Data Structures & Algorithms and The Internet:

1. Data Structures & Algorithms and The Internet: A different way of thinking

2. Comparison Sort vs Bucket Sort • What is bigger? The list of numbers or the alphabet of possible numbers? • O(n log n) vs. O(n+m)

3. Password cracking • Is it easier to try to crack one account by trying a million passwords, • Or is it easier to try to crack a million accounts by trying the same password?

4. Histograms and large vectors • How do search engines represent documents for searching/indexing? • As a histogram of words, e.g. a vector of say 100000 entries for each document, containing word counts • Term frequency – inverse document frequency: enhances rare words, diminishes common words • Math done on vectors to determine similarity / relevance

5. Good Enough is OK • Not the fastest algorithm? Put more computers on it. Cache results. • Not the best results? Not a big deal if user can try again. • The data may be wrong/ambiguous anyway. • Britney Spears / Paris Hilton

6. Graphs are everywhere • Web is a graph. Page rank algorithm says important/relevant pages are linked to by important/relevant pages. • Reverse lookup problem with web links, basically building inverse edges to a directed graph • Spam / porn links to each other… look for bad cliques? • Social networking. Graph of friends. If you respond well to an ad, maybe your friends will too.

7. Liars, spam, hackers, etc. • Algorithms are easier when you can trust your data • Is this email spam? • Does this website have useful content? • Can you make it where if you do N work to detect spammers, they have to do N^2 work to avoid you?

8. Sample Server Architecture cache1 front1 back1 back4 cache2 Load balancer front2 back2 back5 cache3 front3 back3 back6 cache4

9. Denial of Service • A little amount of work by a client causes a lot of work for the server, slowing down or blocking out other users • Either malicious or accidental • Example: fast queries to a search engine that cause cache misses • How to catch?

10. Log IP addresses • Block addresses causing too many cache misses within 10 sec, 1 min, 1 hr • They may be hitting different front ends • HashMap of IP addresses to counters? • But how many IP addresses?

11. Data structures are too large for one computer • Store pieces of data structures across many computers • How do you know what is where? • One solution is a master computer serving as an index, but this is a single point of failure

12. Partitioning • How to divide 4 billion IP addresses among several machines? • We need to know deterministically, given an IP, what machine to look it up on? • Use a hash function that knocks it down to the four machines, i.e. hash has good entropy on AAA.BBB.CCC.DDD • Sometimes can also use a small set of sorted data to divide into ranges that should evenly distribute the larger data

13. Captchkas • Computation easy for person but hard for computer, vision-based problems • How to stop computers from getting fake email addresses? Make it hard to automatically solve a puzzle required for registration • How do spammers bypass? Pay 3rd-world folks to solve puzzles all day, or offer free porn for solving puzzles

14. Databases • Persistent storage for your data • Let’s you do complex queries using a query language • Can add a layer of abstraction over sets, lists, hashes, etc. • Boolean/set logic very important

15. Redundancy and robustness • Is your data fine if a computer goes down • Is your server fine if a computer goes down • Can you undo/revert changes? • If something changes somewhere, how does everyone know about it? • Average runtime vs guaranteed runtime

16. Scaling to more users/data • Is growth as simple as adding another machine? (e.g. linear time / space) • If you get dugg, can you handle a burst of users? • Can you fail softly?