Inverted Indexing for Flexible Search of Raw Machine Data
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Solution Overview
Problem
Existing data analysis tools lack the ability to quickly and easily search and analyze large sets of raw machine data to visually identify data subsets of interest, particularly in diverse data systems with structured, semi-structured, and unstructured data types, leading to challenges in managing and understanding vast amounts of data.
Innovation Solution
A data intake and query system, such as the SPLUNK® ENTERPRISE system, uses a flexible schema and late-binding schema to process and index raw machine data, allowing for efficient retrieval and analysis by applying extraction rules during search time, enabling the use of a common information model across disparate data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pre-processing is applied to extract specified data items before storage, then retrieval efficiency is improved, but data flexibility and analysis capability are worsened because most generated data is discarded
Solution Approach 1:
The system performs preliminary indexing of all generated data at ingestion time without discarding any data, creating an inverted index that maps data items to their locations. This preliminary action enables both efficient retrieval and flexible analysis of the complete dataset, resolving the contradiction between retrieval efficiency and data flexibility
Solution Approach 2:
The inverted index structure serves multiple functions simultaneously: it enables fast retrieval of specified data items, supports flexible analysis of all generated data, and allows for various query types without requiring separate pre-processing pipelines. This multi-functionality resolves the contradiction by making the system adaptable to different analysis needs while maintaining efficiency
2Adaptability or versatility
If all generated raw data is stored for later retrieval, then data flexibility and analysis capability are improved, but storage requirements and system complexity are worsened
Solution Approach 1:
The system creates an inverted index as a copy of the relationships between data items and their locations, rather than storing all raw data in processed form. This copying approach maintains the ability to analyze all generated data while using less storage space and reducing system complexity compared to storing complete raw datasets with multiple processing pipelines
Solution Approach 2:
The system segments the data storage into two components: the generated data itself and the inverted index structure. This segmentation allows the system to maintain raw data for flexibility while using the compact index structure to enable efficient retrieval and reduce overall system complexity
3Ease of operation
If conventional search tools are used to search data systems separately, then search functionality is provided, but analysis efficiency and insight derivation are worsened due to piecemeal result collection
Solution Approach 1:
The system merges multiple data systems into a unified searchable structure using the inverted index, allowing analysts to search across all generated data from diverse sources simultaneously. This combining approach eliminates the need for separate searches and piecemeal result collection, thereby improving analysis efficiency while maintaining ease of operation through a single search interface
Data Source
AI summary
Systems and methods are disclosed involving user interface (UI) search tools for locating data, including tools for summarizing indexed raw machine data that organize and present results to enable expansion and exploration of initial summarizations. The initial summarizations may be explored and refined to help users determine how to identify and best focus a search on data subsets of greater interest.