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Scaling habits of ASP.NET
The document discusses the scaling habits of ASP.NET applications over multiple versions from initial launch to large-scale business success. As an application grows from version 1 with a few users to version N with thousands of users, the key scaling challenges change from fixing logical problems to addressing performance bottlenecks and high availability requirements. The solutions also evolve from simple code optimizations to sophisticated architectures with load balancing, caching, and separate servers for web and database tiers.
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Scaling habits of ASP.NET
- 1. The Scaling Habitsof ASP.NET Applications Richard Campbell
- 2. Richard Campbell • Background – After thirty years, done every job in the computer industry you’ve ever heard of • Currently – Co-Founder of Strangeloop Networks – Co-Host of .NET Rocks! – Host of RunAs Radio
- 3. 50 000 footview Business Success Business Traction Make it Work Right Page Views Make it Work Version 1 Version 2 Version 3 Version N Time
- 4. What are wemeasuring? • Capacity – Total number of known users – Number of active users (aka active sessions) – Number of concurrent users (aka concurrent requests) • Throughput – Page Views per Month – Requests per Second – Transactions per Second • Performance – Load time in milliseconds – Time to first byte (TTFB), Time to last byte (TTLB)
- 5. The Anatomy ofa Web Request
- 6. Performance Equation Legend: R: Response time RTT: Round Trip Time App Turns: Http Requests Concurrent Requests: # server sockets open by browser Cs: Server Side Compute time Cc: Client Compute time Source: Field Guide to Application Delivery Systems, by Peter Sevcik and Rebecca Wetzel, NetForecast
- 7. Where do thenumbers come from? Server Code Timing: 0.8 secs 4.5 sec Client Code Timing: 1.2 secs http://www.speedtest.net/ Ping statistics for 209.162.190.188: Packets: Sent = 4, Received = 4, Lost = 0 (0% loss), Approximate round trip times in milli-seconds: Minimum = 80ms, Maximum = 92ms, Average = 85ms http://www.websiteoptimization.com/services/analyze/
- 8. Performance Spreadsheet Factor 1 Factor 2 Factor 3 Time P1 = Payload/Bandwidth 208 KB Payload 717 KB/Sec Bandwidth 0.29 seconds P2 = AppTurns * Roundtrip Time 51 Appturns 85 ms Roundtrip 2 Concurrent Requests 2.168 seconds P3 = Compute Time at Server 0.8 Seconds 0.8 seconds P4 = Compute Time at Client 1.2 Seconds 1.2 seconds 4.458 seconds
- 9. Version 1: Makeit work • Get Version 1 out the door • Define the initial hardware platform • Meet the launch date The only one who likes your app is you
- 10. Scaling Habits • 10 to 50 requests/second • 5 to 15 users • 15 active sessions at peak • Problems with performance on areas of the site – Multi-User Issues – Complex input screens – Reports
- 11. Solutions for Version1 • Fix logical scaling problems – Multi-user data access • Get user feedback – Humiliating but useful – Fix the actual user pains – Watch your app in use
- 12. Version 2: Makeit work right • Focus on features – What is missing • Bug Fixing • Rethink the App or UI – Some new directions • Larger and more diverse users base Now your boss likes your app too
- 13. Scaling Habits • 50 to 100 requests/second • 15 to 50 users (5-10 are remote!) • 30 active sessions at peak • Problems – Fights with IT over remote access – Reach the single server limit • What does this look like?
- 15. What does itreally look like? • Memory consumption above 80% • Processor consumption at 100% all the time • Request queues start to grow out of hand • Page timeouts (server not available) • Sessions get lost • People can’t finish their work!
- 16. Solutions for Version2 • More Hardware – Dedicated web server – Separate database server (probably shared) • Find the low hanging fruit – Fix querying – Get your page size under control
- 17. Version 3: BusinessTraction • Weighing business priorities – Formal IT transition point – There is budget • Scaling versus Reliability – Which one is more important • 99% verses 100% up time – Cost of Reliability People you don’t know like your app
- 18. Scaling Habits • 300 to 1000 requests/second • 100 to 500 users • 300 active sessions at peak • Problems – Performance is now front and center – Consequences of downtime are now significant
- 19. Network vs. DevelopmentIQ • Network IQ Test • Development IQ Test – Explain each of the – Explain the network Web.config file diagram of your – Explain the load- application balancing scheme – Explain how to access the required by the app production log files – Explain the bottlenecks – Explain the redundancy of the production system model of the production system
- 20. Solutions for Version3 • Move to multiple web servers: You need a load balancer • More bandwidth: Move to a hosting facility • Get methodical, use profiling – Red Gate Ants, SQL Profiler, Web Site Optimizer • Get the facts on the problem areas – Work methodically and for the business on addressing slowest lines of code – Focus on understanding what the right architecture is rather than ad-hoc architecting • Let the caching begin!
- 21. Version N: BusinessSuccess • IT costs now out weigh the software development • Getting new features to production takes months – Or Cowboy it! (which always happens) • IT and Dev process is a focus – Tech Politics It’s no longer your app
- 22. Scaling Habits • 500+ requests/second • 5000+ users • 3000 active sessions at peak • Problems – Running out of memory with inproc sessions – Worker process recycling – Cache Coherency – Session Management
- 23. A Word AboutLoad-balancing Sticky vs. Round Load Balancer Robin vs. WMI Virtual IP Web Server 1 Web Server 2 Web Server 3 Web Server 4 Persistent Data Session?
- 24. Performance and Scale •Now the problem is that scale and performance are intertwined – A new class of ‘timing’ problem shows up under load (and are almost impossible to reproduce outside of production) – Caches are flushed more than expected • And performance plummets
- 25. Solutions for VersionN • Your architecture is now hardware and software – Use third party accelerators – Create a performance team and focus on best practices – Use content routing • Separate and pre-generate all static resources • Cache, cache, and more cache – Output Cache – All static pages are cached – Response.Cache – Look for database gets with few updates
- 26. Summary • Focus onactual user performance problems – What is reality? • Start with low hanging fruit • Use methodical, empirical performance improvement • At large scale, the network is the computer