Dynamic Schema Inference for Time Series Data Storage
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Solution Overview
Problem
Analyzing time series data from numerous sources is challenging due to the vast amount of data and the need for manual schema definition, which is time-consuming and prone to data loss when schema changes occur.
Innovation Solution
A method and system that automatically infer and update schema definitions for time series data, allowing for dynamic modification of data structures without user input, enabling continuous data ingestion and storage without interrupting data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual schema definition is used for time series data, then data storage structure is established, but the process is time-consuming and prone to data loss when schema changes occur
Solution Approach 1:
The system automatically infers schema definitions from incoming events without requiring manual user input. The schema inference engine analyzes event structures and generates appropriate schema definitions autonomously, eliminating the time-consuming manual schema definition process while maintaining data integrity through systematic validation rules.
Solution Approach 2:
The schema definition is made dynamic and adaptable to changing event structures. When new events with different schemas arrive, the system automatically updates the schema definitions to accommodate the new structures, ensuring continuous data intake without interruption and preventing data loss that would occur with static manual schemas.
2Stability of the object's composition
If static schema definitions are used, then data storage structure is fixed, but schema changes require manual intervention and interrupt data transmission
Solution Approach 1:
The system employs dynamic schema evolution where schema definitions are initially stable but can automatically adapt when new event types are detected. The schema inference engine monitors incoming events and triggers schema updates only when necessary, maintaining stability during normal operation while enabling adaptability when new data patterns emerge, all without interrupting data transmission.
Solution Approach 2:
The system performs preliminary schema inference and validation before data storage occurs. By pre-processing events to determine their schema classification and preparing appropriate storage structures in advance, the system maintains stable data organization while being prepared to adapt to new schemas, eliminating the need for manual intervention during data transmission.
3Extent of automation
If automated schema inference is implemented, then manual intervention is reduced, but system complexity increases
Solution Approach 1:
The system segments the schema inference process into distinct modular components: event parsing module, schema analysis module, schema generation module, and validation module. Each component handles a specific aspect of schema inference, reducing overall system complexity by breaking down the automated process into manageable, independently testable units while maintaining high automation levels.
Data Source
AI summary
A method for processing events comprising time series data may include inferring different schema associated with the events. The method may also include storing property definitions corresponding to the events. Each property definition may include a name and a data type. The method may also include storing schema definitions corresponding to the different schema that are inferred. Each schema definition may include a set of one or more properties. The method may also include updating at least one data structure for storing information about the events based on the different schema that are inferred.