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Kuala
Lumpur

Ge#ng	
  to	
  Scale	
  on	
  AWS	
  
Pattrawoot S, CTO, Wongnai.com
• 
• 
• 
• 

Launched in 2010
Restaurant Review Website (Wongnai.com)
Social Network for Food Lovers
Key Features
•  Location-based search for restaurants
•  Write Reviews / Upload Photos / Eat Here (Check-in)
•  Coupons / Guides / Articles
•  Web & Mobile applications (iOS / Android / WP8 / BB10)
•  > 820,000 users
•  > 120,000 restaurants
•  Number of Users
•  Jan 2013: 360k users
•  Mar 2013: 470k users (increase more than 100k)
•  Number of User Contributions
•  3x increase
•  Dynamic and Personalized Contents
•  Time to Scale!
•  Amazon SES (Simple Email Service)
•  > 500,000 email subscribers
•  Over 1 million copies of e-newsletter sent every week
•  Email blast ==> Bandwidth Consumption issue
•  SEO performance
•  Amazon EC2 (Elastic Computing Cloud)
•  Past: One Server (Single Point of Failure)
•  Single Physical Server ==> Multiple Virtual Servers
•  Load Balancing
•  Amazon RDS (Relational Database Service)
•  MySQL
•  Automated Backup
•  Amazon S3 (Simple Storage Service)	
•  Versioning (S3 Object)
Architecture Diagram
www.wongnai.com

Auto scaling Group

Web App
Server

Elastic Load
Balancing

EC2 Instance

Auto scaling Group

Alarm

Solr
Server
Data Volume

Security Group

EC2 Instance

Data Volume

Security Group

EMM
Server
Amazon S3
Bucket

Amazon RDS

EC2 Instance

Region

Amazon SES
What Has Been Improved
• 
• 
• 
• 
• 

Speed
Scalability
Flexibility
Manageability
Time / Cost Savings &
Effectiveness

Plan to Use:
•  Elastic MapReduce
(Hadoop)
•  Simple Notification
Service (SNS)
•  ElastiCache
Getting to Scale
503

Service Temporarily Unavailable
The server is temporarily unable
to service your request due to
maintenance downtime or capacity
problems. Please try again later.
With AWS, scale from one instance…
…to thousands

Fully automated!
How do I scale my architecture to
support my first 10M users?
“Think Big, Start Small, Scale Fast”
Eric Ries, author of NY Times
bestseller “The Lean Startup”
Idea

MVP

Scale

Profitability

01

02

03

04
Getting to Scale
By building a scalable Architecture to
support your first 10M users
1.	
  Dev	
  &	
  Test	
  
3.	
  Beta	
  Release	
  

2.	
  Alpha	
  Release	
  
Production 1.0
Architecture
Database Options
Self-Managed

Database Server
on Amazon EC2
Your choice of
database running on
Amazon EC2
Bring Your Own
License (BYOL)

Fully-Managed

Amazon RDS
Relational Database
as a managed
service
Flexible licensing:
BYOL or License
Included

Amazon
DynamoDB
Managed NoSQL
database service
using SSD storage
Seamless scalability
Zero administration
But how do I choose what
DB technology I need?
SQL? NoSQL?
Some folks won’t like this.
But…
Start with SQL databases
But, but, but, but…
No. You don’t.
Start with SQL databases
Why SQL?
Established and well worn technology
Lots of existing code, communities, books, tools, etc
Clear patterns to scalability
You aren’t going to break SQL DBs in your first 10
million users. No really, you won’t
Amazon	
  Rela&onal	
  Database	
  Service	
  (RDS)	
  
Feature	
  
PlaAorm	
  support	
  
Preconfigured	
  
Automated	
  patching	
  

Details	
  
Create	
  MySQL,	
  SQL	
  Server	
  and	
  Oracle	
  
Get	
  started	
  instantly	
  with	
  sensible	
  default	
  
seAngs	
  
Keep	
  your	
  database	
  plaEorm	
  up	
  to	
  date	
  
automa&cally	
  

Backups	
  

Failover	
  
•  Database-as-a-Service
•  No need to install or manage database
instances
•  Scalable and fault tolerant configurations

Automa&c	
  backups	
  and	
  point	
  in	
  &me	
  
recovery	
  using	
  snapshots	
  
Manual	
  DB	
  snapshots	
  
Automated	
  failover	
  to	
  slave	
  hosts	
  in	
  event	
  of	
  
a	
  failure	
  

ReplicaFon	
  

Easily	
  create	
  read-­‐replicas	
  of	
  your	
  data	
  and	
  
seamlessly	
  replicate	
  data	
  across	
  availability	
  
zones	
  
Auto-Scaling
Automatic resizing of
compute clusters based on
demand
Feature	
  

Amazon	
  
CloudWatch	
  

Trigger	
  auto-­‐scaling	
  policy	
  

Details	
  

Control	
  

Define	
  minimum	
  and	
  maximum	
  instance	
  pool	
  
sizes	
  and	
  when	
  scaling	
  and	
  cool	
  down	
  occurs.	
  

