Building Scalable Applications with Azure Cloud Services and Service Bus

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VS 2013 Azure integration and ease of useCloud Services and Service Bus FeaturesLoosely Coupled designScale Out (not up)Easy to scale, resistant to failurePerformant (serialization, caching, batching, efficient code/frameworks)

Execution Models

Web sitesMobile servicesVirtual machinesCloud services

Cloud Services

Platform as a Service (PaaS)Platform infrastructure managed by AzureUser provides configurationConsist of 1-n worker/web roles

Two Types of Roles–Web Role: IIS enabled–Worker Role: IIS disabled

A role consists of 1-n identical instancesA deployment contains 1-n RolesEach role has an agent

Cloud Service Project

Instances

Non-durable virtual machine (VM)New instances can be created by a roles agent– Failure in VM– Scaling

Create Cloud Service and debug in Visual StudioEnvironment– Visual Studio 2013 Update 2– Windows Azure SDK 2.3– Node V0.10.26– Node.Js Tools for VS 2013 Beta

Windows Azure Scalability Posterhttp://msdn.microsoft.com/library/azure/xx130451.aspx

Azure Service Bus

Queues– reliable, persistent FIFO guaranteed– 1-n competing consumers

Topics– Publish/subscribe messaging (fan out)– Component exchange messages through topic– Supports filter registration

Queue Design

Receiving messages– Polling (pull based)– Queue listeners (push based)

Scale role instances to handle workloadBatch put/get itemsUse multiple queues for higher throughput

Sample Queue

Providers Consumers

Sample Topic

Providers Consumers

Queue Receive Modes

PeekLock– Default mode– After message received, request sent back

ReceiveAndDelete– Both steps in single request– Less throughput– Can lose messages

Using Service Bus

Do not create and close queue/topic clientsBatch items and prefetchingUser asynchronous requestsPartitioning

Brokered Message

Demo Time!

BrokeredMessage, Serialization and Send/Receive

Requirement: Service Bus Namespace Connection StringGo to www.manage.windowsazure.com

Easy to Scale, Resistant to Failure

Avoid single point of failureScale-out not scale-upBe statelessHandle failure gracefully

Error Handling

Handle dropped connectionsHandle transient failures (retry/reconnect)Other failures (log, report, and notify user)If possible, fall back to a different service or workflow

Loosely Coupled Architecture

TestingEasier transition in switching implementationEasier migration to another platform

Useful Design Patterns

Isolate APIs with interfaceFaçade PatternDependency Injection ContainerFactories

Bang for Your Buck!

Batch network calls if appropriateProper serializationChoose frameworks wiselyDon’t server static content from website/web role

Blob Storage

Less load on serverFaster IO than SQLCheapest Azure storageUser for static content >100 kB

Azure Cache Service

Distributed In-memoryPersists during application restartsExpiration: never, absolute, slidingNotifications: AddItem, ReplaceItem, RemoveItemBuilt in back-up

Scaling

ConfigurationManualAPIAuto Scale

Scale by CPU

Scale by Queue

Demo Time

Node.js web roleC# worker role

Questions?

michael.kacher@gmail.com

Slides and code available at www.kcode.me

Building Scalable Applications with Azure Cloud Services and Service Bus

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