Log Event Summarization for Distributed Server Systems
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Solution Overview
Problem
The vast amount of log data generated by web applications is difficult to process manually due to its immense quantity and machine-designed format, often leading to overlooked insights and unidentifiable important information.
Innovation Solution
The system identifies clusters of log lines based on log line templates using n-grams and hash comparisons, communicating cluster information to users through visual interfaces like bubble graphs, which indicate cluster size, age, origin, and other data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If log data is collected from multiple machines in a distributed system, then monitoring coverage and insight quality are improved, but data volume and processing complexity increase exponentially
Solution Approach 1:
The patent merges log data from multiple distributed machines by identifying and grouping similar log lines into clusters. Log lines with matching templates and similar characteristics are combined into single cluster representations, reducing the total data volume while preserving monitoring coverage across all machines in the distributed system.
Solution Approach 2:
The patent extracts essential characteristics from log data by identifying templates and key patterns, then separates the core informational content from redundant details. This extraction process isolates the meaningful insights from the overwhelming volume of raw log data, allowing monitoring without processing every individual log line.
2Loss of information
If log data is processed manually to extract insights, then analysis depth can be improved, but time consumption and operational difficulty increase
Solution Approach 1:
The patent implements self-service log analysis through automated template identification and cluster generation. The system automatically processes log data, identifies patterns, and generates insights without requiring manual intervention, thereby eliminating time consumption while maintaining comprehensive insight extraction quality.
Solution Approach 2:
The patent performs preliminary processing of log data by pre-identifying templates and organizing data into clusters before analysis is needed. This preliminary organization of log data into structured groups enables rapid insight extraction when needed, eliminating the time that would otherwise be required for manual analysis.
3Productivity
If log format is designed for machines with structured data, then data processing efficiency is improved, but human readability and ease of interpretation deteriorate
Solution Approach 1:
The patent applies local quality by presenting different representations of the same log data to different users. Machine processing receives the original structured format for efficient processing, while human users receive cluster summaries with template patterns and key characteristics that are optimized for human readability and interpretation.
Solution Approach 2:
The patent introduces cluster summaries as an intermediary representation between machine-optimized structured log data and human users. These summaries translate machine-readable structured data into human-friendly formats while preserving the essential information, enabling both efficient processing and easy interpretation.
4Measurement precision
If all log data is retained and analyzed in detail, then measurement precision is improved, but system complexity and resource requirements increase
Solution Approach 1:
The patent segments log data into distinct clusters based on template patterns and similarity metrics. This segmentation organizes the vast amount of log data into manageable groups, maintaining measurement precision within each cluster while reducing overall system complexity through hierarchical organization and focused analysis of representative samples.
Data Source
AI summary
Clusters of log lines are identified based on log line templates. The log line templates are based on a punctuality pattern for a log line. Clusters of log lines that match each punctuality pattern can be identified based on comparisons between the log lines. The comparison may determine the similarity of the log lines and ultimately identify whether the log lines are close enough to be clustered. The comparison may be based on generated n-grams for the log lines and performing a hash on the n-grams. The resulting cluster information may be communicated to a user in an interface.