Netlog: What we learned about scalability & high availability

27 mei 2008 Folke Lemaitre Director of Development http://nl.netlog.com/folke What we learned about scalability & high availability

Overview ‣ What is Netlog? ‣ Translations ‣ Network topology ‣ Scaling Databases ‣ Caching ‣ Search ‣ Q&A

What is Netlog?

Social Network ‣ Create your own proﬁle ‣ Discover your friendsʼ activity ‣ Communicate ‣ Explore new content ‣ Applications

Your Proﬁle

What: itʼs personal ‣ You rule: itʼs yours Music YOU ANOTHER Photos Games ANOTHER YOU Videos People Blogs Photos Relations.

Friend Activity ‣ Share & discover friendsʼ activity Pinguke V Mari . reageert Toon Coppens wijzigt haar op haar foto Jan Maarten Willems tekent uploadt een profielfoto het gastenboek nieuwe foto van nico b Jaak Noukens en Jo zijn nu vrienden Stijn Symons uploadt een nieuwe foto Kenny Gryp tekent het gastenboek van Lorenz Bogaert

Communication: Shouts

Communication: Ratings & Comments

Communication: Private messaging

Communication: Chat

Communication: Clans

Explore Blogs Proﬁles Photos Clans Music Events Videos Applications Pages

Applications ‣ OpenSocial • sandbox: http://nl.netlog.com/go/developer/opensocial/sandbox=1 ‣ Ofﬁcially announced tomorrow@ Google I/O • Stay tuned! ‣ Public launch for june

Developer Pages http://nl.netlog.com/go/developer

Itʼs going pretty good ‣ More than 35,000,000 unique members ‣ More than 4,000,000,000 pageviews/Month ‣ 19 languages and more coming up ‣ More than 20 countries ‣ Current Alexa Top-100 ranking (most visited web sites in the world) ‣ Current ComScore Europe Top-10 ranking

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Itʼs going pretty good

Translations

19 languages and alot more coming! Slovenčina Español Català Svenska suomi česky slovenščina Deutsch Magyar Nederlands français Русский Italiano Afrikaans English Dansk Türkçe Polski Hrvatski Lietuvių kalba Eesti Latviešu valoda Português Română български Norsk (bokmål)

Translate Tool

Template

Parsed Template

Translated Template

Generated PhP code

Template Code

Template Output

Network Topology

Overview Netlog Datacenters Database Pools Slave Slave Master Master Slave Slave User Pool Activity Pool Web Cluster Slave Slave Master Master Slave Slave Friendships Pool ... Internet Web Load Balancer Firewall Memcache Pools Static Load Balancer Session Cache Slave Master General Cache Slave Html Cache Primary Pool CDN Storage Servers

Web Servers ‣ Software • Apache 2 • Php 5.2.6 • eAccelerator 0.9.5.2 for bytecode caching • Keepalived for high availability ‣ 200 servers ‣ 450 000 requests per second

Database Servers ‣ MySQL Enterprise 4.1.22 ‣ 200 database servers ‣ 40 thousand tables ‣ 70 billion records ‣ 60 thousand queries per second

Memcache Servers ‣ Memcached 1.2.4 ‣ 60 servers ‣ 250 thousand requests/second ‣ 450 GB of memory

Static servers ‣ Software: • Lighttpd • NginX ‣ Used for: • static ﬁles: css/javascript/images/... • user content: photos, videos ‣ Content Delivery Network: Akamai & Panther

Other servers ‣ OpenSocial: • Shindig • Tomcat ‣ Search: • Sphinx

Scaling Databases

Database & Scalability ‣ Database pools ‣ Replication ‣ Partitioning

Database Pools ‣ Different data on different database pools: • messaging • friendships • blogs • music • videos • ...

