Database Synchronization via Message Broker and UUID Correlation
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Solution Overview
Problem
Large-scale data migration between older relational databases and new document-based databases faces challenges due to disparate data structures, requiring real-time data consistency without service downtime.
Innovation Solution
A method and system for synchronizing data across databases by generating synchronization events in response to changes in source data, publishing these events as messages in a source database internal queue, and using a message broker to synchronize sink data, employing UUIDs for correlation and real-time monitoring to maintain continuous two-way consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data migration is performed between relational databases and document-based databases, then data structure compatibility is improved, but data consistency between disparate data stores deteriorates
Solution Approach 1:
The patent introduces a message broker as an intermediary component between the source relational database and the target document-based database. The message broker receives change data capture events from the source database, transforms them into appropriate formats, and delivers them to the target database. This intermediary layer resolves the data structure incompatibility between disparate database types while maintaining data consistency through reliable message delivery mechanisms and acknowledgment protocols.
Solution Approach 2:
The system dynamically changes data structure parameters by using configurable mapping rules that define how source database schemas transform into target database schemas. These mapping parameters can be adjusted to accommodate different database types, allowing the same synchronization mechanism to work across relational and document-based databases by modifying structural parameters rather than the core synchronization logic.
2Reliability
If real-time data synchronization is implemented between databases, then data consistency is improved, but system complexity deteriorates
Solution Approach 1:
The source database automatically generates change data capture events when data changes occur, without requiring external triggering mechanisms. The message broker autonomously consumes these events, transforms them according to mapping rules, and delivers them to the target database. This self-service approach reduces system complexity by eliminating the need for complex polling mechanisms, scheduled tasks, or manual intervention, while maintaining real-time data consistency.
3Measurement precision
If UUIDs are used for data correlation between databases, then data tracking accuracy is improved, but data processing overhead deteriorates
Solution Approach 1:
The system generates UUIDs for data entities in advance, either during data insertion in the source database or through pre-computed mapping tables. By establishing these unique identifiers beforehand, the system eliminates the need for complex real-time matching algorithms during synchronization, reducing processing overhead while maintaining accurate data tracking and correlation between source and target databases.
Data Source
AI summary
Synchronizing data across databases comprising generating a synchronization event in response to a change in source data in a source database. The synchronization event is published as a message in a source database internal queue. The message is then published in a message broker. Sink data in a sink database is synchronized according to the message in the message broker to match the change in source data in the source database.