This document discusses using domain-driven design principles and patterns for serverless architectures. It begins with an introduction of the speaker and overview of topics to be covered. Then it discusses how bounded contexts from DDD map well to microservices and serverless functions. Several DDD patterns are explained for the serverless context, including repositories using CQRS and event sourcing. Strategic DDD is discussed as an organizing principle to prevent unstructured serverless applications. The document concludes by discussing challenges of measuring productivity for serverless/DDD approaches.

1. Serverless Domain-Driven
Design
At last some productivity
Asher Sterkin
asher.sterkin@gmail.com
Serverless Meetup
April 25, 2017, Tel-Aviv

2. About Myself
● Close to 40 years of experience in the software technology field
● VP Technology, Chief Technology Advisor Group, NDS (2010 - 2012)
● Distinguished Engineer, Office of CTO, Cisco Engineering (2012 - 2016)
● Today CTO at IRKI
● Focusing on software product line strategy
○ Lean Startup
○ Wardley Maps
○ (Strategic) Domain-Driven Design
○ Cynefin
○ Promise Theory
○ Serverless Architecture
○ ...

4. Metallic Sage Green Luster,
Opaque Metallic Matte
Galvanized SF Pink Blush,
Opaque Galvanized
Semi-Matte
Metallic Dark Raspberry
Iris,
Opaque Metallic Matte
Light Olive Luster,
Opaque Metallic Matte

6. Serverless ←→ DDD
● DDD & Microservices
● Serverless → DDD
○ Strategic DDD
○ Tactical DDD
○ Serverless AI → DDD
● Serverless ← DDD
● Serverless DDD → Productivity

7. Video, Slides

8. Key Messages

9. Domain Model
Ubiquitous Language
Bounded Context
Permeable Context Boundaries Result in BBoM
Symptoms of Rotting Design
● Rigidity - hard to change
● Fragility - easy to break
● Immobility - hard to reuse
● Viscosity - hard to do the right thing
● Opacity - disorganized expression
● ...

10. 10
4+1 View of Software Architecture
Conceptual Physical
Process View Deployment View
Logical View
Use-Case View
Implementation View
End-user
Functionality
Programmers
Software management
Performance
Scalability
Throughput
System integrators
System topology
Delivery, installation
communication
System engineering
Analysts/Designers
Structure
P. Krutchen, “Architectural Blueprints - The “4+1” View Model of Software Architecture

11. 11
4+1 View of Software Architecture
Conceptual Physical
Process View Deployment View
Logical View
Use-Case View
Implementation View
End-user
Functionality
Programmers
Software management
Performance
Scalability
Throughput
System integrators
System topology
Delivery, installation
communication
System engineering
Analysts/Designers
Structure
Bounded Context → Microservice

12. Serverless → Strategic
DDD

13. Strategic Domain-Driven Design
Vernon Vaughn, “Implementing Domain-Driven Design”

14. Strategic Domain-Driven Design

15. Strategic Domain-Driven Design
Fashion Accessories

16. Strategic Domain-Driven Design
Fashion Accessories

17. Strategic Domain-Driven Design
Fashion Accessories

18. Disorder

19. Disorder

20. Disorder

21. Pull Up/Push Down
Pull up useful
abstractions
Push down support and
generic stuff; control
through OSS contributions
Fashion Accessories
There is no ideal
core domain model
sitting and waiting to
be discovered.
It’s more like
attractor gradually
emerging out of
multiple “safe-to-fail”
experimentations

22. Strategic Domain-Driven Design
● Compute
● Network
● Storage
● Intelligence
Serverless
● 2D Shape
● 3D Shape
● ...
Geometry
● Color
● ...
Graphics
Microservice = CF Stack?
ERP, CRM,
SCM, PLM,
ECM, CAD, ...
Taxes
Shipment
Web Site
Inventory
....
Fashion Accessories

23. Strategic Domain-Driven Design
● Compute
● Network
● Storage
● Intelligence
Serverless BC
● 2D Shape
● 3D Shape
● ...
Geometry
● Color
● ...
Graphics
Microservice = CF Stack?
ERP, CRM,
SCM, PLM,
ECM, ...
Taxes
Shipment
Web Site
Inventory
....
Fashion Accessories

