The document discusses performance optimization through continuous production profiling, highlighting the importance of reducing costs and improving service level agreements (SLA). It introduces various profiling types, particularly CPU profiling, which aids in optimizing application performance with low overhead. The author, Yonatan Goldschmidt, shares insights on utilizing gprofiler for ongoing performance analysis and implementation of code improvements.

1. © 2021. All rights reserved
Optimizing Performance Using
Continuous Production Profiling
Yonatan Goldschmidt
Principal Engineer and Research Team Lead @ Granulate
Connect on LinkedIn | GitHub: https://github.com/Jongy

2. Meet Yonatan Goldschmidt
●About me:
○ 6 years of service as R&D specialist & team lead
○ Likes everything about computers and software
○ Today, a team lead at Granulate’s Performance Research
●About Granulate:
Enables companies to optimize their workloads, improve performance
and leverage that to reduce compute costs
aka why DevOpsDays accepted my session ;)

3. Why should I optimize?
●Reduce cost
●Improve SLA
Main Reasons:

4. And Why Profile?

5. What is Profiling?
●The dynamic inspection of running programs
●Most commonly, used to aid optimization
○ Can also be used to aid debugging
●Many types (CPU, memory, networking/IO, locks …)
●Many tools

6. Profiling is cool…
… but not common enough!

7. CPU Profiling
●CPU time - not wall time
○ time.sleep(1) vs sum(range(100000000))
●Sampling-based - lower impact & statistically correct
●Directly related to the CPU usage of our app
CPU profiling is easy to apply, has low overhead and gives
impactful conclusions on application performance

8. ●X-axis: relative part from the profile
●Y-axis: stack trace
Additional resources:
●Intro: rbspy.github.io/using-flamegraphs/
●Advanced: brendangregg.com/flamegraphs.html
Flamegraphs

10. ● Production vs local
Continuous and Production Profiling

11. ● Continuous
○ Varying load
○ Code changes
Continuous and Production Profiling

12. From Theory to Practice
Continuously Profile
Analyze Results
Implement Code
Improvements
Verify Results
REPEAT!

13. Live Demo!

14. gProfiler Demo Env.

16. gProfiler Use-cases
●We’re using gProfiler regularly on our systems
○ Ruby - fluentd plugin was rewritten
○ Python example - removeHandlerRef case in one of our servers

20. Takeaways

21. Thank You!
To start free profiling, visit: gprofiler.io
Feel free to DM here or connect on LinkedIn and GitHub (/jongy)

22. Q&A