Class 39: ...and the World Wide Web

Lecture 39: …and the World Wide Web

cs1120 Fall 2011David Evanshttp://www.cs.virginia.edu/evans

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Plan

The World Wide WebBuilding Web ApplicationsHow Google Works

(or, going back to pre-PS5 to make things really fast again!)

cs1120 recap in one (heavily animated) slide!

The World Wide Web

The “Desk Wide Web”

Memex MachineVannevar Bush, As We May Think, LIFE, 1945

WorldWideWeb

First web server and client, 1990(This picture, 1993)

Sir Tim Berners-LeeCERN (Switzerland)

MIT

http://www.w3.org/History/1989/proposal-msw.html

Overview:Many of the discussions of the future at CERN and the LHC era end with the question – “Yes, but how will we ever keep track of such a large project?” This proposal provides an answer to such questions. Firstly, it discusses the problem of information access at CERN. Then, it introduces the idea of linked information systems, and compares them with less flexible ways of finding information.

http://www.w3.org/History/1989/proposal-msw.html

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A Practical Project

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WorldWideWeb

Established a common language for sharing information on computers

Lots of previous attempts (Gopher, WAIS, Archie, Xanadu, etc.) failed

Why the World Wide Web?

– Didn’t attempt to maintain links, just a common way to name things

– Uniform Resource Locators (URL)http://www.cs.virginia.edu/cs1120/index.html

Service Hostname File Path

HyperText Transfer Protocol

World Wide Web succeeded because it was simple!

HyperText Transfer Protocol

Client (Browser)

GET /cs1120/index.html HTTP/1.0

<html><head>…

Contentsof file

Server

HTML HyperText Markup Language

HTML: HyperText Markup Language

• Language for controlling display of web pages• Uses formatting tags: between < and >

Document ::= <html> Header Body </html>Header ::= <head> HeadElements </head>HeadElements ::= HeadElement HeadElementsHeadElements ::= ε | <title> Element </title>Body ::= <body> Elements </body>Elements ::= ε | Element ElementsElement ::= <p> Element </p>Element ::= <center> Element </center>…

Popular Web Site: Strategy 1Static, Authored Web Site

Content Producer

http://www.twinkiesproject.com/

Drawbacks:•Have to do all the work yourself•The world may already have enough Twinkie-experiment websites

Popular Web Site: Strategy 2Dynamic Web Applications

Seed content and function

Web Programmer

eBay in 1997http://web.archive.org/web/19970614001443/http://www.ebay.com/

Produce more content

Attracts users

Popular Web Site: Strategy 2Dynamic Web Applications

Seed content and function

reddit.com in 2005

Produce more content

Attracts users

reddit.com today

Advantages:• Users do most of the work• If you’re lucky, they might even pay you for the privilege!

Disadvantages:• Lose control over the content (you might get sued for things your users do)• Have to know how to program a web application

Dynamic Web SitesPrograms that run on the web server

Can be written in any language (often in Python or Java), just need a way to connect the web server to the program

Program generates HTML (often JavaScript also now)Every useful web site does this

Programs that run on the client’s machineJava, JavaScript (aka, “Scheme for the Web”), Flash, etc.:

language must be supported by the client’s browserResponsive interface: limited round-trips to server

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Searching the Web

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Building a Web Search Engine

• Database of web pages– Crawling the web collecting pages and links– Indexing them efficiently

• Responding to Searches– Spell checking – edit distance– How to find documents that match a query– How to rank the “best” documents

Crawling CrawleractiveURLs = [ “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 pagesWill take very long to get a good view of the webWill annoy web server adminsdownloadPage and extractLinks must be very robust



• Responding to Searches– How to find documents that match a query– How to rank the “best” documents

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)

Google: about 40 Billion pages (1 Trillion URLs, but number actually indexed is a closely kept corporate secret)

* 60 KB (average web page size) = ~2.4 Quadrillion bytes to search!

Linear is not nearly good enough when n is Quadrillions

Hash Table

def lookup(key, table) : searchEntries(table[H(key, len(table))])

Index Key-Value Pairs

0 { <“Colleen”, ? >, <“virginia”, ? >, … }

1 { <“Bob”, ? >, … }

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3

…

[about a million bins?]

Finding a good H is difficultYou can download google’s from http://code.google.com/p/google-sparsehash/

http://code.google.com/p/google-sparsehash/

Google’s Lexicon1998: 14 million words (billions today?)Lookup word in H(word, nbins): maps to WordID

Key Words0 [<“aardvark”, 1024235>, ... ]1 [<“aaa”, 224155>, ..., <“zzz”, 29543> ]... ...

nbins – 1 [<“abba”, 25583>, ..., <“zeit”, 50395> ]

Google’s Reverse IndexWordId ndocs pointer

00000000 3

00000001 15

...

