Embed Size (px)
Citation preview
Build 2015 – BBAzure
Why cloud ?PRO CON
Scaling (”infinite”) Bandwidth (bottleneck)
Metered (Pay-as-you-go) Storage (comparatively expensive)
Redundancy (resilient) Hands-off (lack of control)
Less on-prem admin (TCO) SLA (compromises)
DevOps made possible (eliminate the middleman) Perception (is it safe ?)
SLA (guarantees) Not right for everything
I/P/SaaS
Why Azure ?Azure (Microsoft) AWS (Amazon) GCP (Google)
Cost Per minute – rounded up
Per hour – rounded up
Per minute – rounded up (minimum 10 minutes)
SLA
Technologies .Net, Hyper-V, Hybrid clouds, WinServer, NoSql and Sql storage, HDInsight (Hadoop), ML, etc
Xen hyper-V, NoSql and Sql storage, analytics (Hadoop), ML, etc
KVM hyper-V, Linux, Hadoop, Java/Python (primarily), NoSql and Sql (mysql), etc
”Barrier-to-entry” (for a Microsoft stack company)
Low Higher Higher
Regional presence (why is this relevant ?)
Analogy
Continuous integration / delivery DevOps• “A working product after every check-in”• “The adoption of DevOps is being driven by factors such as:
• Use of agile and other development processes and methodologies• Demand for an increased rate of production releases from application and business unit stakeholders• Wide availability of virtualized and cloud infrastructure from internal and external providers• Increased usage of data center automation and configuration management tools”
• “You’re doing continuous delivery when• Your software is deployable throughout its lifecycle• Your team prioritizes keeping the software deployable over working on new features• Anybody can get fast, automated feedback on the production readiness of their systems any time
somebody makes a change to them• You can perform push-button deployments of any version of the software to any environment on
demand”
MS DevOps stages
The DevOps virtuous cycle
(...AKA Deming Cycle)
Moving on to...
Build 2015 Azure themes (rough bundling)• Azure management and VS integration, ”new” Azure portal, resource groups• Dockers and containerization• App service + web and mobile on Azure• Big data & analytics• Azure Media Services• Azure Elastic Search• Azure Compute platform• Storage updates (DocumentDB, Azure Sql, Blob updates)• Azure Service Fabric• IoT with Azure• Office 365 and Azure
Want everything ? http://channel9.msdn.com/Events/Build/2015?sort=status&direction=asc&term=azure#tab_sortBy_status
Cloud computing trends• Hybrid cloud and piecemeal migration
• Private clouds less so
• Containerization and DevOps • Agile => CI
• Cloud APIs (REST, JSON)• Ease and scalability / automation of resource management
• Portal, API, scripting
• Security• Encryption, automation, orchestration
• Industry clouds• Case in point, Media Services, see also reference architectures
• Continuing commodification• Metering, billing, modes of tweaking resource usage, monitoring
Sources: IBM, Gartner, IDC, Forrester
Storage• SQL Database• SQL Server VM• DocumentDB• Search• HDInsight• Other DBs• Blobs• Files• Queues• Tables• DataLake (Hbase)
Azure storage offerings
Storage architecture
Core storage category API overview
Azure management and VS integration, ”new” Azure portal, resource groups
Mgmt portal and Visual Studio integration• Download the Azure SDK to integrate• Start your VS or open your portal• Portal : logon via Azure AD• VS : use the Azure server explorer• Or use PowerShell (get the Azure cmdlets)• Or use cross-platform xplat-cli• Or write the automation code in any .net
language
Dockers and containerizationDockers and containerization
ALTER DATABASE Milestone MODIFY (edition='Standard')Set-AzureSqlDatabase -ResourceGroupName MS -ServerName MS-Server -DatabaseName Milestone -RequestedServiceObjectiveName Standard;
PUT https://management.azure.com/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/microsoft.sql/servers/{serverName}/databases/{databaseName}?api-version=2014-04-01
https://msdn.microsoft.com/en-us/library/azure/mt163571.aspx
{ "location": “<location>”, "properties": { "requestedServiceObjectiveId":“<serviceObjectiveID>“ }}
Request body
T-SQL:
PowerShell:
REST API:
Dockers and containerization
The problem
The problem
The containerization solution – e.g. Dockers
...the analogy to shipping containers...
