentation Overview Problem Definition.

Design Goals

Google Search Engine Characteristics. Google Architecture

Scalability

Conclusions


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Problem Web is vast and ever expanding. It is getting flooded with data.

This data is heterogeneous and consists of all forms Text Images Ascii Java applets

Lists maintained by Humans cannot keep track of this.

Human attention is confined to 10-1000 documents

Previous search methodologies relied on keyword matching producinginferior quality results.


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Solution = Search Engine Search engines facilitate users to get the text or

documents of their choice within a click of mouse.

Some examples of Search engines:

Google,Altavista,MetaCrawler,Kosmix.

For comprehensive list of search engines do visit:

http://en.wikipedia.org/wiki/List_of_search_engines


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Specific Design Goals Deliver results that have very high precision even

at the expense of recall

Bring search engine technology into academicrealm in order to support novel research activities

Make search engine technology transparent, i.e.

advertising shouldnt bias results

Make system user friendly .


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Google Search Engine Features Uses link structure of web (PageRank) Uses text surrounding hyperlinks to improve accurate

document retrieval

Other features include: Takes into account word proximity in documents

Uses font size, word position, etc. to weight word Storage of full raw html pages


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PageRank For Layman Imagine a web surfer doing a simple random walk on the

entire web for an infinite number of steps.

Occasionally, the surfer will get bored and instead offollowing a link pointing outward from the current page

will jump to another random page.

At some point, the percentage of time spent at each page

will converge to a fixed value. This value is known as the PageRank of the page.


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PageRank For TechiesN(p): # outgoing links from page p

B(p): set of pages that point to p

d: tendency to get bored .R(p): PageRank of p

R(p) = [(1-d)+d*R(q)/N(q)] .


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Why do we need d? In the real world virtually all web graphs are not

connected, i.e. they have dead-ends, islands, etc.

If we dont have d we get ranks leaksfor graphs that are not connected, i.e. leads tonumerical instability.


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Justifications for using PageRankAttempts to model user behavior

Captures the notion that the more a page is pointed to

by important pages, the more it is worth looking at Takes into account global structure of web


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Google ArchitectureImplemented in C and C++ on Solaris and Linux


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PreliminaryHitlist is defined as list of occurrences of a particular

word in a particular document including additionalmeta info:

- position of word in doc- font size

- capitalization

- descriptor type, e.g. title, anchor, etc.


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Google Architecture (cont.)

Keeps track of

URLs that have andneed to be crawled

Compresses and

stores web pages

Multiple crawlers run in parallel.Each crawler keeps its own DNS

lookup cache and ~300 openconnections open at once.

Uncompresses and parsesdocuments. Stores link

information in anchors file.

Stores each link andtext surrounding link.

Converts relative URLsinto absolute URLs.

Contains full html of every webpage. Each document is prefixed

by docID, length, and URL.


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Google Architecture (cont.)Maps absolute URLs into docIDs stored in

Doc Index. Stores anchor text in barrels.Generates database of links (pairs of docIds).

Parses & distributes hit lists

into barrels.

Creates inverted index wherebydocument list containing docIDand hitlists can be retrieved

given wordID.

In-memory hash table thatmaps words to wordIds.

Contains pointer to doclist inbarrel which wordId falls into.

Partially sorted forwardindexes sorted by docID. Each

barrel stores hitlists for agiven range of wordIDs.

DocID keyed index where each entry includes info such as pointer to doc inrepository, checksum, statistics, status, etc. Also contains URL info if doc has

been crawled. If not just contains URL.


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Google Architecture (cont.)

List of wordIds

produced by Sorter andlexicon created byIndexer used to createnew lexicon used by

searcher. Lexicon stores~14 million words.

New lexicon keyed bywordID, inverted docindex keyed by docID,and PageRanks used

to answer queries

2 kinds of barrels.Short barrell which

contain hit list whichinclude title or anchorhits. Long barrell for

all hit lists.


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Google Query Evaluation1. Parse the query.2. Convert words into wordIDs.3. Seek to the start of the doclist in the short barrel for

every word.

4. Scan through the doclists until there is a document thatmatches all the search terms.

5. Compute the rank of that document for the query.6. If we are in the short barrels and at the end of any

doclist, seek to the start of the doclist in the full barrelfor every word and go to step 4.

7. If we are not at the end of any doclist go to step 4.8. Sort the documents that have matched by rank and

return the top k.


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Single Word Query Ranking Hitlist is retrieved for single word

Each hit can be one of several types: title, anchor, URL,large font, small font, etc.

Each hit type is assigned its own weight Type-weights make up vector of weights

# of hits of each type is counted to form count vector

Dot product of two vectors is used to compute IR score

IR score is combined with PageRank to compute final rank


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Multi-word Query Ranking Similar to single-word ranking except now must

analyze proximity

Hits occurring closer together are weighted higher

Each proximity relation is classified into 1 of 10values ranging from a phrase match to not evenclose

Counts are computed for every type of hit andproximity


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Scalability Cluster architecture combined with Moores Law make

for high scalability. At time of writing:

~ 24 million documents indexed in one week

~518 million hyperlinks indexed

Four crawlers collected 100 documents/sec


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Summary of Key Optimization Techniques Each crawler maintains its own DNS lookup cache Use flex to generate lexical analyzer with own stack for parsing

documents Parallelization of indexing phase

In-memory lexicon Compression of repository Compact encoding of hitlists accounting for major space savings Indexer is optimized so it is just faster than the crawler so that

crawling is the bottleneck Document index is updated in bulk Critical data structures placed on local disk Overall architecture designed avoid to disk seeks wherever possible


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References: http://video.google.com/videoplay?docid=-

1400721382961784115

http://google.stanford.edu

http://en.wikipedia.org/wiki/List_of_search_engines

The Anatomy of a Large-Scale Hyper textual

Web Search Engine Sergey Brin and Lawrence

Page(pdf). The audio presentation of my lecture will be posted

on my HomePage and will be given to Dr.Hankley.

www.cis.ksu.edu/~vamsee
http://video.google.com/videoplay?docid=-1400721382961784115http://video.google.com/videoplay?docid=-1400721382961784115http://google.stanford.edu/http://en.wikipedia.org/wiki/List_of_search_engineshttp://en.wikipedia.org/wiki/List_of_search_engineshttp://google.stanford.edu/http://video.google.com/videoplay?docid=-1400721382961784115http://video.google.com/videoplay?docid=-1400721382961784115http://video.google.com/videoplay?docid=-1400721382961784115

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