Federated Search Using Late-Binding Schema for Raw Machine Data
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Solution Overview
Problem
Analyzing and searching massive quantities of machine data from diverse sources is time-consuming and inefficient due to varying data types and formats, necessitating pre-processing that discards significant portions of valuable data.
Innovation Solution
A data intake and query system utilizing a late-binding schema that applies extraction rules during search time, enabling flexible schema development and field-searchable storage of minimally processed machine data across disparate data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pre-processing is applied to machine data to reduce volume and improve search performance, then search efficiency is improved, but data flexibility and analytical depth deteriorate due to discarding valuable data
Solution Approach 1:
The patent segments machine data into multiple fields during pre-processing, extracting relevant information while preserving the ability to access both processed fields and original raw data. This segmentation allows efficient search through indexed fields while maintaining flexibility to query comprehensive data sources.
Solution Approach 2:
The patent implements a nested data structure where processed fields are stored within the context of raw machine data records. This nesting allows the system to access both the optimized processed data for quick retrieval and the comprehensive raw data for deep analysis, resolving the contradiction between search efficiency and data flexibility.
2Speed
If pre-processing extracts specified data items to facilitate efficient retrieval, then retrieval speed is improved, but information completeness deteriorates due to discarding remainder data
Solution Approach 1:
The patent introduces raw machine data as an intermediary layer that preserves complete information while processed fields serve as efficient access points. The raw data acts as a mediator that maintains information completeness while the processed fields enable fast retrieval, allowing users to access both speed and completeness as needed.
Solution Approach 2:
The patent performs preliminary extraction and processing of data items to create indexed fields for fast retrieval, while simultaneously preserving the complete raw data for later comprehensive analysis. This preliminary action creates multiple access pathways without sacrificing information completeness.
3Adaptability or versatility
If massive quantities of raw data are stored for later analysis, then analytical flexibility is improved, but system complexity and resource requirements worsen
Solution Approach 1:
The patent segments data into processed fields and raw data components, allowing the system to manage complexity by organizing data in structured segments. This segmentation enables flexible analysis of comprehensive data while maintaining system organization and reducing operational complexity through clear data architecture.
Data Source
AI summary
A data intake and query system can process a query to identify subquery tokens corresponding to subqueries to be executed by external data systems. The data intake and query system can process the subquery tokens to generate modified subqueries to be executed by the external data systems. The modified subqueries can cause the external data system to return metadata associated with the events processed by the external data systems during executing of the modified subqueries.