This document discusses using aspect-oriented programming (AOP) and Amazon Web Services (AWS) for performance monitoring of server-side applications. It describes how AOP can be used to gather runtime data with minimal boilerplate code. The data is then published to AWS CloudWatch, which provides visualization of metrics over time as well as alarm and notification capabilities. With this approach, developers can monitor important metrics like method execution times, errors and IO operations across a distributed environment in a simple and cost-effective manner.

1. Performance Monitoring
with
AOP & AWS
by Yan Cui
@theburningmonk

2. Server-side Developer @

3. iwi by numbers
• 400k+ DAU
• ~100m requests/day
• 25k+ concurrent users
• 1500+ requests/s
• 7000+ IO opts/s
• 100+ commodity servers
• 75ms average latency

4. What is my code doing at runtime?

5. Runtime Monitoring != Profiling

6. Monitoring
• Difficult in a distributed environment
• Lots of effort to set up

7. Monitoring
• Achievable in 3 simple steps
1. Gather runtime data
2. Publish
3. Visualize

8. Gathering Data
• Method execution time
• Method execution count
• Error count
• Requires (lots) boilerplate to set up

9. AOP to the rescue!

10. AOP
• Aspect Oriented Programming
• Cross-Cutting Concerns
• PostSharp

11. PostSharp
Image by Mike Rohde

12. Code demo goes here

13. Publish
• Where to?...
• Amazon CloudWatch

14. Visualize

15. Alarms

16. Notifications
• Notify via email/SMS

17. CloudWatch
Image by Mike Rohde

18. Visualize
• Amazon Management Console
– Min, Max, Average, Count
– Up to 14 days of data
– Down to 1 minute intervals

19. Amazon CloudWatch
• Cheap
• Supports alarms and notifications
• Visualization tool

20. AOP + Amazon CloudWatch =

21. What to Monitor?
• IO operations
• CPU intensive operations
• Service entry-points

22. Caution
• Overhead
• Only monitor what you need
• Make it configurable/optional?

23. Thank You!
@theburningmonk