Distributed Query Plan Limit Pushdown for Large-Scale Databases
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing database systems face limitations in processing speed due to hardware constraints, data storage methods, and restricted co-processing options, particularly when handling large volumes of data.
Innovation Solution
A distributed database system architecture that utilizes parallelized data input, storage, retrieval, and query processing across multiple computing devices and nodes, optimizing query plans based on storage instructions and resource availability to enhance efficiency and scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a database system processes large volumes of data using traditional hardware and storage methods, then data processing capacity is limited, but processing speed and efficiency deteriorate
Solution Approach 1:
The patent divides the database system into multiple distributed nodes that can independently process different portions of data simultaneously. Each node handles a segment of the overall query workload, enabling parallel processing across the network. This segmentation allows the system to process large volumes of data without sacrificing speed, as the total processing capacity is distributed across multiple independent processing units rather than bottlenecked at a single point.
2Adaptability or versatility
If traditional co-processing options are used, then processing capability is restricted, but system flexibility and performance improvement potential are limited
Solution Approach 1:
The patent creates a universal query processing architecture where any computing device in the distributed network can serve as a query processing node. The system does not rely on specialized co-processors but instead enables general-purpose computers to perform database query operations through standardized protocols. This multi-functionality approach allows diverse hardware configurations to contribute to query processing, increasing both the versatility of co-processing options and the overall execution speed through aggregated computational power.
3Quantity of substance
If data is stored using conventional methods, then storage efficiency is limited, but retrieval speed and query response time deteriorate
Solution Approach 1:
The patent implements segmented data storage where the database is divided into multiple partitions distributed across different storage nodes. Each partition can be independently accessed and processed, allowing parallel data retrieval operations. When a query is executed, the system can simultaneously access multiple storage segments rather than sequentially reading from a single storage unit, dramatically improving retrieval speed while maintaining efficient use of total storage capacity across the distributed network.
Data Source
AI summary
A database system includes a parallelized data input sub-system including a first plurality of nodes, a parallelized data store, retrieve, process sub-system including a second plurality of nodes, and a parallelized query and results sub-system including a third plurality of nodes. A set of nodes of the third plurality of nodes is operable to obtain a query having an initial execution plan and identify a limit operation and a condition for optimizing the initial execution plan. The optimizing involves distributing and pushing down the limit operation within the initial execution plan. The initial execution plan identifies a plurality of pipelines where a pipeline maps nodes of a second set of nodes of the second plurality of nodes to a set of operations of the query. For a first pipeline, the set of nodes is operable to determine an optimal node to assign a distributed limit operation of the limit operation.