1. David Evans http://www.cs.virginia.edu/evans CS150: Computer Science University of Virginia Computer Science Class 38: Googling

2. 2 CS150 Fall 2005: Lecture 38: Googling Google Some searches... “David Evans”David Evans “Dave Evans”Dave Evans “idiot”idiot “lawn lighting”lawn lighting Tomorrow at 6pm (but google doesn’t know that!)

3. 3 CS150 Fall 2005: Lecture 38: Googling Google Building a Web Search Engine Database of web pages –Crawling the web collecting pages and links –Indexing them efficiently Responding to Searches –How to find documents that match a query –How to rank the “best” documents

4. 4 CS150 Fall 2005: Lecture 38: Googling Google Crawling Crawler activeURLs = [ “www.yahoo.com” ] while (len(activeURLs) > 0) : newURLs = [ ] for URL in activeURLs: page = downloadPage (URL) newURLs += extractLinks (page) activeURLs = newURLs Problems: Will keep revisiting the same pages Will take very long to get a good view of the web Will annoy web server admins downloadPage and extractLinks must be very robust

5. 5 CS150 Fall 2005: Lecture 38: Googling Google Crawling Crawler activeURLs = [ “www.yahoo.com” ] visitedURLs = [ ] while (len(activeURLs) > 0) : newURLs = [ ] for URL in activeURLs: visitedURLs += URL page = downloadPage (URL) newURLs += extractLinks (page) - visitedURLs activeURLs = newURLs What is the complexity?

6. 6 CS150 Fall 2005: Lecture 38: Googling Google Distributed Crawler activeURLs = [ “www.yahoo.com” ] visitedURLs = [ ] while (len(activeURLs) > 0) : newURLs = [ ] parfor URL in activeURLs: visitedURLs += URL page = downloadPage (URL) newURLs += extractLinks (page) - visitedURLs activeURLs = newURLs Is this as “easy” as distributing finding aliens?

7. 7 CS150 Fall 2005: Lecture 38: Googling Google Building a Web Search Engine Database of web pages –Crawling the web collecting pages and links –Indexing them efficiently Responding to Searches –How to find documents that match a query –How to rank the “best” documents

8. 8 CS150 Fall 2005: Lecture 38: Googling Google Building an Index What if we just stored all the pages? Answering a query would be  (size of the database) (need to look at all characters in database) For google: about 4 Billion pages (actual size is now considered a corporate secret) * 60 KB (average web page size) = ~184 Trillion Linear is not nearly good enough when n is Trillions

9. 9 CS150 Fall 2005: Lecture 38: Googling Google Reverse Index WordLocations … “David”[ …, http://www.cs.virginia.edu/~evans/index.html:12, …] … “Evans”[ …, http://www.cs.virginia.edu/~evans/index.html:19, …] … What is time complexity of search now?

10. 10 CS150 Fall 2005: Lecture 38: Googling Google Best Possible Searching Searching Problem: –Input: a target key key, a list of n pairs, sorted by key using a comparison function cf –Output: if key is in the list, the value associated with key; otherwise, not found What is the best possible solution to the general searching problem?

11. 11 CS150 Fall 2005: Lecture 38: Googling Google Recall Class 13: Sorting problem is Ω(n log n) There are n! possible orderings Each comparison can eliminate at best ½ of them So, best possible sorting procedure is Ω(log 2 n!) Sterling’s approximation: n! = Ω(n n ) –So, best possible sorting procedure is Ω(log (n n )) = Ω(n log n) Recall log multiplication is normal addition: log mn = log m + log n

12. 12 CS150 Fall 2005: Lecture 38: Googling Google Searching Problem is  (log n ) It is  (log n ) –Each comparison can eliminate at best ½ of all the elements from consideration It is O (log n ) –We know a procedure that solves it in  (log n ) For google: n is the number of distinct words on the web (hundreds of millions?) –  (log n ) is not good enough

13. 13 CS150 Fall 2005: Lecture 38: Googling Google Faster Searching? The proof that searching is  (log n ) relied on knowing that the best a comparison can do is eliminate ½ the entries Can we do better? –Without knowing anything about comparison: no –With knowing about comparison: yes What if one comparison can eliminate O( n ) of the entries?

14. 14 CS150 Fall 2005: Lecture 38: Googling Google Bin Searching First LetterItems a[<“aardvark”, [ http://www.aardvarksareus.com, …]>, … ] b[ … ] … z[ …, ] def binsearch (key, table) : search (key, table[key[0]]) What is time complexity of binsearch?

