Adaptive Time Series Database Schema for Dynamic Partitioning
Find Innovative SolutionsGenerate Solutions
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
1Speed
If conventional relational databases are used to manage time series data, then data storage is possible, but query response time is significantly impacted and scalability is limited due to fixed partitioning schemes
Solution Approach 1:
The patent implements dynamic partitioning schemes that automatically adapt to changing data patterns and query workloads. The system monitors data ingestion rates, query patterns, and system performance metrics to dynamically adjust partitioning strategies, thereby resolving the contradiction between maintaining fast query response times and adapting to varying data characteristics without fixed partitioning constraints
Solution Approach 2:
The system changes partitioning parameters such as partition keys, bucket counts, and distribution strategies based on analyzed data shapes and query patterns. By dynamically modifying these parameters rather than using fixed schemes, the database achieves both fast query performance and adaptability to different data types and access patterns
2Productivity
If data is stored with fixed partitioning schemes, then storage structure is simplified, but query efficiency deteriorates when data patterns change
Solution Approach 1:
The patent implements self-service mechanisms where the database system automatically analyzes incoming data patterns and query workloads to determine optimal partitioning strategies without requiring manual intervention. The system self-adjusts schema parameters, selects appropriate partitioning keys, and reorganizes data storage automatically, thereby maintaining high query efficiency while managing schema complexity internally without burdening the user
3Reliability
If relational databases are relied upon for real-time monitoring analytics, then data storage is achieved, but performance deteriorates due to limited scalability
Solution Approach 1:
The patent implements segmentation by dividing the database into multiple independent time series databases, each handling specific data ranges or types. This segmentation allows the system to scale horizontally by adding more database instances, thereby maintaining reliable real-time analytics performance even as data volume increases significantly beyond what a single relational database can handle
Data Source
AI summary
In a computer-implemented method for maintaining a time series database including a plurality of time series database schemas, time series data including data points are received at an ingestion node of a time series database, the data points comprising a plurality of dimensions. A plurality of time series database schemas of the time series database is determined for storing the time series data. The time series data is ingested according to the plurality of time series database schemas, wherein each data point is stored according to each time series database schema of the plurality of time series database schemas, such that the time series database comprises multiple instances of each data point.