High Scalability by Example – How can Web-Architecture scale like Facebook, Twitter, YouTube, etc.

High Scalability by Example –
How can Web Architecture scale like
Facebook, Twitter?

JAX 2011, 03.05.2011
Robert Mederer, Markus Bukowski
Copyright © 2011 Accenture All Rights Reserved. Accenture, its logo, and High Performance Delivered are trademarks of Accenture.

Speaker

Robert Mederer Markus Bukowski
Lead Technology Architect Senior Software Engineer
Anni-Albers-Straße 11 Kaistraße 20
40221 Düsseldorf
80807 München
Mobil: +49-175-57-64217
Mobil: +49-175-57-68012
markus.bukowski@accenture.com
robert.mederer@accenture.com

Experience
2000 - 2005: Technology Architect and Software 2006 - 2009: Software Engineer in several projects
Engineer in several projects (during studies) for mainly telecommunication
companies
2006: Technical Architecture Lead, Integration and
Execution Architecture for Location-Based Service 2009 - 2011: Senior Software Engineer in several
Provider projects

2009: Technical Architecture Lead, Frontend and 2009/2010: Coach and Software Engineer for a
Execution Architecture Government Agency Health Insurance Fund

2009/2010: Technical Architecture and front-office 2011: Technical Architecture Lead during the
integration build lead, Integration and Execution development of a Document Management Solution
Architecture Financial Services Agency for a Government Agency

2011: Architect and Performance Engineer for
Location Based Services Platform
Copyright © 2010 Accenture. All rights reserved. 2

Accenture
High performance achieved

Company Profile Worldwide Revenues $21.6 billion
•  Global management consulting, (in US$ billion, as of August 31, 2010)
technology services and outsourcing Communications &
company Resources High Tech
•  215.000 employees
•  Rank 47 among the 3.88
4.83
“Best Global Brands 2008”
•  Top 100 Employer
4.32
•  28 of the DAX-30-Companies 2.98
•  96 of the Fortune-Global-100 Public
•  More than three-quarters of the Fortune- Financial 5.53 Service
Services
Global-500
•  87 of our Top 100-clients have been with Products
us for 10 or more years


Local Accenture … ???

Geographic unit
•  Austria
•  Switzerland
•  Germany
Employees Berlin
•  Ca. 6000 Düsseldorf
•  We are hiring!
Exciting Technology work Frankfurt
•  Large scale projects
(100+ people / multiple years)
Munich
•  Most challenging requirements
–  Stock Exchange / Banking / Trading Systems Vienna
–  AEMS Mobility Platform
–  Large Scale Web Applications Zurich
(> 1M page views / day)
–  Batch Architectures


Agenda

•  Introduction
– Scalability & CAP Theorem
– Traditional websites & Social media websites
– High Scalability in Numbers
•  Accenture – High Scalability by Example
•  Architecture at Internet Scale
•  Common concepts of Scalability
•  Conclusion


Scalability

A system’s capacity to uphold the same performance
under heavier volumes.

(Patterns for Performance and Operability: Building and Testing Enterprise Software, Chris
Ford et. al., 2008)


Vertical Scalability

•  Is achieved by increasing the capacity of a
single node
– CPU,
– Memory,
– Bandwidth, …
•  Simple Process
– Application is generally not affected by
those changes

•  Classical Example are Super Computers
like
– HP Integrity Superdome
– IBM Mainframe
Source: Hewlett-Packard


Horizontal Scalability

•  Application is spread on a cluster with
several nodes
•  Nodes can be added to scale out
•  Produces overhead
– Keep cluster consistent
– Node error detection and handling
– Communication between nodes
•  May be used to increase reliability and
availability

•  Distributed Systems and Programs like
– SETI@Home Source: Space Sciences Laboratory, U.C. Berkeley

– World Wide Web
– Domain Name Service


CAP Theorem (Brewer‘s Theorem)

•  Consistency – all clients see the
same data at the same time
Consistency •  Availability – all clients can find all
data even in presence of failure
•  Partition Tolerance – system works
even when one node failed

Partition
Availability
Tolerance

Impossible
Source: PODC-keynote, Towards Robust Distributed Systems, Dr. Eric A. Brewer, 2000

CAP Theorem

Normally, two of these properties for any shared-data system
Consistency + Availability
C •  High data integrity
P A •  Single site, cluster database, LDAP, etc.
•  2-phase commit, data replication, etc.