24. Strategic Domain-Driven Design
● Compute
● Network
● Storage
● Intelligence
Serverless BC
● 2D Shape
● 3D Shape
● ...
Geometry
● Color
● ...
Graphics
Microservice = CF Stack?
ERP, CRM,
SCM, PLM,
ECM, ...
Taxes
Shipment
Web Site
Inventory
....
Fashion Accessories

25. Strategic Domain-Driven Design
● 2D Shape
● 3D Shape
● ...
Geometry
● Color
● ...
Graphics
● Compute
● Network
● Storage
● Intelligence
Serverless BC
Microservice = CF Stack?
ERP, CRM,
SCM, PLM,
ECM, ...
Taxes
Shipment
Web Site
Inventory
....
Fashion Accessories
...

26. Context Map is the Key
● 2D Shape
● 3D Shape
● ...
Geometry
● Color
● ...
Graphics
● Compute
● Network
● Storage
● Intelligence
Serverless BC
Microservice = CF Stack?
ERP, CRM,
SCM, PLM,
ECM, ...
Taxes
Shipment
Web Site
Inventory
....
Fashion Accessories
...
ACL
ACL
ACL

27. Emerging Core
Domain so Far
Types
Components Products
Inventory
Items Customers
Manufacturers
Vendors
Catalog
Items
Shapes
Colors
Pictures

28. Amazon is Eating Software ...
valuechain
evolutionG C P U
eTailer
Service
AIaaS
FaaS
PaaS
IaaS
SCM
...
Web Scale
Operation
New
Service

29. Amazon is Eating Software ...
valuechain
evolutionG C P U
Video
Service
AIaaS
FaaS
PaaS
IaaS
Recommendations
...
Cloud
Native
Ability to build a Web Scale Application is
not a differentiator anymore!

30. Serverless Strategic DDD
Prefer utility AIaaS
Prefer product
or utility SaaS
Major effort
Pull up useful
abstractions
Proper timing for API
Push down support and
generic stuff; control
through OSS contributions

31. Serverless → Tactical
DDD

32. Bounded Context
Tactical DDD Patterns
Domain Model
Ubiquitous Language Nouns, Adjectives, Imperative Verbs, Interrogative Verbs, Perfect Tense Verbs
Entities, Values, Commands, Queries, Events, Aggregates, Repository,
Computational Services, Factories, ACL, Saga (Process Manager)
Serverless Cloud
Environment

33. Repository
Strong Transaction
Consistency + Invariant
DDD Aggregate and Repository
<<Aggregate Root>>
Entity
Entity Value
Value
Eventual
Transaction
Consistency
Storage
Command/
Query
Request
Processor
Command or Query
Requests
Domain Events

34. (AWS) Serverless DDD Patterns
● Repository
○ CRUD
○ CQRS
○ CQRS/ES
○ CQRS/ES IoT Scale
● Computation Service
○ Function
○ Batch
○ Map/Reduce
● Saga (Process Manager)

35. Default Serverless DDD Repository (CRUD)
DynamoDB Streams
Command/Query
Handler
Domain Event
Handler
Strong vs. Eventual Consistency
Bead Shape
Bead Color
Bead
Product Type
Component Type

36. Serverless DDD Repository (CQRS)
DynamoDB Streams
Command Handler Query View Builder
Query Handler
● Ad-hoc vs. precomputed
● Inter- vs. Intra Aggregate
Query View Store
SOT Store
● Command vs. Query
Storage optimization
● Query interface
complexity
Bead Color/Shape Compatibility

37. Serverless DDD Repository (CQRS/ES)
DynamoDB Streams
Command Handler Projection Builders
Query Handler
● Ad-hoc vs. precomputed
● Inter- vs. Intra Aggregate
Query View Store
Event Store
Snapshot Store
● Immutability
● Auditing
Product Spec
Product/Component Inventory

38. Event Store Patterns
● State Change Events (git style)
● Measurement/Observation (telemetry)
● Transaction/Account/Entry (accounting)
● Meaningful Domain Events

39. Snapshot Store Patterns
● Most Recent State
● State Change History
● Multiple Worlds

40. Serverless DDD Repository (CQRS/ES IoT Scale)
Edge
Command/Event
Handler
Cloud
Command/Event
Handler
Thing Event Stream
Thing Event Handlers
Thing Snapshot View
Thing Projection Builder
Thing Event Store

41. Serverless DDD Computation Services
Command/Query/Event
Handler
Computation Service
Bead Color/Shape Permutations
Bead Color
Bead Shape
Bead Component
Most popular products
Most popular colors per country
Minimal inventory level per product
...

