Query optimization and challenges in DDBMS with Review Algorithms.

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
2

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.
01/20/16
3

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
4

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:
01/20/16
5

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.
01/20/16
6

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.
01/20/16
7

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
01/20/16
8

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
9

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
11

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
12

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
13

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.
01/20/16
14

15. Any Questions????
Thanks
01/20/16
15
Email: raj.pradip7@gmail.com
ME_CE_2015
NCIT,balkumari-Lalitpur