This document summarizes several reviews on query optimization in distributed databases. It discusses the key challenges in optimizing queries across multiple data sites including decomposing queries, determining where to move data to reduce communication costs, and calculating an efficient execution plan. The reviews covered explore techniques like dynamic programming, greedy algorithms, and ant colony optimization to generate low-cost query plans in distributed environments. Overall, the document outlines the basic process for optimizing queries in distributed databases and analyzes different approaches from prior work.

1. Pradip Raj Poudel (149-44), Kashiram
Pokharel(149-40)
“QUERY OPTIMIZATION
IN
DISTRIBUTEDDATABASE”
ME_CE III
NCIT, Lalitpur
A Review Article
By: Yasmeen Rm
Umar
Amit R Welekar
01/20/16
1

2. Outline:
 Abstract
 Introduction
 Query Optimization
 Optimization
Challenges
 Steps In Query
Processing
 S. Chaudhuri
Review
 Fan/Xifeng Review
 Chen/YU Review
 Kossman/Stocker
Review
 XUE Lin Review
 Conclusion
First Part Second Part
01/20/16
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3. Abstract:
 Data is Growing over Distributed
Environment, Day By Day so Better
Distributed DBMS is Required.
 Multiple sites with parts of Data’s ,so Query
optimization is a challenges in Distributed
Database.
 Query optimization finds the best execution
plan from various options.
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4. Introduction
 All Data Placed on
Central Computer
location so Easy to
Access/Extract.
 DB Query Easily
Transformed Into
RA operations.
 No overhead
 Data on multiple Sites
but centrally
Administrated.
 Provides
Flexibility/customization
.
 Ex. Location A can
Access data From
location B.
 Location Transparency
 Data Distributed, so
complex for Query
Transformation
Centralized Database Distributed Database
Database: Collection of
Files/Tables.
DBMS: Manage Database( CD or
DD)
01/20/16
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5. Query
Optimization:
 Data Distributed Over Different Sites in
Distributed Database.
 If Query is Given, the response of that query
may Requires data From several Sites.
(DBMS fxn)
 Now the Major task is “ Process A query with
location transparency and Find out Best
Sensible Execution Plan”.
Objective:
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6. Optimization
Challenges:
 1st
Break Query in Distributed Database
Environment.
 2nd
Determine which Sites has less
Data/records.
As less Data ,less Communication and Vice-
versa.
 Then Transfer those Data to Another Site.
More Sites= More Complex/Complication to
Process query.
 Compute Cost using Effective Cost Module.
As Data Distributed in Different Sites, More Challenges To Compute Efficient Query
Plan.
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7. Basic Steps In Query Processing
Plan
a). Query Decomposition:
Decompose into SimplerForm of RA.
OPTIMIZERCOMPONENTS:
a) . Query Engine
b) . Query Optimizer
b). Data localization:
Data Referenced to only one
location.(One Site)
c). Global Optimization:
Optimization of RA/Decision Making
Ex. Which site is efficient to move
data and where query will Execute.
d). Local Optimization:
When the Query Fragmented To
sites ,treat locally and Execute
Query.
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8. Optimizer Components:
Query Engine:
a). Produce O/Pby taking I/P
and Performs Operations By
taking Physical
operators( Join,Sort,Loop).
b). Construct Parse tree which
shows flow of Data fromOne
Operation to AnotherOperation.
Query Optimizer:
a). Receives Parse Tree As I/P
From QE and Produce Best
Possible Execution Plan ,Based
On least Resource Consumption.
b). Not a Easy taskto generate
Efficient Query Plan
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9. Review
 Chaudhari Discussed on Basic Query
Optimization/Search Space/Cost Estimation
Technique.
 Operator Tree having least resources
consumption would be best.
 For Selecting Best plan, Statistical Info and
Execution cost Analyzed.
 Statistical : No of Rows,memory,Joins,Pages
etc.
1. Surajit Chaudhari : Review
01/20/16
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10. Review:
 DD: Multiple Computer With Network.
 GDBMS,LDBMS/CM are Elements of DB.
Distributed Database Manager is global and
local.
 Proposed algorithm to improve semi-
connected sub query optimization to reduce
Network Cost.
But less efficient For Select Query.
2.Fan/XiFeng : Review
01/20/16
10

11. Review:
 More Focused on Communication Cost.
 Focused on Detail Study of Join/Semi join
Query.
 The combination of Join & Semi join Results in
Large Reduction of Communication Cost.
 Determines effect of join operation and find out
best combination of join which reduces
communication cost.
3.Chen/Yu: Review
01/20/16
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12. Review:
 Proposed Algorithm Based on IDP( iterative
Dynamic Programming)
 Good But difficult to apply incase of Complex
queries.
 Thus ,Uses Greedy Algorithm + DP concept
used For best Query plans.
 Memory Requirements not Considered.
4.Kossmann/Stocker:Review
01/20/16
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13. Review:
 User Module: Analyze User Query
 Syntax Analysis Module: done on Global Query
 Query tree Conversion Module
 Optimization Module: receives query tree which is optimized
and creates physical trees and calculates cost of each
physical operator tree.
 Order Processing Module: Distribute Query to Server &
Returns result to user.
 Local Data Dictionary used but table /cpu time/memory
increases.
5.XUE Lin: Review
01/20/16
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14. Conclusion:
 Dynamic Programming/Greedy: Large Space
Complexity.
 Thus New Approach Used Based On Ant Colony
Algorithm, Where Each Relation is Considered as
Domain Value.
 Better Execution Time has Been Achieved.
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15. Any Questions????
Thanks
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Email: raj.pradip7@gmail.com
ME_CE_2015
NCIT,balkumari-Lalitpur