42. Serverless DDD Saga (Process Manager)
Product Picture Album Building
Raw Pictures Catalog Pictures
Product Catalog
Background Cleanup Color/Light Adjustments Resolution Transcoding

43. Serverless AI → DDD

44. ignore AI
Irrelevance
adopt AI
Wasted time
and resources

45. Serverless DDD AI Patterns
● Pereferrial Adapters: audio, video, picture, text
● Application Services: dynamic state machines
● Command/Query Request Processors: classification, matching
● Event Handlers: anomaly detection
● Computation Services: analysis, generation, transformation, optimization
● Batch Processing: feedback and learning

46. Serverless AI ← DDD
Pereferrial Adapters Application Services
Dynamic State
Machines
Application-level Stimulo
Domain Services
Command/Query Requests EventsRaw A/V/T
Inputs
Dynamic
Classifications
Anomaly
Detection
Event Processors
Feedback and
Training
Pinterest/Instagram/Facebook/Twitter Feedback Processing
Best Earrings Match
Best Necklace Match
Historical
Data

47. Serverless → DDD
What is Missing?

48. Serverless → DDD Wish List
● Support for Actor Model
● Support for Reactive Programming
● JavaScript, JSON and YAML are (cloud) machine-level languages - need something at higher
level
● Simple modeling tool (lightweight UML)
● Fully fledged IDE supporting above
● Support for Semantic Maps (kind of AI service)

49. Serverless ← DDD

50. Serverless
Creating this
mess from
Lambda
Functions
would not
make it better

51. DDD for Serverless
Use DDD Strategic and Tactical
patterns as Serverless Architecture
organizing principles to prevent
ending with just a distributed version
of BBoM.

52. One who increases flesh, increases worms; one who
increases possessions, increases worry; one who
increases wives, increases witchcraft; one who increases
maidservants, increases promiscuity; one who increases
man-servants, increases thievery;
Pirkey Avot Chapter 2/7
‫כשפים‬ ‫מרבה‬ ‫נשים‬ ‫מרבה‬ .‫דאגה‬ ‫מרבה‬ ‫נכסים‬ ‫מרבה‬ .‫רימה‬ ‫מרבה‬ ‫בשר‬ ‫.מרבה‬
‫גזל‬ ‫מרבה‬ ‫עבדים‬ ‫מרבה‬ .‫זימה‬ ‫מרבה‬ ‫שפחות‬ ‫מרבה‬
Kohelet Chapter 1/18
18For in much wisdom is much vexation, and he who increases knowledge,
increases pain.
:‫ְאוֹב‬‫כ‬ַ‫מ‬ ‫ִיף‬‫ס‬‫יוֹ‬ ‫ַת‬‫ע‬ַ‫דּ‬ ‫ִיף‬‫ס‬‫ְיוֹ‬‫ו‬ ‫ַס‬‫ע‬ָ‫כּ‬ ‫ב‬ ָ‫ר‬ ‫ָה‬‫מ‬ְ‫כ‬ָ‫ח‬ ‫ֹב‬ ‫ְר‬‫בּ‬ ‫ִי‬‫כּ‬‫יח‬

53. Too Many Moving Parts
...

54. DDD for Serverless
Treat ‘em all as Aggregate
Repositories within the
corresponding Bounded Context of
Generic Infrastructure Sub-Domain

55. At Last Some Productivity

56. How to Measure Productivity?
● Lines of Code - well known anti-pattern
● Story Points - less well known anti-pattern (velocity is not productivity)
● Features - “feature train” (producing many features nobody needs)
● Value - too much depends on Product Owner; not under direct control
● Domain Insights - only domain experts could bring it → a lot of experimentation
“Software Development is a
Learning Process. Running code is
byproduct.”
A.Brandolini

57. “Consensus is poisonous
for innovation”
D. Snowden
The last slide