Query optimization and challenges in DDBMS with Review Algorithms.

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Pradip Raj Poudel (149-44), Kashiram Pokharel(149-40) OPTIMIZATION IN DISTRIBUTED DATABASEME_CE III NCIT, A Review Article By: Yasmeen Rm Umar Amit R Welekar 06/19/22 1

Transcript of Query optimization and challenges in DDBMS with Review Algorithms.

Page 1: Query optimization and challenges in DDBMS with Review Algorithms.

Pradip Raj Poudel (149-44), Kashiram Pokharel(149-40)

“QUERY OPTIMIZATION IN DISTRIBUTED DATABASE”

ME_CE III NCIT, Lalitpur

A Review ArticleBy: Yasmeen Rm

Umar Amit R Welekar05/01/23

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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

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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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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 DatabaseDatabase: Collection of Files/Tables.

DBMS: Manage Database( CD or DD)

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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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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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Basic Steps In Query Processing Plana). Query Decomposition:Decompose into Simpler Form of RA.

OPTIMIZER COMPONENTS:a) . Query Engineb) . Query Optimizer

b). Data localization: Data Referenced to only one location.(One Site)c). Global Optimization:Optimization of RA/Decision MakingEx. 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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Optimizer Components: Query Engine:a). Produce O/P by taking I/P and Performs Operations By taking Physical operators( Join,Sort,Loop).b). Construct Parse tree which shows flow of Data from One Operation to Another Operation.

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 task to generate Efficient Query Plan

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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

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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

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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

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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

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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

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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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Any Questions????

Thanks

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Email: [email protected]

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