Topology-Aware Tracing System for Selective Data Capture
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Solution Overview
Problem
In complex software applications, traditional trace logging generates vast amounts of irrelevant data, leading to performance degradation and delays in diagnosing issues due to the need for technical personnel to sift through extensive logs.
Innovation Solution
A topology-aware tracing system that selectively generates trace data based on defined dimensions, including user, role, application, provider, content, and topology-based attributes, to focus on relevant components and reduce unnecessary data generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If trace logging captures information at the finest possible level of detail, then diagnostic precision is improved, but storage footprint increases and performance degrades
Solution Approach 1:
The system dynamically adjusts the level of trace detail captured based on runtime conditions. During normal operation, tracing operates at a lower level to minimize performance impact. When an abnormal condition is detected, the system automatically increases the trace logging level to capture finer details for diagnosis, thus resolving the contradiction between diagnostic precision and system performance.
Solution Approach 2:
The patent changes the parameter of trace logging detail level from static to adaptive. By monitoring system state and automatically adjusting the granularity of captured information, the system ensures high diagnostic precision when needed while maintaining acceptable performance during normal operation, effectively resolving the trade-off between these two opposing requirements.
2Measurement precision
If trace logging captures information at the finest possible level of detail, then diagnostic precision is improved, but storage footprint increases
Solution Approach 1:
The system dynamically adjusts the volume of trace data generated by adapting the logging level to actual system conditions. During normal operation, minimal trace data is captured, reducing storage requirements. When abnormalities occur, the system temporarily increases logging detail to capture diagnostically valuable information, thus achieving high diagnostic precision without proportionally increasing storage footprint.
Solution Approach 2:
By changing the parameter of trace data granularity from fixed to adaptive, the system optimizes the balance between diagnostic value and storage consumption. The automated adjustment ensures that fine-grained details are captured only when necessary for diagnosis, preventing unnecessary accumulation of trace data and thereby resolving the contradiction between diagnostic precision and storage footprint.
3Measurement precision
If adaptive trace logging increases the level of trace messages during abnormal conditions, then diagnostic capability is improved, but irrelevant data increases
Solution Approach 1:
The patent segments the trace logging function into different levels of detail (e.g., error-level, warning-level, info-level, debug-level). During abnormal conditions, the system selectively activates specific segments (higher detail levels) rather than uniformly capturing all possible information. This segmentation allows the system to improve diagnostic capability while filtering out irrelevant data through selective activation of appropriate trace segments.
Solution Approach 2:
The system applies partial action by capturing only the specific level of trace detail needed for diagnosing abnormal conditions, rather than uniformly capturing excessive information at all levels. By selectively enabling trace messages at appropriate granularity levels based on the nature and severity of detected anomalies, the system improves diagnostic capability while minimizing the generation of irrelevant data.
Data Source
AI summary
Techniques are described herein for performing multi-dimensional selective tracing within a computer system. In one or more embodiments, the tracing profile indicates what trace data to capture based, at least in part on a set of target types and a set of target dependencies. In response to receiving a request to initiate a tracing session using the tracing profile, a topology of a set of target resources that are deployed is identified. A subset of one or more trace providers is then selected, based at least in part on the topology of the set of target resources that are deployed and the tracing profile. The subset of one or more trace providers generate trace data for a subset of target resources in the set of target resources. If the topology changes, then the set of trace providers that generate trace data may also be updated based on the tracing profile.