Automatic Log Analysis for DQL-Free Forensic Troubleshooting
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Solution Overview
Problem
Existing log analysis systems require advanced users to have deep domain knowledge and proficiency in data query language (DQL) to troubleshoot issues, involving labor-intensive indexing and guesswork, leading to prolonged troubleshooting times and increased mean time to recover (MTTR).
Innovation Solution
Implementing automatic log analysis methods that parse log files to generate categories with weight values, identify phrases, and generate sentiment scores, allowing basic users to quickly understand failure causes without DQL queries or indexing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual indexing and DQL queries are used for log analysis, then search precision is improved, but device complexity and user skill requirements increase
Solution Approach 1:
The system performs automatic indexing and log analysis without requiring manual user configuration. The automated indexer continuously processes log files, extracts entities, and builds search indices independently, eliminating the need for users to manually index logs or configure search parameters.
Solution Approach 2:
The system introduces an automated intermediary layer between the raw log data and the user queries. This intermediary includes automatic entity extraction, relationship mapping, and index generation mechanisms that translate complex log structures into simplified search-friendly formats, shielding users from underlying system complexity.
2Speed
If manual indexing is performed, then query speed is improved, but loss of time and labor intensity increase
Solution Approach 1:
The system performs preliminary indexing actions automatically and continuously in the background before user queries are executed. The automated indexer pre-processes log files, extracts entities, and builds search indices proactively, so when users submit queries, the indexing work is already complete, eliminating the trade-off between indexing time and query speed.
Solution Approach 2:
The indexing process runs continuously and asynchronously alongside normal system operations. The automated indexer maintains persistent connections to log sources, continuously extracting and indexing new log entries without interruption, ensuring that the index is always up-to-date and ready for immediate query execution without requiring periodic manual intervention.
3Measurement precision
If advanced search tools with DQL are used, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The system creates simplified visual representations and summaries of complex log data instead of requiring users to work with raw DQL queries. It generates automated search results, entity relationship diagrams, and contextual summaries that copy the essential information from complex query results into user-friendly formats, maintaining precision while eliminating the need for DQL knowledge.
Solution Approach 2:
The system automatically transforms and normalizes log data parameters into standardized formats before presentation to users. It converts diverse log structures into unified entity representations, automatically applies filters and aggregations, and presents data in optimized formats based on query context, shielding users from the complexity of parameter transformation while maintaining analytical precision.
Data Source
AI summary
A method is provided for performing forensic log analysis using automatic log analysis when troubleshooting data logs. Specifically, the method includes obtaining log files from an operation system, an application, a server, or a device. The method also includes parsing data from the log files to generate categories including at least exceptions, keywords, severity levels, resources, or classes, where each type of category has a respective category weight value. The method also includes identifying phrases based at least in part on the categories. The method further includes generating a sentiment score for a log line from the log files based at least in part on the categories, category weight values, or the phases.