Cache is king

Waseem Asif
@folio_3 www.folio3.com Copyright 2015

 Introduction
 Typical Implementation
 Tools
◦ Memcache, Redis
◦ Varnish
◦ HTML 5
 Challenges

 A cache is a component that transparently stores
data so that future requests for that data can be
served faster
 Speeds up the data lookups
 Wikipedia serves 75% content from Cache

 Hardware
◦ CPU (L1, L2)
◦ HDD (Read/Write Buffers)
 Applications
 Database
 Proxies
 Web Servers
 Browsers

 Populating the cache
◦ Upfront Population
◦ Lazy Population
 Keeping the cache and source in sync
◦ Time based Expiry
◦ Active Expiry
◦ Write through Cache
 Managing cache size
◦ Removing data

Upfront Population Lazy Population
 No one-off cache read
delay
 The initial build of the
cache takes time
 Data cached may not
be read at all
 First request is not
found in cache
 Only caches data that
is actually read
 Has no cache build
delay

 Time Based Expiry
◦ Data is removed from cache after some time
◦ Most commonly used
◦ Short expiry time can be a problem
 Active Expiry
◦ Source tells cache to remove data when updated or
removed
 Write-through cache
◦ Allow read and write to cache
◦ Data is added/written to source when written to
cache

 Cache storage is smaller then original data
 Have to manage space smartly
 To make space for new data:
◦ Time based eviction
◦ First in, first out (FIFO)
◦ Least accessed
◦ Least time between access
◦ Many others (http://en.wikipedia.org/wiki/Cache_algorithms#Examples)

 Application
◦ Objects
◦ Arrays / Query results
◦ String/HTML/ Full Page
 Database
◦ Query cache
 Web Server
◦ Static resources
◦ Full page cache
 Browser
◦ Headers
◦ HTML5

 It is Free & Open Source
 Distributed memory based cache
 Key value store
 Easy to deploy
 Client libraries available in most of the
languages

 Since 2003
 Written in C
 Works on Linux, Windows
 Fast by default
 Security can be an issue
◦ Expose only to trusted network
◦ Optional authentication

 Issues with large implementations.
◦ Large works are fine by default
 No communication between Servers
◦ No Data replication
 Capacity issue of single Server
 No warm-up on startup
 Solution
◦ Mick-Router

 Tool by Facebook
 Provides failover, Load balance, warm-up
server, replication of server
 How it works:
◦ https://code.facebook.com/posts/29644273721
3493/introducing-mcrouter-a-memcached-
protocol-router-for-scaling-memcached-
deployments/

 Key-value cache
 Optionally a permanent Store
◦ Snapshotting
◦ Log Based
 Support a lot of Data Structures.
◦ String, Lists (arrays), Sets (Unique), Hashes
(Objects), Sorted Sets, BitMaps, HyperLogLogs etc
◦ http://redis.io/topics/data-types-intro
 Lot more than just SET, GET and DEL
◦ Increment, Append, LPUSH, RPOP, LRANGE
◦ Pipelining (Send multiple commands in one go)
◦ http://redis.io/commands

 Replication
 Client libraries available in most of the languages
◦ C, C#, PHP, Objective-C, Node.Js, Python, Ruby and more
◦ http://redis.io/clients
 Works on Windows
◦ Windows port developed/maintained by Microsoft Open
Tech group
 Available on cloud
◦ AWS, Morpheus, RedisToGo, RackSpace, Microsoft Azure
◦ Comes with a lot of preset like backup, monitoring, high
availability, ACL etc
◦ http://en.wikipedia.org/wiki/Redis#Cloud_deployment

 Performance
◦ Has built-in benchmark, easy to run
◦ It is easy to run test and find out real picture
◦ http://redis.io/topics/benchmarks
 Since 2009
 Written in C

Redis Memcached
 Stores data in a variety of
formats
 Complex to configure (Default
configuration can work)
 Pipelining! Multiple commands at
once
 Locking reads - will wait until
another process writes data to
the cache
 Mass insertion of data to prime a
cache
 Partitions data across multiple
redis instances
 Can back data to disk
 Pub/Sub feature
 Doesn't do anything
besides being an in-
memory key/value store
 Low complexity
 Simple to configure
 Few command macros
making it simple to
master
 Runs like a rock
 Many years in production
 Every programming
language has a
memcached library.

 A lot of bench marks available
◦ http://oldblog.antirez.com/post/redis-
memcached-benchmark.html
◦ http://dormando.livejournal.com/525147.html
◦ http://oldblog.antirez.com/post/update-on-
memcached-redis-benchmark.html

 NCache: Distributed Cache for .NET
 CouchDB: Can be used as replacement of
Memcache
 MemSQL: The Database for Speed, Scale &
Simplicity
 And a lot more
@folio_3 Copyright 2015www.folio3.com

 Sits in front of web server(s)

 Page cache
 Static File Cache
 Load Balancing
 Rewriting and redirecting URLs
 Varnish Configuration Language
◦ Powerful, Can manage rules, Modify headers
 Community/Enterprise Editions
 Becoming the default for specialized cloud
hosting
◦ WP, Magento, Joom, Drupal etc

 ESI
◦ Useful for user customized dynamic content
◦ Help increase TTL for pages
◦ <esi:include src="/news/hot_news.html"/>
 Works for Windows via CYGWIN
◦ Can have IIS on backend
 Stores cache in memory or file
 Paid edition has admin interface
◦ Real-time Statistics
◦ Cache group management
◦ Invalidations of cache etc

 LiteSpeed: Provides cache, replacement for
apache httpd, Load balancer etc
 Apache Httpd: Mod Cache, Mod Mem Cache
 Nginx Cache

 If cached properly:
◦ Resources are not downloaded on next requests
 Set “Expires” Header for Static Resources
 “304 Not Modified” Requests
◦ Involves http round trip but file is not downloaded

 Easy to implement
 Very effective
 Headers can be set on
◦ Application
◦ Web Server

 Application Cache
◦ manifest="demo.appcache“
◦ Define files which are to be cached
 CACHE MANIFEST
 NETWORK: Not to be cached
 FALLBACK: If page not available then fallback to these
◦ http://diveintohtml5.info/offline.html

 Which Data to Cache
◦ Can’t cache every thing
◦ Space is limited
 Expiry Time
◦ Have to be selective, too short or too long can be a problem
 Multiple Cache backend
◦ Use frame works so it is easy to Switch
 Full Page / Parts
◦ Based on needs and usage
 Logged/Logged Out Users
◦ ESI, Logged in user still has part which can be cached
 Removing Data form cache
◦ There must be a way to remove individual item
 Key Scheme
 Monitor Hit/Miss

Cache is king

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Cache is king