Faster apps. faster time to market. faster mean time to repair

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Developers, Test Engineers, QA Engineers, Network Engineers, Operations Managers, Production Managers and Solution Architects joined us in Singapore to learn more about APM Lifecycle

Transcript of Faster apps. faster time to market. faster mean time to repair

  • 1.Faster Apps, Faster Time to Market,Faster Mean Time to RepairBrad GoddardDirector of APM Pre-Sales Engineering - Asia and IndiaCompuwareArdeshir ArfaianSolution Director dynaTrace APACCompuware

2. Compuware Application Performance Management We help organizations optimize the performance of their business-critical applications Web, non-Web, mobile, streaming, cloud-based applications Across all customers, users, browsers, devices, infrastructure, and geographies Rapid issue notification with actionable diagnostics Insight into how these issues affect your business (revenue, brand, cost) SaaS, 4,000+ CustomersGlobal Reach Recognized asCloud-Based and Worldwide Over 80 offices in Industry LeaderOn-Premises 2,500+ enterprise29 countries Gartner: Offerings customersworldwideLeader in APM magic Rapid startup and 1,500+ SMB Strategic servicequadrantpaybackcustomersdelivery Forrester Research: 12 of top 20a complete view of US sitesend user experience* Ovum: Game-changing*Trends: The Diversification Of End User Experiencing Monitoring, Forrester Research, Inc., July 5, 2011 3. Your world is changingApplication visibility and optimization of the customerexperience are more important than ever.Customers: Global New Devices: ProliferatingApplications: Distributed and loosely coupled Virtualization/Cloud: Exploding 4. Impact of to the business 5. The Problem Lifecycle 6. Why Agile Development took off 7. Story Points Its Sprint Time! DevelopmentTesting Estimate Sprint Timeline Remaining Production Team Velocity 8. Story PointsYou are in control! DevelopmeTesting Estimatent Sprint Timeline Remaining Production Team Velocity 9. Story PointsWhat happened? DevelopmeTesting Estimatent Sprint Timeline Remaining Production Team Velocity 10. Story PointsMissed Goals and EstimatesMissed Developme Testing EstimatentProduction Missed Remaining Goal Team Velocity 11. 4 of 5 projects run over time and/or budget. Oxford University Regarding IT Project Success (Saur & Cuthbertson, 2003) 11 12. Problem #1: Different MindsetSource: http://dev2ops.org/blog/2010/2/22/what-is-devops.html 13. Problem #2: Dislocated TeamsSource: http://dev2ops.org/blog/2010/2/22/what-is-devops.html 14. Problem #3: Different ToolsSource: http://dev2ops.org/blog/2010/2/22/what-is-devops.html 15. Problem #4: Over the Fence AttitudeSource: http://dev2ops.org/blog/2010/2/22/what-is-devops.html 16. These Problems lead to Source: http://dev2ops.org/blog/2010/2/22/what-is-devops.html 17. A potential SolutionSource: http://dev2ops.org/blog/2010/2/22/what-is-devops.html 18. Real World Perf Test in Feedback CIONECloud based ToolsetArchitecture ValidationTestingTest inProductionTraditionalLoadTesting 19. Minimize and automate real Load TestsDeveloping Test Run Reproduction Refine Capturing Re Run Tests Reproduction Refine CapturingMultiple Test Iterations needed to analyze Root-cause Re Run Tests Reproduction Problem AnalysisProblem SolvingtimeDeveloping Test Run Reproduction Refine Capturing Re Run Tests Reproduction Refine Capturing Eliminates Test Iterations Go directly to problem analysis Frees up resources for other proje Re Run Tests Reproduction Problem AnalysisProblem Solvingtime 20. Why Web Performance Matters: Impact of PoorPerformancefound that a2second slowdown4.3 % reduction inrevenue/user* determined that a 400 millisecond delay0.59%fewer searches/user*Source: Steve Souders @ Velocity Conference 2009http://radar.oreilly.com/2009/07/velocity-making-your-site-fast.html 21. 21 22. .10000 Smart Phones Sold 22 23. .80000 electronicaccessories sold 23 24. eBay Marketplace = Economy of Scale 22B 10B 10B page views/day URL Requests / day40 40M 9 9 Petabytesof data storage $62$62B lines of code2010 gross merchandise volume100 5 300100M 300Mactive users live listings10000 75 10,0005K75B search engine nodes application servers database calls/day24Commercial data warehouse 100x larger than the research library ofUS Congress 25. Pertinent Problems to be solved @ eBay Search Trust, Fraud and Risk Shipping and Logistics Ease of Payments User Experiences & Site Speed Data , Analytics and Business Intelligence Performance and many more25 26. Benchmark CriteriaS No eBay RequirementsStatus1Deeper insight into the application very quickly, identifying the areas of code where the majority of each transactions time is spent.2Integrate with Silk Performer / JMeter 3Java Diagnosis at method/class level.4API Breakdown chart5Memory Analysis graph6Dashboard showing a comparison between 2 different test runs 7Trace export for QA, Dev 8Business and Technical dashboards9Execution time / Time spent in individual methods of the Application code base 10Time Spent on Service calls. (Entry/Exit times only) 11Performance of SQL Queries. 26 12Reports that would help identify the slow parts of the Application 13To be able to configure and monitor performance of specific business flows. 27. Link to Compuware APM27 28. Selected transactions opensin Compuware APM28 29. How much time isspent on which tier?29Are all my tiers healthy? 30. Detailed view of transaction and flow Each individualtransaction listed Selected transaction spent 42.77 millisecondsLayers Transaction spent time in 30 31. API level Drill down toidentify the method andthe call path havingmaximum performanceimpact 31 32. Global Solution ProviderFinancial Services 33. Transaction Breakdown5secWith increasing load number of Outliers >5sec is increasing 34. Only 85.44% of transactions under 1 secondGoal is to have 90% of transactionsunder 1 second. 35. High Connection Checkin/Checkouttime High RMI execution time 36. JDBC Connection Check-in/Check-out (1) High Avg wait time for a connection (10 seconds) 37. Low CPU / Low Memory consumption / High GC Memory Utilization never climbsabove 25 % on certain JVMs. Even though GC is high. 38. High GC JVM is spending 5.75 minutes per minute on GC 39. GC versus Exec Time ratiocommon.dbservicesJVM is spending 96% of its time on GCFurther analysis showed that mostof GC time are major GCs 40. Root-Cause JVM is running in Client mode 41. GC versus Exec Time ratiocommon.dbservicesAfter switching JVM to servermode, GC time is drastically reduced.Further analysis showed only minorGCs 42. Before (client-mode JVM) / After (server-mode JVM) SLA levels restored With increasing load number ofOutliers >5sec is increasing Moving production load to other datacenter & applying serveroption in meantime 43. Innovationand Getting Acquired 44. Faster Apps, Faster Time to Market,Faster Mean Time to RepairBrad GoddardDirector of APM Pre-Sales Engineering - Asia and IndiaCompuwareArdeshir ArfaianSolution Director dynaTrace APACCompuware