Semantic Data Caching and Replacement

1
Semantic Data Caching and Replacement

• Shaul Dar, Michael J. Frankin, Bjorn T. Jonsson,
• Divesh Srivastava, Michael Tan

Proceedings of the 22nd VLDB Conferences Mumbai
(Bombay), India, 1996
Presented by Kunhao Zhou
2
Outline

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

3
Motivation

• Distributed database
• Client are high-end workstations(fat client)
• High computational power.
• Big local storage

4
Motivation (Contd.)

• Effectively use of client is a key to achieving
  high performance.
• Less network traffic.
• Faster response time.
• Higher server throughput.
• Better scalability.

5
Client Caching Architecture

• Data-Shipping.
• Client process query.
• Data are bought 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.
• Application require associative access to data.
  Eg. As provided by relational query languages.

7
Client Caching Architecture (Contd.)

• Query-Shipping.
• Associative access to data.
• Problems.
• Implementation doesnt support client caching.
  (No 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
Client Caching Architecture (Contd.)

• 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.

10
Client Caching Architecture (Contd.)
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Model of Semantic Caching

• Remainder Query
• Semantic Regions
• Replacement Issues

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Remainder Query

• Relation Re, query Q, client cache V.
• Probe query P(Q,V) Q ÙV can be answered
  locally.
• Remainder query R(Q,V) QÙ(Ø V) should be sent
  to the server.
• Example
• Select from E where.
• salarylt 60,000 and salary gt30,000.
• Client cache all the tuples,
• which salary lt 50,000.
• Q (salarylt 60,000 ) Ù (salary gt30,000).
• V (salary lt50,000).
• P (salarylt50,000) Ù(salary gt30,000).
• R (salarygt50,000) Ù(salarylt 60,000 ).

P
R
Re
V
Q
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Semantic Regions

• Cache management and replacement unit.
• Grouped by semantic value. Each semantic region
  has same replacement value.
• Described by a constrained formula.
• Consideration
• Semantic region merge. (Always not merge)

(a)Original regions
(a)Regions after Q
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Semantic Regions

• Cache management and replacement unit.
• Grouped by semantic value. Each semantic region
  has same replacement value.
• Described by a constrained formula.
• Consideration
• Semantic region merge.(always merge)

(a)Original regions
(a)Regions after Q
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Replacement Issues

• Temporal locality
• LRU, MRU

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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.

O
p1
O
O
o
p2
p1 p2 p2O p1O
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Simulation and Result

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

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Simulation and Result (Contd.)

• Unique2 (Clustered Index).
• Performance
• Almost the same.
• Page-based is slightly better.
• Reason
• Page-based overhead is smaller.

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Simulation and Result (Contd.)

• Unique1(Unclustered Index).
• Performance
• Tuple-based and semantic-based.
• are much better.
• Reason
• Page-based is sensitive to
• clustered.

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Simulation and Result (Contd.)

• Unique3(UnIndexed and Unclustered).
• Performance
• Semantic-based is better.
• Reason
• Remainder enables client and server.
• process query in parallel.

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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)