Customer Stories
H Automotive
Improved System Performance through DBMS Optimization
Improved performance by optimizing data models, simplifying processes, preventing duplicate processing, and enabling parallel processing.
- ClientH Automotive
- ProjectDBMS Optimization
- IndustryManufacturing
- SolutionsDA#, IDO
Large-Scale Data Performance Improvement through Data Model and Processing Optimization
- Purpose & Background
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- Improved response times and infrastructure efficiency during peak system usage periods
- Key Features
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- Approx. 3,000 tables across 3 systems
- Index optimization through object diagnosis
- DBMS configuration optimization
- Problematic SQL identification and performance guidance
Methodology for Large-Scale Data Performance Optimization
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01
Partition Strategy
- Data Modeling Tool: DA#
- Index Optimization Solution: IDO
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02
Optimal Data Model
- Efficient Integrated Data Model
- Optimized Model for Batch Program Access
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03
Data Processing Path Optimization
- Select Join Methods for Large-Scale Data
- Optimize Inefficient SQL for Set-Based Processing
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04
Optimizer Strategy Optimization
- Establish Index Strategies to Reduce Data Processing Scope
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05
Parallel Processing
- Enable Parallel Execution When Needed
- Prevent Unnecessary Parallel Execution
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06
Simplified Operations & Access Optimization
- Bitmap/B-Tree indexing to reduce query load
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07
Non-Procedural Data Processing
- Simplify Repetitive Procedural Processing with SQL
