Here is a detailed summary of the video transcription in markdown format:
Key Takeaways
- The goal of incident management is to find out which of the five causes are responsible for the incident: change, change in inputs, breach of limits, component failure, or dependency failure.
- Troubleshooting involves navigating quickly between different observability tools and data sources to find the issue, without getting stuck or overwhelmed.
- Comprehensive application instrumentation, using tools like OpenTelemetry, is crucial for fast and effective troubleshooting.
- The ability to break down metrics and logs by various dimensions (e.g., instance, API, customer) is essential for isolating the root cause.
- Automating the troubleshooting process with AI-driven tools can help teams investigate and mitigate issues more efficiently.
Troubleshooting Approach
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Identify the Five Causes
- Change: Was there a recent change (deployment, configuration, etc.) that could have caused the issue?
- Change in Inputs: Has the workload or request pattern changed, leading to overload or other issues?
- Breach of Limits: Have you hit a scaling limit (CPU, memory, etc.) or a dependency limit (certificates, quotas, etc.)?
- Component Failure: Has a specific component (instance, availability zone, etc.) failed or is performing worse than others?
- Dependency Failure: Has a dependency (remote service, database, etc.) failed or is performing poorly?
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Navigate Efficiently
- Use observability tools to quickly navigate between infrastructure, applications, and dependencies.
- Leverage "information scent" to follow the most promising leads and avoid getting stuck.
- Automate navigation as much as possible to reduce the need for manual steps and context switching.
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Leverage Comprehensive Instrumentation
- Instrument applications with OpenTelemetry to capture detailed telemetry (metrics, logs, traces).
- Ensure the instrumentation allows for breaking down metrics and logs by relevant dimensions (e.g., instance, API, customer).
- Use indexing and other features to enable fast, efficient querying of the telemetry data.
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Accelerate Investigation with AI
- Leverage AI-driven tools that can automatically investigate the issue, follow the five causes, and provide hypotheses and recommended actions.
- These tools can help teams parallelize the investigation and avoid getting stuck or missing important clues.
Demonstration
The speaker demonstrated the troubleshooting process using AWS CloudWatch and related tools:
- Navigated from the initial alarm to the load balancer, instances, and application-level metrics and logs to identify the issue.
- Recognized that the problem was likely in a dependent service (bot-forge) and shifted the investigation there.
- Leveraged the Application Insights feature to visualize the distributed tracing and identify the specific error (access denied) in the bot-forge service.
- Used CloudTrail to quickly find the recent change (a resource policy update) that was the root cause of the issue.
- Demonstrated the new CloudWatch Investigator feature, which automatically followed the five causes, identified the root issue, and provided a hypothesis and recommended actions.
The speaker emphasized the importance of comprehensive instrumentation, efficient navigation between observability data sources, and the value of AI-driven troubleshooting tools in accelerating the investigation and mitigation process.