Building Scalable 'NET Applications

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Building Scalable .NET Applications
Guy Nirpaz, EVP RD, GigaSpaces Technologies
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Who am I?

• 3 Years with GigaSpaces
• VP of RD
• Speaker at dev conferences
• Agile, Technology, Architecture
• Veteran of several Startups
• Spent most of my life in design and architecture
  of complex systems
• Financial Services, Command and Control, Teleco
• Mercury, IBM, others
• Contact
• guyn_at_gigspaces.com
• _at_gnirpaz Twitter
• jroller.com/gnirpaz - Blog

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About GigaSpaces

• A Scale-Out Platform, optimized for distributed
  and virtualized environments
• Any deployment environments clouds, grids,
  commodity servers, multi-core
• Any languages Spring/Java, .Net, C, Dynamic
• Driven by the need for
• confidence to handle unpredictable demand and
  peak loads
• guaranteed performance under any processing
  loads
• cost-effective, on-demand scalability for clouds
  and grids
• rapidly develop, change and scale applications

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Global 1000 Companies Rely on GigaSpaces
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• Explore technical and business challenges of
  building scalable applications. Analyze
  fundamental architecture limitations and propose
  cost-effective alternative

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The typical scenario
Michael Jackson Tickets are on Sale!
Your Application
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Solution Linear and Dynamic Scalability
Linear Dynamic Scalability
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Traditional Tier Based Architecture is not
scalable
Business tier

• Relies on centralized resources
• Hard to install
• Bound to static resources (IPs, disk drives,
  etc.)
• Separate clustering model for each tier
• Hard to maintain
• Non-scalable

Web Tier
Load Balancer
Back-up
Back-up
Back-up
Messaging
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Peak loads are unpredictable

• Constant transaction, data and user growth
• Volatile and unpredictable loads

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Scalability Disasters Are More Common Than Ever

• Lost customers
• Lost revenues
• Brand damage

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Micro-Blogging (ala Twitter) Example
IIS
Data Base
Users
Load Balancer
IIS
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Reader/Publisher Service
namespace MicroBlog.Services public
interface IReaderService ICollectionltPostgt
GetUserPosts(String userID) ICollectionltPostgt
GetUserPosts(String userID, DateTime fromDate)

namespace MicroBlog.Services public
interface IPublisherService void
PublishPost(Post post)
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What happens on success
Users
The database becomes the bottleneck
IIS
Data Base
IIS
Load Balancer
IIS
IIS
IIS
IIS
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Reader Database Implementation
public ICollectionltPostgt GetUserPosts(string
userID, DateTime fromDate) // Create
command IDbCommand dbCommand
_dbConnection.CreateCommand() dbCommand.Com
mandText String.Format( "SELECT FROM Post
WHERE UserID'0' AND PostedOn gt
1", userID, fromDate) // Execute
command IDataReader dataReader
dbCommand.ExecuteReader() // Translate results
from db records to .NET objects ListltPostgt
result ReadPostsFromDataReader(dataReader) //
Return results return result
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Step I Remove DB Bottlenecks with Caching

• Reduce I/O bottlenecks less DB access
• In-Memory caching
• Reduced Network hops (if in-process)
• Suitable for read-mostly apps

Users
Load Balancer
Data Base
Cache Service
IIS
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Reader Space Implementation
public ICollectionltPostgt GetUserPosts(String
userID) // Create a template to get all posts
by a user id Post template new
Post() template.UserID userID // Use space
proxy to read entries matching template Post
result _spaceProxy.ReadMultiple(template) //
Return result return result
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Step II Linear Scalability Partitioning and
Collocation
IIS
Users
Load Balancer
Data Base
IIS
IIS
IIS
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Post Life Cycle
Writer
Reader
Poster
Load Balancer
Post.NEW
IIS
ReaderClient
Post.VALID
Space
Writer Client
Reader
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Writer Client
public class SpacePublisherService
IPublisherService private readonly
ISpaceProxy _spaceProxy public void
PublishPost(Post post) this._spaceProxy.Writ
e(post)
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Event Driven Programming Writer Example
PollingEventDriven, TransactionalEvent public
class PendingPostsProcessor
DataEventHandler public Post
ProcessPendingPost(Post post)
PostStatus newStatus PostStatus.Published
foreach (String illegalWord in _illegalWords)
if (post.Subject.Contains(illegalWord)
post.Body.Contains(illegalWord))
newStatus PostStatus.Rejected break
// Set new status
post.Status newStatus return post
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Read Life Cycle
Writer
Reader
Poster
Load Balancer
IIS
ReaderClient
Space
Writer Client
Post
Post
Post
Reader
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Step II Linear Scalability Partitioning and
Collocation
IIS
Users
Load Balancer
Data Base
IIS
IIS

• Collocation write/read within the same process
• Partitioning and Content-Based Routing
• Async Writes to the Database

IIS
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Step III Dynamic Scalability
Monitor
Provision
Users
Load Balancer
Data Base
IIS
IIS

• SLA Driven Policies
• Dynamic Scalability
• Self healing

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Space Based Architecture Values

• Linear Scalability
• Predictable cost model pay per value
• Predictable growth model
• Dynamic
• On demand grow only when needed
• Scale back when resources are not needed anymore
• SLA Driven
• Automatic
• Self healing
• Application aware
• Simple
• Non intrusive programming model
• Single clustering Model

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Questions?
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GigaSpaces Home Pagehttp//www.gigaspaces.com/
GigaSpaces XAP Product Overview http//www.gigasp
aces.com/wiki/display/XAP7/Concepts GigaSpaces
XAP for the Cloud http//www.gigaspaces.com/clou
d