Clustered Extraction Rules for Heterogeneous Event Segments
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Solution Overview
Problem
Analyzing and searching massive quantities of machine-generated data from diverse sources is challenging due to the varying formats and types of data, which can be time-consuming and inefficient with existing systems that typically discard unprocessed data during pre-processing.
Innovation Solution
A data intake and query system utilizing a late-binding schema that applies extraction rules at search time, enabling flexible data analysis and retention of minimally processed data for later retrieval, combined with acceleration techniques like parallel processing and keyword indexing to enhance query performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is pre-processed and filtered during data intake, then processing speed is improved, but data completeness and flexibility for later analysis deteriorate
Solution Approach 1:
The system performs preliminary indexing of raw data without full processing, creating a searchable structure that enables fast retrieval while preserving complete data for later analysis. The keyword index is built from raw data during intake, allowing subsequent queries to be executed quickly without re-processing the entire dataset.
Solution Approach 2:
The system extracts only the essential indexing information (keywords, metadata) from raw data during intake, separating this from the complete raw data storage. This extraction enables fast searching through the index while the full data remains intact for comprehensive analysis when needed.
2Productivity
If extraction rules are applied during data intake, then data analysis efficiency is improved, but processing time and resource consumption worsen
Solution Approach 1:
The system makes extraction rules dynamic and configurable, allowing users to apply different extraction rules at different times based on analysis needs. Extraction rules can be added, modified, or removed without reprocessing historical data, enabling flexible analysis efficiency optimization.
Solution Approach 2:
The system creates extracted field values and stores them as separate data structures during intake, creating a copy of the essential information from raw data. This copying enables fast analysis operations on the extracted fields without repeatedly processing the original raw data.
3Adaptability or versatility
If diverse data formats are retained in raw form, then data versatility is improved, but search and analysis complexity worsen
Solution Approach 1:
The system segments data into raw data portions and extracted field portions, allowing each to be handled differently. Raw data maintains its original format versatility, while extracted fields provide structured, standardized access points that simplify search and analysis operations.
Solution Approach 2:
The system introduces extracted fields as an intermediary layer between diverse raw data formats and analysis operations. This intermediary layer standardizes access to data content without requiring transformation of the underlying diverse formats, reducing search and analysis complexity.
4Reliability
If minimal processing is applied to data during intake, then data retention quality is improved, but query performance deteriorates
Solution Approach 1:
The system performs preliminary indexing operations during data intake, creating search-optimized structures from minimally processed data. This preliminary action prepares the data for fast querying without requiring extensive processing that would compromise data retention quality.
Solution Approach 2:
The system extracts key searchable elements (keywords, metadata fields) from minimally processed data during intake, creating an index that enables fast query performance while the complete minimally processed data remains intact for high-quality analysis.
Data Source
AI summary
Determining a set of extraction rules include clustering event segments into at least a first group of event segments, and determining, using first field data in the first group of event segments, a first set of extraction rules for extracting the first field data from each event segment of the first group of event segments. A determination is made that the first set of extraction rules fails to successfully extract all of the first field data. Responsive to the determination, the event segments are re-clustered into at least a second group of event segments and a third group of event segments until a successful set of extraction rules are identified. The successful set of extraction rules are stored in computer memory.