OPS08-BP03 Analyze workload traces
Analyzing trace data is crucial for achieving a comprehensive view of an application's operational journey. By visualizing and understanding the interactions between various components, performance can be fine-tuned, bottlenecks identified, and user experiences enhanced.
Desired outcome: Achieve clear visibility into your application's distributed operations, enabling quicker issue resolution and an enhanced user experience.
Common anti-patterns:
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Overlooking trace data, relying solely on logs and metrics.
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Not correlating trace data with associated logs.
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Ignoring the metrics derived from traces, such as latency and fault rates.
Benefits of establishing this best practice:
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Improve troubleshooting and reduce mean time to resolution (MTTR).
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Gain insights into dependencies and their impact.
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Swift identification and rectification of performance issues.
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Leveraging trace-derived metrics for informed decision-making.
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Improved user experiences through optimized component interactions.
Level of risk exposed if this best practice is not established: Medium
Implementation guidance
AWS X-Ray
Implementation steps
The following steps offer a structured approach to effectively implementing trace data analysis using AWS services:
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Integrate AWS X-Ray: Ensure X-Ray is integrated with your applications to capture trace data.
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Analyze X-Ray metrics: Delve into metrics derived from X-Ray traces, such as latency, request rates, fault rates, and response time distributions, using the service map to monitor application health.
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Use ServiceLens: Leverage the ServiceLens map for enhanced observability of your services and applications. This allows for integrated viewing of traces, metrics, logs, alarms, and other health information.
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Enable X-Ray Insights:
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Turn on X-Ray Insights for automated anomaly detection in traces.
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Examine insights to pinpoint patterns and ascertain root causes, such as increased fault rates or latencies.
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Consult the insights timeline for a chronological analysis of detected issues.
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Use X-Ray Analytics: X-Ray Analytics allows you to thoroughly explore trace data, pinpoint patterns, and extract insights.
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Use groups in X-Ray: Create groups in X-Ray to filter traces based on criteria such as high latency, allowing for more targeted analysis.
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Incorporate Amazon DevOps Guru: Engage Amazon DevOps Guru
to benefit from machine learning models pinpointing operational anomalies in traces. -
Use CloudWatch Synthetics: Use CloudWatch Synthetics to create canaries for continually monitoring your endpoints and workflows. These canaries can integrate with X-Ray to provide trace data for in-depth analysis of the applications being tested.
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Use Real User Monitoring (RUM): With AWS X-Ray and CloudWatch RUM, you can analyze and debug the request path starting from end users of your application through downstream AWS managed services. This helps you identify latency trends and errors that impact your end users.
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Correlate with logs: Correlate trace data with related logs within the X-Ray trace view for a granular perspective on application behavior. This allows you to view log events directly associated with traced transactions.
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Implement CloudWatch cross-account observability: Monitor and troubleshoot applications that span multiple accounts within a Region.
Level of effort for the implementation plan: Medium
Resources
Related best practices:
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