Tenant Dataset Schema Mapping for Storage Efficiency
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Solution Overview
Problem
Online database systems face challenges in storing datasets from multiple tenants due to schema mismatches, leading to potential data loss when tenant datasets are stored, as the tenant schema may not be supported by the database schema.
Innovation Solution
An online database system analyzes the tenant dataset to determine its custom schema, identifies differences with the database schema, and generates a tenant-specific mapping that converts the tenant schema to the database schema, prioritizing frequently used data entries and compressing less frequently used ones to ensure seamless integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If tenant datasets are stored directly in the online database system, then storage efficiency is improved, but data loss occurs due to schema mismatch
Solution Approach 1:
The patent introduces a schema mapping layer as an intermediary between the tenant dataset and the online database system. This mapping layer contains correspondence relationships that translate tenant-specific schema attributes to standard database schema attributes, enabling data to be stored without loss while maintaining compatibility with the database system's storage requirements.
2Reliability
If tenant schema is converted to database schema, then data integrity is improved, but conversion complexity increases
Solution Approach 1:
The patent performs schema analysis and mapping generation in advance, before actual data storage operations. By pre-establishing the correspondence relationships between tenant schema and database schema, the system eliminates the need for complex real-time conversion during data storage, thereby maintaining data integrity while reducing operational complexity.
3Ease of operation
If all tenant data entries are stored as individual entries, then data accessibility is improved, but storage space consumption increases
Solution Approach 1:
The patent applies different storage strategies to different data entries based on their characteristics and usage patterns. Frequently accessed or important entries are maintained as individual entries for easy accessibility, while less critical or redundant entries are compressed or aggregated. This localized differentiation optimizes both accessibility and storage efficiency.
Data Source
AI summary
An online database system receives a tenant dataset from a tenant and automatically generates a mapping that can be used to convert the tenant dataset from a tenant schema to a database schema used by the online database system. To generate the mapping, the online database system analyzes individual entries of the tenant dataset received from the tenant and determines a likelihood of usage. The mapping maintains entries in the tenant dataset that are likely to be used as individual entries in the converted dataset and compresses entries that are less likely to be used into fewer entries to satisfy the database schema. The online database system converts the tenant dataset from the tenant schema to the database schema using the mapping.