16777215 105

(Based on 1998 paper…definitely changed some since then, but now they are secretive!)

Lexicon: 293 MB (1998)Today: many GB?

“InvertedBarrels”:

41 GB (1998)Today: many TB?

Inverted Barrelsdocid (27 bits) nhits (5 bits) hits (16 bits

each)

7630486927 23...

plain hit:capitalized: 1 bitfont size: 3 bitsposition: 12 bits first 4095 chars, everything else

extra info foranchors, titles(less position bits)

Suggested experiment for winter break: is the position field still only 12 bits?



• Responding to Searches– Spell checking – edit distance– How to find documents that match a query– How to rank the “best” documents

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

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PageRank

If a site is important and interesting, other sites will link to it.

But…not all links are equal: if a lot of highly-ranked sites link to this site, this site should be highly-ranked.

Don’t ever take <a href=http://www.cs.virginia.edu/cs1120>cs1120</a>!

PageRank

def pageRank (u): rank = 0 for b in linksToPage (u) rank = rank + PageRank (b) / Links (b) return rank

Would this work?

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) replace the old ranks with the new ranks

How do initial ranks effect results?How many iterations are necessary?

PageRank: 1998• Crawlable web (1998): • 150 million pages, 1.7 Billion links• Database of 322 million links– Converges in about 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

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Do we have a search engine?

Google’s First Server

Theoretician: Sure!

Ali G: No way! It’ll blow up.

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How do we make our service fast enough to index the whole web and serve billions of requests?

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Counting Word Occurrences

“When in the Course of human events, it becomes necessary for one people to dissolve the political bands which have connected them with another, …”

[ <“When”, 1>, <“in”, 1>, <“the”, 2> … ]

“We the People of the United States, in Order to form a more perfect Union, establish Justice, insure domestic Tranquility, provide for the …”

[ <“We”, 1>, <“in”, 1>, <“the”, 2> … ]

map(doc, countWords)

If we have enough machines, can we do this fast for the whole web?

[ <“When”, 1>, <“in”, 1>, <“the”, 2> … ]

[ <“We”, 1>, <“in”, 1>, <“the”, 2> … ]

[ <“a”, 5>, <“in”, 3>, <“the”, 2> … ]

[ <“apple”, 1>, <“in”, 1>, <“the”, 7> … ]

reduce

reduce

reduce[ <“a”, 5>, <“in”, 6>, … ]

[ <“We”, 1>, <“in”, 2>, … ]

[ <“a”, 5>, <“in”, 4>, … ]

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MapReduce

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Key to Massive Parallel Execution

Get rid of state and mutation!

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Functional Programming(PS 1-4)

(define (count-matches p b) (list-sum (map (lambda (v) (if (eq? v b) 1 0)) p)))

Mechanical Logic

“Magic” Transistors

AND NOT

A B C R1 R0

0 0 0 0 00 0 1 0 1… … … … …

Any Discrete Function

(or a b) (not (and (not a) (not b)))

Any Mechanical Computation

Interpreters

1 2

# 1 0 1 1 0 1 1... ...

1 0 1 1 0 1 1 1 #

3 Turing Machine

def meval(expr, env): … return evalApplication(expr, env)



Mechanical Logic


AND NOT

A B C R1 R0

0 0 0 0 00 0 1 0 1… … … … …




Interpreters

1 2

# 1 0 1 1 0 1 1... ...

1 0 1 1 0 1 1 1 #

3 Turing Machine




A B C R1 R0

0 0 0 0 00 0 1 0 1… … … … …




Interpreters

1 2

# 1 0 1 1 0 1 1... ...

1 0 1 1 0 1 1 1 #

3 Turing Machine


State and Mutation

Objects

1m1: 2 3

SimObject

PhysicalObjectPlace

MobileObject



A B C R1 R0

0 0 0 0 00 0 1 0 1… … … … …




Interpreters

1 2

# 1 0 1 1 0 1 1... ...

1 0 1 1 0 1 1 1 #

3 Turing Machine


State and Mutation

Objects

1m1: 2 3

SimObject

PhysicalObjectPlace

MobileObject


Mechanical Logic




Interpreters

State and Mutation

Objects

Recursive Definitions

Universality

Abstraction

Charge

Now, you know almost everythingyou need to build the next reddit or google!

Technology

Class 39: ...and the World Wide Web