This document provides a technical deep dive into the Windows Azure platform. It begins with some introductory notes and an example application called TicketDirect that will be used to demonstrate the Azure architecture. It then covers key aspects of the Azure architecture including roles, storage, queues, scaling, configuration, monitoring and SQL Azure. Throughout it provides examples and considerations for designing applications on the Azure platform.

1. Windows Azure Platform
Technical Deep Dive
Chris J.T. Auld
Director
Intergen Ltd
chris.auld@intergen.co.nz
twitter.com/cauld
1

2. Notes Before We Begin
I don’t work for Microsoft
Will call a ‘spade a spade’ today
Azure is a young technology
Best practices are still emerging
We focus on architecture in this session
A Few Demos only
No prior experience assumed
We have lots of attendees and just a little time
Questions at the end please

3. TicketDirect: An example application
TicketDirect is a ticketing company in Australia
and New Zealand
Ticketing is uniquely suited to the cloud
Will use TicketDirect as real world example
today

4. TicketDirect Architecture
Azure Roles Client Applications
.Net Service Bus Castellan.Azure
• Box Office sales
• Ticket Printing
• System Administration
• Venue/Event Management
http:// TicketDirect .* Dynamic Worker Distributed Cache Partitioner WCF • Partitioning
(tasks uploaded Worker Worker
as blobs)
Azure Storage On Premise
Blobs to store
-- ----
Tables to Queues for SQL Server
web and
worker role
-- record
server &
communication
between clients
resources
-- ----
-- partition and roles
information Castellan Venue
SQL Azure
Castellan Castellan Castellan
Castellan Castellan Castellan ...
Castellan Venue DB 1
Venue
Venue DB’s Venue DB 2
Venue
Venue DB’s Venue DB N
Venue
Venue DB’s
Partition(s) Partition(s) Partition(s) Castellan.old (VB6)
One application DB, many venue DB’s – each partitioned in to many parts (40+)
4

5. A Global Hardware Platform
Global Foundation Services
http://www.globalfoundatonservices.com
6 Azure Data Centers
Europe - West/North
Asia – East/Southeast
USA – South Central/North
Central
New containerised data
centers
Approaching PUE of 1.2
Thousands of computation
units
5

6. The High Scale Application Archetype
Windows Azure provides a ‘pay-as-you-go’ scale out application platform
Intelligent Network Load Balancer
Network Activation
Stateless Web and/or Application Servers
Stateless ‘Worker’ Machines
Async Activation
State ‘NoSQL’ Partitioned Shared
Queues
Tier Datastores RDBMS Filesystem
6

7. Azure Service Architecture
The Internet
The Internet via TCP or HTTP
LB LB
LB
Queues
Web Site Worker
Worker Role
Worker
Web Site
Web ASMX, WCF)
(ASPX, Role Service
Managed
(ASPX, ASMX, WCF)
IIS as Host Service
Interface Calll
Blobs
Tables
Storage
Windows Azure Data Center
7

8. Cloud != Cloud
8

9. Windows Azure
Your
Service
D
N
S
L
B
Web Portal
(API)
L
Fabric B
Controller
9

10. Service Deployment
Your
Service Model
Service
D
N
S
L
B
Web Portal
(API)
DNS
config L
Fabric B
Controller
10

11. Hello Windows Azure
11

12. Service Update
Your
Service Model
Service
Service
D
production
N
S
staging
Service
L
B
Web Portal
(API)
DNS
config
config L
Fabric B
Controller
12

13. Upgrading Your Application
Two Models: VIP Swap and In-Place Upgrade
VIP Swap:
Uses Staging and Production environments.
Allows to quickly swap environments.
Production: v1  Staging: v2, after swap then
Production: v2  Staging: v1.
In-Place Upgrade
Performs a rolling upgrade on live service.
Entire service or a single role
Manual or Automatic across update domains
Cannot change Service Model
13

14. Fault and Upgrade Domains
Instance Instance Instance
Instance Instance Instance
14

15. Configuration
Service Configuration
Serviceconfiguration.csdef – Service Model
ServiceConfiguration.cscfg – instance data
RoleEnvironment.GetConfigurationSettingValue()
Don’t use web.config for values you wish to
change at runtime
Web.config change requires re-deploy
15

16. Service Scaling
Your
Service Service
D
Service
N
S
Service
Service Service
L Service
B
Service
Service
Web Portal
(API)
L
Fabric B
Controller
Model
16

17. Rule Based Auto- Scaling
Use Service Management API
Predicable or Periodic Demand
Time based rules
Unpredictable demand
Monitor metrics and react accordingly

18. Monitor metrics
Primary metrics (actual work done)
Requests per Second
Queue messages processed / interval
Secondary metrics
CPU Utilization
Queue length
Response time
Derivative metrics
Rate of change of queue length
Use ‘historical’ data to help predict requirements

19. Evaluating Business Rules
Are requests taking too long?
Do I have too many jobs in my queue?
How much money have I spent this month?
Could write these into code.
Could build some sort of rules engine.
Could use WF rules engine.

