guide to application performance: planning to continued optimization

Guide to Application Performance

Aaron WeikleCEO@MS3

Mark HuangSystem Solutions Architect, Mulesoft

Performance enables outcomes

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Better user experiences Scalable applications Resource efficiency

Performance a First Class Citizen

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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

Case Study

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

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Large Global Enterprise - NA Deployment

Case Study - Architecture

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Backend Data-sources

Traffic Generator

CloudHub Customer Backend

proxy app VPN GW VPN GW

Complex solution involving many moving part

Case Study - Approach

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Traffic Generator



Analysed and understood traffic pattern:- Mixed load matters- Large Payload is a trigger


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Traffic Generator



Analysed time spent at each hop


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Traffic Generator



Setup traffic simulation at each hop- Isolated the issue to proxy app- Saw: CPU not scaling- Conclusion: Likely blocking


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Traffic Generator



Blocking Issue Resolved

TLSv1.2

Improved performance but still short- Saw: CPU scaling but Maxed out- Found: TLSv1.2 in Java 7 slow


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Traffic Generator



Blocking Issue Patched

TLSv1.1

Passing Future Production Targets- Exposed backend bottlenecks

resolved by customer- Successful Live Deployment

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

Performance EngineeringPresented by:

Mountain State Software Solutions

Aaron Weikle, CEO

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

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

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

Virtual Machine Tuning• Garbage Collection

– Use Pools of Reusable Objects instead of dynamiccreation 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

Testing Tools

MUnit

Monitoring Tools

Conclusion• Performance engineering is key to

successful API-led connectivity• Attention to detail will pay dividends • Think you need more help?

Contact: Aaron Weikle [email protected]

mailto:[email protected]

Questions?

Get started with the Anypoint Platform performance whitepaper✓ Key architectural features of Mule runtime engine✓ Performance benchmark data✓ Customer use caseshttps://www.mulesoft.com/lp/whitepaper/api/anypoint-platform-performance