C Consistency + Partition
•  Distributed database, distributed locking, etc.
P A •  Pessimistic locking, etc.

Availability + Partition
C •  High scalability
P A •  Distributed cache, DNS, etc.
•  Optimistic locking, expiration/leases (timeout), etc.

Source: “Architecting Cloudy Applications”, David Chou

Agenda

•  Introduction
•  Conclusion


Traditional Websites

(Scaling the Social Graph: Infrastructure at Facebook, Jason Sobel, QCon 2011)


Social Media Websites

(Scaling the Social Graph: Infrastructure at Facebook, Jason Sobel, QCon 2011)


Agenda

•  Introduction
•  Conclusion


High Scalability in Numbers

The World Is Obsessed With Facebook (Video)

Source: http://www.youtube.com/watch?v=xJXOavGwAW8


facebook twitter

Active users 500.000.000 New accounts per day 500.000

Active users logging in 250.000.000 Tweets per day 140.000.000
daily (a year ago) (50.000.000)

Active mobile users 250.000.000 Max tweets per second 6.939

In 20 minutes …

Links shared 1.000.000

Status updates 1.851.000

Photos uploaded 2.716.000

Source: http://www.facebook.com/press/info.php?statistics, http://www.allfacebook.com/
Comments made 10.208.000 16
Copyright © 2011 Accenture. All rights reserved.


LinkedIn
Members >100.000.000
Connections between Members 1.300.000.000

Visitors per month 128.000.000
% of global internet users who visit LinkedIn daily 4%

Source: http://blog.linkedin.com, http://press.linkedin.com/about/, LinkedIn Demographics 20111


Agenda

•  Introduction
– The Royal Wedding Website
– US Based Professional Sport League .com Website
•  Conclusion


Agenda

•  Introduction
– Facebook
– Twitter
– LinkedIn
– Challenges
•  Conclusion


Facebook – Key Facts

•  Biggest Social Network

•  People are linked together
•  People share and exchange information in real-time

•  Facebook provides a
– A personalized News Feed containing news about friends
– Picture Service including people tagging / linking
– Messaging Service to stay in connect with friends
– Platform to play Games with friends


Facebook – Architecture (1)

•  Facebook „main“ architecture is based Memcache
on a LAMP stack
•  The application logic resides in Web
Server layer (PHP based)
•  The application layer takes care of the
data distribution

•  Some services do not fit (C++, Java)
– Search MySQL
– Ads
– People You May Know
Invalidation logic is implemented in
– Multifeed
the application!


Facebook – Architecture (2)

Multifeed / Newsfeed
•  Distributed System

•  Every user gets
– 45 best rated updates
–  on every reload

•  Every DB-Update results in
a Scribe Notification
–  Leaf Nodes / Server are
notified

Source: “High Performance at Massive Scale – Lessons learned at Facebook”, Jeff Rothschild, 2009
Inspired By: “Architecting Cloudy Applications”, David Chou, 2010

Agenda

•  Introduction
– Facebook
– Twitter
– LinkedIn
– Challenges
•  Conclusion


Twitter – Key Facts

•  Twitter is a real-time information network

•  Twitter‘s key characteristics
– Tweets are used for sharing information
– Timeline of tweets must be kept
– A Social Graph is maintained to deliver
information


Twitter - Architecture

Load Balancers
Web
Frontend
Apache mod_proxy

Apps & Distributed
Rails (Unicorn) Cache
Services

Flock memcached Kestrel Queues
Partitioned
Data Distributed
MySQL Cassandra
Storage

Daemons

Agenda

•  Introduction
– Facebook
– Twitter
– LinkedIn
– Challenges
•  Conclusion


LinkedIn – Key Facts

•  A social media networking platform to find
connections to recommended job candidates,
industry experts and business partners
•  99% pure Java

LinkedIn – Applications
•  Groups
•  Job market
•  Mailing
•  Profiles (Friends and Companies)
•  Mobile Applications (Android, iPhone,
Blackberry, Palm)


