Time Series Database Adaptive Storage Mode Selection
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Solution Overview
Problem
Time series databases face inefficiencies in storage and query operations due to varying data collection frequencies and measurement points across different application environments, leading to high resource overheads and suboptimal performance.
Innovation Solution
A method and apparatus that monitor operations accessing the database to identify query and insert types, calculate costs for different storage modes, and select the mode with the minimum cost for efficient data storage and retrieval, using storage modes such as group and high-frequency modes tailored to specific application environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is collected at high frequency with many measurement points, then data completeness and monitoring accuracy are improved, but storage resource overhead and system complexity increase significantly
Solution Approach 1:
The patent segments time series data into different categories based on measurement point characteristics (e.g., high-frequency vs. low-frequency, critical vs. non-critical). Different storage strategies are applied to different segments, allowing the system to handle large volumes of data from numerous measurement points without uniformly applying complex storage mechanisms to all data, thus reducing overall system complexity while maintaining monitoring accuracy.
Solution Approach 2:
The patent implements local quality by applying different storage modes and compression techniques to different types of measurement data based on their specific characteristics. Critical measurement points with high importance receive more robust storage handling, while less critical points use more compact storage, optimizing the balance between monitoring accuracy and system complexity.
2Productivity
If diverse storage modes are implemented to handle different query types, then query efficiency is improved, but storage system complexity increases
Solution Approach 1:
The patent implements dynamic storage mode selection that adapts to different query requirements. The system can switch between different storage modes (e.g., time-based, measurement-point-based, hybrid) depending on the type of query being executed. This dynamic approach allows the system to optimize query efficiency for different operation types without permanently maintaining multiple complex storage structures, thereby reducing overall storage system complexity.
3Loss of energy
If adaptive storage mode selection is implemented, then resource overhead is reduced, but computational overhead for mode selection increases
Solution Approach 1:
The patent performs preliminary analysis of query patterns and measurement point characteristics to pre-determine optimal storage modes before actual data storage operations. By anticipating storage requirements based on historical patterns and data characteristics, the system reduces the need for complex real-time computational decisions, thereby lowering computational overhead while still achieving adaptive resource optimization.
Data Source
AI summary
A method for managing a time series database, includes: monitoring multiple operations that access the time series database, so as to identify types of the multiple operations, the types of the multiple operations comprising at least one of the query types or insert types; with respect to a storage mode among multiple storage modes, obtaining costs that the multiple operations access the time series database based on the types, respectively; selecting a storage mode with the minimum cost from the multiple storage modes; and during a predetermined time period, storing into the time series database data values that are collected from multiple measurement points according to the selected storage mode. In one embodiment, there is provided an apparatus for managing a time series database. By means of the method and apparatus of the present invention, the storage and query efficiency with respect to the time series database can be increased.