1. INFO 344 Web Tools And Development
CK Wang
University of Washington
Spring 2014

2. Announcements Azure + AWS credits sent
Azure = see slides on Canvas (today)
AWS = google it, how to use the credits

3. Programming Assignment #2

4. Build a query suggestion service
My favorite programming assignment

5. Rewarding! Does something useful Does something you use everyday
10 hours of work looks like 10 days of results
Challenging but FUN.

6. Query Suggestion
All modern websites with user input has this, it’s a must-have.
Super useful.
Needs to be fast.

7. Final Product AJAX Web service hosted on Azure, super scalable
Written in C#, follow best practices & things taught in class
No SDKs/external libraries except for jQuery for AJAX

8. Great User Experience Must be AJAX
Must be fast, i.e. year web service returning the results needs to be < 100ms.

9. Deliverables Due on April 30, 11pm PST Submit on Canvas
Please submit the following as a single zip file:
Readme.txt with URL to your Azure instance & GitHub repro
Screenshot of your Azure dashboard with Instance running (azure-compute.jpg)
C# source code (we should be able to run this locally)
Write up explaining how you implemented everything. Make sure to address each of the requirements, writeup.txt (~500 words)
Extra credits – short paragraph in extracredits.txt for each extra credit (how to see/trigger/evaluate/run your extra credit feature and how you implemented it)

10. Start Early!!! Today!!!
This is the hardest programming assignment all quarter

11. Hint
Google search “trie”, you will need this to store your data for fast retrieval
Your algorithm will likely be recursive/depth first search
Trim results to 10 to improve performance
Run out of memory = store as much data as you can!
Only store titles with a-z, A-z, (space), ignore everything else.

12. Big O notation Run time complexity as ‘n’ increases Common ones are
n = number of elements
Common ones are
O(n), O(n^2), O(log n), O(n log n)
Some proofs may be complicated (see Algo’s class)
Linear search = O(n)
Binary search = O(log n)

13. Big O for PA2 Data = millions of titles/phrases!
Linear search O(n) – too slow!
Trie search O(log n) – very fast! The log-base number is likely 26 (or 27)
10 instructions in O(n) searches 10 titles…
10 instructions in O(log n) searches 26^(10) titles!

14. Always use Trie? 10 vs. 26^10 OMG. Always use Trie!
Not for free! Space-time tradeoff
Linear search O(n) = less than 500mb
Trie O(log n) = maybe over 10GB to fit everything!

15. Challenge Code for this assignment == hard so start early!
If you can write the code in 4 hours, you will pass any coding interview but this will likely take hours esp if you’ve never heard of a ‘trie’
2 weeks for this assignment so start now! This will be fun and very worthwhile to take the time and learn.

16. Extra Credit [10pts] Popularity (page view) based query suggestion*
[10pts] Hybrid List & Trie data structure (convert to Trie after > X entries in node)
[10pts] Handle misspelling gracefully**
[5pts] Query suggestion based on user searches
These are actually really really fun.

17. Questions?