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A DevOps team manages a critical microservices application on Azure Kubernetes Service (AKS). They want to improve the detectability of issues across their distributed services. They need to trace requests as they flow through multiple services, identify bottlenecks, and pinpoint service dependencies. Which monitoring approach is best suited for this requirement?
- AMetrics Collection
- BLog Aggregation
- CSynthetic Monitoring
- DDistributed Tracing
Show answer & explanationAnswer & explanation
Correct answer: D. Distributed Tracing
Distributed tracing is specifically designed to track requests as they traverse multiple services in a microservices architecture. It provides a complete view of the request path, allowing teams to identify latency, errors, and dependencies, which directly addresses the team's need to trace requests and pinpoint bottlenecks.
Why the other options are wrong
- A. Metrics collection gathers numerical data (e.g., CPU, memory, request count) but doesn't provide the end-to-end context of a single request across services.
- B. Log aggregation collects logs from various services but doesn't inherently link them together to form a complete request flow across services.
- C. Synthetic monitoring simulates user transactions but doesn't provide granular insight into the internal flow and dependencies of a live request within the system.
Distributed Tracing
An observability technique used to monitor requests as they propagate through multiple services in a distributed system, providing end-to-end visibility of transaction flow.
- Tracks a single request across service boundaries.
- Helps identify latency and errors in microservices.
- Often implemented with OpenTelemetry or similar standards.
Memory trick: For microservices, you need a tracing thread to follow the path.