VM
Virtualization spectrum – where VMs and Containers fit
Azure Media Services• Upload and storage• Encoding, packaging, indexing• Content protection (encryption, signing)• Live and VoD streaming• Azure Media Player (cross platform)• Azure Media Indexer (NLP, transcripts, search, CC)• Ingest => encode => encrypt => publish => deliver
App service / Service Fabric
Service Fabric Applications
Azure Private Clouds
Applications composed of microservices
High Availability
Hyper-Scale
Hybrid Operations
High Density
Rolling Upgrades Stateful
services
Low Latency Fast startup & shutdown
Container Orchestration & lifecycle management Replication &
FailoverSimple
programming models
Load balancing
Self-healingData Partitioning
Automated Rollback
Health Monitoring
Placement Constraints
Service Fabric
Reliable Actors API Reliable Services API
What is a microservice?• Is (logic + state) that is independently versioned, deployed,
and scaled• Has a unique name that can be resolved
e.g. fabric:/myapplication/myservice
• Interacts with other microservices over well defined interfaces and protocols like REST
• Remains always logically consistent in the presence of failures• Hosted inside a “container” (code + config)• Can be written in any language and framework• node.js, Java VMs, any EXE
Abstraction, encapsulation, decoupling
• Reliable actors API (“Orleans”)• Build reliable stateless and stateful objects with a virtual Actor Programming Model
• Suitable for applications with multiple independent units of state and compute
• Automatic state management and turn based concurrency (single threaded execution)
• Reliable services API • Build stateless services using existing technologies such as ASP.NET
• Build stateful services using reliable collections
• Manage the concurrency and granularity of state changes using transactions
• Communicate with services using the technology of your choice (e.g WebAPI, WCF)
“Self-contained, independent, substitutable containers vs interdependent tiers”
Reliable Collections
• Atomically update one or more collections using transactions• Changes are replicated and durably stored on multiple replicas• Reads are repeatable within the transaction• Enumerations are snapshot based
IReliableDictionary<K,V>
IReliableQueue<T>
Reliable services sample
Reliable actors sample
Azure App Service• Abstraction layer over Web Apps, Moble Apps, API Apps, Logic Apps• “Point-and-click” / CASE-like functionality• Seamless authentication• API definition and generation, auto-discovery and update using
Swagger / Swashbuckle• Galleries (Marketplace)
App Service – four categories of app
Point-n-click in the Azure portal
Big data & analytics
Elastic database resource provisioning
Elastic database management
Data lake concept
Event data and analyticsPresentation and action
Storage andBatch Analysis
StreamAnalysis
IngestionCollectionEvent production
Event hubs
Cloud gateways(web APIs)
Field gateways
Applications
Legacy IOT (custom protocols)
Devices
IP-capable devices(Windows/Linux)
Low-power devices (RTOS)
Search and query
Data analytics(Power BI)
Web/thick client dashboardsEvent Hubs
SQL DB
Storage Tables
Power BI
Storage Blobs
Stream Analytics
Devices to take action
MachineLearning
more to come…
Code example, Azure Stream Analytics
{“XO”, 4, “Win10”} {“Jo”, 0, “Surface”} {“Foo”,4, “Bing”}Twitter Stream: {“Dip”, 2, “XBox”}
{“XO”, 0, “Win10”} {“Dip”, 0, “Xbox”}{“Jo”, 4, “Surface”} {“Foo”, 0, “Bing”}Twitter Stream:(same stream,further down the timeline)
SELECT TS1.UserName, TS1.TopicFROM TwitterStream TS1 TIMESTAMP BY CreatedAt JOIN TwitterStream TS2 TIMESTAMP BY CreatedAt
ON TS1.UserName = TS2.UserName AND TS1.Topic = TS2.Topic
AND DateDiff(second, TS1, TS2) BETWEEN 1 AND 60WHERE TS1.SentimentScore != TS2.SentimentScore
time
“List all users and the topics on which they switched their sentiment within a minute“
Parallelized stream analytics of event hubs
WITH Step1 AS ( SELECT Count(*) AS CountTweets, TopicFROM TwitterStream PARTITION BY PartitionIdGROUP BY TumblingWindow(second, 3), Topic, PartitionId
),Step2 AS (
SELECT Avg(CountTweets) FROM Step1GROUP BY TumblingWindow(minute, 3)
)SELECT * INTO Output1 FROM Step1SELECT * INTO Output2 FROM Step2SELECT * INTO Output3 FROM Step2
• A query can have multiple steps to enable pipeline execution
• A step is a sub-query defined using WITH (“common table expression”)
• Can be used to develop complex queries more elegantly by creating a intermediary named result
• Creates unit of execution for scaling out when PARTITION BY is used
• Each step’s output can be sent to multiple output targets using INTO
Azure Machine Learning Studio
ML Studio - detail
Resources• Everything Azure @ Build2015
• https://channel9.msdn.com/Events/Build/2015?sort=status&direction=asc&term=azure#tab_sortBy_status
• MS Azure Blog & Announcements• https://azure.microsoft.com/blog/
• All links referenced for this slide pack =>• Try-an-app :
• https://tryappservice.azure.com/
• Azure storage explorers• http://blogs.msdn.com/b/windowsazurestorage/archive/2014/03/11/windows-azure-
storage-explorers-2014.aspx
links.txt
