Centralized Log Management System for Distributed Error Diagnosis
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Solution Overview
Problem
In distributed computing environments, managing and diagnosing errors across multiple components is challenging due to the inefficiencies of local log files, which consume resources and make it difficult to identify and analyze errors effectively.
Innovation Solution
A log management system that centralizes log data by writing messages to topics instead of local log files, allowing for remote processing and analysis, and applies enrichments and analytics using machine learning models to facilitate error diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If local log files are used for error logging in distributed computing environments, then each component can independently store error messages, but resource consumption increases and error diagnosis becomes difficult
Solution Approach 1:
The patent introduces a centralized log management system as an intermediary between distributed computing components and error logging. Instead of each component maintaining local log files, all error messages are routed through the centralized system which manages storage, processing, and retrieval of log data, thereby reducing overall resource consumption while maintaining reliable error logging across the distributed environment
Solution Approach 2:
The patent consolidates multiple distributed log files into a unified centralized log repository. By merging the logging functionality across all components into a single system, the patent reduces redundant resource usage and enables efficient error diagnosis through centralized access and analysis capabilities
2Reliability
If local log files are used for error logging, then error messages can be stored locally, but it becomes difficult to identify and analyze errors across multiple components
Solution Approach 1:
The centralized log management system acts as an intermediary that collects, standardizes, and organizes error messages from multiple distributed components. This intermediary structure enables unified search, filtering, and analysis capabilities that make error identification and diagnosis significantly easier compared to searching through scattered local log files
Solution Approach 2:
The centralized log management system provides universal access and analysis capabilities for all error messages across the distributed environment. It implements multiple functions including centralized storage, standardized formatting, searchability, filtering, and analytical processing, making error detection and measurement straightforward regardless of which component generated the error
3Adaptability or versatility
If multiple components write to local log files, then each component can independently log errors, but the collection of log files becomes difficult to maintain and parse
Solution Approach 1:
The patent extracts the log file management complexity from individual components and centralizes it in a dedicated log management system. Each component maintains its independent error logging capability through simplified interfaces, while the complex tasks of log file maintenance, parsing, and organization are handled by the specialized centralized system
Solution Approach 2:
The centralized log management system serves as an intermediary that handles all complex log management operations. Components interact with this intermediary through simple standardized interfaces, allowing them to maintain independent logging while the intermediary manages the complexity of maintaining and parsing the collective log data
Data Source
AI summary
In order to provide effective diagnostics and logging of error messages produced during the execution of processes across multiple components, techniques are disclosed for the generating, managing, and processing centralized logs containing those error messages. In particular, the components may write error messages to a centralized log instead of writing the error messages to local log files. The various error messages in the centralized log can be read, identified, and organized. Furthermore, enrichments and/or analytics may be applied to the error messages based on information from a knowledge source or the application of one or more machine learning models. The organized error messages, enrichments, and analytics can be stored in an output log that can be easily retrieved and viewed through a graphical interface. The organized error messages, enrichments, and analytics work together to allow for more effective diagnosing of execution errors.