Incident Analyzer Root Cause Identification
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Solution Overview
Problem
Conventional methods for instrumenting software in complex user environments with multiple micro-transactions and infrastructure platforms struggle to efficiently identify the root cause of errors, as they require manual tracing and are time-consuming due to the complexity of handling various hosts, containers, and infrastructure platforms.
Innovation Solution
An incident analyzer attributes a high error rate in an endpoint to an offending infrastructure element, allowing for efficient identification of the root cause by querying error traces, ranking them using statistical methods, and analyzing infrastructure causes, thereby streamlining the diagnosis and correction process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional instrumentation techniques are used in complex micro-service environments, then error tracking can be implemented, but the time required to identify root causes increases significantly
Solution Approach 1:
The system segments the complex error analysis problem into distinct components: error trace collection, statistical analysis, and root cause identification. By dividing the monolithic error tracking process into modular segments that can be independently processed and analyzed, the system reduces the time required to identify root causes while maintaining comprehensive error tracking capability across distributed micro-service environments.
Solution Approach 2:
The system performs preliminary actions by pre-collecting and storing error traces with metadata from multiple sources before analysis is needed. Error traces including stack traces, log data, and performance metrics are captured and organized in advance, allowing rapid querying and analysis when errors occur, thereby reducing the time to identify root causes without sacrificing tracking precision.
2Loss of information
If manual tracing methods are used to track errors through multiple hosts and containers, then detailed error information can be obtained, but the complexity of the analysis process increases
Solution Approach 1:
The system introduces an intermediary statistical analysis layer that automatically processes error traces from multiple hosts and containers. This intermediary component performs chi-squared tests and other statistical analyses to identify correlated errors and potential root causes, reducing the complexity of manual analysis while preserving complete error information through automated correlation detection across distributed systems.
Solution Approach 2:
The system creates simplified copies or representations of complex error trace data through statistical summaries and correlation matrices. Instead of manually analyzing raw error traces from multiple sources, the system generates condensed statistical representations that maintain the essential error information while reducing analysis complexity, allowing rapid identification of root causes across distributed micro-service environments.
3Reliability
If comprehensive error tracing is performed across all services and infrastructure, then complete error context is captured, but the computational resources required increase
Solution Approach 1:
The system applies partial action by selectively analyzing error traces based on statistical significance rather than processing all traces uniformly. Using chi-squared tests and correlation analysis, the system identifies and focuses computational resources on error traces that show statistically significant correlations with potential root causes, maintaining complete error context for reliable analysis while reducing overall computational resource requirements by excluding statistically insignificant data.
Data Source
AI summary
A process for analyzing an incident includes setting up an alert for a high error rate on a particular endpoint. Once the alert is triggered, a set of traces for transactions exhibiting errors on the offending endpoint is queried. All traces for other services/operations that include errors on the offending endpoint are also enumerated. A set of baseline transactions that involve the offending endpoint, but do not result in error may be utilized to determine whether the errors are always present, or are distinctive for certain offending transactions. All traces are ranked based on a statistic. Once the traces have been ranked, they may be traced down to a deepest/most terminal error. A set of transactions that correlate to the terminal error may also be analyzed to determine infrastructure causes.