Advertisement
Advertisement

More Related Content

Advertisement
Advertisement

Scaling Systems: Architectures that Grow

  1. Scaling Systems: Architectures that Grow Fundamental Patterns for scaling you can implement incrementally
  2. Who Am I? • Kendall Miller • One of the Founders of Gibraltar Software – Small Independent Software Vendor Founded in 2008 – Developers of VistaDB and Gibraltar – Engineers, not Sales People • Enterprise Systems Architect & Developer since 1995 • BSE in Computer Engineering, University of Illinois Urbana-Champaign (UIUC) • Twitter: @KendallMiller
  3. Fair Warning
  4. What is Scale? Scaling is the ability to cope and perform under an increasing workload.
  5. What is Scale? Scaling to a load = available sustaining that load
  6. What is Scale? Being available is really about a request being completed in a period of time.
  7. What’s your Target? 0.00E+00 1.00E+07 2.00E+07 3.00E+07 4.00E+07 5.00E+07 6.00E+07 7.00E+07 Microsoft.com Twitter.com Amazon.com Target.com Slashdot.org DevExpress.com Hanselman.com Gibraltar Software Average daily traffic in Visitors / Day
  8. What’s your Target? 1.00E+00 1.00E+01 1.00E+02 1.00E+03 1.00E+04 1.00E+05 1.00E+06 1.00E+07 1.00E+08 Microsoft.com Twitter.com Amazon.com Target.com Slashdot.org DevExpress.com Hanselman.com Gibraltar Software Average daily traffic in Visitors / Day
  9. What’s your Target? 25,000 Visitors/Day = 125,000 Pages/Day 11 High Traffic Hours/Day = 12,000 Pages/Hour 12,000 Pages/Hour = 3.3 Pages/Second
  10. Specific Architectures • Gossip • Load Balancers + Shared • Map Reduce Nothing Units • Tree of Responsibility • Load Balancers + • Stream Processing Stateless Nodes + Scalable Storage • Scalable Storage • Content Addressable • Publish/Subscribe Networks • Distributed Queues • General Peer to Peer
  11. ACD/C • Async – Do the work whenever • Caching – Don’t do any work you don’t have to • Distribution – Get as many people to do the work as you can • Consistency – We all agree on these key things
  12. Async • Decouple operations so you do the minimum amount of work in performance critical paths • Queue work that can be completed later to smooth out load • Speculative Execution • Scheduled Requests (Nightly processes)
  13. Caching • Save results of earlier work nearby where they are handy to use again later • Apply in front of anything that’s time consuming • Easiest to apply from the left to the right • Simple strategies can be really effective (EF Dump all on update)
  14. Why Caching? • Loading the world is impractical • Apps ask a lot of repeating questions. – Stateless applications even more so • Answers don’t change often • Authoritative information is expensive
  15. Distribution • Distribute requests across multiple systems • Classic web “Scale Out” approach • The less state held, the easier to distribute work. – Distributed database = hard – Distributed static content server = easy • Request routing for distribution can serve other availability purposes
  16. Consistency • The degree to which all parties observe the same state of the system at the same time • Scaling inevitably requires compromise – Forces one source of the truth for absolute consistency and requires extensive locking to ensure parties agree – The real world doesn’t require the consistency we tend to demand of our systems
  17. Consistency Challenges • Singleton Data Structures (Order numbers..) • State held between the endpoints of a process • Consistent results of queries across partitioned datasets
  18. Typical Application Session State Transaction Isolation SSL Session Reader/Writer Locks Log Contention Singleton Data Structures Memory Allocation/GC Network Sockets Request Queue Client Server (Web (Web Storage Browser) Server) (Database)
  19. Caching 100% 50% 10% 1% Client Server (Web (Web Storage Browser) Server) (Database)
  20. Distribution Session State and Identity need to be factored out Partition (Sticky Session) First, then stateless nodes Client Server (Web (Web Client Browser) Server) (Web Client Storage Browser) (Web (Database) Client Server Browser) (Web (Web Browser) Server)
  21. Partitioned Storage Zones Server Client (Web Server (Web Server) (Web Storage Client (Database) Browser) Server) (Web Client Browser) (Web Client Server Browser) (Web (Web Browser) Server Server) Storage (Web Server) (Database)
  22. Partitioned Storage Intra-Zone Client Server Orders (Web Customer B (Web Server Client Browser) Server) (Web (Web Server Client Server) Browser) (Web (Web Server Client Server) Browser) (Web Products (Web Server) Browser) Inventory
  23. Asynchronous Processing Server Orders (Web Order Server Server) Queue (Web Server Server) (Web Server Server) (Web Products Server) Order Processing Server Inventory
  24. Fallacies of Distributed Computing • The network is reliable • Latency is zero • Bandwidth is infinite • The network is secure • Topology doesn’t change • There is one administrator • Transport cost is zero • The network is homogeneous
  25. Fresh Problems: Partial Failures Client Server (Web (Web Client Browser) Server) (Web Client Storage Browser) (Web (Database) Client Server Browser) (Web (Web Browser) Server)
  26. Fresh Problems: Partial Failures 1. Break system into individual failure zones 2. Monitor each instance of each zone for problems 3. Route around bad instances
  27. Without monitoring, redundancy is worthless
  28. Fresh Problems: Upgrades Server Client (Web Server (Web Server) (Web Storage Client (Database) Browser) Server) (Web Client Browser) (Web Client Server Browser) (Web (Web Browser) Server Server) Storage (Web Server) (Database)
  29. Fresh Problems: Upgrades 1. Break system into individual upgrade zones 2. Upgrade each zone – Drain & Stop, Upgrade, Verify. 3. Cut traffic over to updated zones
  30. Design for Software Update From the Start • Don’t forget Data Schemas
  31. Bringing Home the Bacon Testing Testing Testing
  32. Critical Lessons Learned • ACD/C • Clear Consistency Strategy • Build in monitoring and management
  33. Additional Information: Websites – www.GibraltarSoftware.com – www.eSymmetrix.com Follow Up – Kendall.Miller@eSymmetrix.com – Twitter: kendallmiller

