Schema Change Alerts in Data Intake Systems
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Analyzing and searching massive quantities of machine-generated data from diverse sources is challenging due to its complexity and volume, as existing systems often discard non-preprocessed data, limiting flexibility and insights.
Innovation Solution
A data intake and query system that employs a late-binding schema and event-based processing, allowing for flexible data ingestion, indexing, and querying, with features like keyword indexing and high-performance analytics to accelerate search operations and report generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is preprocessed and stored in a fixed schema, then data organization and retrieval efficiency is improved, but flexibility and adaptability to schema changes deteriorates
Solution Approach 1:
The patent implements a dynamic schema system where data schemas are not fixed but can evolve over time. The system automatically detects schema changes in incoming data and adapts the schema accordingly, allowing the system to maintain both efficient data organization and flexibility to accommodate changing data structures from various sources.
Solution Approach 2:
The system changes schema parameters dynamically based on detected data patterns. When schema changes are detected, the system modifies schema definitions, field types, and relationships to match the new data structure, thereby maintaining retrieval efficiency while adapting to schema evolution.
2Loss of information
If comprehensive data processing is performed, then data analysis capability is improved, but processing time and system complexity deteriorates
Solution Approach 1:
The system performs preliminary schema detection and validation during data ingestion, so that when data is queried later, the schema is already optimized and ready. This preliminary action reduces processing time during analysis while maintaining comprehensive data processing capabilities.
Solution Approach 2:
The patent extracts only the essential schema information and metadata needed for analysis, separating the core data from unnecessary processing overhead. This allows comprehensive data analysis capability while reducing processing time by focusing only on critical data elements.
3Adaptability or versatility
If data is stored in minimal processing format, then data integrity and flexibility are improved, but data organization and retrieval efficiency deteriorates
Solution Approach 1:
The system segments data into raw data storage and processed data representations. Raw data is stored in minimal processing format to maintain integrity and flexibility, while separate schema-based structures organize the data for efficient retrieval. This segmentation allows both requirements to be satisfied simultaneously.
Solution Approach 2:
The patent introduces schema definitions as an intermediary layer between raw data and query operations. The schema acts as a mediator that translates minimal processing data into organized, efficiently retrievable structures without compromising data flexibility or integrity.
4Reliability
If schema changes are detected and alerted, then data quality monitoring is improved, but system complexity and processing overhead deteriorates
Solution Approach 1:
The system implements feedback mechanisms that automatically detect schema changes and generate alerts when modifications are detected. This feedback loop maintains data quality monitoring by continuously comparing incoming data against current schema definitions and notifying users of changes, while the automation reduces manual complexity.
Data Source
AI summary
A schema consistency mechanism monitors data ingested by a data intake and query system for changes to the structure, or data schema, associated with the data. A schema consistency monitor obtains data from a data source (or, more generally, from any number of separate data sources) at a plurality of points in time. The data is analyzed to determine whether a first portion of the data received at a first point in time conforms to a first data schema and that a second portion of the data received at a second point in time conforms to a second data schema that is different from the first data schema (thereby indicating a change to the associated data schema). A graphical user interface (GUI) can be generated that includes indications of identified changes to one or more data schemas associated with data.