Building Scalable .NET Web Applications

Building Scalable .NET Web Applications
Buu Nguyen, MVP (ASP.NET)
www.buunguyen.net/blog
buunguyen@kms-technology.com
Microsoft Confidential
1

Agenda
Scalability
Scaling Web Client Tier
Scaling Web & Application Server Tiers
Scaling Database Tier

Scalability

Scalability
A system is scalable when it can accommodate more loads and larger data set by increasing hardware power
Scalability implies performance but not the other way around

Vertical Scaling vs. Horizontal Scaling
http://www.ourofficebox.co.uk/Images/scalability2%20750.png
http://www.gigaspaces.com/files/pics/online_gaming_scalability_diagram_website.jpg

Don't Underestimate Vertical Scaling
StackOverflow.com
1M page views per day
500K questions and millions of posts
817th largest site
Hardware
2 web servers (1 Xeon 4-core processor, 8GB RAM)
1 database server (2 Xeon 4-core processors, 48GB RAM)
Software
IIS 7.5 on Windows Server 2008 R2
HAProxy (inside Linux VM)
SQL Server 2008 Enterprise
ASP.NET MVC and .NET 3.5
Source: http://www.microsoft.com/casestudies/Case_Study_Detail.aspx?casestudyid=4000006676
6

Scaling Web Client Tier

Client Resource Management
Minify and Gzip JavaScript, CSS
MS Ajax Minifier, YUI Compressor, Google Closure Compiler
Combine JavaScript and CSS
MS Script Loader
CSS on top
JavaScript at bottom
Add Expires headers for all resources

Combres
Currently 2.0, Apache license
http://combres.codeplex.com/
Key Features
Combine, compress, minify, cache (server & client) JavaScript and CSS resources
Automatic change detection
Extensible architecture
User Guide
http://www.codeproject.com/KB/aspnet/combres2.aspx
9

JavaScript & Ajax Optimization
Reverse-Ajax (Comet) instead of polling
Use dedicated Comet server
Examples: StreamHub, Meteor
http://cometdaily.com/maturity.html
Yield to timer by chunking processing
Avoid outer-scope lookup
Split initial payload (i.e. before onload)
Employ non-blocking JS loading techniques
MS Ajax Library Script Loader, YUI Script Loader

Scaling Web and Application Server Tiers

Load Balancing
The act of properly distribute workload across machines/resources in a cluster
Approaches
DNS’ “A” records
Poor man’s hand-coded redirection
Software, e.g. HAProxy, NLB, LVS
Hardware, e.g. F5
Considerations
Session state
View state

Domain Sharding
Partition resources across different hosts
By type, e.g. static/dynamic, JS/CSS etc.
By functionality, e.g. forum module
Benefits
Balance loads
Parallel downloads
Avoid redundant cookies
Isolated optimization

Content Delivery Network (CDN)
Services
Microsoft, Google, Akamai etc.
Static resources only
Benefits
As domain sharding
Redundancy and availability
Smart DNS routing
High chance of browser cache hit

Distributed Cache
Enable persistent cache for a server cluster
Fast (in-memory)
Distributed – ignorant by clients
Solutions
AppFabric Cache (Velocity)
Support POCO, XML, binary
ASP.NET integration
Extensible cache provider
Others: memcached

Concurrency
It’s a waste of cores if you don’t have enough threads
Solutions
Parallel Task Library (PTL)
PLINQ
F#

Others
Compensation over distributed transactions
Asynchronous over synchronous services
Related: asynchronous controller in MVC 2.0

Side Notes on Entity Framework
Singleton ObjectContext doesn’t fit web apps
Not thread-safe
Consume RAM
Might fit rich client
One OC per request is the common strategy for web apps
Use compiled query to reuse generated EF command tree

Scaling Database Tier

Indexes
Clustered vs. non-clustered indexes
Guidelines
Indexing columns in WHERE and SELECT
Indexing columns with highly unique values
Cluster-indexing primary keys
Avoid over-indexing

Replication
Publisher-subscribers configuration
i.e. master-slave in MySQL
Write to subscribers won’t propagate back
Common options
Transactional (near real-time)
Snapshot (time interval)
Suitable for read-intensive applications

…Replication
Peer-2-peer configuration
i.e. master-master in MySQL
Issues
Identity conflicts
No “true” copy at any point of time
High network latency

Partitioning
Vertical Partitioning (Clustering)
Horizontal Partitioning (Sharding)
Distribute tables into multiple DBs, each representing a cluster of related tables, e.g.
Customer DB, product DB, forum DB etc.
Application layer aggregates data
Distribute table rows into logical groups, e.g.
US customers, European customers
Application layer picks shards & aggregates data

NoSQL
Partitioning makes relational databases not so relational any more
Complexity in DB design & application layer
NoSQL, “not-only-relational”, is about DBs built with scalability in mind
Sacrifice integrity & ACID to a certain extent
Apache Cassandra
Auto load balancing
Identical nodes
Elastic capacity
Flexible schema
Others: MongoDB, Voldermort, Tokyo Cabinet

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Building Scalable .NET Web Applications

by Buu Nguyen on Apr 18, 2010

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Building Scalable .NET Web Applications — Presentation Transcript