Adaptive Time Series Database Schema for Dynamic Partitioning
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Solution Overview
Problem
Conventional relational databases face challenges in managing large quantities of time-sensitive data, particularly in dynamic environments, due to limitations in scalability and query response times, which are impacted by fixed partitioning schemes.
Innovation Solution
The development of an adaptive time series database schema that dynamically adjusts based on data shape analysis and query frequency, allowing for variable partitioning and self-tuning to optimize data storage and retrieval efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If fixed partitioning schemes are used in conventional databases, then data storage structure is simple, but query response time is significantly impacted and scalability is limited
Solution Approach 1:
The patent implements dynamic schema adaptation where the database system automatically adjusts partitioning schemes based on observed query patterns and data characteristics. The schema evolves over time to optimize query performance without requiring manual intervention, transforming the static database structure into a dynamic one that adapts to changing workloads.
Solution Approach 2:
The system changes key parameters of the database schema including partitioning strategies, indexing methods, and storage formats based on analyzed query patterns and data shapes. These parameter adjustments are made automatically to optimize query response time while managing complexity through systematic adaptation rules.
2Quantity of substance
If conventional relational databases are used, then ease of operation is maintained, but scalability and ability to manage large quantities of time-sensitive data is deficient
Solution Approach 1:
The patent segments the database into multiple specialized schemas optimized for different types of queries and data patterns. By dividing the database system into distinct partitioning strategies and indexing approaches, it can simultaneously handle large volumes of time-sensitive data while maintaining high processing throughput for diverse query workloads.
Solution Approach 2:
The adaptive schema system provides multi-functionality by automatically selecting and switching between different partitioning and indexing strategies based on query characteristics. This universal approach allows a single database system to efficiently handle various types of queries and data patterns without requiring separate specialized databases.
3Adaptability or versatility
If fixed partitioning schemes are used, then device complexity is low, but adaptability to different query patterns and data shapes is poor
Solution Approach 1:
The database system performs self-service by automatically analyzing query patterns, data characteristics, and workload characteristics to determine optimal schema configurations. This self-adaptation mechanism eliminates the need for manual schema tuning while achieving high adaptability to different query patterns and data shapes, managing complexity through automated decision-making.
Solution Approach 2:
The system implements feedback loops where query performance metrics and data characteristics are continuously monitored and fed back into the schema adaptation process. This feedback mechanism enables the database to learn from actual usage patterns and automatically adjust partitioning and indexing strategies to optimize performance for observed workloads.
Data Source
AI summary
In a computer-implemented method for querying a variably partitioned time series database, a query of a time series database is received, the query including a time range and a predicate comprising at least one dimension, wherein the time series database comprises a plurality of time series database schemas. At least one time series database schema of the time series database corresponding to the time range is determined. The query is divided into a plurality of sub-queries, wherein each sub-query of the plurality of sub-queries corresponds to one time series database schema of the plurality of time series database schemas. The plurality of sub-queries is executed to return a plurality of results.