Bi-directional Data Replication with Transformation Configuration
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Solution Overview
Problem
Current cloud computing systems lack efficient methods for replicating data between instances while applying transformations to the replicated data and ensuring that changes made to the replicated data are accurately reflected back to the source instance, particularly in a scalable and secure manner.
Innovation Solution
A method for replicating instance data involves setting transformation configuration data on a consumer instance to map and transform incoming data from a producer instance, using a logging infrastructure to publish and subscribe data modification events, and enabling bi-directional replication to ensure that changes made to the replicated data are updated on the source instance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is replicated between cloud instances without transformation configuration, then replication speed is improved, but data consistency and adaptability to different instance schemas deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-configuring transformation rules and mappings between source and target table schemas before replication occurs. This allows the replication process to execute quickly while transformations are applied systematically, resolving the contradiction between speed and data consistency.
Solution Approach 2:
The system changes parameters by dynamically adjusting transformation configurations, mapping rules, and data format conversions based on the specific source and target instance schemas. This enables adaptable data transformation that maintains consistency across different instances without sacrificing replication efficiency.
2Reliability
If bi-directional replication is implemented to synchronize changes back to source, then data consistency is improved, but system complexity and processing overhead increase
Solution Approach 1:
The system implements feedback mechanisms where changes made at consumer instances are automatically detected, transformed, and propagated back to producer instances. This bi-directional synchronization maintains data consistency while using structured feedback loops to manage complexity.
Solution Approach 2:
The replication system is designed with universal components that handle both unidirectional and bi-directional replication, transformation configurations, and change tracking through a unified architecture. This multi-functionality reduces overall system complexity by consolidating multiple functions into integrated components.
3Adaptability or versatility
If transformation configuration data is set for each consumer replication set, then adaptability to different instances is improved, but configuration complexity and setup time increase
Solution Approach 1:
The system employs dynamic configuration where transformation rules and mappings can be adjusted based on the specific source and target instances. This allows the replication system to adapt to different schemas and data formats automatically, reducing manual configuration complexity while maintaining high adaptability.
Solution Approach 2:
The configuration system is segmented into modular components including transformation rules, mapping definitions, and instance-specific settings. This segmentation allows administrators to configure transformations incrementally and reuse configurations across multiple replication sets, reducing overall configuration complexity.
4Loss of time
If real-time data transformation and replication is performed, then data availability is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs data transformation and replication in periodic batches or at scheduled intervals rather than continuously for every single data change. This periodic action maintains real-time data availability while reducing peak computational resource consumption and processing overhead.
Data Source
AI summary
Transformation configuration data is set for a consumer replication set on a consumer instance to replicate on the consumer instance data of a source table included in a producer replication set on a producer instance. The transformation configuration data includes configuration data of at least one of: (i) a target table from among a plurality of tables on the consumer instance that is specified in the consumer replication set as a table for loading on the consumer instance, incoming data from the source table; and (ii) a specified mapping of incoming fields of the source table with respective fields of the target table. Replication event data of a data modification event associated with a record on the source table is received. The received replication event data is transformed based on the set transformation configuration data, and loaded on the target table.