Integrated	
  to	
  Amazon	
  
CloudWatch	
  

Use	
  metrics	
  gathered	
  by	
  CloudWatch	
  to	
  drive	
  
scaling.	
  

Instance	
  types	
  

Run	
  Auto	
  Scaling	
  for	
  On-­‐Demand	
  and	
  Spot	
  
Instances.	
  Compa&ble	
  with	
  VPC.	
  

as-create-auto-scaling-group MyGroup
--launch-configuration MyConfig
--availability-zones us-east-1a
--min-size 4
--max-size 200
AWS Cloud Kata | Bangkok - Getting to Scale on AWS
AWS Cloud Kata | Bangkok - Getting to Scale on AWS
AWS Cloud Kata | Bangkok - Getting to Scale on AWS
AWS Cloud Kata | Bangkok - Getting to Scale on AWS
AWS Cloud Kata | Bangkok - Getting to Scale on AWS
AWS Cloud Kata | Bangkok - Getting to Scale on AWS
AWS Cloud Kata | Bangkok - Getting to Scale on AWS
AWS Cloud Kata | Bangkok - Getting to Scale on AWS
ProducFon	
  1.0	
  
Architecture	
  
Production 1.0 Architecture
Well-­‐designed,	
  2	
  Tier	
  architecture	
  
Highly	
  Available	
  due	
  to	
  Mul&ple	
  Availability	
  Zone	
  
Load	
  Balancing	
  &	
  Auto-­‐Scaling	
  for	
  full	
  scalability	
  
Fully	
  managed	
  Database	
  included	
  	
  
Capable	
  of	
  serving	
  >10K-­‐100Ks	
  users	
  
	
  
BUT…
Production 1.0 Architecture
Wasted	
  server	
  capacity	
  for	
  sta&c	
  content	
  
Reliability	
  and	
  durability	
  are	
  not	
  yet	
  op&mal	
  
End-­‐user	
  experience	
  could	
  be	
  improved	
  thru	
  
offloading	
  &	
  caching	
  
SO…
Let’s add
Simple Storage Service (S3)
CloudFront
to optimize the end-user experience
Simple Storage Service (S3)
Feature	
  
Flexible	
  object	
  store	
  
Access	
  control	
  
Server-­‐side	
  encrypFon	
  
MulF-­‐part	
  uploads	
  
Object	
  versioning	
  
Object	
  expiry	
  

Durable storage, any object
99.999999999% durability of objects
Unlimited storage of objects of any type
Up to 5TB size per object

Access	
  logging	
  
Web	
  content	
  hosFng	
  
NoFficaFons	
  
Import/Export	
  

Details	
  
Buckets	
  act	
  like	
  drives,	
  folder	
  structures	
  within	
  
Granular	
  control	
  over	
  object	
  permissions	
  
256bit	
  AES	
  encryp&on	
  of	
  objects	
  
Improved	
  throughput	
  &	
  control	
  
Archive	
  old	
  objects	
  and	
  version	
  new	
  ones	
  
Automa&cally	
  remove	
  old	
  objects	
  
Full	
  audit	
  log	
  of	
  bucket/object	
  ac&ons	
  
Serve	
  content	
  as	
  web	
  site	
  with	
  built	
  in	
  page	
  handling	
  
Receive	
  no&fica&ons	
  on	
  key	
  events	
  
Physical	
  device	
  import/export	
  service	
  
 CloudFront	
  
•  World-wide content distribution
network
•  Easily distribute content to end
users with low latency, high data
transfer speeds, and no
commitments
Feature	
  
Fast	
  
Integrated	
  with	
  other	
  services	
  
Dynamic	
  content	
  
Streaming	
  

Details	
  



Mul&ple	
  world-­‐wide	
  edge	
  loca&ons	
  to	
  serve	
  content	
  as	
  close	
  to	
  your	
  users	
  as	
  possible	
  
