Database Configuration Objects for In-Query Machine Learning
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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 that employs parallelized data input, storage, and query processing systems, utilizing a configuration sub-system to optimize query plans and execute them across multiple computing devices with independent processing core resources, enabling efficient storage and retrieval of data segments based on segmenting factors and redundancy encoding schemes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional database systems use conventional hardware and storage methods, then system simplicity is maintained, but processing speed and execution efficiency are limited
Solution Approach 1:
The database system is divided into multiple independent nodes that can process queries concurrently. Each node handles a portion of the workload, enabling parallel processing and improving overall speed while distributing complexity across multiple simpler units rather than one complex system.
Solution Approach 2:
The system transitions from sequential processing to multi-dimensional concurrent processing by introducing additional processing nodes and parallel execution paths. This dimensional expansion allows multiple queries to be handled simultaneously, significantly improving processing speed without proportionally increasing individual node complexity.
2Speed
If data is stored using conventional methods, then storage simplicity is maintained, but query execution speed is limited
Solution Approach 1:
Data is partitioned and distributed across multiple nodes rather than stored monolithically. This segmentation enables parallel query processing and improves execution speed by allowing simultaneous access to different data portions, while each node maintains simple storage structures.
3Speed
If co-processing options are restricted, then system simplicity is maintained, but execution speed for complex queries is limited
Solution Approach 1:
Each node in the system is designed with universal functionality to handle multiple types of operations and queries independently. This multi-functionality enables complex queries to be processed concurrently across nodes without requiring specialized co-processing hardware, improving execution speed while keeping individual node designs simple.
4Adaptability or versatility
If machine learning models are integrated into the database system, then analytical capabilities are improved, but system complexity increases
Solution Approach 1:
Machine learning models are distributed across separate nodes rather than integrated monolithically into the database. This allows ML capabilities to be added as independent services that can be invoked by database nodes, improving analytical versatility while containing complexity in isolated modules rather than the core system.
Data Source
AI summary
A database system is operable to execute a first to generate the machine learning model from a training set of rows based on accessing and processing the training set of rows via a plurality of operators. system configuration data tracking a plurality of objects of a database system is updated to further track the machine learning model as a corresponding first object tracked via the system configuration data. A query output for a second query indicating applying of the machine learning model via execution of the second query based on applying the machine learning model to a set of rows in accordance with at least one property of the corresponding first object based on accessing the system configuration data.