Initial deck on WebSphere eXtreme Scale with WebSphere Commerce Server

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This is the deck used to show how IBM WebSphere eXtreme Scale improves the usability of WebSphere Commerce Server by replacing private per JVM disk based caches with a shared datagrid based one for page fragment caching.

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Initial deck on WebSphere eXtreme Scale with WebSphere Commerce Server

  1. 1. 2121: WebSphere eXtreme Scale and Distributed Caching in Commerce Solutions
  2. 2. 2 Smarter Planet Solutions Require a Dynamic Application Infrastructure • Scale quickly and efficiently • Optimize workload performance • Flexibly flow resources • Avoid downtime • Save energy • Automate management tasks Smart regions Smart weather Smart countries Smart supply chains Smart cities Smart industries
  3. 3. 3 Business Needs Adoption Patterns “Meet business objectives consistently, nimbly, cost-effectively” Application Foundation “Enable applications to adapt to changing market conditions” Intelligent Management “Address extreme demands of clients & business models” Extreme Transaction Processing Dynamic Application Infrastructure Builds on Smart SOA
  4. 4. 4 4 Dynacache Disk Offload • This allows a JVM to have a private disk based cache. • It’s a feature heavily exploited by WebSphere Commerce Server and other stack products. • It allows caches much larger than is possible with a memory only conventional cache. • This is a 3 tier cache. The JVM has a small local cache, then there is the file system cache and finally the disk itself. 4
  5. 5. 5 5 Dynacache disk offload Server diagram 5 Disk File Cache File system cache App Cache File system cache App Cache File system cache App Disk File Disk File
  6. 6. 6 6 WebSphere eXtreme Scale • Organizes the memory from a number of JVMs as a single logical shared cache. • Clients can attach to the ‘cache’ using the network and can also have an in process cache to reduce trips to the remote cache when possible. • No dependency on a large file system cache. • No disk dependency, no SAN required. • Cache is as large as the memory in the ‘grid’. • Each record is stored once in the grid and shared by all clients. 6
  7. 7. 7 7 WebSphere eXtreme Scale Server 7 WXS Near Cache App WXS Near Cache App WXS Near Cache App WXS Container WXS Container WXS Container WXS Container Network
  8. 8. 8 8 Test description • WebSphere Application Server 6.1.0.26 • WebSphere eXtreme Scale V7.0 • Hardware: Two Socket Unix box, 16GB RAM and normal disk. • Gigabit ethernet • Servlet generates a 72kbyte page. • Dynacache being used to cache servlet page. • 20Gb of data, 10% of which is ‘hot’. 8
  9. 9. 9 9 Topology of test 9 Rational Load Driver Rational Load Driver ND WXS ND ND ND WXS WXSWXS All boxes are 2 socket with 16GB RAM Network is Gigabit
  10. 10. 10 10 Results using Dynacache disk offload • File system cache too small: – 273 pages/sec @ 730ms and 16% CPU – 400 Disk IOPS • File system cache large enough to stop all disk I/O – 1620 page/sec @ 121ms and 42% CPU – Network bottlenecks on HTTP side 10
  11. 11. 11 11 Results: Remote WXS grid, no local cache AT ALL • WXS – 1700 pages/sec @ 116ms 73% CPU – Network bottlenecked on the HTTP side – No file system cache needed per JVM – Data is compressed (2.5:1) – Cost of fetching data from grid is therefore: • 73%-42% = 31% of CPU – Using a WXS Near cache will eliminate this ‘cost’. 11
  12. 12. 12 12 WXS CPU usage • The box running the WXS grid used 15% CPU at this load of 1700 page views/sec. • This was with no near cache. A near cache will lower this CPU significantly. • BUT, 1700 pages view/sec is a lot of page views. One similar box can serve up 11k cached page views/sec but would require 10Gb ethernet. 12
  13. 13. 13 13 Scaling Disk offload versus WXS • WXS runs on commodity boxes and manages them so that it’s fully fault tolerant in software, it doesn’t need expensive reliable hardware to run on reliably. • WXS can be scaled incrementally simply by adding another box while it’s running. Perfect linear scaling. • Disk offload almost always uses a SAN. • SAN has a per gigabyte charge. You can’t incrementally scale a SAN, you replace it. 13
  14. 14. 14 14 Cache warmup is faster and cheaper with WXS • The cache is shared between all WAS servers. • Each cached entry is only generated ONCE, not ONCE PER JVM as with disk offload. • It’s about 2x faster to load the WXS cache versus a disk offload based cache. 14
  15. 15. 15 15 Invalidation/update once versus Invalidate/update all • When the cache is invalidated with disk off load, the entry must be regenerated on EVERY JVM in the cluster. • WXS invalidates the cache entry ONCE per cluster. • Only one WAS JVM needs to update the invalidated entry for EVERY JVM as the cache is shared! • This allows more frequent invalidates whilst cutting CPU and disk I/O by 1/N over before. 15
  16. 16. 16 16 Benefits summary • Invalidation can occur more frequently as they cost less to do using WXS than disk offload. • No need for SAN costs for disk offload. Use the existing boxes for memory/CPU/network. • Faster/more efficient warm up and JVM instance starting because of shared rather than private cache. • Modern Web 2.0 like architecture. 16
  17. 17. 17 Learn More About Dynamic Application Infrastructure! Application Foundation ibm.com/appfoundation Intelligent Management ibm.com/intellmgmt Extreme Transaction Processing ibm.com/xtp ibm.com/appinfrastructure
  18. 18. 18 Thank you for Attending. We Value Your Feedback ! • Please complete the session survey for this session by: • Accessing the SmartSite on your smart phone or computer at: http://imp2010.confnav.com – Surveys / My Session Evaluations • Visiting any onsite event kiosk – Surveys / My Session Evaluations • Each completed survey increases your chance to win an Apple iPod Touch with daily drawing sponsored by Alliance Tech
  19. 19. 19 Questions?
  20. 20. 20 Copyright and Trademarks © IBM Corporation 2009. All rights reserved. IBM, the IBM logo, ibm.com and the globe design are trademarks of International Business Machines Corporation, registered in many jurisdictions worldwide. A current list of IBM trademarks is available on the Web at "Copyright and trademark information" at www.ibm.com/legal/copytrade.shtml. Other company, product, or service names may be trademarks or service marks of others.

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