Replication ‣ write to one master ‣ read from multiple slaves (and master) ‣ pros • easy to implement • read intensive applications scale very well ‣ cons • write intensive applications donʼt scale

Partitioning (sharding) ‣ Divide data on primary key: • all user data for users with id 1 - 10 in database1 • all user data for users with id 11 - 20 in database2 • ... ‣ Best scaling possible ‣ How? • managed in code • MySQL partitioning (available from version 5.1)

Analyse, analyse, analyse! ‣ Tag your queries • SELECT * FROM USER WHERE userid = 123 /*User::getUser():11 */ ‣ Analyse mysql slow logs ‣ Analyse process lists ‣ Analyse based on tags • 1023 User:getUser():230 • 512 User::isOnline():124 • 10 Activities:getActivity():320 ‣ minutely cron that checks for “too many connections” • if “too many connections”, log process list

Caching

Introduction to memcached ‣ Developed by Danga Interactive: • http://www.danga.com/ ‣ Initially developed for LiveJournal: • http://www.livejournal.com/ ‣ OpenSource

Introduction to memcached ‣ Least Recently Used ‣ Fast! ‣ Distributed ‣ Automatic failover ‣ Big Hash table: set/add/get/delete

What to cache? ‣ sessions ‣ query caching ‣ processed data ‣ generated html

Session Cache ‣ 99% hit ratio ‣ Time to live is 20 minutes ‣ Faster than session database

Query Cache ‣ Why memcache and not MySQL query cache? • MySQL invalidates cached queries on a table on every update • different query cache for different replicated databases ‣ Add to generic database classes • Cache key is query

Processed data ‣ Better to cache processed data than query results

HTML Caching

HTML Caching ‣ Proﬁle blocks are fully cached ‣ Data needed to generate html is also cached ‣ When data changes, html is invalidated, cached data updated ‣ High cache hit rate on proﬁle pages

3 ways of caching ‣ Cache with TTL ‣ Cache forever with invalidate ‣ Cache forever with update

Cache with TTL ‣ The good: • Quickly achieve better performance on existing code ‣ The bad: • Users see outdated information • TTL can not be high • Caching efﬁciency is minimal

Cache with TTL ‣ Cache friends for 5 minutes

Cache forever with invalidate ‣ The Good: • fairly easy to implement • user never sees outdated data

Cache friends forever ‣ For memcached this means ttl=0

Invalidate Cache

Cache forever with update ‣ The Good: • Best caching possible • Can reduce your select queries to the minimum

Update Cache (array) ‣ Only update cache when no db queries needed

Update Cache (simple value) ‣ No need to check cache

Global Locking ‣ Use memcache as locking mechanism

Global Locking: Chat Example ‣ Example: add new message to cached shared chat thread

Flooding detection ‣ User can only redo action A after a timeout • a guestbook message can only be posted once every 2 minutes ‣ User can not do action A more than X times in T minutes • only 12 failed login attempts per hour are allowed

Flooding detection

Flooding detection ‣ User can only redo action A after a timeout • a guestbook message can only be posted once every 2 minutes ‣ User can not do action A more than X times in T minutes • only 12 failed login attempts per hour are allowed

Search

MySQL full-text search ‣ Initially used for our search • can be very slow • extra load on most of our databases, since most content is searchable ‣ Better search engine needed • Sphinx! • OpenSource search engine developed by Andrew Aksyonoff (http://sphinxsearch.com/)

Sphinx Features ‣ very fast indexing ‣ very fast searching • 0.04 seconds average • 5 million searches / day • 60 searches / second ‣ distributed ‣ document ﬁelds ‣ stopwords ‣ api available in many languages • PhP, Java, Python, Ruby, Perl, C++, ...

Sphinx Indexer ‣ Index is read-only (except for attributes) ‣ Build new index while searching old one ‣ How we index: • rebuild full index from data once in a while (daily, weekly) • generate delta indexes often (every minute, 5 minutes) • contains changes for search index since last full index merge • full index merge of previous index and delta (every hour)

Sphinx Search ‣ Search query returns list of ids ‣ For every result page shown, we fetch data associated with ids • data is cached with memcache for every id

Thank you! Questions?

Netlog: What we learned about scalability & high availability

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