The document discusses scaling systems to handle elastic demand through horizontal and vertical scaling, as well as scaling software, hardware, and services. It describes challenges faced with poor performance, unpredictability, and crashes when scaling up. Solutions involved using Scala, AngularJS, a new automation framework with Apache JMeter, and performance testing tools. Through iterations of performance testing and improvements, the system performance improved from three times slower than baseline to seven times faster than baseline. The key lessons are to use automation testing early to identify issues, write performant code, and leverage tools to deliver scalable systems.

1. Scaling up using
Automation and
Performance testing
Bogdan Molocea
Ness
[25th of October 2014]

3. What Scaling is?
Scaling is the ability of a
system to handle elastic
demand.
The inner cube represents
resource consumption that is
required at any point in time
The outer cube represents
potential resource deployment

4. What Scaling is?
The X axis is scaling by capability or depth and the
Y and Z axes are horizontal and vertical scaling
We can apply this model to
hardware, software, slushware
and services scaling

5. The “perfect” product myth
Quality
Performance
Scalability
Minimal costs

6. Our Journey

7. “Problems”
• Poor user experience
• Poor performance
• Unpredictable delays
• Process crashes
• Poor Automation Framework
• Badly designed SOAP API
• High maintenance costs
• Angry end users

8. Solutions
Scala
AngularJS
New Automation Framework
Apache JMeter

9. Challenges
Skilled Scala developers
Learn the rules of the system
Build test environments
Documentation
Availability

10. Automation testing

11. Automation testing
Page Object and BDD

12. Performance testing

13. Performance testing tools

14. Performance testing tools

15. Performance testing tools

16. Performance testing tools

17. Performance testing tools

18. Performance Evolution
Iteration 1:
Three times slower than
the Baseline results
We discovered and
fixed a few slow DB
queries.

19. Performance Evolution
Iteration 2:
Twice slower
We introduced
parallel processing
logic and switched to
bulk inserts into DB.

20. Performance Evolution
Iteration 3:
Same performance as
Baseline
A major refactoring
was done.

21. Performance Evolution
Iteration 4:
Twice slower
The newly
introduced defects
were fixed.

22. Performance Evolution
Iteration 5:
Twice faster
The number of actors
was increased.

23. Performance Evolution
Iteration 6:
Seven times faster

24. How we did it?
• Right tools
• Reliable Automation tests
• Performance improvements
• Different settings for DB interactions
• Different settings for concurrent processing

25. What to remember?
•Make use of automation to identify
performance and scalability issues
• Involve Performance Testing early if possible
• Developers should write code having
performance in mind
• Make use of any tool that can help you to
deliver a performant and scalable product

26. Q&A

28. Please fill the online evaluation form after event
Scaling up using Automation and Performance testing
Bogdan Molocea
Ness
[25th of October 2014]