Amazon

Introduction

As Amazon’s cloud infrastructure grows, so does the complexity of its internal systems. The Local Region Affinity (LRA) system, designed to optimize request routing, introduced a new abstraction layer that made it difficult for service owners to see how their endpoints were being resolved at runtime. This lack of visibility led to frustration among engineers and customers alike, who needed to understand and troubleshoot regional routing behavior.

I was tasked with addressing this pain point as a Software Development Engineer Intern on the LRA team. My goal was to design and implement a solution that would give service owners the clarity and confidence they needed to operate and troubleshoot their services effectively.

Target Users

• Amazon service owners and engineers responsible for LRA-enabled endpoints
• Strategic customers (e.g., OPF) onboarding to LRA who require operational transparency

The Challenge

• Lack of visibility into how endpoints were resolved at runtime
• Difficulty verifying and troubleshooting regional routing
• Complexity in understanding traffic distribution across regions

These issues surfaced in technical reviews, risk assessments, and direct customer feedback. Service owners needed a way to answer questions like:

“Where did my endpoint resolve last week? Did traffic go to the right region? Why did a request fail?”

User Research & Insights

To better understand the problem, I reviewed feedback from senior engineers, technical risk assessments, and onboarding sessions with strategic customers. The recurring theme was a desire for historical, queryable data on endpoint resolutions—something that would allow teams to investigate incidents, verify routing, and build operational confidence.

Solution

I designed and built the resolution-history command for the LRA Mechanic CLI. This tool empowers service owners to:

• Query and analyze historical regional endpoint resolutions
• Filter by timeframe, caller region, and AWS account
• View summary statistics and detailed resolution data
• Export results for further analysis

Key Metrics:

• Up to 1 week of historical data
• Data updated every 5 minutes
• Fast queries for both small and large time windows, powered by Amazon Athena

Implementation

I chose to implement the solution directly in the Mechanic CLI, leveraging Amazon Athena for scalable, serverless querying of large datasets. This approach allowed for:

• Efficient, on-demand access to historical data
• Seamless integration with existing AWS services (S3, Glue)
• Minimal operational overhead for the LRA team

Architecture Overview:

• Mechanic CLI issues queries to Athena
• Athena reads from S3, cataloged by Glue
• Results are returned to the CLI for user-friendly display

User Experience

The new command provides a simple, powerful interface:

mechanic execute lra health resolution-history --endpoint=service.lra.amazon --start-time='2025-06-01T00:00:00' --end-time='2025-06-01T02:00:00' --regions=dub,zaz --account=123456789012

Sample Output:

Impact

• Operational Confidence: Service owners can now verify endpoint resolution behavior across regions and time periods.
• Simplified Troubleshooting: Historical data enables faster, more accurate incident investigation.
• Enhanced Visibility: Teams gain insight into traffic distribution and regional routing patterns.
• Reduced Mean Time to Detect (MTTD): Issues are identified and resolved more quickly.

Customer Feedback

“The resolution-history command has significantly improved our ability to monitor and troubleshoot our LRA endpoints. It's become an essential tool in our operational toolkit.”
— OPF Team Lead

Future Enhancements

• Add comparison features for DNS resolution patterns across time periods
• Integrate with monitoring and alerting systems
• Develop visualization tools for long-term trend analysis

Conclusion

By focusing on the real needs of service owners and leveraging AWS’s serverless analytics stack, I delivered a tool that closes a critical visibility gap in the LRA system. The resolution-history command empowers teams to operate with confidence, troubleshoot efficiently, and deliver better outcomes for Amazon’s customers.