This document discusses the benefits of combining domain-driven design (DDD) and reactive programming approaches with an in-memory data grid like Apache Geode or Pivotal GemFire. It notes that DDD helps reduce complexity by developing a shared domain language within bounded contexts, while reactive programming allows different bounded contexts to integrate via domain events. The document then demonstrates how to use these techniques with GemFire/Geode for a high-speed algorithmic trading system, including converting price quotes to bars, calculating analytics, and submitting orders.

1. Why Domain-Driven Design and
Reactive Programming?
Vaughn Vernon – Distinguished Author and Consultant
Wes Williams – Pivotal Software Designer & Architect

2. My Best Selling Books
@VaughnVernon

3. What Causes Software
Projects to Lack Well-
Designed Domains?

4. Data-Only Focus When You Should Aim Higher to
Capitalize on Your Unique Business Drivers

5. Business Concepts are Seen Only as Data-Centric
Anemic Entities

6. Here’s How it Happens

7. Oh Yeah, and …

8. Well, There are Also …

9. Don’t Forget …

10. Wouldn’t That Be …

11. Don’t Limit

12. It’s Getting Muddy

13. Cavaet: Big Ball of Mud
Your entities grow in number until no one
understands the tangled web.

14. Team is Blinded by Data and Lack of Core Business
Knowledge and Collaboration

15. What is a Good Domain Model and How
Does it Help with Reducing Complexity?

16. Invest in Business Knowledge
Developers Domain Experts

17. Business Value: Unify Mental Models Through a
Team-Developed Domain Language
??????

18. Segregate the Language Into a Well-Defined
Software Model

19. Strategic Design Tool: Bounded Context
Strategic Design Tool: Bounded Context

20. Inside the Boundary the Concepts Have Well
Defined Context

21. Outside The Boundary Other Concepts Will Not
Adhere to The Context of our Model

22. Using These Principles We Can Challenge The
Tangle and Identify Our Core Language

30. Actual Core

31. Multiple Bounded Contexts

32. What is Reactive Software and How
Does it Help Developers Solve
Complex Application and
Integration Problems?

33. Must Integrate Multiple Bounded Contexts

34. Inside Bounded Contexts Are Aggregate
Components

35. Aggregates Produce Facts: Domain Events

36. In App: Cross-Aggregates are Reactive
new

37. One Transaction Saves Aggregate and Event
Other Bounded Contexts react to Domain Events

38. How Can You Use These Techniques
To Reduce Time-to-Market and
Improve Quality as You Build
Software That is More Flexible, More
Scalable, and More Tightly Aligned to
Business Goals?

39. Time-to-Market is Reduced Through Modeling with
Business Knowledge

40. Software Quality is Enhanced and Business Goals
Achieved Through the Bounded Context and
Ubiquitous Language

41. Software is More Scalable Through Smaller
Focused Bounded Contexts

42. How Can In-Memory Data Grids Like Open
Source Apache Geode and Pivotal GemFire
(Based on Apache Geode) Fit With These
Modern Concepts?

43. Pivotal Software
Wes Williams
Pivotal Data Engineering
wwilliams@pivotal.io
Largest startup in history of Silicon Valley
Helping enterprises scale with
Process + OS Software + Data Engineering

44. Reactor & DDD - Demo
High-speed
Price Feed
Pivotal
GemFire
Execution
Management
System
Algorithmic
Trading System
DDD Style
https://github.com/reactor/reactor-kafka
Stream – convert quotes to price bars
Wall Street
Automated Trading Strategy
https://github.com/VaughnVernon/IDDD_NYSE

45. Reactive Kafka – Why?
High-speed
streaming
transactions
Pivotal
GemFire
High-speed
streaming
transactions
Pivotal
GemFire
20,000 txns/ sec on a 4-core 2.8 GHz MAC
1,000,000 txn / sec on
a 4-core 2.8 GHz MAC
Asynch put
micro-batches
Auto-replay batches in
case of commit failure

46. GemFire – Why?
Region
get(key)
Aggregate
In-memory
object grid
Business logic and
data are co-located
DDD + GemFire =
A natural complement!
Data must be
serialized first
from across
network.

47. GemFire – Eventing Architecture
Applicatio
n
Applicatio
n
DB or other
Data Store
Orders
Update!
Continuous Query
Select * from /EventStore where type
= “Buy Order”
Cache
Write
Algorithmic Orders
EventStore
Listening for
new orders
Built-in Event-Driven
Architecture

48. GemFire – Fast Calculations
High-speed
calculations
Algorithmic Trading
System
Pivotal
GemFire
Flight Scheduler Target Marketing
<Your company’s
analytic here>

49. Demo Features
1. Windowing – Convert a stream of quotes into a 1-second price bar.
2. Analytics – Calculate the Volume-Weighted Average Price on each bar
3. Submit Buy Order for 25,000 shares
4. Algorithmically execute the order in 1,000-share “slices” until filled
Using DDD & Reactor technology with an In-memory Data Grid

50. Demo – Convert Prices to …
High-speed
Price Feed
Pivotal
GemFire
Price feed - emits price
quotes in a pseudo-random
wave pattern with a 5%
range
5-second price bars for AAPL
Creating price bars in a
1-second window
open
highclose
low

51. Demo - Analytics
Volume-Weighted Average Price
Execute trade when VWAP < target price
open
close
 Calculate VWAP  Evaluate Trade
Σ (price * volume)
________________________________________________________
Σ (volume)
VWAP =

52. GemFire – Eventing Architecture
Applicatio
n
Applicatio
n
Orders
Update!
Continuous Query
Select * from /EventStore
where type = “Buy Order”
Algo Orders
EventStore
Listening for
new orders
Submit a “Buy”
request for
25,000 shares
Submit Order to
trade
algorithmically
Will submit orders 1,000
shares at a time until the
entire order is filled

53. Demo

54. Demo Summary
1. DDD and Reactor – Powerful best practices and practical for the
enterprise
2. GemFire – A natural complement to DDD due to being an object IMDG,
built-in eventing. Can use with Reactor streaming for Fast Analytics.
3. Reactor Kafka - Enables extremely high throughput
Thanks to Vaughn Vernon for modifying the trading system to DDD style

55. Next Steps

56. My Best Selling Books
@VaughnVernon

57. http://idddworkshop.com

58. Pivotal Resources
• Pivotal GemFire
• Features, Benefits, Use Cases:
https://pivotal.io/big-data/pivotal-gemfire
• Free Intro Online Course: https://pivotal.io/academy/course/introduction-to-
gemfire
• Today’s Demo:
https://github.com/VaughnVernon/IDDD_NYSE
https://github.com/reactor/reactor-kafka
• Apache Geode
• Site: http://geode.apache.org/
• Geode Summit: https://2016.event.geodesummit.com/schedule
• Upcoming Community Discussion with Vaughn Vernon: Weds, Sept 21, 9AM
Pacific
Meeting URL: https://pivotal.zoom.us/j/540483502