Adaptive Time Series Database Schema for Query Optimization
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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 fixed partitioning schemes, which impact query response times and efficiency in real-time monitoring analytics.
Innovation Solution
An adaptive time series database schema that dynamically adjusts based on the data shape and query patterns, allowing for variable partitioning and multiple schemas to optimize storage and retrieval of time series data, reducing the need for re-indexing and improving query performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional relational databases are used with fixed partitioning schemes, then data storage is simple and consistent, but query response time deteriorates significantly when data quantities grow large
Solution Approach 1:
The patent implements dynamic partitioning schemes where the database schema automatically adapts its partitioning strategy based on the actual data distribution patterns and query workloads. Instead of using static fixed partitioning, the system continuously monitors data characteristics and reconfigures partition boundaries and strategies to optimize query performance as data grows and patterns change over time.
Solution Approach 2:
The system changes partitioning parameters such as partition boundaries, partition keys, and partition strategies based on analyzed data characteristics. By dynamically adjusting these parameters according to actual data distribution and query patterns, the database optimizes query response time without requiring complete schema redesigns.
2Productivity
If fixed partitioning schemes are used in conventional databases, then schema design is simple and consistent, but scalability deteriorates when managing large quantities of time-sensitive data
Solution Approach 1:
The patent employs dynamic schema evolution capabilities that allow the database to automatically scale its partitioning structure as data quantities grow. The system monitors data volume and distribution patterns, then dynamically creates new partitions, adjusts partition strategies, and reconfigures storage layouts to maintain scalability without manual intervention or complex manual schema design.
3Productivity
If conventional databases rely on fixed partitioning, then data storage is consistent and simple, but real-time monitoring analytics efficiency deteriorates
Solution Approach 1:
The patent implements dynamic partitioning adaptation specifically optimized for real-time analytics workloads. The system monitors analytics query patterns and data distribution, then dynamically adjusts partitioning strategies to maximize analytics efficiency. This includes optimizing partition boundaries for common query ranges and adjusting partition keys based on analytics requirements, enabling fast real-time monitoring without sacrificing partitioning flexibility.
Data Source
AI summary
In a computer-implemented method for adapting time series database schema, a plurality of queries to a time series database received over a time period is accessed, wherein time series data is ingested into the time series database according to a time series database schema, wherein time series data comprises a plurality of dimensions. The plurality of queries of the time period is analyzed to determine a relative frequency of the plurality of dimensions within the plurality of queries over the time period. It is determined whether to adapt the time series database schema based at least in part on the relative frequency of the plurality of dimensions within the plurality of queries over the time period.