Parallel Database Subsystems for Hardware-Limited Query Speed
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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 data processing and query execution.
Innovation Solution
A database system architecture with parallelized sub-systems for data input, storage, and query processing, utilizing a network of computing devices and optimized query planning to distribute and execute operations efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a database system uses traditional single-processing architecture, then hardware constraints limit processing speed, but implementing parallel processing increases system complexity
Solution Approach 1:
The database system is divided into multiple independent subsystems (e.g., query processing subsystem, data storage subsystem, index management subsystem) that can operate in parallel. Each subsystem handles specific tasks independently, enabling concurrent processing and improving overall system speed without requiring complete redesign of the entire architecture.
Solution Approach 2:
The system transitions from single-threaded sequential processing to multi-dimensional parallel processing by introducing additional processing dimensions through multiple threads and processes. This dimensional expansion allows simultaneous execution of database operations across different subsystems, overcoming hardware limitations while managing complexity through structured organization.
2Speed
If data is stored in traditional formats, then storage is simple, but query execution speed is limited
Solution Approach 1:
The data storage structure is segmented into multiple data partitions that can be independently accessed and processed. Each partition is optimized for specific query patterns, allowing parallel retrieval and processing of data segments, which significantly improves query execution speed while maintaining manageable storage organization through partitioning strategies.
3Productivity
If co-processing options are restricted, then system design is simpler, but processing efficiency is reduced
Solution Approach 1:
The subsystems are designed with universal interfaces and standardized communication protocols that enable them to perform multiple functions. This multi-functionality allows flexible co-processing configurations where subsystems can be dynamically assigned different tasks based on workload requirements, improving processing efficiency without creating rigid complex architecture.
Data Source
AI summary
A database system includes a data ingest sub-system, a store and compute sub-system, and a query and response subsystem. Each sub-system includes a plurality of clusters of computing devices (e.g., a first, second, and third respectively). A cluster of computing devices of the first plurality of clusters of computing devices includes a plurality of loader nodes operable to collectively ingest and temporarily store data as an ingested data set. A cluster of computing devices of the second plurality of clusters of computing devices includes a plurality of foundation nodes operable to collectively stores at least a portion of the ingested data set and execute a set of query operational instructions in accordance with machine learning models on the at least a portion of the ingested data set to produce a partial query response. A cluster of computing devices of the third plurality of clusters of computing devices includes a plurality of query instruction nodes operable to collectively generate the set of query operational instructions and generates an output query response based on the partial query response.