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Building Performant, Reliable,
Scalable Integrations with Mule ESB
Ryan Hoegg, IntegrationArchitect,Confluex
RupeshRamac...
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Service Level Agreement
First Class Requirements
Precision Matters
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Reliability SLAs
Availability
Uptime
Time toRecovery
Message Loss
Maximum Guarantee
Detection
Recovery
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Scalability SLAs
Capacity
Peak
Sustained
(Degraded)
Message Volume
Message Size
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Performance SLAs
Response Time
Throughput
Concurrency
Sustainability
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Performance Tuning: Big picture
Mule ESB
JVM
Operating System
File System
Network
Downstream Systems
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Performance: Best Practices
Asynchronous vs Synchronous
Real-time vs Batch
Stateful vs Stateless (web scale)
On-Prem...
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Performance: Manage SLA’s
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Performance: Case Study
Use Case:
API Gateway
XML to JSON
transforms
Mixed Payload Sizes
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Performance: Case Study
Test Case Mule ESB 3.5 EE as API Gateway
Infrastructure Amazon EC2 with 10GbE network
Throughpu...
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Performance Characteristics:
Mule ESB 3.5 EE
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Making SLAs a Reality
Prioritize
Model
Measure
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Tuning: Focusing on What Matters
Observe
Identify hot spot
Generate load
Compare with SLA
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Tuning: Improve Scalability
Scale Up, Scale Out
SEDA
Store and Forward
Message Oriented Middleware
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Case Study: Gaming Platform
Public Beta Launch
“Code Complete”
Players are unforgiving
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Case Study: Gaming Platform
1. Catalog Services, Estimate Load
2. Prioritize
3. Isolate Muleand Instrument
4. Generate ...
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Case Study: Gaming Platform
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Case Study: Gaming Platform
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Tuning: Improve Reliability
Reliable Acquisition Pattern
Transactions
Retry
Delegate
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Case Study: Retail
Business Critical Integration
“Code Complete”
Losing Purchase Orders
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Case Study: Retail
1. Determine failure modes
2. Decide how torespond
3. Induce and observe
4. Apply reliability patter...
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Case Study: Retail
25
Questions?
Please visit Confluex and
MuleSoft experts in the Expo Hall
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Building Performant, Reliable, and Scalable Integrations with Mule ESB

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How to build integrations with Mule ESB that meet your customers' needs around performance, scalability, and reliability. Presented with Rupesh Ramachandran at MuleSoft CONNECT 2014.

Published in: Software
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Building Performant, Reliable, and Scalable Integrations with Mule ESB

  1. 1. 1
  2. 2. 2 Building Performant, Reliable, Scalable Integrations with Mule ESB Ryan Hoegg, IntegrationArchitect,Confluex RupeshRamachandran,SolutionsArchitect, MuleSoft
  3. 3. 3 Service Level Agreement First Class Requirements Precision Matters
  4. 4. 4
  5. 5. 5 Reliability SLAs Availability Uptime Time toRecovery Message Loss Maximum Guarantee Detection Recovery
  6. 6. 6 Scalability SLAs Capacity Peak Sustained (Degraded) Message Volume Message Size
  7. 7. 7 Performance SLAs Response Time Throughput Concurrency Sustainability
  8. 8. 8 Performance Tuning: Big picture Mule ESB JVM Operating System File System Network Downstream Systems
  9. 9. 9 Performance: Best Practices Asynchronous vs Synchronous Real-time vs Batch Stateful vs Stateless (web scale) On-Premise vs iPaaS
  10. 10. 10 Performance: Manage SLA’s
  11. 11. 11 Performance: Case Study Use Case: API Gateway XML to JSON transforms Mixed Payload Sizes
  12. 12. 12 Performance: Case Study Test Case Mule ESB 3.5 EE as API Gateway Infrastructure Amazon EC2 with 10GbE network Throughput ~8000tps Latency ~5ms Scale Linear scale out, to 6 boxes*
  13. 13. 13 Performance Characteristics: Mule ESB 3.5 EE
  14. 14. 14 Making SLAs a Reality Prioritize Model Measure
  15. 15. 15 Tuning: Focusing on What Matters Observe Identify hot spot Generate load Compare with SLA
  16. 16. 16 Tuning: Improve Scalability Scale Up, Scale Out SEDA Store and Forward Message Oriented Middleware
  17. 17. 17 Case Study: Gaming Platform Public Beta Launch “Code Complete” Players are unforgiving
  18. 18. 18 Case Study: Gaming Platform 1. Catalog Services, Estimate Load 2. Prioritize 3. Isolate Muleand Instrument 4. Generate Load 5. Observe 6. Tune
  19. 19. 19 Case Study: Gaming Platform
  20. 20. 20 Case Study: Gaming Platform
  21. 21. 21 Tuning: Improve Reliability Reliable Acquisition Pattern Transactions Retry Delegate
  22. 22. 22 Case Study: Retail Business Critical Integration “Code Complete” Losing Purchase Orders
  23. 23. 23 Case Study: Retail 1. Determine failure modes 2. Decide how torespond 3. Induce and observe 4. Apply reliability patterns
  24. 24. 24 Case Study: Retail
  25. 25. 25 Questions? Please visit Confluex and MuleSoft experts in the Expo Hall
  26. 26. 26

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