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Scaling Systems: Architectures that GrowFundamental Patterns for scaling you can implement incrementally

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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

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Fair Warning

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What is Scale?Scaling is the ability to cope and perform under an increasing workload.

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What is Scale?Scaling to a load = available sustaining that load

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What is Scale? Being available is really about a request beingcompleted in a period of time.

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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

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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

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What’s your Target? 25,000 Visitors/Day = 125,000 Pages/Day11 High Traffic Hours/Day = 12,000 Pages/Hour 12,000 Pages/Hour = 3.3 Pages/Second

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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

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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

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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)

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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)

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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

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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

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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

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Consistency Challenges• Singleton Data Structures (Order numbers..)• State held between the endpoints of a process• Consistent results of queries across partitioned datasets

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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 StorageBrowser) Server) (Database)

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Caching100% 50% 10% 1% Client Server (Web (Web StorageBrowser) Server) (Database)

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Distribution Session State and Identity need to be factored out Partition (Sticky Session) First, then stateless nodes Client Server (Web (Web ClientBrowser) Server) (Web Client StorageBrowser) (Web (Database) Client ServerBrowser) (Web (WebBrowser) Server)

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Partitioned Storage Zones Server Client (Web Server (Web Server) (Web Storage Client (Database)Browser) Server) (Web ClientBrowser) (Web Client ServerBrowser) (Web (WebBrowser) Server Server) Storage (Web Server) (Database)

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Partitioned Storage Intra-Zone Client Server Orders (Web Customer B (Web Server ClientBrowser) Server) (Web (Web Server Client Server)Browser) (Web (Web Server Client Server)Browser) (Web Products (Web Server)Browser) Inventory

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Asynchronous ProcessingServer Orders (Web OrderServerServer) Queue (WebServerServer) (WebServerServer) (Web ProductsServer) Order Processing Server Inventory

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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

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Fresh Problems: Partial Failures Client Server (Web (Web ClientBrowser) Server) (Web Client StorageBrowser) (Web (Database) Client ServerBrowser) (Web (WebBrowser) Server)

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Fresh Problems: Partial Failures1. Break system into individual failure zones2. Monitor each instance of each zone for problems3. Route around bad instances

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Withoutmonitoring, redundancy is worthless

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Fresh Problems: Upgrades Server Client (Web Server (Web Server) (Web Storage Client (Database)Browser) Server) (Web ClientBrowser) (Web Client ServerBrowser) (Web (WebBrowser) Server Server) Storage (Web Server) (Database)

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Fresh Problems: Upgrades1. Break system into individual upgrade zones2. Upgrade each zone – Drain & Stop, Upgrade, Verify.3. Cut traffic over to updated zones

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Design for Software Update From the Start • Don’t forget Data Schemas

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Bringing Home the Bacon Testing Testing Testing

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Critical Lessons Learned• ACD/C• Clear Consistency Strategy• Build in monitoring and management

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Additional Information: Websites – www.GibraltarSoftware.com – www.eSymmetrix.com Follow Up – Kendall.Miller@eSymmetrix.com – Twitter: kendallmiller