Data Intake Query System Late-Binding Schema
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Analyzing and searching massive quantities of machine-generated data from diverse sources is challenging due to the complexity and volume of data, requiring efficient data intake and query systems that can handle various formats and provide flexible analysis capabilities.
Innovation Solution
The implementation of a data intake and query system, such as the SPLUNK® ENTERPRISE system, which uses a late-binding schema to extract information from event data, allowing for flexible schema development and refinement at search time, and provides tools for data modeling, visualization, and report generation, enabling users to analyze and correlate large datasets effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a traditional data analysis system processes massive machine-generated data from diverse sources, then the system can handle various data formats, but the complexity and volume of data make analysis and searching challenging
Solution Approach 1:
The patent segments the data analysis system into distinct functional modules: data intake components that receive and normalize data from diverse sources, schema development tools that create structured representations, and query processing components that execute analysis. This modular segmentation allows each component to handle specific tasks independently, reducing overall system complexity while maintaining versatility in processing different data formats.
Solution Approach 2:
The patent introduces an intermediary schema layer that acts as a mediator between raw machine-generated data and analysis queries. This schema layer standardizes diverse data formats into a common structure, enabling simplified query processing without requiring the underlying system to directly handle the complexity of multiple data formats. The schema serves as an intermediary representation that decouples data ingestion from data analysis.
2Adaptability or versatility
If users directly query massive datasets without predefined schemas, then flexible analysis is possible, but the lack of structured approach makes data extraction inefficient
Solution Approach 1:
The patent implements preliminary schema development that creates structured templates and data models before users execute queries. These pre-defined schemas organize data into logical groupings and relationships, allowing users to perform flexible analysis on structured data rather than unstructured raw data. The preliminary schema creation enables the system to efficiently locate and extract relevant information based on organized data structures.
Solution Approach 2:
The patent implements dynamic schema refinement capabilities that allow schemas to evolve based on user interactions and analysis needs. Users can modify and extend predefined schemas during query execution, and the system adapts the data structure dynamically to accommodate new analysis requirements. This dynamic approach maintains query flexibility while preserving the efficiency benefits of structured data organization.
3Quantity of substance
If the system stores all raw machine-generated data without processing, then complete data availability is maintained, but the volume and diversity of data make searching and analysis difficult
Solution Approach 1:
The patent segments stored data into organized hierarchical structures based on schemas, where raw data is divided into logical components such as events, fields, and records. Each segment is tagged with metadata and organized according to predefined data models. This segmentation maintains complete data availability while enabling efficient searching through structured indexes and hierarchical navigation, allowing users to locate specific information without scanning entire datasets.
Data Source
AI summary
Operational machine components of an information technology (IT) or other microprocessor- or microcontroller-permeated environment generate disparate forms of machine data. Network connections are established between these components and processors of an automatic data intake and query system (DIQS). The DIQS conducts network transactions on a periodic and/or continuous basis with the machine components to receive the disparate data and ingest certain of the data as measurement entries of a DIQS metrics datastore that is searchable for DIQS query processing. The DIQS may receive search queries to process against the received and ingested data via an exposed network interface. In one example embodiment, a query building component conducts a user interface using a network attached client device. The query building component may elicit search criteria via the user interface using a natural language interface, construct a proper query therefrom, and present new information based on results returned from the DIQS.