Level-Based Query Execution in Database Systems
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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 queries in large-scale data processing networks.
Innovation Solution
The implementation of a parallelized data processing system that divides data into segments, optimizes query plans, and utilizes multiple computing devices and clusters to process queries concurrently, with a segment scheduler module that schedules retrieval and processing of segments based on drive utilization to maximize efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is stored in a traditional sequential manner, then data integrity is maintained, but query processing speed deteriorates
Solution Approach 1:
The patent divides data into multiple segments distributed across different storage devices, allowing parallel query processing. Each segment can be accessed independently by different processing units simultaneously, thereby increasing query processing speed while maintaining data integrity through proper segment management and reconstruction protocols.
Solution Approach 2:
The patent transitions from sequential single-dimension data storage to a multi-dimensional distributed storage architecture. Data is organized across multiple dimensions including spatial distribution across devices, temporal versioning, and hierarchical segmentation, enabling parallel access paths and significantly improving query processing speed.
2Productivity
If multiple queries are processed sequentially, then resource utilization is simplified, but productivity deteriorates
Solution Approach 1:
The patent segments queries into multiple independent processing tasks that can be executed in parallel across different computing units. Each query is divided into sub-queries operating on different data segments, allowing simultaneous execution and significantly increasing overall query processing throughput while maintaining manageable complexity through structured task distribution.
Solution Approach 2:
The patent implements a query processing system where computing resources continuously execute multiple queries in parallel without idle time. The system maintains a pipeline of active query executions across different processing units, ensuring continuous utilization of computational resources and maximizing productivity through sustained parallel operation.
3Speed
If hardware resources are increased, then processing speed improves, but system cost and complexity increase
Solution Approach 1:
The patent divides the data processing system into multiple independent processing units that can operate in parallel. Each unit handles specific data segments, allowing the system to achieve higher processing speeds through distributed computation rather than requiring a single powerful hardware configuration, thereby managing complexity through modular architecture.
Solution Approach 2:
The patent creates a multi-functional database system where the same hardware infrastructure supports multiple query types, data formats, and processing operations simultaneously. The system can dynamically allocate resources across different workloads and adapt to various query patterns, achieving high processing speeds for diverse operations without requiring specialized hardware for each function.
4Loss of time
If data is divided into segments for parallel processing, then query execution speed improves, but data retrieval complexity increases
Solution Approach 1:
The patent performs preliminary organization and indexing of data segments during the data loading and storage phase. Metadata about segment locations, contents, and relationships is pre-computed and stored, enabling rapid segment identification and retrieval during query execution without requiring complex real-time analysis, thus reducing query execution time while managing retrieval complexity through advance preparation.
Data Source
AI summary
A method for execution by a node of a computing device includes: determining level assignment data indicating assignment to a subset of levels of a plurality of levels of a query execution plan, wherein the plurality of levels includes a lowest level, at least one inner level, and a highest level; determining a query for execution; determining a query discriminator number corresponding to the query; determining to process the query at one of the plurality of levels based on determining the one of the plurality of levels is included in the subset of levels; generating query resultant data by facilitating execution of at least one query operator of the query in accordance with the one of the plurality of levels; selecting one of a plurality of possible parent nodes in a sequentially higher one of the plurality of levels based on the query discriminator number; and sending the query resultant data to the one of the plurality of possible parent nodes for processing.