Dimensional Data Structure for Log Entry Organization
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Solution Overview
Problem
In distributed or complex systems, managing numerous log entries across multiple nodes is challenging due to duplication and variation, leading to a significant backlog of identical or similar log entries, which programmers struggle to organize effectively.
Innovation Solution
A computer-implemented method that identifies log entries and applies criteria to generate a dimensional data structure, allowing for the organization and loading of log entries based on attributes such as timestamp, log level, and context, enabling efficient categorization and comparison of log entries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If log entries are stored on multiple nodes in distributed systems, then system scalability and fault tolerance are improved, but log management complexity and duplication increase
Solution Approach 1:
The patent segments log entries into distinct categories based on their content and origin, organizing them into separate data structures (e.g., application logs, system logs, security logs). This segmentation allows distributed systems to manage logs from multiple nodes independently while maintaining a unified overview, reducing management complexity despite system scalability.
Solution Approach 2:
The patent introduces multiple dimensions for organizing log data, including temporal dimensions (timestamps, time ranges), hierarchical dimensions (log levels, categories), and spatial dimensions (source nodes, destinations). This multi-dimensional organization enables efficient log management in distributed systems by providing multiple sorting and filtering perspectives simultaneously.
2Reliability
If numerous log entries are collected from distributed nodes, then system monitoring coverage is improved, but the volume of duplicate and similar entries increases
Solution Approach 1:
The patent merges duplicate and similar log entries by identifying entries with identical or semantically equivalent content across different nodes. Similar entries are consolidated into representative samples, reducing the total volume of log data while preserving complete monitoring coverage through the retained representative entries.
Solution Approach 2:
The patent extracts and removes duplicate entries from the log collection process. By identifying and filtering out redundant log entries before they enter the storage system, the patent reduces the volume of data to be managed while maintaining accurate system monitoring coverage through the extracted unique entries.
3Measurement precision
If log entries are organized by multiple criteria, then log analysis precision is improved, but data structure complexity increases
Solution Approach 1:
The patent creates a universal log organization framework that can accommodate multiple sorting and filtering criteria through a single data structure design. The structure supports multi-dimensional queries and analyses by implementing flexible indexing and search mechanisms, allowing precise log analysis across various dimensions without requiring separate complex data structures for each criterion.
Data Source
AI summary
A computer-implemented method includes identifying a plurality of log entries. The method includes receiving one or more criteria. The one or more criteria are applicable to the plurality of log entries. The method includes generating a dimensional data structure. The dimensional data structure is defined in a dimension for each of the one or more criteria. The method includes loading the plurality of log entries into the dimensional data structure based on the one or more criteria. A corresponding computer program product and computer system are also disclosed.