Works	
  seamlessly	
  with	
  S3	
  and	
  EC2	
  origin	
  servers	
  
Supports	
  sta&c	
  and	
  dynamic	
  content	
  from	
  origin	
  servers	
  
Supports	
  rtmp	
  from	
  S3	
  and	
  includes	
  support	
  for	
  live	
  streaming	
  from	
  Adobe	
  FMS	
  and	
  Microsod	
  
Media	
  Server	
  
Production 1.2
Architecture
Production 1.2 Architecture
Well-­‐designed,	
  2	
  Tier	
  architecture	
  
Highly	
  Available	
  due	
  to	
  Mul&ple	
  Availability	
  Zone	
  
Load	
  Balancing	
  &	
  Auto-­‐Scaling	
  for	
  full	
  scalability	
  
Fully	
  managed	
  Database	
  included	
  	
  
Sta&c	
  content	
  stored	
  in	
  durable,	
  consistent	
  way	
  
Improved	
  end-­‐user	
  experience	
  through	
  CDN	
  
Capable	
  of	
  serving	
  >100K-­‐1M+	
  users	
  
BUT…
Production 1.2 Architecture
You are now at Scale…
…with lots of data…
…and need to optimize continuously.
But how and where?
SO…
Let’s add
Big Data
for analytics of web, mobile, gaming,
and log data
Multiple managed AWS services for Big Data
Elastic MapReduce (EMR)
•  Managed, elastic Hadoop cluster
•  Integrates with S3 & DynamoDB
•  Leverage Hive & Pig analytics scripts
Feature	
  
Scalable	
  
Integrated	
  with	
  other	
  
services	
  
Comprehensive	
  

Cost	
  effecFve	
  
Monitoring	
  

Details	
  
Use	
  as	
  many	
  or	
  as	
  few	
  compute	
  instances	
  running	
  Hadoop	
  as	
  you	
  want.	
  Modify	
  the	
  number	
  of	
  
instances	
  while	
  your	
  job	
  flow	
  is	
  running	
  	
  
Works	
  seamlessly	
  with	
  S3	
  as	
  origin	
  and	
  output.	
  Integrates	
  with	
  DynamoDB	
  
Supports	
  languages	
  such	
  as	
  Hive	
  and	
  Pig	
  for	
  defining	
  analy&cs,	
  and	
  allows	
  complex	
  defini&ons	
  in	
  
Cascading,	
  Java,	
  Ruby,	
  Perl,	
  Python,	
  PHP,	
  R,	
  or	
  C++	
  
Works	
  with	
  Spot	
  instance	
  types	
  
Monitor	
  job	
  flows	
  from	
  with	
  the	
  management	
  console	
  
Foursquare…

…generates a lot of Data

Founded in 2009
112M in Venture Capital
33 million users
1.3 million businesses using the service

3.5 billion check-ins
15M+ venues,
Terabytes of log data
Uses EMR for
Evaluation of new features
Machine learning
Exploratory analysis
Daily customer usage reporting
Long-term trend analysis
Benefits of EMR
Ease-of-Use
“We have decreased the processing time for urgent data-analysis”

Flexibility
To deal with changing requirements & dynamically expand reporting clusters

Costs
“We have reduced our analytics costs by over 50%”
Production 1.3
Architecture
Production 1.3 Architecture
Well-­‐designed,	
  2	
  Tier	
  architecture	
  
Highly	
  Available	
  due	
  to	
  Mul&ple	
  Availability	
  Zone	
  
Load	
  Balancing	
  &	
  Auto-­‐Scaling	
  for	
  full	
  scalability	
  
Sta&c	
  content	
  stored	
  in	
  durable,	
  consistent	
  way	
  
Improved	
  end-­‐user	
  experience	
  through	
  CDN	
  

Big	
  Data	
  analy&cs	
  built	
  in	
  for	
  con&nuous	
  op&miza&on	
  
Capable	
  of	
  serving	
  >1m-­‐10M+	
  users	
  
DEMO	
  

Ge#ng	
  to	
  Scale	
  
AWS Cloud Kata | Bangkok - Getting to Scale on AWS
AWS Cloud Kata | Bangkok - Getting to Scale on AWS
Thank You
aws.amazon.com/start-­‐ups	
  
aws.amazon.com/acFvate/cloudkata	
  

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AWS Cloud Kata | Bangkok - Getting to Scale on AWS

