Olston, Ailamaki, Garrod, Maggs, Manjhi, Mowry, Carnegie Mellon University Conference on Innovative Data System Research, 2005 A Scalability Service for Dynamic Web Applications Raihan Al-Ekram University of Waterloo October 11, 2006

2 Outline  Scalability Problem  A Solution  Challenges  Proposed System  Architecture  Operation  Consistency Management  Channel-By-Query  Channel-By-Update  View Invalidation Strategies  Conclusion  Discussion

3 Scalability Problem  Sudden Popularity Spikes  Slashdot effect  Civic Emergency  9/11, Hurricane Katrina

4 A Solution  Scalability as a Third Party Service  Content Distribution Network

5 Challenges  Strong Consistency  Inconsistency could cost lives  Precludes TTL based caching  Ownership of Data  Security concerns  Data corruption risks  Precludes distributed data replication  Invalidate cache instead of updating

6 Observations  Read domination  Modifications only in Home Servers  Predefined Query and Update Templates  Cache invalidation based on query/update independence analysis  Strong cache consistency

7 System Architecture

8 System Operation  Fixed set of query and update templates at the proxy servers  Statically analyze the templates for conflicting query templates for each update template  When an update template is instantiated, all proxies containing an instantiation of a conflicting query template is notified for invalidation  Query template instantiations are organized in multicast groups and notifications are sent to only to proper multicast groups

9 Consistency Management  Update Templates 1.insert into inv values (name=?, id=?, qty=?, date=now()) 2.update inv set qty=? Where id=?  Query Templates 1.select qty from inv where name=? 2.Select * from inv where date>? 3.select * from inv where qty<?

10 Channel-By-Query  Multicast Channels  One per query template, parameter independent oQ1-PI, Q2-PI, Q3-PI  One per parameter binding of an equality comparison oQ1-PS1, Q1-PS2, Q1-PS3 …  Query Template Instantiation  Subscribe own channels oQ1: Q1-PI, Q1-PSx oQ2: Q2-PI oQ3: Q3-PI  Update Template Instantiation  Notify conflicting query channels oU1: Q1-PSx, Q2-PI, Q3-PI oU2: Q1-PI, Q2-PI, Q3-PI  Receive Notification  Unsubscribe channels

11 Channel-By-Update  Multicast Channels  One per update template, parameter independent oU1-PI, U2-PI  One per parameter binding oU1-PS1, U1-PS2, …, U2-PS1, U2-PS2, …  Query Template Instantiation  Subscribe conflicting update channels oQ1: U1-PSx, U2-PI oQ2: U1-PI, U2-PI oQ3: U1-PI, U2-PI  Update Template Instantiation  Notify own channels oU1: U1-PSx, U1-PI oU2: U2-PI  Receive Notification  Unsubscribe to channels

12 Comparison  Channel-By-Query  Fewer channel subscriptions  More multicast messages  Channel-By-Update  More channel subscriptions  Fewer multicast messages

13 View Invalidation  Strategies  Black-box, only query and update available  View-Inspection, access to cache data is also available  Full-Access, access to base data is also available  Example  Books (Title, Author, Subject)  Authors (Author, Award, Country)

14 View Invalidation  View  create view MyView (Author, Award) as select A.Author, B.Award from Authors A, Books B where A.Author=B.Author and A.Country=“USA” and B.Subject=“History”  Updates 1.update Authors set Country=“France” whereAuthor=“Tocqueville” 2.update Books set Subject=“Fiction” whereTilte=“Napoleon’s Television”  Can benefit from view- inspection if there is no Author=“Tocqueville”  Can benefit from full- access if before the update Subject “History”

15 Conclusion  Ongoing work  Scalable and consistent data caching  Co-operative caches  Minimal home server involvement

16 Discussion  How are the updates in the home servers propagated to the proxies?  How do the proxies determine if the client requested data is in the cache?  Can this scheme provide 1SR or SI guarantee?