Distributed Database Query Routing for 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 execution of operations, especially in large-scale data processing environments.
Innovation Solution
A scalable database system architecture that utilizes a parallelized data input, storage, and processing sub-system, along with a query and response sub-system, to distribute data across multiple computing devices and nodes, enabling efficient storage and processing of massive data sets through optimized query planning and execution across a network of resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single centralized database system is used to store and process data, then the system structure is simple and easy to manage, but the processing speed is limited by hardware constraints and cannot scale effectively
Solution Approach 1:
The database system is divided into multiple distributed database nodes that operate independently across different computing devices. Each node stores and processes a portion of the total data, allowing parallel processing operations that significantly increase overall data processing speed while maintaining manageable node-level complexity
Solution Approach 2:
The system transitions from a single centralized processing unit to a multi-dimensional distributed architecture where data and processing operations are spread across multiple computing devices connected by a network. This dimensional expansion enables concurrent processing of multiple queries and operations simultaneously, overcoming hardware limitations of single-node systems
2Productivity
If data is stored in a traditional single-location format, then storage structure is simple, but the ability to process massive data sets in real-time is limited
Solution Approach 1:
Massive data sets are segmented and distributed across multiple database nodes in the network. Each node stores a subset of data locally, enabling real-time processing operations on specific data partitions without requiring access to the entire data set, thus improving real-time analysis capability
Solution Approach 2:
A query router acts as an intermediary component that receives data requests, determines the optimal path through the distributed network, and directs queries to appropriate database nodes. This intermediary layer simplifies the complexity of data storage structure by providing intelligent data location and access management
3Productivity
If limited co-processing options are provided in the database system, then the system is easier to operate and maintain, but the speed of operation execution is restricted
Solution Approach 1:
The co-processing architecture dynamically adapts to different operation types and data characteristics by automatically selecting appropriate processing nodes and methods. The system can dynamically route operations to nodes with optimal resources available at any given time, increasing operation execution speed without requiring manual configuration
Solution Approach 2:
The distributed database system performs self-optimization by automatically determining the best processing paths and resource allocations based on current network conditions and data distribution. This self-service capability enables high-speed operation execution while maintaining ease of operation, as users simply issue queries without configuring complex co-processing parameters
Data Source
AI summary
A database system is operable to generate a query operation energy utilization estimation function based on historic energy utilization data and historic query operation execution data. A query is determined for execution and energy utilization estimation input data is determined for the query operation. The query operation energy utilization estimation function is performed upon the energy utilization estimation input data to generate energy utilization estimate data for the query operation. An energy efficiency strategy for the database system is applied based on the energy utilization estimate data generated for the query operation.