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![Memcached Example
employee_id = 1234
employee_json = {
name => ‘Ryan Lowe’,
title => ‘Production Engineer’ }
set(employee_id, employee_json)
get(employee_id) [Returns employee_json]
22](https://image.slidesharecdn.com/redismemcachedpdf-120808142024-phpapp01/85/Redis-memcached-pdf-22-320.jpg)
![About the Benchmarks
• 1 Hour
• Redis 2.6 & Memcached 1.4.5
• 64,000,000 Keys
"KEY_#{i.to_s}"
• 51-Character Values
(0...50).map{ ('a'..'z').to_a[rand(26)] }.join
36](https://image.slidesharecdn.com/redismemcachedpdf-120808142024-phpapp01/85/Redis-memcached-pdf-36-320.jpg)
Redis memcached pdf
- 1. Caching Memcached vs. Redis San Francisco MySQL Meetup Ryan Lowe Erin O’Neill 1
- 2. Databases WELOVE THEM ... Except when we don’t 2
- 3. When Databases Rule •Many access patterns on the same set of data • Transactions (both monetary and isolated units of work) • Don’t know what the end state access patterns will be • Always :) 3
- 4. When Databases Suck •Lots of concurrent users • ORMs • Big Data Sets • Small Pockets of VERY hot data 4
- 5. How Caching Works •External vs. built-in caching • MySQL Query Cache • InnoDB Buffer Pool • Rails SOMETHING 5
- 6. Caching Architecture Becomes 6
- 7.
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- 9.
- 10. There are onlytwo hard problems in Computer Science: cache invalidation, naming things, and off-by- one errors. -- Martin Fowler 10
- 11. Problems with Caching •Cache Misses • Thundering Herd • Stale Data • Warm-Up Times • Overly-Aggressive Caching • Poor Cache Design 11
- 12. Cache Misses Cache Hit: 1 Operation Cache Miss: 3 Operations 12
- 13. Thundering Herd • KeyTOP_10_VIDEOS expires @ 9:00 • Generating the K/V takes three seconds • Page gets 100 req/s = 100*3 = 300 threads! 13
- 14. Stale Data • Mustmaintain consistency between the database and the cache from within the application • Extremely difficult to validate correctness 14
- 15. Cache Warm-Up • Allattempts to read from the cache are CACHE MISSES, which require three operations. • This can result in a significant degradation of response time. • Usually accompanied by a Thundering Herd 15
- 16. Use Cases • Sessions • Tag Clouds • Popular Items • Auto-suggest lists • Full Page Cache • Relationships • Profile • User Information Information • Online Users • User Preferences • Statistics 16
- 17. Memcached Memcached is anin-memory key- value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering. 17
- 18. Redis Redis is anopen source, advanced key-value store. It is often referred to as a “data structure server” since keys can contain strings, hashes, lists, sets and sorted sets. 18
- 19. In-Memory Means We’re BoundBy RAM 19
- 20. Consistent Hashing • EachKey deterministically goes to a particular server. Think (KEY % SERVERS) 20
- 21. Memcached • Dead Simple& Battle Tested • Fast • Non-Blocking get()/set() • Multi-Threaded • Consistent Hashing 21
- 22. Memcached Example employee_id =1234 employee_json = { name => ‘Ryan Lowe’, title => ‘Production Engineer’ } set(employee_id, employee_json) get(employee_id) [Returns employee_json] 22
- 23. But I don’twant all the data • What if I just want the name? • 64 Bytes for the object vs. 10 for just the name :-( • 6x network traffic • More work for the application • Fatter applications 23
- 24. Redis • Advanced DataTypes • Replication • Persistence • Usually Fast • Very Cool Atomic Operations 24
- 25. Redis: The Bad •Single-Threaded • Limited client support for consistent hashing • Significant overhead for persistence (do be discussed later) • Not widely deployed (compared to Memcached) 25
- 26. Redis: Datatypes • Strings(just like Memcached) • Lists • Sets • Sorted Sets • Hashes 26
- 27. Redis: Lists • Storedin sorted order • Can push/pop • Fast head/tail access • Index access (yay) 27
- 28. Redis: Lists r.lpush(‘employees’, ‘RyanLowe’) r.lpush(‘employees’, ‘Dave Apgar’) r.lrange(‘employees’, 0, -1) (‘Dave Apgar’, ‘Ryan Lowe’) r.rpush(‘employees’, ‘Evan Miller’) r.lrange(‘employees’, 0, -1) (‘Dave Apgar’, ‘Ryan Lowe’, ‘Evan Miller’) 28
- 29. Redis: Sets • Un-orderedcollections of strings • Unique (no repeated members) • diff, intersect, merge 29
- 30. Redis: Sets sadd(‘employees’, ‘RyanLowe’) sadd(‘former_employees’, ‘Bryan Lowe’) sdiff(‘former_employees’, ‘employees’) (‘Ryan Lowe’,‘Bryan Lowe’) 30
- 31. Redis: Sorted Sets •Same as Sets but ordered by a score 31
- 32. Redis: Hashes r.hset(‘employees’, ‘count’,1234) r.hset(‘employees’,‘females’, 1000) r.hset(‘employees’,‘males’, 234) hget(‘employees’,‘count’) “1234” hgetall(‘employees’) { ‘count’ => 1234, ‘females’ => 1000, ‘males’ => 234 } 32
- 33. Memcached vs. Redis Memcached Redis (multi)get ✓ ✓ (multi)set ✓ ✓ incr/decr ✓ ✓ delete ✓ ✓ Expiration ✓ ✓ prepend/append ✓ Range Queries ✓ Data Types! ✓ Persistence (sorta) ✓ Multi-Threaded ✓ Replication (sorta) ✓ 33
- 34. Instrumentation • Redis: info •Memcached: stats • Both give system information, connections, hits, misses, etc. • Graphite most of the metrics!!! 34
- 35. Benchmarks 35
- 36. About the Benchmarks •1 Hour • Redis 2.6 & Memcached 1.4.5 • 64,000,000 Keys "KEY_#{i.to_s}" • 51-Character Values (0...50).map{ ('a'..'z').to_a[rand(26)] }.join 36
- 37.
