Query Resource Estimation Model for Distributed Database Systems
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Solution Overview
Problem
In online advertising, large-scale data systems face challenges in efficiently estimating resource usage for queries, leading to inefficient use of data clusters and difficulty in predicting query complexity and execution time, especially in dynamic environments with varying data input and cluster usage.
Innovation Solution
A query resource estimation system that uses a machine learning-based approach to generate and adjust models for predicting resource usage, providing estimates of execution time and suggesting query changes to reduce resource usage, by analyzing feature vectors and system status, and dynamically adjusting weights based on actual usage comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional resource estimation methods are used for queries in large-scale distributed database systems, then the system can process queries without complex prediction mechanisms, but the accuracy of resource usage prediction deteriorates leading to inefficient use of data clusters
Solution Approach 1:
The system implements feedback by continuously monitoring actual resource usage of executed queries and using this information to retrain and adjust the machine learning model. The model compares predicted resource usage with actual usage, and the discrepancy feedback is used to improve future predictions through dynamic weight adjustment in the linear regression model.
Solution Approach 2:
The system employs self-service by automatically generating feature vectors from query parameters and system status, autonomously training the machine learning model using historical data, and dynamically adjusting prediction weights without requiring manual intervention. The model serves itself by continuously improving through automated feedback loops.
2Measurement precision
If machine learning models are continuously trained and adjusted for each query, then prediction accuracy improves, but the computational overhead and time consumption increase
Solution Approach 1:
The system performs preliminary action by pre-training the machine learning model using historical query data before actual query execution. The model is trained offline on accumulated historical data, and this pre-trained model is then used for rapid online predictions. This separates the time-consuming training phase from the quick prediction phase.
Solution Approach 2:
The system applies partial action by selectively adjusting only the necessary weights in the linear regression model based on actual query feedback, rather than completely retraining the entire model for each query. The model updates are incremental and targeted, modifying only the weights relevant to the current query's feature vector.
3Ease of operation
If the system provides detailed resource usage estimates for all queries, then users can make informed decisions about query execution, but the system complexity and processing overhead increase
Solution Approach 1:
The system extracts only the essential feature vectors from complex query parameters and system status data. Instead of analyzing all possible query attributes, the model identifies and extracts key features that most significantly impact resource usage, such as query type, data volume, and cluster status, simplifying the estimation process.
Solution Approach 2:
The system changes parameters by dynamically adjusting the weights of different features in the linear regression model based on actual query performance feedback. The model adapts its parameter weights to reflect changing system conditions and query patterns, providing accurate estimates without requiring complex structural changes.
Data Source
AI summary
Disclosed are systems and methods for managing queries on on-line advertisement data. The system includes a query engine device for receiving queries from and outputting query results to query client devices and a training engine for generating and adjusting a model for predicting an estimation of resource usage for execution of each received query based on each query's corresponding feature vector having values pertaining to the query and a system status. The query engine device is further configured to provide the estimation of resource usage for each query to the corresponding query client device and, in response, receive input from such corresponding query client device and specifying whether to proceed with the corresponding query. A database system receives input from each query's corresponding query client device as to whether to proceed with the query and, in response, initiates or inhibits execution of such query with respect to a database storage system.