Semantic Data Caching and Replacement

Outline

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

Motivation

Distributed database Clients are high-end workstations (fat client)

High computational power. Big local storage

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.

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.

Client Caching Architecture (Contd.)

Data-Shipping. Problem.

Applications require associative access to data, that is, as provided by relational query languages.

Client Caching Architecture (Contd.)

Query-Shipping. Associative access to data. Problems.

Implementations do not support client caching.

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.

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.)

Client Caching Architecture (Contd.)

Data Granularity

Missing Data

Cache Replacement

Page Caching

Group Faulting Temporal locality (LRU, MRU)

Spatial locality (Clustering)

Tuple Caching

Single Faulting

Semantic Caching

Dynamically Group

Remainder Queries

Semantic Locality

Model of Semantic Caching

Remainder Query Semantic Regions Replacement Issues

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< 60,000 and salary >30,000.

Client cache all the tuples,

which salary < 50,000.Q = (salary< 60,000 ) salary >30,000).V = (salary <50,000).P = (salary<50,000) salary >30,000).R = (salary>=50,000) (salary< 60,000 ).

P

Re

V Q

R

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

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

Replacement Issues

Temporal locality LRU, MRU

Replacement Issues (Contd.)

Semantic locality Manhattan distance

(Note) Manhattan distance Definition: The distance between two points measured along axes at right angles. In a plane with p1 at (x1, y1) and p2 at (x2, y2), it is |x1 - x2| + |y1 -

y2|.

p1

p2o| p1 p2 | = | p2O | + | p1O |

O

O

O

Simulation and Result

Relation has three candidate keys, Unique2 is indexed and clustered, Unique1 is indexed and unclustered, Unique3 is unindexed and unclustered.

RelSize 10000 Relation size (tuples)

TupleSize 200 Size of tuple (bytes)

TuplePerPage 20 How many tuples per page

QuerySize 1-10% % of relation selected by each query

Skew 90% % of queries within a hot region

HotSpot 10% Size of the hot region (% of relation)

CacheSize 250 Client Cache size (kb)

Simulation and Result (Contd.)

Unique2 (Clustered Index). Performance:

Almost the same. Page-based is slightly better.

Reason: Page-based overhead is smaller.

Simulation and Result (Contd.)

Unique1(Unclustered Index). Performance:

Tuple-based and semantic-based.

are much better.

Reason: Page-based is sensitive to

clustered.

Simulation and Result (Contd.)

Unique3(UnIndexed and Unclustered). Performance:

Semantic-based is better.

Reason: Remainder enables client and server.

process query in parallel.

Simulation and Result (Contd.)

Semantic locality / Manhattan

distance on Unique1. Performance:

Manhattan distance

is better than LRU.

Reason: “Cold regions” will be replaced

faster.

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)