  • 1. Kuala Lumpur Ge#ng  to  Scale  on  AWS   Pattrawoot S, CTO, Wongnai.com
  • 2. •  •  •  •  Launched in 2010 Restaurant Review Website (Wongnai.com) Social Network for Food Lovers Key Features •  Location-based search for restaurants •  Write Reviews / Upload Photos / Eat Here (Check-in) •  Coupons / Guides / Articles •  Web & Mobile applications (iOS / Android / WP8 / BB10) •  > 820,000 users •  > 120,000 restaurants
  • 3. •  Number of Users •  Jan 2013: 360k users •  Mar 2013: 470k users (increase more than 100k) •  Number of User Contributions •  3x increase •  Dynamic and Personalized Contents •  Time to Scale!
  • 4. •  Amazon SES (Simple Email Service) •  > 500,000 email subscribers •  Over 1 million copies of e-newsletter sent every week •  Email blast ==> Bandwidth Consumption issue •  SEO performance •  Amazon EC2 (Elastic Computing Cloud) •  Past: One Server (Single Point of Failure) •  Single Physical Server ==> Multiple Virtual Servers •  Load Balancing
  • 5. •  Amazon RDS (Relational Database Service) •  MySQL •  Automated Backup •  Amazon S3 (Simple Storage Service) •  Versioning (S3 Object)
  • 6. Architecture Diagram www.wongnai.com Auto scaling Group Web App Server Elastic Load Balancing EC2 Instance Auto scaling Group Alarm Solr Server Data Volume Security Group EC2 Instance Data Volume Security Group EMM Server Amazon S3 Bucket Amazon RDS EC2 Instance Region Amazon SES
  • 7. What Has Been Improved •  •  •  •  •  Speed Scalability Flexibility Manageability Time / Cost Savings & Effectiveness Plan to Use: •  Elastic MapReduce (Hadoop) •  Simple Notification Service (SNS) •  ElastiCache
  • 9. 503 Service Temporarily Unavailable The server is temporarily unable to service your request due to maintenance downtime or capacity problems. Please try again later.
  • 10. With AWS, scale from one instance…
  • 12. How do I scale my architecture to support my first 10M users?
  • 13. “Think Big, Start Small, Scale Fast” Eric Ries, author of NY Times bestseller “The Lean Startup”
  • 15. Getting to Scale By building a scalable Architecture to support your first 10M users
  • 16. 1.  Dev  &  Test   3.  Beta  Release   2.  Alpha  Release  
  • 18. Database Options Self-Managed Database Server on Amazon EC2 Your choice of database running on Amazon EC2 Bring Your Own License (BYOL) Fully-Managed Amazon RDS Relational Database as a managed service Flexible licensing: BYOL or License Included Amazon DynamoDB Managed NoSQL database service using SSD storage Seamless scalability Zero administration
  • 19. But how do I choose what DB technology I need? SQL? NoSQL?
  • 20. Some folks won’t like this. But…
  • 21. Start with SQL databases
  • 22. But, but, but, but…
  • 24. Start with SQL databases
  • 25. Why SQL? Established and well worn technology Lots of existing code, communities, books, tools, etc Clear patterns to scalability You aren’t going to break SQL DBs in your first 10 million users. No really, you won’t
  • 26. Amazon  Rela&onal  Database  Service  (RDS)   Feature   PlaAorm  support   Preconfigured   Automated  patching   Details   Create  MySQL,  SQL  Server  and  Oracle   Get  started  instantly  with  sensible  default   seAngs   Keep  your  database  plaEorm  up  to  date   automa&cally   Backups   Failover   •  Database-as-a-Service •  No need to install or manage database instances •  Scalable and fault tolerant configurations Automa&c  backups  and  point  in  &me   recovery  using  snapshots   Manual  DB  snapshots   Automated  failover  to  slave  hosts  in  event  of   a  failure   ReplicaFon   Easily  create  read-­‐replicas  of  your  data  and   seamlessly  replicate  data  across  availability   zones  
  • 27. Auto-Scaling Automatic resizing of compute clusters based on demand Feature   Amazon   CloudWatch   Trigger  auto-­‐scaling  policy   Details   Control   Define  minimum  and  maximum  instance  pool   sizes  and  when  scaling  and  cool  down  occurs.   Integrated  to  Amazon   CloudWatch   Use  metrics  gathered  by  CloudWatch  to  drive   scaling.   Instance  types   Run  Auto  Scaling  for  On-­‐Demand  and  Spot   Instances.  Compa&ble  with  VPC.   as-create-auto-scaling-group MyGroup --launch-configuration MyConfig --availability-zones us-east-1a --min-size 4 --max-size 200
