1. Semantic Data Caching and Replacement

2. Outline Motivation Client Caching Architecture Model of Semantic Caching Simulations and Results Conclusion and Future Work

3. Motivation Distributed database Clients are high-end workstations (fat client) High computational power. Big local storage

4. Motivation (Contd.) Effective use of a client is the key to achieving high performance. Less network traffic. Faster response time. Higher server throughput. Better scalability.

5. Client Caching Architecture Data-Shipping. Clients process query. Data is brought on-demand from servers. Navigational access. Object ID (Tuple ID or Page ID). Can be categorized as tuple-based or page-based Cache Replacement Policies: LRU. MRU.

6. Client Caching Architecture (Contd.) Data-Shipping. Problem. Applications require associative access to data, that is, as provided by relational query languages.

7. Client Caching Architecture (Contd.) Query-Shipping. Associative access to data. Problems. Implementations do not support client caching.

8. Client Caching Architecture (Contd.) Semantic Caching. A model that integrates support for associative access into an architecture based on data-shipping. Advantage. Exploit the semantic information to effectively manage client cache.

9. Semantic Caching. Semantic description of the data rather than use record-id or page-id. Can be used to generate remainder query to send to server if the requested tuples are not available locally. Information for replacement is maintained as semantic regions. Low overhead, insensitive to bad clustering. Cache replacement use value function based on semantic description. Not just LRU or MRU. Client Caching Architecture (Contd.)

10. Data Granularity Missing Data Cache Replacement Page Caching GroupFaultingTemporal locality (LRU, MRU) Spatial locality (Clustering) Tuple Caching SingleFaulting Semantic Caching Dynamically Group Remainder Queries Semantic Locality

11. Model of Semantic Caching Remainder Query Semantic Regions Replacement Issues

12. Remainder Query Relation Re, query Q, client cache V. Probe query P(Q,V) = Q V can be answered locally. Remainder query R(Q,V) = Q V) should be sent to the server. Example: Select * from E where. salary 30,000. Client cache all the tuples, which salary < 50,000. Q = (salary 30,000). V = (salary <50,000). P = (salary 30,000). R = (salary>=50,000) (salary< 60,000 ). P Re V Q R

13. Semantic Regions Cache management and replacement unit. Grouped by semantic value. Each semantic region has a single replacement value. Described by a constrained formula. Consideration: Semantic region merge. (a)Original regions(a)Regions after Q

14. Semantic Regions Cache management and replacement unit. Grouped by semantic value. Each semantic region has a single replacement value. Described by a constrained formula. Consideration: Semantic region merge.(always merge) (a)Original regions(a)Regions after Q

15. Replacement Issues Temporal locality LRU, MRU

16. Replacement Issues (Contd.) Semantic locality Manhattan distance (Note) Manhattan distanceDefinition: The distance between two points measured along axes at right angles. In a plane with p 1 at (x 1, y 1 ) and p 2 at (x 2, y 2 ), it is |x 1 - x 2 | + |y 1 - y 2 |. p1p1 p2p2 o | p 1 p 2 | = | p 2 O | + | p 1 O | O O O

17. Simulation and Result Relation has three candidate keys, Unique2 is indexed and clustered, Unique1 is indexed and unclustered, Unique3 is unindexed and unclustered. RelSize10000Relation size (tuples) TupleSize200Size of tuple (bytes) TuplePerPage20How many tuples per page QuerySize1-10% of relation selected by each query Skew90% of queries within a hot region HotSpot10%Size of the hot region (% of relation) CacheSize250Client Cache size (kb)

18. Simulation and Result (Contd.) Unique2 (Clustered Index). Performance: Almost the same. Page-based is slightly better. Reason: Page-based overhead is smaller.

19. Simulation and Result (Contd.) Unique1(Unclustered Index). Performance: Tuple-based and semantic-based. are much better. Reason: Page-based is sensitive to clustered.

20. Simulation and Result (Contd.) Unique3(UnIndexed and Unclustered). Performance: Semantic-based is better. Reason: Remainder enables client and server. process query in parallel.

21. Simulation and Result (Contd.) Semantic locality / Manhattan distance on Unique1. Performance: Manhattan distance is better than LRU. Reason: Cold regions will be replaced faster.

22. Conclusion and Future Work Conclusion. A simple model with selection query, semantic caching provides better performance. Future work. Implementation issues for complex query, update, deletion, and insertion: Concurrency control. Consistency. Completeness. A Predicate-based caching scheme for client-server database architecture. (Arthur M. Keller and Julie Basu)