Log Data Management for Application Fault Diagnosis
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Solution Overview
Problem
Existing log management methods face challenges in efficiently storing and retrieving log data from application systems, as they either reduce data volume by decreasing log frequency, leading to decreased accuracy, or store only high-severity logs, resulting in incomplete and discontinuous data that hinders fault diagnosis.
Innovation Solution
A method that continuously receives and caches log data, storing only logs associated with exceptions within a predetermined range before and after the exception occurs, allowing for comprehensive fault diagnosis without the need to store normal state logs, thus reducing storage space while maintaining data accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all log data is stored to monitor application system state, then fault diagnosis accuracy is improved, but storage space consumption increases significantly
Solution Approach 1:
The patent extracts and stores only log data associated with exceptions and their surrounding context, rather than storing all log data. When an exception is detected, the system identifies the exception log and stores it along with logs within a predetermined time range before and after the exception, effectively extracting only the necessary diagnostic information while discarding normal operation logs that consume storage space.
2Quantity of substance
If only high-severity exception logs are stored to reduce storage space, then storage efficiency is improved, but fault diagnosis completeness deteriorates due to missing context
Solution Approach 1:
The patent performs preliminary action by storing log data within a predetermined time range before an exception occurs, in addition to the exception log itself. This ensures that contextual information leading up to the exception is preserved, enabling complete fault diagnosis without storing all normal operation logs. The system proactively captures the necessary context window around each exception event.
3Quantity of substance
If log frequency is decreased to reduce data volume, then storage requirements are reduced, but monitoring accuracy deteriorates
Solution Approach 1:
The patent implements a dynamic log storage strategy where the logging behavior changes based on system state. During normal operation, logs are not stored or are stored at lower frequency. When an exception occurs, the system dynamically increases logging activity by capturing the exception log and surrounding context logs with higher density. This dynamic approach maintains monitoring accuracy for critical events while reducing overall data volume.
Data Source
AI summary
Techniques manage log data. Such techniques involve receiving log data describing a state of an application system at a target time point. Such techniques further involve determining whether a level of the log data satisfies a predetermined level. Such techniques further involve, in response to determining that the level of the log data satisfies the predetermined level, storing log data within a predetermined range adjacent to the target time point to a log repository of the application system, the predetermined range indicating the amount of log data expected to be stored.