Relational SaaS Data Backup With Hierarchical Cascading Restore
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Solution Overview
Problem
Existing data management systems face challenges in efficiently backing up and restoring data for relational SaaS applications due to their hierarchical nature and the need to maintain parent-child relationships, which complicates operations when accessing data through multiple APIs.
Innovation Solution
A data management system (DMS) employs a discovery and backup framework that identifies and maintains hierarchical relationships between computing objects and tables, organizing them as logical entities and using APIs to facilitate cascading restores, ensuring that parent data is backed up or restored before child data to maintain parent-child relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is accessed through multiple APIs for relational SaaS applications, then data completeness and relationship accuracy are improved, but operation complexity and time consumption increase
Solution Approach 1:
The patent segments the backup process into distinct phases: discovery phase (identifying computing objects and their relationships), backup phase (capturing data snapshots), and restore phase (reconstructing data with relationships). This segmentation allows each phase to handle specific tasks efficiently, reducing overall operational complexity while maintaining data integrity through systematic processing.
Solution Approach 2:
The patent performs preliminary actions by establishing hierarchical relationships and dependency mappings between computing objects before the actual backup or restore operations. This pre-processing creates a roadmap that guides subsequent operations, ensuring data integrity is maintained without requiring complex real-time coordination during backup/restore execution.
2Reliability
If hierarchical relationships are maintained during backup and restore, then data integrity is improved, but processing time and operational complexity increase
Solution Approach 1:
The patent performs preliminary discovery and relationship mapping before backup and restore operations. By pre-establishing the hierarchical structure and dependencies between parent and child computing objects, the system can execute backups and restores more efficiently without real-time complexity, as the execution phase simply follows the pre-planned sequence.
Solution Approach 2:
The patent implements periodic discovery processes that identify computing objects and their relationships at scheduled intervals. This periodic approach allows the system to update its understanding of data relationships without requiring continuous analysis, balancing relationship integrity maintenance with acceptable processing time.
3Ease of operation
If cascading restore functionality is implemented for relational SaaS data, then ease of recovery is improved, but system complexity increases
Solution Approach 1:
The patent implements self-service cascading restore functionality where the system automatically identifies and restores dependent computing objects based on pre-established hierarchical relationships. When a user initiates a restore for a parent computing object, the system autonomously determines which child objects need restoration and executes the cascade without requiring manual intervention, simplifying user operations while managing complexity internally.
Solution Approach 2:
The patent introduces an intermediary discovery layer that mediates between the user's restore request and the actual restore execution. This intermediary component analyzes the hierarchical relationships, determines the cascading scope, and coordinates the restore sequence, thereby shielding users from system complexity while enabling comprehensive recovery functionality.
4Adaptability or versatility
If multiple APIs are used to access computing objects, then data accessibility is improved, but coordination difficulty and error potential increase
Solution Approach 1:
The patent creates a universal discovery framework that works across multiple different APIs for accessing computing objects. This framework provides a common interface and standardized approach for identifying objects and relationships regardless of which specific API is used, allowing the system to leverage multiple APIs for data accessibility while managing their coordination through a unified methodology.
Data Source
AI summary
Methods, systems, and devices for data management are described. Software-as-a-service (SaaS) applications may host customers' information in a distributed environment that is not directly accessible to the customers. SaaS applications may store data in the form of relational tables. Snapshots for SaaS applications may maintain hierarchical relationships between computing objects and tables to enable cascading recovery. A data management system (DMS) may identify the computing object hierarchy for an organization's data as hosted by a SaaS application and also may identify the APIs associated with accessing each computing object, which may each include multiple tables. Tables that are accessed via a same API may be organized as logical entities. The DMS may store the hierarchical relationship between the tables, and as the tables are stored as logical entities, the DMS may organize the tables in the backup database based on which APIs are used to access the relevant tables.