Query Execution on Query Execution on NeNetTtTravelerraveler
Angel L. Villalaín-GarcíaAngel L. Villalaín-GarcíaManuel RodrManuel Rodrííguez-Martguez-MartííneznezUniversity of Puerto Rico - Mayaguez University of Puerto Rico - Mayaguez CampusCampus
WALSAIP 04/21/23
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ObjectivesObjectives
Develop a framework for Parallel and Develop a framework for Parallel and Distributed Query Optimization and Distributed Query Optimization and Execution on NetTravelerExecution on NetTraveler
Facilitate and Optimize the access of Facilitate and Optimize the access of data across WANsdata across WANs Transparent data accessTransparent data access Uniform access interfaceUniform access interface
Robust operation by exploiting Robust operation by exploiting replicationreplication
WALSAIP 04/21/23
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Road MapRoad Map
ObjectivesObjectives Motivation Motivation Problem FormulationProblem Formulation Proposed SolutionProposed Solution Execution ExampleExecution Example ContributionsContributions Technical DetailsTechnical Details StatusStatus Next Steps Next Steps SummarySummary Questions & DemoQuestions & Demo
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MotivationMotivation
INTERNET
Mobile Devices
Relational DBMS(Oracle,
PostgreSQL, etc)
XML Repositories
Other Data Repositories
Wireless Sensors
LAN
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Problem FormulationProblem Formulation
INTERNET
Mobile Devices
Relational DBMS(Oracle,
PostgreSQL, etc)
XML Repositories
Other Data Repositories
Wireless Sensors
LAN
Dispersed and Heterogeneous data sources
No uniformity on WANs•Several limitations
•Bandwidth
•Memory
•Power
•Processing Capabilities
WALSAIP 04/21/23
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Problem Formulation (cont.)Problem Formulation (cont.)
Traditional DBMS Plan
Parser
Semantic Analyzer
Logical Optimizer
Physical Optimizer
Query Plan Execution
Centralized Query Optimizer
Scan Relations
Select A.id, A.name from A,B where A.id = B.id and A.sage <30
WALSAIP 04/21/23
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Proposed SolutionProposed Solution
Parser
Semantic Analyzer
Logical Optimizer
Physical Optimizer
Query Plan Execution
Physical Optimizer
Query Plan Execution
Decentralized Query OptimizerDistributed and Parallel DBMS Plan
Scan Replicated Relations
Select A.id, A.name from A,B where A.id = B.id and A.sage <30
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Execution ExampleExecution Example
Q
QSB1Knows:
QSB2, QSB3, QSB4
QSB2
QSB3
QSB4
IG2
IG1
R
R
R
R
IG3
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ReplicatesManagement
Manage Partitions
Pre Hashed On The Fly
Hashing MechanismParallel Ops
IG Level Operations
Memory Management
SchedulersMechanisms Hash Join Exchange Op
Parallel OpsQSB Level Operations
Site ManagementScheduling Management
Parallel & Distributed Ops
Physical OptimizerLogical Optimizer
Optimizer Level Operations
Technical DetailsTechnical Details
WALSAIP 04/21/23
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ContributionsContributions
Facilitate Integration for scientific Facilitate Integration for scientific applicationsapplications Heterogeneous data sourcesHeterogeneous data sources Heterogeneous schemas Heterogeneous schemas
Load BalancingLoad Balancing Spread work to various nodesSpread work to various nodes
RobustnessRobustness Can get data from multiple sourcesCan get data from multiple sources
AsynchronousAsynchronous Dynamically replace nodes used for processingDynamically replace nodes used for processing
Decentralized Query OptimizationDecentralized Query Optimization
WALSAIP 04/21/23
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StatusStatus
Manage Replicates
Manage Partitions
Pre Hashed On The Fly
Hashing MechanismParallel Ops
IG Level Operations
Memory Management
SchedulersMechanisms Hash Join Exchange Op
Parallel OpsQSB Level Operations
Site ManagementScheduling Management
Parallel & Distributed Ops
Physical OptimizerLogical Optimizer
Optimizer Level Operations
WALSAIP 04/21/23
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Next StepsNext Steps
StudStudy y Scheduling Effect aScheduling Effect and nd improvementsimprovements
Hash Join operators and functionalitiesHash Join operators and functionalities
User interface for configuration and User interface for configuration and demonstration purposesdemonstration purposes
WALSAIP 04/21/23
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Additional Areas of ResearchAdditional Areas of Research
Distributed Catalog ManagerDistributed Catalog Manager Oliver MorenoOliver Moreno
Server-Side Query Recovery MechanismServer-Side Query Recovery Mechanism Victor KarehVictor Kareh
NetTraveler System AdministrationNetTraveler System Administration Osvaldo FerreroOsvaldo Ferrero
WALSAIP 04/21/23
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Summary Summary
Facilitate and Optimize the access of Facilitate and Optimize the access of data across WANsdata across WANs Query Parallelization and ExecutionQuery Parallelization and Execution Exploiting ReplicationExploiting Replication Response Time improvementResponse Time improvement
See website for API and user manualSee website for API and user manual http://amadeus.ece.uprm.edu/~s08413http://amadeus.ece.uprm.edu/~s08413
WALSAIP 04/21/23
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Demo and QuestionsDemo and Questions