Distributed Database Parallel Processing Speed
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Solution Overview
Problem
Current database systems face limitations in processing speed due to hardware constraints, data storage methods, and restricted co-processing options, which hinder efficient data processing and query execution.
Innovation Solution
The proposed solution involves a distributed database system architecture that includes a parallelized data input sub-system, a parallelized data store, retrieve, and process sub-system, and a parallelized query and response sub-system. This architecture divides data into segments, restructures them for efficient storage, and optimizes query plans for execution across multiple storage clusters and nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a traditional database system processes data sequentially on a single hardware system, then the system structure remains simple, but the processing speed is limited by hardware constraints
Solution Approach 1:
The database system is divided into multiple independent storage clusters, each capable of processing data segments in parallel. The data is partitioned across multiple nodes, allowing simultaneous processing operations on different data segments, thereby increasing processing speed while maintaining manageable system complexity through modular architecture
Solution Approach 2:
The system transitions from single-node sequential processing to multi-node parallel processing by adding a distributed dimension. Multiple storage clusters operate concurrently across different hardware nodes, utilizing parallel processing to overcome hardware limitations of individual systems
2Speed
If data is stored in a traditional single-location format, then the storage method remains simple, but the processing speed is limited by data storage structure
Solution Approach 1:
Data is divided into segments and distributed across multiple storage clusters. This segmentation allows parallel retrieval and processing of data segments, improving query execution speed. The segmented storage structure enables independent access to different data portions simultaneously
Solution Approach 2:
The data storage structure is designed to be dynamic and flexible, allowing data to be redistributed and reorganized across storage clusters based on processing needs. This dynamic reconfiguration capability enables optimization of data access patterns for different query types
3Speed
If co-processing options are restricted to a single processor, then the system configuration remains simple, but the execution speed is limited by co-processing capabilities
Solution Approach 1:
Processing functions are segmented and distributed across multiple co-processors in different storage clusters. Each co-processing function operates independently on assigned tasks, enabling parallel execution that significantly increases function execution speed compared to single-processor systems
Solution Approach 2:
Multiple storage clusters and co-processors are configured to work together in a unified system. The co-processing architecture is designed to be universal, allowing different types of processing operations to be performed across the distributed system, enhancing both speed and functional capability
Data Source
AI summary
A database system is operable to generating, via a first node, an update to system state data mediated via the set of nodes via a consensus protocol. A second node determines to perform a database system functionality requiring utilization of a most recent version of the system state data and requests system state data. The second node requests current system state data and receives a set of responses. The second node updates the current version of the system state data locally stored via the local storage of the second node as the most recent version of the system state data and performs the database system functionality via accessing the most recent version of the system state data.