20. Take Action
Add/Remove Instances
Use Service Management API
Don’t forget billing window is 1hr
Change role size
Requires change to *.csdef
Most suited to Worker Roles
Send notifications
Email
IM
Manage momentum

21. Service Monitoring & Recovery
Your
Service
D
Service
N
S
Service
L
B Service Service
Web Portal
(API)
L
Fabric B
Controller
Model
21

22. Monitoring
No Debugging in Cloud
Instrument your application using Trace, Debug
Use Diagnostics API to Configure and Collect
Event Logs
Performance Counters
Trace/Debug information (logging)
IIS Logs, Failed Request Logs
Request data on demand or scheduled
Transferred into your table and/or blob storage
Everything is remotely configurable
22

23. Hello Windows Azure v2
23

24. Storage
Scalable storage in Azure Datacenter
100tb per storage account
Accessible via RESTful Web Service API
Access from Azure Compute
Access from anywhere via internet
Supporting .NET Client Library
Various storage types
Table
Queue
Blob
Drives
24

25. Storage
Tables Blobs
Table = group of entities Large binary storage
Entity = name/value pairs Stored in container
Partitioned by key Unlimited containers
Scale out to Bns of entities CDN Deliverable
Not an RDBMS
Queues
Simple message queue
Not transactional
Read at least once
Delete to remove message,
otherwise is returned to
queue
25
Partitioned by Queue Name

26. Using Queues for Async Processing
1. User uploads large image file
2. Image inserted into blog storage
3. Message placed on queue incl BLOB URI and metadata
4. Worker role is polling queue. Reads message from queue
30mb JPEG
5. Worker role processes message, reads from BLOB storage, generates thumbnail
6. Thumnail and metadata stored in Table storage
7. Message deleted from queue
Web Role Worker Role
Web Role Table Worker Role
Worker Role
Worker Role
Blob30mb JPEG
Container
LB LB
Queue
Storage
26

27. Idempotency
f(x) = f(f(x))
Queues are NOT transactional
27

28. First Step For Software Architects
If you expect to write documents mentioning
idempotency
Open word
Type idempotency
Right click
Choose add to dictionary
WARNING: Failure to follow these steps will surely see you sending an
important architecture and design document to a client with the ‘corrected’
spelling of the word...impotency
28

29. Messages Process At Least Once
1. Debit bank account $100 message
2. Worker role reads message
3. Balance debited $100
4. Worker role is torn before message can be deleted
5. 3 minutes later, message re-appears on queue
6. Worker role reads message
7. Balance debited $100
8. Message deleted from queue
9. Chaos ensues.....
10. Customer calls bank.....
Balance = $800
$900
Balance = $1000
Web Role Worker Role
Web Role Worker Role
Worker Role
Worker Role
LB LB
Queue
29 Storage

30. Solving The Idempotency Problem
1. Debit bank account $100 message with transaction ID
2. Worker role reads message. Checks transaction ID not present.
3. Writes transaction ID with state ‘Started’ to ‘Replay Log’
4. Balance debited $100
5. Worker role is torn before message can be deleted
6. 3 minutes later, message re-appears on queue
7. Worker role reads message. Checks transaction ID. It is present in state started
8. Compensating message written to another queue
9. Message deleted from queue
10. Compensatory message processed.
Balance = $1000
Balance = $900
Web Role Query Worker Role
Web Role Worker Role
Table Worker Role
Worker Role
Queue
LB LB
Queue
30
Storage

31. Azure Tables
Not an RDBMS
31

32. Azure Table Storage – Key Points
Partition Key is the killer feature
Partitions are Auto-Balanced
No need to partition into equal bins
Hot partitions may be scaled up
Azure fabric may dedicate more resources to
partitions with high Tx load
Partition Key AND Row Key = Primary Key
Must include PartitionKey for
Create,Update,Delete
Select queries across partitions parallelized,
resource intensive and potentially more
32
expensive!

