Data Operation Manager for Integrated Database Systems
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Solution Overview
Problem
Current caching mechanisms in integrated database systems are inadequate for managing large quantities of data, leading to bottlenecks and insufficient flexibility, as they do not account for data being used by multiple parts of the system and have limited storage capacity.
Innovation Solution
The proposed solution involves copying data sets from a primary data source into a temporary data store, performing operations on the stored data set, and then replicating the changes back to the primary data source, using a data operation manager module that handles lock modes to manage concurrent access and updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If current caching mechanisms are used to store data from multiple database systems, then data consolidation is achieved, but bottlenecks occur near the connection to the single database system and the cache size limits the amount of data that can be stored
Solution Approach 1:
The patent divides the monolithic cache into multiple distributed caches across different servers. Each cache stores a portion of the consolidated data, allowing the system to handle larger quantities of data without creating a single point of bottleneck. The data operation manager coordinates access across these segmented caches, maintaining productivity while increasing storage capacity.
Solution Approach 2:
The patent transitions from a single-dimensional cache architecture to a multi-dimensional distributed cache network. By adding the dimension of distribution across multiple servers and introducing the data operation manager as a coordination layer, the system can scale data storage capacity without proportionally increasing bottleneck risks at any single connection point.
2Adaptability or versatility
If data is consolidated from different database systems into a single database system, then data integration is achieved, but flexibility is reduced due to bottlenecks and limited cache capacity
Solution Approach 1:
The data operation manager serves as an intermediary between application programs and the distributed cache system. It handles the complexity of coordinating data operations across multiple caches, translating high-level data access requests into appropriate cache operations. This mediator approach increases system flexibility while managing the complexity of distributed data operations centrally.
Solution Approach 2:
The data operation manager provides universal functionality for managing data operations across the entire distributed cache system. It handles various operations (read, write, update, delete) uniformly across all caches, allowing the system to adapt to different data access patterns and requirements without requiring separate management mechanisms for each cache.
3Reliability
If a legacy source code is used for data consolidation, then existing data integration logic is maintained, but modification becomes difficult without breaking other logic in the code
Solution Approach 1:
The patent extracts the data operation management logic from the legacy source code and places it in the separate data operation manager module. This extraction allows the legacy consolidation logic to remain unchanged and reliable, while new functionality and modifications can be implemented in the manager without risking breaks to existing data integration logic.
Solution Approach 2:
The data operation manager performs preliminary actions by pre-fetching and pre-processing data from the distributed cache before the legacy source code needs it. This preliminary action reduces the burden on the legacy code, allowing modifications to the manager without affecting the integrity of existing data integration logic that relies on these pre-prepared data sets.
Data Source
AI summary
A mechanism for managing data operations in an integrated database system. The method includes receiving a request to perform a data operation and retrieving a data set from a primary data source (PDS) in view of the request. The method also includes storing the data set in a temporary data store (TDS). The method further includes performing the data operation on the stored data set in the TDS.