  • 37. Production 1.0 Architecture Well-­‐designed,  2  Tier  architecture   Highly  Available  due  to  Mul&ple  Availability  Zone   Load  Balancing  &  Auto-­‐Scaling  for  full  scalability   Fully  managed  Database  included     Capable  of  serving  >10K-­‐100Ks  users    
  • 39. Production 1.0 Architecture Wasted  server  capacity  for  sta&c  content   Reliability  and  durability  are  not  yet  op&mal   End-­‐user  experience  could  be  improved  thru   offloading  &  caching  
  • 40. SO…
  • 41. Let’s add Simple Storage Service (S3) CloudFront to optimize the end-user experience
  • 42. Simple Storage Service (S3) Feature   Flexible  object  store   Access  control   Server-­‐side  encrypFon   MulF-­‐part  uploads   Object  versioning   Object  expiry   Durable storage, any object 99.999999999% durability of objects Unlimited storage of objects of any type Up to 5TB size per object Access  logging   Web  content  hosFng   NoFficaFons   Import/Export   Details   Buckets  act  like  drives,  folder  structures  within   Granular  control  over  object  permissions   256bit  AES  encryp&on  of  objects   Improved  throughput  &  control   Archive  old  objects  and  version  new  ones   Automa&cally  remove  old  objects   Full  audit  log  of  bucket/object  ac&ons   Serve  content  as  web  site  with  built  in  page  handling   Receive  no&fica&ons  on  key  events   Physical  device  import/export  service  
  • 43.  CloudFront   •  World-wide content distribution network •  Easily distribute content to end users with low latency, high data transfer speeds, and no commitments Feature   Fast   Integrated  with  other  services   Dynamic  content   Streaming   Details   Mul&ple  world-­‐wide  edge  loca&ons  to  serve  content  as  close  to  your  users  as  possible   Works  seamlessly  with  S3  and  EC2  origin  servers   Supports  sta&c  and  dynamic  content  from  origin  servers   Supports  rtmp  from  S3  and  includes  support  for  live  streaming  from  Adobe  FMS  and  Microsod   Media  Server  
  • 45. Production 1.2 Architecture Well-­‐designed,  2  Tier  architecture   Highly  Available  due  to  Mul&ple  Availability  Zone   Load  Balancing  &  Auto-­‐Scaling  for  full  scalability   Fully  managed  Database  included     Sta&c  content  stored  in  durable,  consistent  way   Improved  end-­‐user  experience  through  CDN   Capable  of  serving  >100K-­‐1M+  users  
  • 47. Production 1.2 Architecture You are now at Scale… …with lots of data… …and need to optimize continuously. But how and where?
  • 48. SO…
  • 49. Let’s add Big Data for analytics of web, mobile, gaming, and log data
  • 50. Multiple managed AWS services for Big Data
  • 51. Elastic MapReduce (EMR) •  Managed, elastic Hadoop cluster •  Integrates with S3 & DynamoDB •  Leverage Hive & Pig analytics scripts Feature   Scalable   Integrated  with  other   services   Comprehensive   Cost  effecFve   Monitoring   Details   Use  as  many  or  as  few  compute  instances  running  Hadoop  as  you  want.  Modify  the  number  of   instances  while  your  job  flow  is  running     Works  seamlessly  with  S3  as  origin  and  output.  Integrates  with  DynamoDB   Supports  languages  such  as  Hive  and  Pig  for  defining  analy&cs,  and  allows  complex  defini&ons  in   Cascading,  Java,  Ruby,  Perl,  Python,  PHP,  R,  or  C++   Works  with  Spot  instance  types   Monitor  job  flows  from  with  the  management  console  
  • 52. Foursquare… …generates a lot of Data Founded in 2009 112M in Venture Capital 33 million users 1.3 million businesses using the service 3.5 billion check-ins 15M+ venues, Terabytes of log data
  • 53. Uses EMR for Evaluation of new features Machine learning Exploratory analysis Daily customer usage reporting Long-term trend analysis
  • 54. Benefits of EMR Ease-of-Use “We have decreased the processing time for urgent data-analysis” Flexibility To deal with changing requirements & dynamically expand reporting clusters Costs “We have reduced our analytics costs by over 50%”
  • 56. Production 1.3 Architecture Well-­‐designed,  2  Tier  architecture   Highly  Available  due  to  Mul&ple  Availability  Zone   Load  Balancing  &  Auto-­‐Scaling  for  full  scalability   Sta&c  content  stored  in  durable,  consistent  way   Improved  end-­‐user  experience  through  CDN   Big  Data  analy&cs  built  in  for  con&nuous  op&miza&on   Capable  of  serving  >1m-­‐10M+  users  
  • 57. DEMO   Ge#ng  to  Scale