Static-to-Dynamic Schema Transition for Lower Database Memory Use
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Solution Overview
Problem
Static schema entities in databases consume significant memory due to unused field data, leading to inefficient memory usage and incompatibility issues when removed, while dynamic schema entities maintain compatibility but at the cost of performance.
Innovation Solution
A mechanism to transition static schema to dynamic schema by generating a software development kit (SDK) that supports dropped fields, allowing unused fields to be omitted at compile time, reducing memory usage and maintaining compatibility with third-party code.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If static schema entities are used, then access performance is high, but memory consumption increases due to unused field data
Solution Approach 1:
The schema is segmented into static schema entities (for frequently accessed fields) and dynamic schema entities (for unused or less frequently accessed fields). This segmentation allows the system to load only necessary fields into memory while maintaining high performance access for critical data, thereby reducing overall memory consumption without sacrificing access speed for important fields.
Solution Approach 2:
The system introduces dynamic schema entities that can be loaded and unloaded at runtime based on usage requirements. Unlike static schemas that remain fixed in memory, dynamic schemas can be dynamically allocated only when needed, allowing the system to optimize memory usage while maintaining high performance access for frequently used static fields.
2Quantity of substance
If static schema entities are removed, then memory usage decreases, but third-party code compatibility is broken
Solution Approach 1:
The system introduces an intermediary layer that includes both static and dynamic schema entities. The static schema entities maintain compatibility with existing third-party code by preserving the expected interface and structure, while the dynamic schema entities provide the flexibility to reduce memory usage. This intermediary static schema layer acts as a bridge, ensuring that third-party code continues to function correctly while the system benefits from reduced memory consumption through selective loading.
3Reliability
If dynamic schema entities are used, then code compatibility is maintained, but access performance decreases
Solution Approach 1:
The schema is divided into static and dynamic segments, where static schema entities are optimized for high-performance access with compile-time optimization, while dynamic schema entities handle compatibility requirements. By segmenting the schema, the system can apply different optimization strategies to each segment, ensuring that frequently accessed fields maintain high performance while less critical fields use dynamic schemas for compatibility.
Solution Approach 2:
Different performance characteristics are applied locally to different schema entities based on their usage patterns. Static schema entities with high access frequency receive optimized access paths for high performance, while dynamic schema entities used primarily for compatibility maintain their flexible, load-on-demand characteristics. This local differentiation allows the system to achieve overall high performance while maintaining code compatibility.
Data Source
AI summary
Systems and methods that provide a mechanism to transition static schema to dynamic schema while maintaining backwards capability. Simple removal of static schema elements, followed by replacement with dynamic schema elements, make a third-party code incompatible since the third-party code references schema entities that no longer exist. Provided is a mechanism to decrease the memory use of non-material static schema entities. Transitioning static schema to dynamic schema allows the database to avoid loading non-material schema entities, thereby decreasing overall memory usage.