Query optimization and challenges in DDBMS with Review Algorithms.
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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.
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