This document discusses performance testing for web applications. It introduces why performance testing is important, what it is, and how to conduct it. Performance testing determines how a system performs under different workloads by measuring aspects like availability, response time, and throughput. The document recommends testing at various load levels, monitoring applications during tests, using the right tools to minimize impact, and automating testing processes. It promotes continuous performance testing with sufficient data and time.

1. Performance
Testing
for web-scale

2. ● SA at EPAM Systems
● primary skill is Java
● hands-on-coding with Groovy, Ruby
● exploring FP with Erlang/Elixir
● passionate about agile, clean code and devops

3. Agenda
● Why?
● What?
● How to..?
● Tools
● Demo
● Summary
● References
● Q&A

4. Why...
should I Care?..

5. http://positionly.com/blog/seo/mobile-search-position-boost

6. $1.6B
~10 * $150M

7. What...
is Performance
Testing?

8. Performance testing
is in general testing performed
to determine how a system performs
in terms of responsiveness
and stability
under a particular workload.

9. What...
are
Key
Characteristics?

10. Availability

11. Concurrency

12. Response Time

13. Throughput

14. How…
to Decide when to
Test?

15. Readiness

16. Enough Data

17. Enough Time

18. Continuously

19. How…
to Choose what to
Test?

20. System Load
Test Duration (mins)
Normal Operation
Max Designed Operation Capacity
Stress Test
Soak Test
Peak Load
Test
Spike Test
Types

21. Consensus

22. Scenarios

23. How…
to Execute Test?

24. Data Volume

25. Network

26. Monitoring

27. Above Limits

28. Tool Impact

29. How…
to Collect Results?

30. Measures

31. Distributed Log

32. Tools

33. Many..
- Iago http://twitter.github.io/iago/index.html
- Tsung https://www.process-one.net/en/tsung/
- Locust http://locust.io/
- Zopkio https://github.com/linkedin/Zopkio

34. Gatling http://gatling.io/
- load testing framework based on Scala, Akka and Netty
- provides DSL along with UI for recording
- beautiful reports with right measurements
- easy for distributed load
- real-time monitoring

35. Demo
time

36. Takeaways
- Do performance testing continuously
- Do performance testing with right amount of data
- Monitor application during performance tests run
- User right measurements (percentiles)
- Choose right tool to load with less impact
- Automate deployment and testing processes

37. References
- The Art of App Performance Testing http://goo.gl/CEgfSQ
- Performance Testing Crash Course https://goo.gl/jDDcP4
- InfluxDB https://influxdb.com/
- Grafana http://grafana.org/
- Docker Compose https://docs.docker.com/compose/
- Sources https://github.com/webdizz/web-scale-perf-testing

38. Q&A

39. http://epam.com/careers

40. Izzet Mustafayev@EPAM Systems
@webdizz webdizz
izzetmustafaiev