Deep Dive into Amazon ElastiCache Architecture and Design Patterns (DAT307) | AWS re:Invent 2013
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Peek behind the scenes to learn about Amazon ElastiCache's design and architecture. See common design patterns of our Memcached and Redis offerings and how customers have used them for in-memory operations and achieved improved latency and throughput for applications. During this session, we review best practices, design patterns, and anti-patterns related to Amazon ElastiCache. We also include a demo where we enable Amazon ElastiCache for a web application and show the resulting performance improvements.
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Deep Dive into Amazon ElastiCache Architecture and Design Patterns (DAT307) | AWS re:Invent 2013
- 1. DAT307 - Deep Dive into Amazon ElastiCache Architecture and Design Patterns Nate Wiger, Principal Solutions Architect November 14, 2013 © 2013 Amazon.com, Inc. and its affiliates. All rights reserved. May not be copied, modified, or distributed in whole or in part without the express consent of Amazon.com, Inc.
- 2. Contents • • • • • Caching: What’s all this then? Amazon ElastiCache Laziness, impatience, and hubris From one to a dozen nodes Memcached vs. Redis showdown
- 4. Device Fragmentation • • • • • • Phones, tablets, PCs, toasters HTML, apps, JSON APIs Presentation differs Data is the same CDN for static images, videos Doesn’t help “Welcome Back, Kotter!”
- 5. Death By 1000 Queries • • • • • Login, session New messages, recent posts Calls to Facebook, Twitter APIs Your friends love the new Coldplay album!!! Sudden viral traffic spikes
- 7. cache (noun) a group of things that have been stored in a secret place because they are illegal or have been stolen
- 8. Typical Web 2.0 App External APIs ELB App
- 9. Amazon ElastiCache • • • • • Managed cache service Memcached or Redis Launch cluster of nodes Scale up / down Monitoring + alerts
- 11. Fire It Up
- 12. Fire It Up
- 13. Fire It Up
- 14. Wire It Up
- 15. Wire It Up
- 16. Wire It Up # Ruby require ‘dalli’ cache = Dalli::Client([ ’mycache.z2vq55.0001.usw2.cache.amazonaws.com:11211’, ’mycache.z2vq55.0002.usw2.cache.amazonaws.com:11211’ ]) cache.set("some_key", "Some value") value = cache.get("some_key") cache.set("another_key", 3) cache.delete("another_key”)
- 17. Multiple Cache Nodes External APIs ELB App
- 18. Sharding Across Nodes server_list = [ ’mycache.z2vq55.0001.usw2.cache.amazonaws.com:11211’, ’mycache.z2vq55.0002.usw2.cache.amazonaws.com:11211’ ] server_index = hash(key) % server_list.length server = server_list[server_index]
- 19. Sharding Across Nodes server_list = [ ’mycache.z2vq55.0001.usw2.cache.amazonaws.com:11211’, ’mycache.z2vq55.0002.usw2.cache.amazonaws.com:11211’ ] server_index = hash(key) % server_list.length server = server_list[server_index] BAD
- 21. It’s All Been Done Before • • • • • Ruby – Dalli Python – HashRing / MemcacheRing Node.js – node-memcached PHP – libketama or ElastiCache Client Java – SpyMemcached or ElastiCache Client
- 22. So Far • • • • Launched a cache cluster Got the node names Connected our client Figured out sharding
- 24. What To Cache? • • • • • Everything! Database records Full HTML pages Page fragments Remote API calls
- 25. How To Cache It? • Lazy population • Write-through • Timed refresh
- 26. Laziness is a Virtue # Python def get_user(user_id): record = cache.get(user_id) if record is None: # Run a DB query record = db.query("select * from users where id = ?", user_id) cache.set(user_id, record) return record # App code user = get_user(17)
- 27. Ship It • • • • • Most data is never accessed Ensures cache is filled Caches fail and scale But cache miss penalty Best approach for most data
- 28. Foresight is 20-20 # Python def save_user(user_id, values): record = db.query("update users ... where id = ?", user_id, values) cache.set(user_id, record) return record # App code user = save_user(17, {"name": "Nate Dogg"})
- 29. Laziness vs. Impatience • • • • • Ensures cache is always current Write penalty vs. read penalty But missing data on scale up Plus excess data / cache churn Still need lazy fetch too
- 30. Combo Move! def save_user(user_id, values): record = db.query("update users ... where id = ?", user_id, values) cache.set(user_id, record, 300) # ttl return record def get_user(user_id): record = cache.get(user_id) if record is None: record = db.query("select * from users where id = ?", user_id) cache.set(user_id, record, 300) # ttl return record # App code save_user(17, {"name": "Nate Diddy"}) user = get_user(17)
- 31. Timed Refresh • • • • Run job to periodically update cache Good for Top-N lists Time-intensive rankings Trending items
- 34. Monitoring • • • • • Integration with CloudWatch metrics Setup alarms to send via email Memory usage Evictions Which ElastiCache metrics should I monitor?
- 40. Node Discovery • Setup an Amazon SNS topic for ElastiCache • Have app listen for events – ElastiCache:AddCacheNodeComplete – ElastiCache:RemoveCacheNodeComplete • Reconfigure connections • See Event Notifications and Amazon SNS
- 41. Programmable Scaling External APIs SNS Add Node ELB App
- 42. Node Auto-Discovery # PHP $server_endpoint = "mycache.z2vq55.cfg.usw2.cache.amazonaws.com"; $server_port = 11211; $cache = new Memcached(); $cache->setOption( Memcached::OPT_CLIENT_MODE, Memcached::DYNAMIC_CLIENT_MODE); # Set config endpoint as only server $cache->addServer($server_endpoint, $server_port); # Lib auto-locates nodes $cache->set("key", "value");
- 43. Redis • • • • • • Also in-memory Advanced data types Atomic operations Single-threaded Persistence Read replicas
- 44. Leaderboard with Sorted Sets ZADD ZADD ZADD ZADD leaderboard leaderboard leaderboard leaderboard 556 819 105 1312 "Andy" "Barry" "Carl" "Derek" ZREVRANGE leaderboard 0 -1 1) "Derek" 2) "Barry" 3) "Andy" 4) "Carl" ZREVRANK "Barry" 2
- 45. Follow the Leader def save_score(user, score): record = db.query("update users ... where id = ?", user_id, score) redis.zadd("leaderboard", score, user) def get_rank(user) return redis.zrevrank(user) + 1 # App code save_score("Andy", 556) save_score("Barry", 819) save_score("Carl", 105) save_score("Derek", 1312) get_rank("Barry") # 2
- 47. Redis Sharding • Same concept as Memcached • BUT • Can't shard – Lists – Sets / sorted sets – Hashes • Require single in-memory structure
- 48. Anti-Pattern: Dedicated Nodes • Spawn multiple nodes • Use for different features – Leaderboard – Counters • Can still shard key-value ops
- 49. Dedicated Redis Nodes External APIs ELB App Leaderboard Counters
- 50. Summary • • • • • Caching is good Good caching is hard ElastiCache eases deployment Memcached or Redis More to come
- 51. Please give us your feedback on this presentation DAT307 - Nate Wiger As a thank you, we will select prize winners daily for completed surveys!