33. Azure Table Storage – Key Points
Continuation Tokens May Be Returned from
Cross Partition Queries
Any query not including the PartitionKey needs
to handle Continuation tokens
http://tinyurl.com/ContToken
Key Columns Up to 1KB in size
Should aim to keep to 260 char URI limit
Be aggressive
e.g. Only ever query by an ID?
RowKey = PartitionKey
All queries should include partition key
33

34. NoSQL/Non-Relational
Data Modelling
34

35. Azure Tables != RDBMS
Storage is cheap
Cross partition queries are resource intensive
De-normalization and massive duplication often
name of the game

36. E.g. Tweet Storage
Tweet
TweetID
UserID
DateTimeStamp
Message
36

37. E.g. Tweet Storage
Tweet TweetWord Word
… TweetID WordID
Message WordID Word (IX)
Tweet Word
… WordID
Message Word (IX)
37

38. E.g. Tweet Storage
Tweet TweetIndex
TweetID (RK) TweetID (RK)
UserID (PK) UserID
DateTimeStamp DateTimeStamp
Message Message
Word (PK)
38

39. E.g. Tweet Storage
Tweet MentionIndex
TweetID (RK) TweetID (RK)
UserID (PK) UserID
DateTimeStamp DateTimeStamp
Message Message
UserID (PK)
39

40. Pricing
Compute
$0.12 / CPU hour (or part thereof)
~ 1.7 GHz, 2GB Ram, Single Core
$2.88 / Day
$86.4 / 30 days (billing period)
2 instances = $172.80 / month
Storage
$0.15 / GB/Month
$.01 / 10,000 calls to storage web service
Bandwidth
$0.30 /GB inbound to Asian datacenters
40
$0.45 /GB outbound from Asian datacenters

41. Design Considerations
Scale and availability are the design points
Storage isn’t a relational database
Stateless
Stateless front ends, store state in storage
Use queues to decouple components
Instrument your application (Trace)
Once you are on - stay on
Think about patching & updates
41

42. SQL Azure
Business
Data Sync Reporting
Intelligence
Initial Services
Database
Core SQL Server database capabilities in cloud optimized topology
Highly compatible with on premise SQL Server
Future Services
Data Sync – Enables the sync framework
Additional SQL Server capabilities available as a service:
Business Intelligence and Reporting

43. SQL Azure Details
SQL Azure provides logical SQL Server
Gateway server that understands TDS protocol
Looks like SQL Server to TDS Client
Actual data stored on multiple backend data nodes
Logical optimisations supported
Indexes, Query plans etc..
Physical optimisations not supported
File Groups, Partitions etc…
SQL Azure transparently manages physical storage

44. SQL Azure
Deployment
Web Portal
(API)
DB
Script
SQL Azure
TDS
44

45. SQL Azure
Accessing databases
Web Portal
(API)
Your SQL Azure
TDS
App
Change Connection String
45

46. SQL Azure
46

47. Database Replicas
Replica 1
Replica 2
DB
Replica 3
47

48. Shared Environment
C
D
A B D
Hardware Boundary Hardware Boundary
C C
A B
A B D
Hardware Boundary Hardware Boundary
48

49. SQL Azure
Database Monitoring & Recovery
Web Portal
(API)
Your
App
SQL Azure
TDS
!
49

50. Design Considerations
1 x 10GB database
1 Instances
10 x 1GB databases
10 Instances
Partition for
Data volume
Query load
50

51. SQL Azure – Key Points
Partition for
Data volume > 10GB
Transaction throttle (non deterministic)
Always code for retry
All partition logic up to the developer
Algorithmic
Lookup based
Partitions are not Auto-Balanced
Need to aim for ‘equal’ partitions
‘Equal’ not necessarily the same size
51

52. Choosing a Partition Key
Natural Keys
Country
First letter, last name
Date
Mathematical
Hash functions
Modulo operator
Lookup Based
Lookup table to resolve value to partitions
52

53. Using Modulo
The remainder of a division
Nice properties for partitioning:
Given two positive integers M and N
M mod N will return a number between 0 and N-1
Want equi-sized partitions?
Given an appropriate distribution of M we will get N
‘equally full’ buckets.
53

54. Using Hash Values
Using A Hash Function Projects One
Distribution into Another
Use a hash function that projects a random
distribution
Do NOT use a cryptographic hash function
Plenty of choice on the web
http://tinyurl.com/part-hash
Be careful if using Object.GetHashCode()
54

