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Modern Arch. Patterns for the cloud
Transcript of Modern Arch. Patterns for the cloud
for the cloud
What is the cloud?
Computing power/storage over the internet, provided as a service. Benefits include:
Zero or low upfront cost
Elasticity on demand (Scale up or down)
Maintenance/upgrades/patches etc is someone else's problem
Seemingly infinite computing power at your disposal
Design for failure (resilience)
Design for availability
Design for elasticity
Design for the future
ASP NET MVC or Web Forms
WS-* web services
SQL Server database
Demo App for this talk!
Create an app better than Twitter!
Queue Centric Workflow
Command Query Responsibility Separation
Performs an action
The "Write" part of CQRS
In the CAP theorem, offers Consistency and Availability
No Read functionality: domain model offers true encapsulation
Domain model only exposes behaviours
Returns data to the user through a query
"Read" part of CQRS
In the CAP theorem, offers Availability and Partitionability
Can skip the domain model altogether - thin read layer
Reads can go direct to DB - No complexity
Reads are quick
A way of persisting app state, by storing history of events
Something that has happened in the past
Chess application: storing every move that was made as a series of events
Accounting application: storing every payment made, every payment received as events
Can get the state of the application, by replaying events
The old way
Create Shopping Cart
Add book "Introducing .NET 4.5"
Add book "SL 4 for Dummies"
The new way
Shopping Cart has been created
Book "Introducing .NET 4.5" has been added
Book "SL 4 for Dummies" has been added
Cart has been checked out
BASE, not ACID
Relational database do not scale well
Enter....No SQL Databases
Isolate units of work and execute them asynchronously
"Commands" are queued up
Multiple instances can work on a queue (scaling)
Idempotent processing of repeated messages
Poison message handling
Xx Joe ...
1 Xx Blah1 ...
2 Xx Blah2 ...
How it all hangs together
Shopping cart Data
Book 1 added Data
Book 2 added Data
Cart checked Data
In the Microsoft Eco-system
Non-relational data that has no fixed schema
Highly scalable, and performant
Eventually consistent - BASE/not ACID
Stores Partitioned key-value pairs in tables
Examples - Google's BigTable, Azure's Table Storage, Amazon's DynamoDB, Redis, etc
Stores Documents in datastores
Documents can be XML, JSON, BSON, etc
Examples - Raven, Mongo, Couch
Works on "Graph theory" concept. Stores data as Nodes, Properties and Edges
Examples - Neo4J, Allegro, Virtuso
Data is always available, even if data is stale.
State may change.
There may be a delay before the final state is reached. But, it will.
Pattern to deal with Big Data. Azure uses Hadoop (HD Insight)
Data is processed in 2 steps:
Map: Data is divided up into sub-problems and distributed to nodes. Nodes will process and deliver answer
Reduce: Takes the answers of sub-problems and combines them to form eventual answer
Can repeat Map/Reduce multiple times to get the answer you need
Concept of breaking down services to be small enough so that they can be independently deployed, and versioned
Rather than have one or two monolithic services, you end up with a large set of "micro" services
Sacrifices performance for flexibility - but that can be remediated
In a workflow environment, a document can move from one step to another
Document enrichment can be used to embellish the document at each step to make it more meaningful and useful for next step
Example - Doing a payrun