Database Query Cache Invalidation for Resultant Data Refresh
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Solution Overview
Problem
Existing database systems face limitations in processing speed due to hardware constraints, data storage methods, and restricted co-processing options, leading to inefficiencies in handling large volumes of data and complex queries.
Innovation Solution
A parallelized database system architecture that includes a data input sub-system, data store and process sub-system, query and response sub-system, administrative sub-system, and configuration sub-system, utilizing multiple computing devices and nodes with independent processing core resources to handle massive data volumes and concurrent queries efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a traditional database system processes queries sequentially, then hardware constraints limit processing speed, but implementing parallel processing increases system complexity
Solution Approach 1:
The database system is divided into multiple independent processing cores that can execute queries simultaneously. Each processing core operates as an independent unit with its own execution engine, allowing parallel query processing while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The system transitions from single-threaded sequential processing to multi-threaded parallel processing, adding the dimension of concurrency. This enables multiple queries to be executed simultaneously across different processing cores, significantly improving throughput without requiring fundamental redesign of core processing functions.
2Speed
If data is stored in traditional formats, then storage is simple, but processing speed is limited by data storage methods
Solution Approach 1:
Data is segmented into smaller units that can be processed in parallel by multiple processing cores. The system implements columnar storage organization where data is divided into segments that can be independently accessed and processed, enabling efficient parallel data processing while maintaining storage integrity.
3Speed
If co-processing options are restricted, then system simplicity is maintained, but execution speed is limited
Solution Approach 1:
Each processing core is designed as a universal unit capable of executing various types of queries and operations. The processing cores can handle different query workloads and data types independently, providing versatile co-processing capabilities without requiring specialized hardware for each function.
Data Source
AI summary
A database system is operable to cache first resultant data generated via executing a first query in cache memory resources during a first temporal period. An instruction to re-execute the first query is processed during the first temporal period by accessing the first resultant data in the cache memory resources. A cached resultant invalidation instruction indicating the first relational database table is received. The first resultant data from the cache memory resources is removed during a second temporal period based on processing the cached resultant invalidation instruction. A third instruction to re-execute the first query is processed after the second temporal period by re-executing the first query via access to the first relational database table in the first storage resources to re-generate corresponding first resultant data for the first query.