55. SQL Azure Partitioning
55

56. Just in time Partitioning
In SQL Azure Partitions Cost Money
In highly elastic scenarios partitions may be
needed for just a few hours or days
If load is predictable
Partition before load commences
De-partition after load has subsided
56

57. Pricing
WEB Edition Business Edition
1 GB Database 10GB Database
$9.99 / month $99.99 / month
Bandwidth Bandwidth
$0.10 /GB inbound $0.10 /GB inbound
$0.15 /GB outbound $0.15 /GB outbound
• Pro rated by the day or part thereof
• Can move up and down between sizes
• SQL Azure has no query charge
• Excessively long transactions or high query load may result in throttling
• 50GB database size in Beta
57

58. Windows Azure Platform AppFabric
Extending .NET to the cloud with Internet Scale Utility Services
Service Bus:
General purpose application messaging bus
Access Control:
Rules-driven, claims-based access control
58

59. Simplified, Secure Connectivity for the Cloud
Service Bus and Access Control in Windows Azure platform
AppFabric are powerful building blocks.
Secure Connectivity
• Bridge cloud services, on-premises apps, and hosted
assets
• Build distributed apps for your business or to
collaborate with partners
AppFabric SERVICE BUS Across boundaries
Connect apps & services • Navigate network and security boundaries, securely
and simply
• Federate identity and access across organizations
and ID providers
• Simplify claims-based authorization for distributed
apps and web services
At Cloud Scale
AppFabric ACCESS CONTROL • Scale up and down as your business requires
Control & secure access • Automated service mgmt. and dynamic scale
• Interoperate with a variety of languages and
industry standards

60. AppFabric Service Bus Connectivity
Text
XML
Graphics
Binary Data Receive Receive
Streaming
Firewall
Send Send
Exchange messages between loosely
coupled, composite applications.
Direct Connection facilitated by
Application #1 Service Bus if that is best Application #2
connection mechanism.

61. Service Bus
61

62. Architecture of AppFabric Access Control
3. Map input claims to
output claims based
1. Define access on access control rules
control rules Your Access
Control Project
0. Trust exchanged; 4. Return token
secrets, certs (output claims from 3)
2. Send token (initial
6. Check for claims
claims; e.g. identity)
Your App User
(Relying Part) (Application)
5. Send token with request

63. Pricing & SLA
$1.99 / 100k ACS transactions
Connections
$3.99/connection/month
Packages available
Bandwidth
$0.10 /GB inbound
$0.15 /GB outbound
63

64. TicketDirect: An example application
TicketDirect is a ticketing company in Australia
and New Zealand
Ticketing is uniquely suited to the cloud
Will use TicketDirect as real world example
today

65. TicketDirect Architecture
Azure Roles Client Applications
.Net Service Bus Castellan.Azure
• Box Office sales
• Ticket Printing
• System Administration
• Venue/Event Management
http:// TicketDirect .* Dynamic Worker Distributed Cache Partitioner WCF • Partitioning
(tasks uploaded Worker Worker
as blobs)
Azure Storage On Premise
Blobs to store
-- ----
Tables to Queues for SQL Server
web and
worker role
-- record
server &
communication
between clients
resources
-- ----
-- partition and roles
information Castellan Venue
SQL Azure
Castellan Castellan Castellan
Castellan Castellan Castellan ...
Castellan Venue DB 1
Venue
Venue DB’s Venue DB 2
Venue
Venue DB’s Venue DB N
Venue
Venue DB’s
Partition(s) Partition(s) Partition(s) Castellan.old (VB6)
One application DB, many venue DB’s – each partitioned in to many parts (40+)
65

66. Windows Azure Platform Benefits
The Cloud Windows Azure
New Economic Model High Level of Abstraction
Low Capex Hardware
Pay as you Go Server OS
Elastic Scale Network Infrastructure
Only solvable via Cloud Web Server
Global Distribution Availability
Global data centers Automated Service Management
Azure CDN
Scalability
Instance & Partitions
Developer Experience
Familiar Developer Tools
66

67. Windows Azure Platform Benefits
AppFabric SQL Azure
High Performance Messaging Higher Level of Abstraction
Massively scalable Hardware
HTTP and Raw TCP Server OS
Access Control Network Infrastructure
Less brittle apps due to factoring Database Server
out rules Availability
Developer Experience Automated Database
Familiar Developer Tools Management & Replication
WCF bindings Scalability
Databases Partitioning
Developer Experience
Familiar SQL Environment
67