LinkedIn - Architecture

Web Frontend
LinkedIn
Spring Search
Grails MVC
Queuing Spring
Caching / Messaging
DWR
Spring MVC Grails DWR Spring Framework (IoC, Web
Lucene Searchprocessing AOP,
Model-View-Controller DI, for
Asynchronous Engine
•  Direct Web Remoting (AJAX for
Voldemort
Open-source web application
Frameworktriggered writes
•  Java Library) Concerns,..) by
Separation of open "coding
Aims to bring the source in
•  Apache combines high-
•  Voldemort
user
Apps & Services •  RPC library toparadigm to the text
Used performance, full-featured
LinkedIn extensions with Groovy
memory caching
for
convention" call Java functions
search engine library written
storage system so that a
Service Service LinkedIn Spring Service Service from JavaScript and (lin:bean,
•  XML Vocabulary to call
StorageentirelyLoad of Page not
Initial
•  Why Grails? incaching tier is
Valdemort
Hadoop
separate Java
Storagerequired a more from Javaweb
Sensei
JavaScript functions productive
•  lin:component, …)
Needed software
Hadoop isOperations framework
Pull a
•  ZoieProperty Editors a major
Valdemort used as
•  Forframework
•  Used supports data-intensive
distributed database
distributed key-value data cache
Lucene
Zoie Bobo Caching Voldemort ehcache Memcache •  that realtime indexing/search
ehcache
•  …
•  New kind of application context
Search Index •  Asynchronous Communication
for• the social graph to (map/
horizontallyprototyping
Rapid
•  distributed applications source,
system scaledopen
•  for life-cycle management with
Ehcache is an
•  Push Operations with and
demonstrate feasibility
•  reduce /Consistency cachehigh
standards-based
relaxed distributed file system) used
Queuing / Messaging
Bobo productivity gain
Advantage:
components
durabilityis usedwith engine offload
•  …
to boost performance,
guarantees majorjava/
•  Hadoop database and simplify the
Integration as a
faceted search existing for
ActiveMQ the concurrent read
Fast
Socialkey-value data
Graph
distributed index building store
•  spring-based solutions
scalability
Fast
•  handles semi-structured and
•  for the social graph
Memcache structured data
Storage
•  Bottleneck:
•  Distributed Cache
MySQL Sensei Hadoop Voldemort •  Read data
•  Index building

• Keep core data ACID
• Keep replicated and cached data BASE
• Cache data on a cheap memory (memcached)
• Use a hint to route the client to his / her’s ACID data
Source: Project Voldemort, Jay Kreps Source: http://sna-projects.com

Agenda

•  Introduction
•  Conclusion


Common concepts of Scalability

parallelization

asynchronous idempotent
7 Habits of operations
Good
partitioned data Distributed fault-tolerance
Systems

optimistic concurrency shared nothing

Source: highscalability.com

Common concepts of Scalability
Hybrid architectures

• Scale-out (horizontal) • Scale-up (vertical)
– BASE – ACID
– focus on “commit” – availability first
– optimistic locking – pessimistic locking
– shared nothing – transactional
– maximize scalability – favor accuracy/consistency

Most distributed systems realize both


Agenda

•  Introduction
•  Conclusion
–  Questionnaire
–  Déjà vu - Common concepts
–  Don’t forget the Infrastructure


Conclusion
Questionnaire

•  Is there a need to scale my application?
–  Vertical scaling is more easy to achieve
–  Use horizontal scaling only when required

•  Is there a plan to proof your designed solution?
–  Plan to do a lot of realistic Proof-of-Concepts

•  Is there a one size fits all solution?
–  NO!

•  How important is ACID?
– Is BASE enough?
– Can a NoSQL solution be used?

Conclusion
Déjà vu - Common concepts

•  Asynchronous Processing
– Keep Facebook, Twitter and LinkedIn in mind
–  Asynchronous writes and even UI

•  Data Partioining
–  You must understand your data
–  Is a hybrid solution as used by LinkedIn applicable?

•  Design To Failure
–  Lessons learned from Amazon‘s cloud solution outage


Conclusion
Don’t forget the Infrastructure

•  Disk IO compared to RAM is slow
  Avoid „slow“ IO access whenever possible
  Caching, Caching, Caching
  Bulk Updates (Batch) over small updates

•  Network Latency
  Keep in mind that there is a network
  Where are the users located?
  How are your users connected? mobile application?
  Is there a need for a datacenter distribution?

•  Hardware configuration
–  Is the CPU Power Saving working right?

Questions ?


High Scalability by Example – How can Web-Architecture scale like Facebook, Twitter, YouTube, etc.

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