About Caching

Jan 5, 20150 likes1,683 views

Caching is used to optimize performance by taking advantage of different access speeds of storage mediums. It stores frequently accessed data in faster storage like memory or disk cache. There are different types of caches like browser cache, server cache using memcached, and cache within a request. Memcached is mainly used for read-through caching but has issues like requiring separate deployment and CAS operations. GroupCache is an alternative that avoids duplicated calls to the backend and has better performance without network IO or CAS. Write-through caching aims to have zero reads from the backend but is challenging to implement transactionally at scale.

Purpose of Caching
• Different accessing speed of different medium
• In-Process > Memory > Network > Disk
• Cache for Performance Optimization

Web Page Cache
• Browser Cache
• etag / lastmodiﬁed / max-age
• Server Cache
• memcached: Full page/pagelet cache
• Donut Cache: Layout cache

memcached issues
• Deployment
• modifying nodes in cluster
• Duplicated call to backend when cache miss on multiple client
• Performance
• Network latency
• Memory slot
• CAS: check and save
• Mainly used for Read-through cache

GroupCache
• https://github.com/golang/groupcache
• No separated deployment required
• singleﬂight builtin: avoid duplicated call to backend
• Better performance
• No network IO
• No CAS
• Go 1.4 Continuous Stack: ﬁx memslot issue

• Ideally
• ZERO read to backend
• Only write
• Data warm up + complete write-through cache

Complete Write-Through
• Available API for application？
• Can support SQL？
• select * from users where sex = ‘f’ order by createAt limit 10, 10

Implementation Examples
• https://github.com/nkallen/cache-money
• https://github.com/Wuvist/pysonic

GroupCache + Write-Through？
https://github.com/Wuvist/groupcache/commits/group_delete

–Phil Karlton
“There are only two hard things in Computer Science: cache
invalidation and naming things.”

• Easy to implement『eventual consistency』
• Atomic：Distributed transaction is difﬁcult
• Paxos
• System provision is more difﬁcult
• http://en.wikipedia.org/wiki/Queueing_theory

Connection Pool
• Pool is also a kind of “cache”
• Database connection pool
• Application connection pool
• php FPM / rails unicorn

Multiple Facebook teams spent
two years
https://code.facebook.com/posts/1499322996995183/solving-the-mystery-of-link-
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About Caching

1. About Caching Weng Wei, Dec 2014

2. Purpose of Caching • Different accessing speed of different medium • In-Process > Memory > Network > Disk • Cache for Performance Optimization

3. Web Page Cache • Browser Cache • etag / lastmodiﬁed / max-age • Server Cache • memcached: Full page/pagelet cache • Donut Cache: Layout cache

4. memcached

5. Cache within Request

6. Read-Through Cache

7. memcached issues • Deployment • modifying nodes in cluster • Duplicated call to backend when cache miss on multiple client • Performance • Network latency • Memory slot • CAS: check and save • Mainly used for Read-through cache

8. GroupCache • https://github.com/golang/groupcache • No separated deployment required • singleﬂight builtin: avoid duplicated call to backend • Better performance • No network IO • No CAS • Go 1.4 Continuous Stack: ﬁx memslot issue

9. Write Though

10. • Ideally • ZERO read to backend • Only write • Data warm up + complete write-through cache

11. Complete Write-Through • Available API for application？ • Can support SQL？ • select * from users where sex = ‘f’ order by createAt limit 10, 10

12. Implementation Examples • https://github.com/nkallen/cache-money • https://github.com/Wuvist/pysonic

13. GroupCache + Write-Through？ https://github.com/Wuvist/groupcache/commits/group_delete

14. –Phil Karlton “There are only two hard things in Computer Science: cache invalidation and naming things.”

15. • Easy to implement『eventual consistency』 • Atomic：Distributed transaction is difﬁcult • Paxos • System provision is more difﬁcult • http://en.wikipedia.org/wiki/Queueing_theory

16. Which is faster?

17. proper stress test to conﬁrm!

18. Connection Pool • Pool is also a kind of “cache” • Database connection pool • Application connection pool • php FPM / rails unicorn

19. How to release connection？ MRU or LRU？

20. Multiple Facebook teams spent two years https://code.facebook.com/posts/1499322996995183/solving-the-mystery-of-link- imbalance-a-metastable-failure-state-at-scale/

21. Q & A

About Caching

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