Late-Binding Schema for Heterogeneous Data Querying
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Solution Overview
Problem
Current tools lack the capability to efficiently and flexibly search and analyze large sets of raw machine data from diverse sources, leading to challenges in identifying data subsets of interest, particularly in IT environments where massive amounts of structured, semi-structured, and unstructured data are generated.
Innovation Solution
A data intake and query system that utilizes a flexible schema, known as a late-binding schema, to process and store raw machine data, allowing for real-time search and analysis through field-searchable events with configurable extraction rules applied during query execution, enabling the identification of data subsets of interest across disparate data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is pre-processed to extract specified data items for efficient retrieval, then retrieval efficiency is improved, but data flexibility and ability to analyze all generated data is reduced
Solution Approach 1:
The system performs preliminary actions by collecting and storing all raw machine data without pre-processing or filtering. This preliminary data collection enables later flexible analysis while maintaining the ability to efficiently retrieve data when needed, resolving the contradiction between retrieval efficiency and analysis flexibility
Solution Approach 2:
The system dynamically adapts its processing approach based on query requirements. Rather than static pre-processing, the system can apply different levels of data processing and filtering at query time, allowing it to optimize for both retrieval efficiency and analysis flexibility as needed
2Adaptability or versatility
If tools search data systems separately and collect results over a network, then data can be retrieved from diverse sources, but analysis capability and ease of use are reduced
Solution Approach 1:
The system merges multiple data systems and their search capabilities into a single unified interface. This consolidation allows users to search across diverse data sources simultaneously through one tool, maintaining comprehensive data source coverage while dramatically improving ease of operation and analysis capability
Solution Approach 2:
The system creates a universal search tool that can handle multiple data types, formats, and sources through a single interface. This multi-functional approach eliminates the need for separate tools for different data systems, enhancing both versatility in data source coverage and ease of operation
3Adaptability or versatility
If massive quantities of raw data are stored for later retrieval, then data analysis flexibility is improved, but data management complexity increases
Solution Approach 1:
The system introduces an intermediary layer (the unified search platform) between the stored raw data and the analysis tools. This intermediary automatically handles the complexity of managing massive raw data, including indexing, searching, and retrieval operations, thereby maintaining data analysis flexibility while reducing the apparent complexity for users
Data Source
AI summary
Systems and methods are disclosed for flexibly applying a query term to heterogeneous data. A query system can receive a query that includes a data-determinant query term. As the system executes the query it can generate interim search results. As the system query processes the interim search results based on the query, it can apply the data-determinant query term to records of the interims search results based on the structure of the records.