The document discusses building web-scale applications using AWS. It covers principles like elasticity, scaling, and loose coupling of components. It describes using AWS services like S3, DynamoDB, ELB, Route53, CloudFront, and Auto Scaling to build applications that can handle large amounts of traffic and data flexibly. As an example, it discusses how a large UK job board company called Totaljobs migrated their infrastructure to AWS, which reduced costs while improving availability, scalability and performance compared to their previous data center setup.

1. Building Web-Scale
Applications with AWS
Glen Robinson, Manager Solution Architecture
Ryan Shuttleworth, Technical Evangelist

2. What are we going to cover?
What’s a web scale application?
Three principles to build upon
Layering the cake:
Data
Application
Total Jobs Group – their story

3. What’s a web scale
application?

4. Some web scale apps you can hear from today…

5. What do they have in
common?

6. Elastic capacity No need to guess capacity requirements and over-provision
Elastic Capacity
Demand
Actual demand
Customer
dissatisfaction
Predicted demand
Waste
Time

7. Elastic capacity No need to guess capacity requirements and over-provision
Elastic Capacity
Demand
Elastic capacity
Time

8. Built on a global footprint
Built across regional availability zones
9 Regions
25 Availability Zones
Continuous Expansion

9. Architected using services
Relational Database Service
Use RDS for databases Database-as-a-Service
No need to install or manage database instances
Scalable and fault tolerant configurations
DynamoDB Use DynamoDB for
Provisioned throughput NoSQL database high performance key-
Fast, predictable performance
value DB
Fully distributed, fault tolerant architecture

10. Architected using services
Amazon SQS Reliable message
Processing results Reliable, highly scalable, queue service
queuing without
for storing messages as they travel
Amazon SQS between instances
additional software
1
Processing
task/processing
trigger 2
Push inter-process Simple Workflow Task A
workflows into the Reliably coordinate processing steps
Task B 3
across applications
cloud with SWF (Auto-scaling)
Integrate AWS and non-AWS resources
Manage distributed state in complex
systems Task C

11. Architected using services
Document
Cloud Search Server
Don’t install search Elastic search engine based upon
software, use Amazon A9 search engine
Fully managed service with
CloudSearch
sophisticated feature set Search
Scales automatically Server
Results
Elastic MapReduce
Elastic Hadoop cluster
Process large volumes
Integrates with S3 & DynamoDB of data cost effectively
Leverage Hive & Pig analytics scripts with EMR
Integrates with instance types such as
spot

12. Three principles to
build upon…

13. Scale
1

14. Elasticity
Scale
1

15. Elasticity
State Data
Scale
1

16. Elasticity
State Data
Scale
2
Security

17. Elasticity
State Data
Scale
Inherent
2
Security

18. Elasticity
State Data
Scale
VPC
Inherent
2
Security
Groups

19. Elasticity
State Data
Scale
VPC
Inherent
3 Failure
Security
Groups

20. Elasticity
State Data
Scale
VPC
Inherent
3 Failure Expected
Security
Groups

21. Elasticity
State Data
Scale
VPC
Inherent
3 Failure
Automation
Expected
Security
Groups Testing

22. Elasticity
State Data
Scale
VPC Automation
Inherent Security Failure Expected
Groups Testing

23. Layering the cake

24. Web scale data
Object storage
Data

25. Highly scalable web access
to objects
You put it in S3
AWS stores with 99.999999999% durability
Data
Multiple redundant copies
in a region

26. Highly scalable data storage
A web store,
not a file system Access via APIs
What is S3?
Fast Economical
Data Highly available & durable

27. Data

28. Web scale data
Object storage
Data

29. Web scale data
Object storage
Relational data
Data

30. Master/Slave Horizontal Scaling
Reasonably simple to implement
Leverage PIOPs for raw performance
Easy to change instances sizes
Has an upper limit
Data

31. Sharded Horizontal Scaling
More complex at the application layer
No practical limit on scalability
Operation complexity/sophistication
Shard by function or key space
RDBMS or NoSQL
A D
hash
ring
C
B
Data

32. Web scale data
Object storage
Relational data
Data

33. Web scale data
Object storage
Relational data
NoSQL
Data

34. Horizontal Scaling - Fully
Managed
DynamoDB
Provisioned throughput NoSQL database
Fast, predictable performance
Fully distributed, fault tolerant architecture
Considerations for non-uniform data
Data

35. DynamoDB
Provisioned read/write performance per table
Predictable high performance scaled via console or
API
Dial it up
Data

36. Low provisioned throughput
Table
Partition
SSD
Data
Region
Illustrative diagram only

37. Increased provisioned
throughput
Table Table Table Table Table
Partition Partition Partition Partition Partition
SS SS SS SS SS
D D D D D
Table Table Table Table Table
Partition Partition Partition Partition Partition
SS SS SS SS SS
D D D D D
Data
Region
Illustrative diagram only

38. High provisioned throughput
Table Table Table Table Table Table Table Table Table Table
Partition Partition Partition Partition Partition Partition Partition Partition Partition Partition
Table Table Table Table Table Table Table Table Table Table
Partition Partition Partition Partition Partition Partition Partition Partition Partition Partition
Table Table Table Table Table Table Table Table Table Table
Partition Partition Partition Partition Partition Partition Partition Partition Partition Partition
Table Table Table Table Table Table Table Table Table Table
Partition Partition Partition Partition Partition Partition Partition Partition Partition Partition
Data
Region
Illustrative diagram only

39. Loose coupling sets you free!
The looser they're coupled, the bigger
they scale
Independent components
Design everything as a black box
Application Decouple interactions
Load-balance clusters
Data

