The Search Engine Architecture
CSCI 572: Information Retrieval and
Search Engines
Summer 2010
Outline
• Introduction
• Google
– The PageRank algorithm
– The Google Architecture
– Architectural components
– Architectural interconnections
– Architectural data structures
– Evaluation of Google
• Summary
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Problems with search engines circa the
last decade
• Human maintenance
– Subjective
• Example: Ranking hits based on $$$
• Automated search engines
– Quality of result
• Neglect to take user’s context into account
• Searching process
– High quality results aren’t always at the top of the list
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The Typical Search Engine Process
Search Engine Page
Yes
No
Filter Through Results
No
In what stages is the
most time spent?
Select Result
Exhausted
Results
Assess Page Quality
Good
Match
Yes
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How to scale to modern times?
• Currently
–
–
–
–
Efficient index
Petabyte scale storage space
Efficient Crawling
Cost effectiveness of hardware
• Future
– Qualitative context
• Maintaining localization data
– Perhaps send indexing to clients
– Client computers help gather Google’s index in a distributed,
decentralized fashion?
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Google
• The whole idea is to keep up with the growth of the web
• Design Goals:
-Remove Junk
Results
-Scalable document
indices
• Use of link structure to improve quality filtering
• Use as an academic digital library
– Provide search engine datasets
– Search engine infrastructure and evolution
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Google
• Archival of information
– Use of compression
– Efficient data structures
– Proprietary file system
• Leverage of usage data
• PageRank algorithm
– Sort of a “lineage” of a source of information
• Citation graph
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PageRank Algorithm
•
Numerical method to calculate page’s importance
– this approach might well be followed by people doing research
•
Page Rank of a page A
–
–
–
–
•
With damping factor d
Where PR(x) = Page Rank of page X
Where C(x) = the amount of outgoing links from page x
Where T1…Tn is the set of pages with incoming links to page
A
– PR(A)=(1-d)+d(PR(T1)/C(T1)+…+PR(Tn)/C(Tn))
It’s actually a bit more complicated than it first looks
– For instance, what’s PR(T1) and PR(T2) and so on?
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PageRank Algorithm
• An excellent explanation
– http://www.iprcom.com/papers/pagerank/
• Since the PR(A) equation is a probability
distribution over all web pages linking to web page
A…
– And because of the (1-d) term and the d*(PR….) term
– The PageRanks of all the web pages on the web will sum
to 1
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PageRank: Example
• So, where do you start?
• It turns out that you can
effectively “guess” what
the PageRanks for the web
pages are initially
– In our example, guess 0 for all of
the pages
• Then you run the PR function to calculate PR for all the web pages
iteratively
• You do this until…
– The page ranks for each web page stop changing in each iteration
– They “settle down”
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PageRank: Example
Below is the iterative calculation that we would run
PR(a) = 1 - $damp + $damp * PR(c);
PR(b) = 1 - $damp + $damp * (PR(a)/2)
PR(c) = 1 - $damp + $damp * (PR(a)/2 + PR(b) + PR(d));
PR(d) = 1 - $damp;
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PageRank Algorithm: First 18 iterations
a: 0.00000 b: 0.00000 c: 0.00000 d: 0.00000
a: 0.15000 b: 0.21375 c: 0.39544 d: 0.15000
a: 0.48612 b: 0.35660 c: 0.78721 d: 0.15000
a: 0.81913 b: 0.49813 c: 1.04904 d: 0.15000
a: 1.04169 b: 0.59272 c: 1.22403 d: 0.15000
a: 1.19042 b: 0.65593 c: 1.34097 d: 0.15000
a: 1.28982 b: 0.69818 c: 1.41912 d: 0.15000
a: 1.35626 b: 0.72641 c: 1.47136 d: 0.15000
a: 1.40065 b: 0.74528 c: 1.50626 d: 0.15000
a: 1.43032 b: 0.75789 c: 1.52959 d: 0.15000
a: 1.45015 b: 0.76632 c: 1.54518 d: 0.15000
a: 1.46341 b: 0.77195 c: 1.55560 d: 0.15000
a: 1.47226 b: 0.77571 c: 1.56257 d: 0.15000
a: 1.47818 b: 0.77823 c: 1.56722 d: 0.15000
a: 1.48214 b: 0.77991 c: 1.57033 d: 0.15000
a: 1.48478 b: 0.78103 c: 1.57241 d: 0.15000
a: 1.48655 b: 0.78178 c: 1.57380 d: 0.15000
a: 1.48773 b: 0.78228 c: 1.57473 d: 0.15000
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Still changing too
much
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PageRank: next 13 iterations
a: 1.48852 b: 0.78262 c: 1.57535 d: 0.15000
a: 1.48904 b: 0.78284 c: 1.57576 d: 0.15000
a: 1.48940 b: 0.78299 c: 1.57604 d: 0.15000
a: 1.48963 b: 0.78309 c: 1.57622 d: 0.15000
a: 1.48979 b: 0.78316 c: 1.57635 d: 0.15000
a: 1.48990 b: 0.78321 c: 1.57643 d: 0.15000
a: 1.48997 b: 0.78324 c: 1.57649 d: 0.15000
a: 1.49001 b: 0.78326 c: 1.57652 d: 0.15000
a: 1.49004 b: 0.78327 c: 1.57655 d: 0.15000
a: 1.49007 b: 0.78328 c: 1.57656 d: 0.15000
a: 1.49008 b: 0.78328 c: 1.57657 d: 0.15000
a: 1.49009 b: 0.78329 c: 1.57658 d: 0.15000
a: 1.49009 b: 0.78329 c: 1.57659 d: 0.15000
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Starting to stabilize
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PageRank: Last 9 iterations
a: 1.49010 b: 0.78329 c: 1.57659 d: 0.15000
a: 1.49010 b: 0.78329 c: 1.57659 d: 0.15000
a: 1.49010 b: 0.78329 c: 1.57659 d: 0.15000
a: 1.49010 b: 0.78329 c: 1.57659 d: 0.15000
a: 1.49011 b: 0.78329 c: 1.57660 d: 0.15000
a: 1.49011 b: 0.78330 c: 1.57660 d: 0.15000
a: 1.49011 b: 0.78330 c: 1.57660 d: 0.15000
a: 1.49011 b: 0.78330 c: 1.57660 d: 0.15000
a: 1.49011 b: 0.78330 c: 1.57660 d: 0.15000
Average pagerank = 1.0000
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Stabilized
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Google Architecture




Key components
Interconnections
Data structures
A reference
architecture for
search engines?
