Database Query Processing Duplicate Column Expressions
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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, particularly in large-scale data processing networks.
Innovation Solution
The implementation of a parallelized data processing network that includes a database system with sub-systems for data input, storage, retrieval, and query execution, utilizing multiple computing devices and nodes with independent processing core resources to distribute processing tasks and optimize query execution plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data processing is performed using a single database system with conventional hardware, then the system structure is simple, but the processing speed is limited by hardware constraints
Solution Approach 1:
The patent divides the database system into multiple independent nodes, each capable of autonomous data processing. This segmentation allows parallel execution of query operations across different nodes, thereby increasing overall processing speed while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent transitions from a single-dimensional sequential processing model to a multi-dimensional parallel processing architecture. By distributing data and processing tasks across multiple spatial dimensions (different nodes and cores), the system achieves exponential speedup without proportionally increasing operational complexity.
2Speed
If large datasets are processed using conventional storage methods, then the storage structure is simple, but the retrieval speed is limited
Solution Approach 1:
The storage system is segmented into distributed storage units across multiple nodes. Data is partitioned and stored in parallel across these units, enabling simultaneous retrieval operations from different segments. This increases retrieval speed while keeping each individual storage unit relatively simple.
Solution Approach 2:
The patent implements data replication across multiple nodes, creating redundant copies of datasets. This allows parallel access to identical or different portions of data from multiple locations simultaneously, significantly improving retrieval speed without requiring complex sequential access mechanisms.
3Loss of time
If query execution is performed without parallelization, then the execution plan is simple, but the response time is prolonged
Solution Approach 1:
The system performs preliminary analysis of query execution plans to identify operations that can be parallelized. By pre-determining which tasks can execute concurrently and distributing them across available nodes before actual query execution, the system reduces response time while managing complexity through automated plan generation.
Solution Approach 2:
The patent implements dynamic query execution plans that can adaptively distribute tasks across varying numbers of processing nodes based on available resources and data characteristics. This dynamic approach optimizes response time by utilizing all available computational power while keeping the execution logic flexible rather than statically complex.
4Productivity
If co-processing options are restricted to conventional methods, then the system architecture is simple, but the processing capability is limited
Solution Approach 1:
Each node in the distributed database system is designed with universal co-processing capabilities, allowing any node to perform any type of data processing task. This multi-functionality increases overall processing capability while maintaining architectural simplicity through standardized, interchangeable processing units rather than specialized components.
Data Source
AI summary
A database system is operable to determine a query expression for execution indicating processing of a set of columns. Multiple ones of the set of columns are defined via duplicate instances of a same column expression. A query corresponding to the query expression is executed based on processing a set of column streams corresponding to the set of columns. Based on the multiple ones of the set of columns being defined via the duplicate instances of the same column expression, the set of column streams includes: a first column stream corresponding to a first one of the set of columns that includes a plurality of column values; and at least one additional column stream corresponding to other ones of the set of columns that includes at least one memory reference indicating at least one memory location of the first column stream.