OPS04-BP05 Implement distributed tracing
Distributed tracing offers a way to monitor and visualize requests as they traverse through various components of a distributed system. By capturing trace data from multiple sources and analyzing it in a unified view, teams can better understand how requests flow, where bottlenecks exist, and where optimization efforts should focus.
Desired outcome: Achieve a holistic view of requests flowing through your distributed system, allowing for precise debugging, optimized performance, and improved user experiences.
Common anti-patterns:
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Inconsistent instrumentation: Not all services in a distributed system are instrumented for tracing.
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Ignoring latency: Only focusing on errors and not considering the latency or gradual performance degradations.
Benefits of establishing this best practice:
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Comprehensive system overview: Visualizing the entire path of requests, from entry to exit.
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Enhanced debugging: Quickly identifying where failures or performance issues occur.
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Improved user experience: Monitoring and optimizing based on actual user data, ensuring the system meets real-world demands.
Level of risk exposed if this best practice is not established: High
Implementation guidance
Begin by identifying all of the elements of your workload that require instrumentation. Once all components are accounted for, leverage tools such as AWS X-Ray and OpenTelemetry to gather trace data for analysis with tools like X-Ray and Amazon CloudWatch ServiceLens Map. Engage in regular reviews with developers, and supplement these discussions with tools like Amazon DevOps Guru, X-Ray Analytics and X-Ray Insights to help uncover deeper findings. Establish alerts from trace data to notify when outcomes, as defined in the workload monitoring plan, are at risk.
Implementation steps
To implement distributed tracing effectively:
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Adopt AWS X-Ray
: Integrate X-Ray into your application to gain insights into its behavior, understand its performance, and pinpoint bottlenecks. Utilize X-Ray Insights for automatic trace analysis. -
Instrument your services: Verify that every service, from an AWS Lambda
function to an EC2 instance , sends trace data. The more services you instrument, the clearer the end-to-end view. -
Incorporate CloudWatch Real User Monitoring and synthetic monitoring: Integrate Real User Monitoring (RUM) and synthetic monitoring with X-Ray. This allows for capturing real-world user experiences and simulating user interactions to identify potential issues.
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Use the CloudWatch agent: The agent can send traces from either X-Ray or OpenTelemetry, enhancing the depth of insights obtained.
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Use Amazon DevOps Guru
: DevOps Guru uses data from X-Ray, CloudWatch, AWS Config, and AWS CloudTrail to provide actionable recommendations. -
Analyze traces: Regularly review the trace data to discern patterns, anomalies, or bottlenecks that might impact your application's performance.
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Set up alerts: Configure alarms in CloudWatch
for unusual patterns or extended latencies, allowing proactive issue addressing. -
Continuous improvement: Revisit your tracing strategy as services are added or modified to capture all relevant data points.
Level of effort for the implementation plan: Medium
Resources
Related best practices:
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