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Guide to Application Performance: Planning to Continued Optimization
The document provides a comprehensive guide on application performance, emphasizing the importance of understanding the entire solution architecture and effectively tuning performance to enhance user experience. It includes a detailed case study of a global digital transformation initiative using Anypoint Platform, highlighting key takeaways on identifying bottlenecks and the significance of collaboration in troubleshooting. The conclusion stresses that effective performance engineering and attention to detail are critical for successful API-led connectivity.
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Guide to Application Performance: Planning to Continued Optimization
- 1. Guide to ApplicationPerformance
- 2. Aaron Weikle CEO@MS3 Mark Huang SystemSolutions Architect, Mulesoft
- 3. Performance enables outcomes 3 Betteruser experiences Scalable applications Resource efficiency
- 4. Performance a FirstClass Citizen 4 Part of the design process • Understand the entire solution architecture • You’re only as strong as your weakest point • Define what performance means - Throughput, Latency, Resource Utilization
- 5.
- 6. Case Study -Overview • Global Digital Transformation Initiative • Anypoint Platform one of many moving parts • Approximately 3 Million Transactions per hour • Average Target response time of < 400ms • Variable payload size from few hundred bytes to 1.4 MB 6 Large Global Enterprise - NA Deployment
- 7. Case Study -Architecture 7 Backend Data-sources Traffic Generator CloudHub Customer Backend proxy app VPN GW VPN GW Complex solution involving many moving part
- 8. Case Study -Approach 8 Backend Data-sources Traffic Generator CloudHub Customer Backend proxy app VPN GW VPN GW Analysed and understood traffic pattern: - Mixed load matters - Large Payload is a trigger
- 9. Case Study -Approach 9 Backend Data-sources Traffic Generator CloudHub Customer Backend proxy app VPN GW VPN GW Analysed time spent at each hop
- 10. Case Study -Approach 10 Backend Data-sources Traffic Generator CloudHub Customer Backend proxy app VPN GW VPN GW Setup traffic simulation at each hop - Isolated the issue to proxy app - Saw: CPU not scaling - Conclusion: Likely blocking
- 11. Case Study -Approach 11 Backend Data-sources Traffic Generator CloudHub Customer Backend proxy app VPN GW VPN GW Blocking Issue Resolved TLSv1.2 Improved performance but still short - Saw: CPU scaling but Maxed out - Found: TLSv1.2 in Java 7 slow
- 12. Case Study -Approach 12 Backend Data-sources Traffic Generator CloudHub Customer Backend proxy app VPN GW VPN GW Blocking Issue Patched TLSv1.1 Passing Future Production Targets - Exposed backend bottlenecks resolved by customer - Successful Live Deployment
- 13. Key Take Aways •Performance Tuning Best Practices: - Understand all pieces of the chain - Systematically isolate potential bottleneck and address them - Iterate over the approach • Partnership - Optimized application + fine grained logging led to key insights - Collaborative troubleshooting → systematically isolated the issue Lessons Learned
- 14. Performance Engineering Presented by: MountainState Software Solutions Aaron Weikle, CEO
- 16. On-premises vs. CloudHub •Server clustering – Several servers working on a collection of jobs – Hazelcast data synchronization • Multi-worker – Several workers working on the same job – CloudHub Fabric
- 18. Traffic Management • Volumes –Batch: How much and when? – Real-time: How much and SLA needs – Computationally expensive transactions • Identification and Simulation – Record and playback – Synthetic transaction processing • Planning and Scheduling
- 20. Application Design • EfficientThoughtful Designs • Caching – Application Based vs. Distributed Caching – 3rd Party Caching Server • Distributed Processing – VMs – Partitioning for load balancing – Replication for High Availability
- 21. Virtual Machine Tuning •Garbage Collection – Use Pools of Reusable Objects instead of dynamic creation of objects – Make use of local variables not declared in classes – Avoid using object wrappers under primitive types • Java Heap / PermGen – Properly size heap size based on size of live objects – Set Min and Max Heap Size to the same value
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- 25. Conclusion • Performance engineeringis key to successful API-led connectivity • Attention to detail will pay dividends • Think you need more help? Contact: Aaron Weikle akw@ms3-inc.com
- 26. Questions? Get started withthe Anypoint Platform performance whitepaper ✓ Key architectural features of Mule runtime engine ✓ Performance benchmark data ✓ Customer use cases https://www.mulesoft.com/lp/whitepaper/api/anypoint-platform- performance