Inverted Index Sampling for Faster Raw Machine Data Search
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Solution Overview
Problem
Existing data analysis tools lack the ability to efficiently search and analyze large sets of raw machine data from diverse data systems, particularly in a visually understandable and straightforward manner, hindering the derivation of insights from vast amounts of machine-generated data.
Innovation Solution
The SPLUNK® ENTERPRISE system employs a late-binding schema and extraction rules to process and index raw machine data, allowing for flexible schema development and refinement during search time, enabling the extraction of valuable insights from heterogeneous data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If tools search and analyze large sets of raw machine data from diverse data systems, then analysts can derive comprehensive insights and maintain data flexibility, but the search and analysis process becomes computationally intensive and time-consuming
Solution Approach 1:
The system performs preliminary actions by building inverted indexes during data ingestion, organizing data by field values and storing positional information before search operations occur. This pre-processing enables rapid query execution without sacrificing the ability to analyze all raw data, resolving the contradiction between comprehensive data analysis and search time.
Solution Approach 2:
The inverted index acts as an intermediary structure between the raw data storage and the search query processing. It provides a efficient lookup mechanism that enables fast retrieval of data subsets without requiring full data scanning, thus reducing search time while maintaining the capability to analyze comprehensive raw data when needed.
2Adaptability or versatility
If the system maintains all raw machine data for later retrieval and analysis, then data flexibility and insight potential increase, but storage requirements and data processing complexity increase
Solution Approach 1:
The system segments the raw data into discrete events with structured fields during ingestion. Each event is parsed into field-value pairs that can be independently indexed and retrieved. This segmentation enables flexible querying of specific fields without processing the entire raw data set, reducing processing complexity while maintaining data flexibility.
Solution Approach 2:
The inverted index structure serves as an intermediary layer between the stored raw data and query operations. It provides a simplified access path that reduces the complexity of searching through massive raw data sets by organizing data according to query-relevant fields, thus enabling flexible retrieval without proportional increases in processing complexity.
3Productivity
If the system pre-processes data by extracting specified data items and storing them in a data system, then retrieval efficiency improves, but data flexibility decreases because most generated data is discarded
Solution Approach 1:
The system applies local quality by creating inverted indexes specifically for fields that are likely to be queried, rather than uniformly processing all data. This selective indexing approach improves retrieval efficiency for common query patterns while maintaining the raw data for flexible analysis when needed, avoiding the loss of data flexibility associated with comprehensive pre-processing.
Solution Approach 2:
The system performs partial pre-processing by building inverted indexes for subsets of data fields that are most relevant to expected queries. This partial action provides retrieval efficiency benefits without the need to pre-process and store all possible data combinations, thereby maintaining data flexibility while improving productivity for common search scenarios.
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.