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Google Data Components
• BigFiles
• Repository
– Use zlib to compress
• Lexicon
– Word base
• Hit Lists
– Word->document ID map
• Document Indexing
– Forward Index
– Inverted Index
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Google File System (GFS)
• BigFiles
–
–
–
–
A.k.a. Google’s Proprietary Filesystem
64-bit addressable
Compression
Conventional operating systems don’t suffice
• No explanation of why?
– GFS: http://labs.google.com/papers/gfs.html
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Google Key Data Components
 Repository
 Stores full text of web
pages
 Use zlib to compress
 Zlib less efficient than bzip
 Tradeoff of time complexity versus space efficiency
 Bzip more space efficient, but slower
 Why is it important to compress the pages?
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Google Lexicon
• Lexicon
– Contains 14 million words
– Implemented as a hash table of pointers to words
– Full explanation beyond the scope of this discussion
• Why is it important to have a lexicon?
–
–
–
–
Tokenization
Analysis
Language Identification
SPAM
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Mapping queries to hits
 HitLists

wordID->(docID,position,font,capitalization)
mapping
 Takes up most of the
space in the forward
and inverted indices
 Types:
Fancy,Plain,Anchor
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Document Indexing
• Document Indexing
– Forward Index
• docIDs->wordIDs
• Partially sorted
• Duplicated doc IDs
– Makes it easier for final indexing and coding
– Inverted Index
• wordIDs->docIDs
• 2 sets of inverted barrels
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Crawling and Indexing
• Crawling
– Distributed, Parallel
– Social issues
• Bringing down web servers: politeness
• Copyright issues
• Text versus code
• Indexing
– Developed their own web page parser
– Barrels
• Distribution of compressed documents
– Sorting
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Google’s Query Evaluation
• 1: Parse the query
• 2: Convert words into WordIDs
– Using Lexicon
• 3: Select the barrels that contain documents which match the
WordIDs
• 4: Search through documents in the selected barrels until one is
discovered that matches all the search terms
• 5: Compute that document’s rank (using PageRank as one of the
components)
• 6: Repeat step 4 until no documents are found and we’ve went
through all the barrels
• 7: Sort the set of returned documents by document rank and return
the top k documents
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Google Evaluation
•
Performed by generating
numerical results
–
Query satisfaction
• Bill Clinton Example
–
Storage requirements
• 55GB Total
–
System Performance
• 9 days to download
26 million pages
• 63 hours to get the final
11 million (at the time)
–
Search Performance
• Between 1 and 10 seconds
for most queries (at the time)
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Wrapup
• Loads of future work
– Even at that time, there were issues of:
• Information extraction from semi-structured sources (such as web
pages)
– Still an active area of research
• Search engines as a digital library
– What services, APIs and toolkits should a search engine provide?
– What storage methods are the most efficient?
– From 2005 to 2010 to ???
• Enhancing metadata
– Automatic markup and generation
– What are the appropriate fields?
• Automatic Concept Extraction
– Present the Searcher with a context
• Searching languages: beyond context-free queries
• Other types of search: Facet, GIS, etc.
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The Future?
• User poses keyword query search
– “Google-like” result page comes back
– Along with each link returned, there will be
• A “Concept Map” outlining – using extraction methods – what
the “real” content of the document is
– This basically allows you to “visually” see what the page rank is
– Discover information visually
– Existing evidence that this works well
• http://vivisimo.com/
• Carrot2/3 clustering
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Concept Map
Science Data Systems
Software Architecture
Data Grid
Data
Publications
Software
Chris’s Homepage
http://sunset.usc.edu/~mattmann
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CSCI 572: Information Retrieval and Search Engines