Editor's Notes

  1. What level of scaling are we talking about?Scaling is the ability to cope and perform under an increasing workload.
  2. This is VISITORS per DAYMicrosoft.com: 60M Twitter.com: 35MAmazon.com: 15MTarget.com: 2MDevExpress.com & Telerik.com: 25KHanselman.com: 12KGibraltar Software: 1K
  3. This is VISITORS per DAYMicrosoft.com: 60M Twitter.com: 35MAmazon.com: 15MTarget.com: 2MDevExpress.com & Telerik.com: 25KHanselman.com: 12KGibraltar Software: 1K
  4. THIS IS NOT ABOUT ASYNC FOR FASTER PERCEIVED PERFORMANCE
  5. Improve response under loadDo only the work you have to Up to 95% of the work on the typical site can be pulled from cache
  6. Add reverse proxy (Load Balancer)Add additional middle tier serversSession state and identity need to be factored outPartition (“Sticky session”) first, then true load balancing with no state in center
  7. Break down traffic by easy to determine characteristic: Customer, product category, etc.Add storage regions that are self-consistentCan vary exact mix of what data is in each container and how you partitionTypically some parts may be shared like IdentityCross-zone aggregation is slowCross-zone coherency strategy
  8. Middle tier routes storage requests based on easy to determine characteristicConsistency strategy complexity (reports may reflect delayed data, different parties may not see the same view of the world)
  9. Separate long running, dangerous, or serialized tasks from general workWorkflow consistency strategy requiredComplications with deployment and versioningDeferred failure scenarios.
  10. Add reverse proxy (Load Balancer)Add additional middle tier serversSession state and identity need to be factored outPartition (“Sticky session”) first, then true load balancing with no state in center
  11. Break down traffic by easy to determine characteristic: Customer, product category, etc.Add storage regions that are self-consistentCan vary exact mix of what data is in each container and how you partitionTypically some parts may be shared like IdentityCross-zone aggregation is slowCross-zone coherency strategy
Advertisement