How to Design a High-Performance Database for User-Defined Fields (UDFs)?

Efficient Database Design: Addressing the Performance Challenges of Custom Fields (UDFs)

Introduction

Storing and managing custom fields (UDFs) can pose technical challenges, especially when it comes to performance and scalability optimizations. This article explores several database design options for efficiently handling UDFs.

Program Evaluation

Option 1: Single table common column

❌ Not recommended - Inefficient storage and slow query performance due to large number of null values ​​and lack of data type coercion.

Option 2: Dynamic table

❌ Not Recommended - Indexes are challenging and have poor performance due to the need to frequently add columns.

Option 3: Store UDF details and views in a single table

❓ Feasibility is questionable - requires complex parsing logic and query views, which may affect performance.

Option 4: Divide multiple tables by data type

✅ Recommended - Separate UDFs by type, reduce storage overhead, and support optimized data processing, indexing and aggregation.

Option 5: XML data

❓ Feasibility is questionable - further investigation is required to determine its suitability for UDF processing and performance characteristics.

Best solution: Option 6 - Use multiple tables per UDF

Advantages:

• Efficient storage and indexing, tailored specifically for each UDF.
• Reduce the amount of data processing for aggregation and analysis through pre-aggregated table joins.
• Clear data semantics, using meaningful table and column names.
• Strict data type enforcement and constraint management.
• Excellent referential integrity handling through built-in RDBMS mechanisms.

Disadvantages:

• Potentially generate large number of tables (managed through schema separation).
• Additional application code is required to manage UDFs.

Other notes

• Group UDFs based on logical relationships to reduce the number of tables and optimize data retrieval.
• If performance issues arise, consider moving heavily used UDFs into the main table.
• For particularly large data volumes, explore table partitioning options.

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