15. 15 CS150 Fall 2005: Lecture 38: Googling Google Searching in O(1) To do better than  (log n ) the number of bins must scale with n –Average number of elements in a bin must be O(1) –One comparison must eliminate O( n ) of the elements

16. 16 CS150 Fall 2005: Lecture 38: Googling Google Hash Tables Bin = H (key, number of bins) –H is a hash function –We’ve seen cryptographic hash functions where H must be collision resistant –For this, we don’t need that just need H must distribute the keys well across the bins Finding a good H is difficult –You can download google’s from http://goog-sparsehash.sourceforge.net/

17. 17 CS150 Fall 2005: Lecture 38: Googling Google Google’s Lexicon 1998: 14 million words (much more today) Lookup word in H ( word, nbins ) Maps to WordID KeyWords 0[,... ] 1[,..., ]... nbins – 1[,..., ]

18. 18 CS150 Fall 2005: Lecture 38: Googling Google Google’s Reverse Index WordIdndocspointer 000000003 0000000115... 16777215105 (From 1998 paper...may have changed some since then) Lexicon: 293 MB (1998) Inverted Barrels: 41 GB (1998)

19. 19 CS150 Fall 2005: Lecture 38: Googling Google Inverted Barrels docid (27 bits)nhits (5 bits)hits (16 bits each) 763048692723... plain hit: capitalized: 1 bit font size: 3 bits position: 12 bits first 4095 chars, everything else extra info for anchors, titles (less position bits)

20. 20 CS150 Fall 2005: Lecture 38: Googling Google Building a Web Search Engine Database of web pages –Crawling the web collecting pages and links –Indexing them efficiently Responding to Searches –How to find documents that match a query –How to rank the “best” documents

21. 21 CS150 Fall 2005: Lecture 38: Googling Google Finding the “Best” Documents Humans rate them –“Jerry and David’s Guide to the World Wide Web” (became Yahoo!) Machines rate them –Count number of occurrences of keyword Easy for sites to rig this –Machine language understanding not good enough Business Model –Whoever pays you the most is listed first

22. 22 CS150 Fall 2005: Lecture 38: Googling Google Random Walk Model Initialize all page ranks = 0 p = select a random URL for as long as you feel like p.rank = p.rank + 1 p = select random link from Links (p) Eventually, ranks measure probability a random websurfer would encounter a page Problems with this?

23. 23 CS150 Fall 2005: Lecture 38: Googling Google Back Links http://www.google.com/search?hl=en&lr=&q=link%3Awww.cs.virginia.edu%2F%7Eevans%2Findex.html&btnG=Search = 219 backlinks

24. 24 CS150 Fall 2005: Lecture 38: Googling Google Counting Back Links link:http://www.deainc.com/ –109 backlinks (hey, I should be first!) Back links are not a good measure –Most of mine are from my own pages But Google doesn’t know that (always) –Some pages are more important than others

25. 25 CS150 Fall 2005: Lecture 38: Googling Google PageRank Weight the back links by the popularity of the linking page def PageRank (u): rank = 0 for b in BackLinks (u) rank = rank + PageRank (b) / Links (b) return rank Would this work?

26. 26 CS150 Fall 2005: Lecture 38: Googling Google Converging PageRank Ranks of all pages depend on ranks of all other pages Keep recalculating ranks until they converge def CalculatePageRanks (urls): initially, every rank is 1 for as many times as necessary calculate a new rank for each page (using old ranks of other pages) replace the old ranks with the new ranks How do initial ranks effect results? How many iterations are necessary?

27. 27 CS150 Fall 2005: Lecture 38: Googling Google PageRank Crawlable web (1998): 150 million pages, 1.7 Billion links Database of 322 million links –Converges in ~50 iterations Initialization matters –All pages = 1: very democratic, models browser equally likely to start on random page –www.yahoo.com = 1,..., all others = 0 More like what Google probably uses

28. 28 CS150 Fall 2005: Lecture 38: Googling Google Query Work To respond to 1 query (2002) –Read 100 MB of data –10s of Billions of CPU cycles Google in 2002: –15,000 commodity PCs Racks of 88 2GB PCs, $278,000 each rack Power: 10 MW-h/month ($1,500) –If you have 15,000 PCs, there always be some with faults: load balancing, data partitioning

29. 29 CS150 Fall 2005: Lecture 38: Googling Google Building a Web Search Engine Database of web pages –Crawling the web collecting pages and links –Indexing them efficiently Responding to Searches –How to find documents that match a query –How to rank the “best” documents Ready to go become the next google?

30. 30 CS150 Fall 2005: Lecture 38: Googling Google Charge Before becoming the next Google, you need to finish PS8! Tomorrow: 6pm, Lighting of the Lawn Friday’s class: –A few other neat things about Google –Guidelines for project presentations –Exam review – email me your topics and questions Monday: project presentations