Netlog: What we learned about scalability & high availability

transcript

What we learned about scalability & high availability

Folke LemaitreDirector of Development

http://nl.netlog.com/folke

27 mei 2008

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

YOU Videos

Relations.

Photos

People

Photos

ANOTHER

Friend Activity

‣ Share & discover friendsʼ activity

Toon Coppens uploadt een nieuwe foto

Jaak Noukens en Jo zijn nu vrienden

Mari . reageertop haar foto

Pinguke V wijzigt haar profielfoto

Stijn Symons uploadt een nieuwe foto

Jan Maarten Willems tekent het gastenboek van nico b

Kenny Gryp tekent het gastenboek van Lorenz Bogaert

Communication: Shouts

Communication: Ratings & Comments

Communication: Private messaging

Communication: Chat

Communication: Clans

Explore

Profiles

Videos

Photos

Events

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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Monthly Unique VisitorsNorthern Europe3%Americas

Western Asia16%

Eastern Europe3%

Southern Europe22%

Western Europe46%

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Monthly Visits

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5.000.000.000

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Monthly Page Requests

Translations

19 languages and alot more coming!

Català

中文 česky

Nederlands

English

français

Deutsch

Italiano

Lietuvių kalba

Norsk (bokmål)

Polski

PortuguêsRomână

Русский

slovenščina

EspañolSvenska

TürkçeAfrikaans

български

Hrvatski

Magyar

Latviešu valoda

Slovenčina

Translate Tool

Template

Parsed Template

Translated Template

Generated PhP code

Template Code

Template Output

Network Topology

Overview

Internet

Netlog Datacenters

Firewall Web Load Balancer

Web Cluster

Master

Primary Pool

Memcache Pools

Session Cache

General Cache Html Cache

Database Pools

Master

User Pool

Master

Activity Pool

Master

Friendships Pool

Master

Static Load Balancer

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

‣ 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

‣ 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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