- 38. Redis Set (1Server) 1 Client 2 Clients 4 Clients 8 Clients 12 Clients 16 Clients 24 Clients 32 Clients 12000 10500 9000 7500 6000 4500 3000 1500 0 38
- 39. Redis Set (1Server) 1 Client 2 Clients 4 Clients 8 Clients 12 Clients 16 Clients 24 Clients 32 Clients 12000 WTF?! 10500 9000 7500 6000 4500 3000 1500 0 39
- 40. Redis Set (1Server) 1 Client 2 Clients 4 Clients 8 Clients 12 Clients 16 Clients 24 Clients 10000 8000 6000 4000 2000 0 40
- 41. Redis Set (2Servers) 1 Client 2 Clients 4 Clients 8 Clients 12 Clients 16 Clients 24 Clients 32 Clients 16000 12000 8000 4000 0 41
- 42. Redis Set (2Servers) 1 Client 2 Clients 4 Clients 8 Clients 12 Clients 16 Clients 24 Clients 32 Clients 16000 12000 8000 WTF?! 4000 0 42
- 43. Redis Set (4Servers) 1 Client 2 Clients 4 Clients 8 Clients 12 Clients 16 Clients 24 Clients 32 Clients 16000 12000 8000 4000 0 43
- 44. Redis Set (8Servers) 1 Client 2 Clients 4 Clients 8 Clients 12 Clients 16 Clients 24 Clients 32 Clients 16000 12000 8000 4000 0 44
- 45. Hash Ring Balance(%) Server 1 Server 2 100 50 0 Redis Memcached 45
- 46. Hash Ring Balance(%) Server 1 Server 2 Server 3 Server 4 50 25 0 Redis Memcached 46
- 47. Hash Ring Balance(%) Server 1 Server 2 Server 3 Server 4 Server 5 Server 6 Server 7 Server 8 25 12.5 0 Redis Memcached 47
- 48. Redis Get (1Server) 1 Client 2 Clients 4 Clients 8 Clients 12 Clients 16 Clients 24 Clients 32 Clients 125000 100000 75000 50000 25000 0 48
- 49. Redis Get (2Servers) 1 Client 2 Clients 4 Clients 8 Clients 12 Clients 16 Clients 24 Clients 32 Clients 125000 100000 75000 50000 25000 0 49
- 50. Redis Get (4Servers) 1 Client 2 Clients 4 Clients 8 Clients 12 Clients 16 Clients 24 Clients 32 Clients 125000 100000 75000 50000 25000 0 50
- 51. Redis Get (8Servers) 1 Client 2 Clients 4 Clients 8 Clients 12 Clients 16 Clients 24 Clients 32 Clients 125000 100000 75000 50000 25000 0 51
- 52. The Cost of Persistence Redis No Persistence Redis BGSAVE (FIO) Redis AOF 12000 10000 8000 6000 4000 52
- 53. Redis & Memcached Benchmarks 53
- 54. Set Operations (1 Server, 24 Clients) Memcached Redis 10000 9825 9650 9475 9300 54
- 55. Get Operations (1 Server, 24 Clients) Memcached Redis 130000 117500 105000 92500 80000 55
- 56. Set Operations (2 Servers, 24 Clients) Memcached Redis 13000 11750 10500 9250 8000 56
- 57. Get Operations (2 Servers, 24 Clients) Memcached Redis 140000 107000 74000 41000 8000 57
- 58. Set Operations (4 Servers, 24 Clients) Memcached Redis 13000 11750 10500 9250 8000 58
- 59. Get Operations (4 Servers, 24 Clients) Memcached Redis 140000 107000 74000 41000 8000 59
- 60. Set Operations (8 Servers, 24 Clients) Memcached Redis 12000 11000 10000 9000 8000 60
- 61. Get Operations (8 Servers, 24 Clients) Memcached Redis 150000 114500 79000 43500 8000 61
- 62. Conclusions • Redis inconsistentunder heavy load • We need more benchmarks! • (Redis) Datatype-specific • Big Memory • (Redis) Big Keys 62
- 63. Questions? 63