Devoxx UK 2019 - Exploring Microservice Boundaries Through Network Science

Talk given at Devoxx UK 2019, with video available here: https://www.youtube.com/watch?v=IvQGT1Yv3wE

Talk Overview: https://devoxxuk19.confinabox.com/talk/LDW-8683/Exploring_Microservices_boundaries_through_network_science

Transcript

1. @techiewatt EXPLORING MICROSERVICE BOUNDARIES THROUGH NETWORK SCIENCE Nicki Watt - CTO @techiewatt opencredo.com
2. @techiewatt EXPLORING MICROSERVICE BOUNDARIES THROUGH NETWORK SCIENCE Nicki Watt - CTO @techiewatt opencredo.com
3. @techiewatt EXPLORING MICROSERVICE APP ARCHITECTURE THROUGH NETWORK SCIENCE Nicki Watt - CTO @techiewatt opencredo.com
4. AGENDA OVERVIEW • HYPOTHESIS
 • A BIT OF THEORY
 • MICROSERVICE EXPLORING
 • SUMMARY @techiewatt
5. HYPOTHESIS @techiewatt
6. HYPOTHESIS @techiewatt General Hypothesis:
 Data Driven Architectural Improvement
 
 You can extract metrics and KPIs from a microservices architecture using network science AND use these to gain insight into the structure and characteristics of your microservices architecture
7. HYPOTHESIS @techiewatt Specifically 
 Can we use these metrics to detect bad microservice architectural smells and anti-patterns like a tightly coupled architecture (distributed monolith)
8. A QUICK BIT OF THEORY @techiewatt
9. GRAPH THEORY 101 Nodes / 
 Vertices Relationships / 
 Edges @techiewatt A graph is a way to formally represent a network, or collection of related objects, in a mathematical way
10. Show me some network science stuff ... @techiewatt
11. DEGREE How connected is a specific node? @techiewatt 1
12. DEGREE How connected is a specific node? A is more highly connected than 
 B and C 6 2 2 @techiewatt 1
13. CLUSTER COEFFICIENT How tightly is a group is clustered, compared to how tightly it could be clustered? @techiewatt 2
14. CLUSTER COEFFICIENT @techiewatt 2
15. CLUSTER COEFFICIENT @techiewatt 2
16. CLUSTER COEFFICIENT @techiewatt 2
17. CLUSTER COEFFICIENT @techiewatt 0.66 2
18. CLUSTER COEFFICIENT @techiewatt 0.66 2
19. CLUSTER COEFFICIENT @techiewatt 0.66 1.0 2
20. COMMUNITY DETECTION Used to find related communities and quantify the quality of groupings @techiewatt 3
21. DIVE IN! EXPLORING MICROSERVICES (AS A NETWORK) @techiewatt
22. #1 A “Microservice” Architecture 
 (v1.0) @techiewatt
23. @techiewatt V1.0
24. @techiewatt V1.0 - NETWORK STATS Density: 0.19 Number of nodes: 20 Number of edges: 36 Average degree: 3.6000 Average Clustering Co-eff: 0.30
25. @techiewatt V1.0 - DEGREE
26. @techiewatt V1.0 - CLUSTER COEFFICIENT
27. V1.0 - ARCHITECTURE DIAGRAM @techiewatt Front End Service Back End Service External Integration Adapter Service Database API API API API API API
28. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 2 4 6 8 10 12 14 16 ShippingFacade Order PricingCalculator User Product Paym entFacade W eb API M ob API B2B API UPS DHL Royal M ail Shipper X Shipper Y Shipper Z M asterCard PayPal Provider A Provider B Provider C v1 - degree & cluster coefficient degree cluster coefficient V1.0 ANALYSIS @techiewatt Average degree: 3.6 Average Clustering 
 Coefficient: 0.3
29. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 2 4 6 8 10 12 14 16 ShippingFacade Order PricingCalculator User Product Paym entFacade W eb API M ob API B2B API UPS DHL Royal M ail Shipper X Shipper Y Shipper Z M asterCard PayPal Provider A Provider B Provider C v1 - degree & cluster coefficient degree cluster coefficient V1.0 ANALYSIS @techiewatt
30. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 2 4 6 8 10 12 14 16 ShippingFacade Order PricingCalculator User Product Paym entFacade W eb API M ob API B2B API UPS DHL Royal M ail Shipper X Shipper Y Shipper Z M asterCard PayPal Provider A Provider B Provider C v1 - degree & cluster coefficient degree cluster coefficient V1.0 ANALYSIS @techiewatt
31. V1.0 OBSERVATIONS @techiewatt - Entity Services 
 Anti-Pattern - Distributed 
 Monolith
32. V1.0 - COMMUNITY DETECTION @techiewatt
33. V1.0 - OBSERVATIONS CONTINUED @techiewatt - Help detect tightly coupled areas - Not always DDD 
 aligned (** see Erich Eichinger’s blog “Heuristics for Identifying Service Boundaries” 
 - https://opencredo.com/blogs/identify-service-boundary-heuristics/)
34. #2
 The Revised Microservice Arch 
 User Domain Decoupling
 (v2.0) @techiewatt
35. Recap … V1.0 @techiewatt
36. V2.0 @techiewatt
37. V2.0 - NETWORK STATS @techiewatt Density: 0.11 Number of nodes: 29 Number of edges: 46 Average degree: 3.1724 Average Clustering Co-eff: 0.20
38. V2.0 - COMBINED STATS @techiewatt
39. V2.0 - COMBINED STATS @techiewatt
40. V2.0 - COMBINED STATS @techiewatt
41. V2.0 - COMMUNITY DETECTION @techiewatt
42. SUMMARY @techiewatt
43. HYPOTHESIS @techiewatt General Hypothesis:
 Data Driven Architectural Improvement
 
 You can extract metrics and KPIs from a microservices architecture using network science AND use these to gain insight into the structure and characteristics of your microservices architecture
44. HYPOTHESIS @techiewatt General Hypothesis:
 Data Driven Architectural Improvement
 
 You can extract metrics and KPIs from a microservices architecture using network science AND use these to gain insight into the structure and characteristics of your microservices architecture
45. CONCLUSION @techiewatt Demonstrated using degree and cluster coefficient measures to detect tightly coupled (distributed monolith) architectures Demonstrated using community detection algorithms to uncover related groupings (boundaries) of microservices
46. HYPOTHESIS @techiewatt Specifically 
 Can we use these metrics to detect bad microservice architectural smells and anti-patterns like a tightly coupled architecture (distributed monolith)?
47. RECOMMENDED LEARNING RESOURCES @techiewatt
48. @techiewatt Come chat with me at our booth! Need a Microservice arch review? We’re hiring! Want to find out more?