This document discusses distributed caching and its benefits. It provides examples of how caching is used in browsers and for user login requests. It then discusses how companies like Facebook and Naver implement distributed caching at large scale using Memcached to improve performance and scalability. The key points are: 1) Caching stores data to serve future requests faster. It is commonly used in browsers, databases for login requests, and at companies like Facebook and Naver. 2) Facebook used Memcached to cache content and improved performance, serving over 1 billion users per day with 800 Memcached servers and 1,800 MySQL servers. 3) Naver uses a distributed file system with multiple cheap servers and disks instead of a single

1. 분산 캐시
Park Chunduck. Hallym univ.

2. What is cache?
• Cache is a component that transparently stores
data so that future requests for that data can
be served faster –wikipedia
• Cache 는 나중에 요청 받을 데이터를 미리 저장해두었다가 빠르게
서비스 해 주는 것

3. Data and Cache
• Lots of Data
• cache

4. CPU cache

5. CPU cache

6. Brower cache 1)

7. Brower cache 2)

8. Cache use case : Login
• Common case
– Read from DB
• Select * from users where id=‘sogangori’;

9. Normal DB composition
일반적인 DB구성
• All traffic processed by Master 모든 트래픽 담당
– Overhead!
• Slave for emergency prepare 장애 대비

10. How Reduce Overhead?
• Scale UP vs Scale Out
– No Scale Down 이런 거 없다
scale Up!

11. Scale up

12. Scale out

13. Scale up/Scale down
• Simpleness
– High / Low
• Prices
– High / Low
• Expandability 확장성
– Low / High
• OK! Let’s Go Scale Down

14. Request analysis
• Read 70%, Write 30%
– Let’s distribute Read Request!!
• One Write Master + Multi Read Slave

15. Composition
• At early stage… 초기에는 좋았다

16. Performance Decrease
• Further Workload bigger, performance decreased.
– 부하가 더 커지니 성능 향상 효과가 미비해지다.
• Databases eventual consumed by writing.

17. Solution 1 : SSD
• Performance !
• Money ?

18. Solution 2 : Partitioning
• Let’s go Scalable Partitioning
– Performance Up!
– Management Issue Up!!??
– Cost Up??

19. Use Case Detail
• Login
– Read from cache

20. General Cache Layer

21. Cache Type
• 1 key – N Items
• 1 key – 1 Item

22. Cache Type Pros&Cons 장단점
• 1 key – 1 Item
– Pros : if 1 item is changed, just change that
item.
– Cons : some items can be removed
• 1 key – N Items
– Pros : just 1 get
– Cons :
• if 1 item is changed, need cache update
• Race codition 경합 조건

23. Divide Data
Changeable Data
Divide
Constable data

24. Memcache Performance
• Memcache vs Mysql
– 136 vs 1,613 (seconds)

25. Memcached in Facebook
• Facebook has a billion(10억) users in a
single day, says Mark Zuckerberg
– 28 August 2015
– 1 in 7 people on Earth used Facebook

26. Memcached in Facebook
• 2010. 4 When day user was half of billion(Today
is billion 10억명)
– Active User 70 million
– Web Request 40 million
– Web Server 10,000 Memcached Server 800 -> 15TB
– Mysql Server 1,800 Mater/slave(900+900)
• Mem:25TB, SQL Query 500,000 on second

27. Naver Datacenter 각
• IDC(Internet Data Center) in ChunCheon city

28. NAS vs 1 unit size Server
• NAS(network attached storage)
– Price High
– Performance High
– Extention?
• Buy one more..

29. NAS vs 1 unit size Server
• 1 unit size server
– Price low
– Performance low
– Extention High

30. • http://datacenter.navercorp.com/ko/index.html

32. Naver Owner-based File System
• Use multy cheap servers, multy cheap disks
• 3 copy Policy(back up) for Safety