Distributed Database Update Timing Acceleration
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Solution Overview
Problem
In distributed database systems, updating data across multiple nodes can lead to delays, where a node's updated data may not be immediately reflected across all nodes, causing clients to access outdated values, especially for high-access-frequency data records.
Innovation Solution
A distributed database system with a data management device that manages timestamps and update status information, allowing for accelerated data updates by promoting updates in nodes storing non-latest data records and ensuring consistency through management information updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is distributed across multiple nodes for parallel access, then access speed and throughput are improved, but data consistency and update timeliness deteriorate
Solution Approach 1:
The system performs preliminary actions by propagating update values to candidate nodes before the update is fully committed. The data management device identifies candidate nodes that will benefit from early update propagation and sends them the update value in advance, allowing these nodes to prepare for the update before it becomes official.
Solution Approach 2:
The system dynamically adjusts the update propagation process based on data access patterns. High-access-frequency data records are updated more quickly across nodes, while less frequently accessed data follows the standard update timeline. This dynamic approach optimizes the balance between consistency and access performance.
2Reliability
If update propagation is performed orderly across all nodes, then data consistency is maintained, but update time and latency increase
Solution Approach 1:
The system applies different update propagation strategies to different nodes based on their local characteristics and data access patterns. Candidate nodes identified as having high access frequency receive updates earlier than other nodes, creating localized quality differences in update timing that optimize overall system performance.
Solution Approach 2:
The system changes the update propagation parameter (timing) based on data characteristics. For high-access-frequency data records, the update propagation is accelerated to candidate nodes before full commitment, while standard data follows the traditional orderly propagation approach, thus adapting the update time parameter to different data types.
3Reliability
If simultaneous updates are prevented across nodes, then data consistency is ensured, but access efficiency and parallel processing capability are reduced
Solution Approach 1:
The system performs preliminary updates to candidate nodes before the official update is committed. This preliminary action allows candidate nodes to have the updated value ready in advance, reducing the time clients wait for updated data while maintaining consistency through the official commitment process.
Data Source
AI summary
A distributed database system accesses data by using management information managed by another data store, and accelerates a timing to update a data record having high access frequency. Thereby the latest data can be easily acquired. A distributed database system according to one embodiment comprises a distributed database including a plurality of nodes that stores data, and a data management device that manages the data stored in the plurality of nodes. Each of the nodes comprises a data storage unit that stores the data related to a time stamp representing a time when the data is stored and a key identifying the data, a data update unit that updates the data, a data sending unit acquires the data stored in the data storage unit, and a data update promotion unit that requests the data update unit to update the acquired data when the acquired data is not latest data.