Supporting everything from mobile apps with thousands of concurrent users to global deployments processing millions of requests daily, Anypoint Platform has been put to test. In this session, MuleSoft experts will talk through case studies from our most demanding deployments and provide a best practice approach to designing and tuning applications for optimal performance.

1. Guide to Application Performance

2. Aaron Weikle
CEO@MS3
Mark Huang
System Solutions Architect, Mulesoft

3. Performance enables outcomes
3
Better user experiences Scalable applications Resource efficiency

4. Performance a First Class 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. Case Study

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:
Mountain State 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
• Efficient Thoughtful 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

22. Tools

23. Testing Tools
MUnit

24. Monitoring Tools

25. Conclusion
• Performance engineering is 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 with the 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