Handling Scale with Reactive Programming

Summary of reactive programming concept and basics

Transcript

1. Handling Scale with Reactive Programming Alex Xandra Albert Sim
2. Disclaimer Presentations are intended for educational purposes only and not to replace independent professional judgment. The views and opinions expressed in this presentation do not necessarily reflect the official policy or position of blibli.com. Audience discretion is advised.
3. Who am I? • Alex Xandra Albert Sim • Principal Research and Development Engineer at blibli.com • alex.sim@gdn-commerce.com • bertzzie(.sim)
4. Reactive Manifesto
5. Reactive Manifesto A good reactive systems are: • Responsive • Resilient • Elastic • Message Driven
6. Reactive Manifesto A good reactive systems are: • Responsive • Resilient • Elastic • Message Driven
7. Reactive Manifesto A good reactive systems are: • Responsive • Resilient • Elastic • Message Driven
8. Reactive Manifesto A good reactive systems are: • Responsive • Resilient • Elastic • Message Driven
9. Reactive Manifesto A good reactive systems are: • Responsive • Resilient • Elastic • Message Driven
10. ACHIEVING RESPONSIVENESS
11. The Classic Web Architecture
12. The Classic Web Architecture
13. The Normal Flow Users Internet Web app (tomcat, netty, php, etc) Application process
14. What’s in “application process”?
15. Classical Code Sample
16. Classical Code Sample
17. Potensi Bottleneck
18. Little’s Law ! = # × % L : Jumlah koneksi ke database λ : Load dari request W : Waktu respons database
19. Little’s Law (Contoh) Untuk kasus di mana: • Terdapat 500 request / detik • Database memberikan response dalam 30ms • Jumlah koneksi db yang diperlukan adalah 15 L = λ x W L = 500 rps x 0.03s L = 15
20. Alur Kehancuran Sistem (1)
21. Solusi? • Naikkan jumlah pool / queue? • NO, karena hanya membantu di average case • Kenapa? • DB down 1 menit, 500 rps tetap masuk
22. Solusi 1
23. Solusi 2
24. Solusi Queue • Membuat sistem konsisten. • Misal: 3 queue berarti sistem akan memberikan respon max 120ms • Average menurun, tapi lebih stabil • Belum hitung cache! • + cache rate 50%, average bisa < 50ms
25. Queue dan Timeout • Timeout == batas sistem dianggap gagal • Request tidak selalu terdistribusi rapi • Average tidak sepenting Max Response Time
26. ON PARALLELISM
27. Sequential Execution Start a b c Result Latency
28. Parallel Execution Latency Start a b c Result
29. Composable Future
30. Keterbatasan Parallelisme
31. Keterbatasan Parallelisme
32. What we miss L • Resilience: Compartmentalization • Resilience: Circuit Breaker • Resilience: Supervision pattern • Message passing • Actor system • BASE Pattern
33. Maybe next time!
34. QUESTION AND ANSWERS
35. Come with us. We have cookies.
36. Thank You