Change Data Capture System for Distributed Databases
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Solution Overview
Problem
Traditional data replication systems face challenges in handling large data sets, particularly in big data environments, due to issues with availability, scalability, and fault-tolerance, and struggle with latency when moving data between different database environments.
Innovation Solution
A system and method for capturing change data from a distributed data source system, supporting features like distributed source topology-awareness, initial load, deduplication, and recovery, which enables incremental change capture, automatic deduplication, and seamless node management across multiple nodes, ensuring data integrity and availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data replication systems are used to handle large data sets in big data environments, then data can be replicated between databases, but the systems face challenges with availability, scalability, and fault-tolerance
Solution Approach 1:
The system segments the distributed data source into multiple nodes, each maintaining its own state and data. The replication system processes changes from individual nodes independently, allowing the system to scale by simply adding more nodes without requiring complex reconfiguration of the entire system.
Solution Approach 2:
The replication system is designed to work with heterogeneous database targets and support multiple distributed data source types (Apache Cassandra, Kafka, MongoDB, Oracle NoSQL, Google Bigtable) through a unified interface, making it universally applicable across different big data environments while maintaining consistent replication behavior.
2Reliability
If data is replicated from distributed data sources to ensure availability and fault-tolerance, then data redundancy is improved, but handling dynamic topology changes and node failures increases system complexity
Solution Approach 1:
The replication system automatically detects node failures and topology changes in the distributed data source, dynamically adjusts its replication strategy, and recovers from failures without requiring manual intervention. The system self-manages the complexity of handling dynamic topology changes while maintaining fault-tolerance.
Solution Approach 2:
The system continuously monitors the state of distributed data source nodes and uses this feedback to dynamically adjust replication operations. When nodes are added or removed, the system receives feedback about topology changes and automatically reconfigures replication to maintain data integrity and fault-tolerance.
3Loss of information
If change data is captured from multiple nodes in a distributed system, then data completeness is improved, but duplicate data may be replicated to targets
Solution Approach 1:
The system introduces an intermediary deduplication layer between the distributed data source nodes and the target database. This intermediary component receives change data from multiple nodes, identifies and eliminates duplicates using various strategies (exact match, fuzzy match, or based on primary key), and forwards only unique changes to the target, ensuring data completeness without duplication.
4Reliability
If the replication system processes changes from all distributed data source nodes, then data synchronization is improved, but latency increases when moving data between different database environments
Solution Approach 1:
The system implements selective replication where changes are processed and replicated based on configuration and priority. High-priority changes are replicated immediately, while lower-priority changes may be batched or delayed. This partial action approach maintains data synchronization for critical data while reducing overall latency by not processing every single change with the same urgency.
Data Source
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AI summary
In accordance with an embodiment, described herein is a system and method for capture of change data from a distributed data source system, for example a distributed database or a distributed data stream, and preparation of a canonical format output, for use with one or more heterogeneous targets, for example a database or message queue. The change data capture system can include support for features such as distributed source topology-awareness, initial load, deduplication, and recovery. A technical purpose of the systems and methods described herein includes determination and communication of changes performed to data at a distributed data source that includes a large amount of data across a plurality of nodes, to one or more target computer systems.