40. Amazon SQS
Reliable, highly scalable, queue service for storing
messages as they travel between instances
Processing results
Amazon SQS
Application
Data Processing
task/processing trigger

41. Tight Coupling
Controller A Controller B Controller C
Application
Data

42. Tight Coupling
Controller A Controller B Controller C
Q Q Q
Application
Controller A Controller B Controller C
Data Loose Coupling

43. Trigger auto-
scaling
policy
Auto Scaling
Automatic resizing of compute clusters based on demand
Application Feature Details
Control Define minimum and maximum instance pool
sizes and when scaling and cool down occurs.
Integrated to Use metrics gathered by CloudWatch to drive
Data Amazon scaling.
CloudWatch
Instance types Run Auto Scaling for On-Demand and Spot
Instances. Compatible with VPC.

44. Where
does state
Application reside?
Data

45. Browser Framework
cookies session
handler
Where
does state
Application reside?
Memory
session Session
manager database
Data

46. State store should be:
Performant
Scalable
Application
Data Reliable

47. Where should state reside?
Trigger auto-
scaling
policy
Application
Data

48. Where should state reside?
Trigger auto-
scaling
policy
Application
Not here
Data

49. Where should state reside?
Trigger auto-
scaling
policy
Application
Not here
Data State must reside OUTSIDE the
Session state
scope of the elements you wish to
service scale

50. Performant Where should state reside?
Scalable
Reliable
Application
Data

51. Elastic Load Balancing
Create highly scalable applications
Distribute load across EC2 instances
in multiple availability zones
Load Balancing
Feature Details
Application Available Load balance across instances in multiple Availability
Zones
Health checks Automatically checks health of instances and takes
them in or out of service
Session stickiness Route requests to the same instance
Data Secure sockets layer Supports SSL offload from web and application
servers with flexible cipher support
Monitoring Publishes metrics to CloudWatch

52. Route53
Global DNS service
Request Route53
Distribution
Route53
Load Balancing
Region Region
A B
Application
Data

53. Route53
Global DNS service
Request Route53
Distribution
Route53
16ms 92ms
Load Balancing
Region Region
A B
Application
Data

54. Route53
Global DNS service
Request Route53
Distribution
Route53
16ms 92ms
Load Balancing
Region Region
A B
Application
Data

55. Route53
Global DNS service
Request Route53
Distribution Region A DNS entry
Route53
16ms 92ms
Load Balancing
Region Region
A B
Application
Data

56. CloudFront
World-wide content distribution
Distribution network
3 Served from S3
/images/*
Load Balancing
Application
London 2 Served from EC2
*.php
Data Paris
1 Single CNAME
NY
www.mysite.com

57. CloudFront
World-wide content distribution
Distribution network
No CDN CDN for CDN for
Static Static &
Load Balancing Content Dynamic
Content
Application
Response Time
Response Time
Response Time
Server Load
Server
Server
Load
Load
Data

58. Management
Distribution
Load Balancing
Application
Data

59. Management
Distribution
10 instances
Load Balancing manageable
Application
Data

60. Management
Distribution
100 instances
Load Balancing at a push
Application
Data

61. Management
Distribution
1,000 instances
Load Balancing not a chance
Application
Data

62. Management
Distribution
Automation &
Load Balancing management
Web scale enabler
Application
Data

63. Management
CloudFormation EC2
Distribution
Load Balancing
Application
Data
OpsWorks Elastic Beanstalk

64. Management control
CloudFormation EC2
Distribution
Load Balancing
Application
Data
OpsWorks Elastic Beanstalk
convenience

65. Summary
Summary
Use these techniques (and many others) as appropriate
Awareness of the options is the first step to good design
Scaling is the ability to move the bottlenecks around to
the least expensive part of the architecture
AWS makes this easier – so your application is not a
victim of its own success

66. From theory to practice
a real story

67. Scale in AWS
Oliver Burns – Lead Architect
23 April 2013

68. Who are we …

69. Facts and Figures…
• Founded in 1999
• Largest job board company in the UK
• 450 staff
• 200K jobs
• 7 million job seekers a month
• 3.5 million page views per day
• 6.7 million searchable CVs
• 4 million job applications per month
• 3.5 million daily email job alerts

71. The Challenge
Before the end of 2012

72. www.totaljobs.com
Akamai
www.origin.totaljobs.com
Availability Zone 1 Availability Zone 2 Route53
ELB
DNS
Replication
Active
Peer to Peer Passive
Replication

73. Technology
Service Data Centre AWS
Web Servers Virtual Machines (VMWare) m1.xlarge (15GB, 4 cores)
(IIS)
DB Servers Blades cc2.8xlarge (60GB, 32 cores)
(SQL Server 2012)
Storage SAN Elastic Block Store (EBS) standard + high
iops
Load Balancing Cisco Load Balancer + Elastic Load Balancing
Cisco Global Site Selector
Backup Tape zManda, using Simple Storage Service
(S3)
Security Hardware IPS Akamai WAF
Email Cisco IronPorts powerMTA

74. Financials
• Estimated like for like data centre costs:
£730,000 p/a
• Projected AWS costs:
£450,000 p/a

75. Cost savings

76. Scaling

77. Scaling
• Lots of commodity servers
– Resiliency
– Smaller granularity cost increases
– Auto scaling

78. So….
Does AWS live up to its
promises?

79. Benefits
• Lower, flat, running costs
• Flexible provisioning
• Capacity and new technology opportunities =>
performance

80. Thank you