why domain-driven design and reactive programming?
TRANSCRIPT
Why Domain-Driven Design and Reactive Programming?
Vaughn Vernon – Distinguished Author and ConsultantWes Williams – Pivotal Software Designer & Architect
My Best Selling Books
@VaughnVernon
What Causes Software Projects to Lack Well-Designed Domains?
Data-Only Focus When You Should Aim Higher to Capitalize on Your Unique Business Drivers
Business Concepts are Seen Only as Data-Centric Anemic Entities
Here’s How it Happens
Oh Yeah, and …
Well, There are Also …
Don’t Forget …
Wouldn’t That Be …
Don’t Limit
It’s Getting Muddy
Cavaet: Big Ball of Mud
Your entities grow in number until no one understands the tangled web.
Team is Blinded by Data and Lack of Core Business Knowledge and Collaboration
What is a Good Domain Model and How Does it Help with Reducing Complexity?
Invest in Business Knowledge
Developers Domain Experts
Business Value: Unify Mental Models Through a Team-Developed Domain Language
??????
Segregate the Language Into a Well-Defined Software Model
Strategic Design Tool: Bounded Context
Strategic Design Tool: Bounded Context
Inside the Boundary the Concepts Have Well Defined Context
Outside The Boundary Other Concepts Will Not Adhere to The Context of our Model
Using These Principles We Can Challenge The Tangle and Identify Our Core Language
Actual Core
Multiple Bounded Contexts
What is Reactive Software and How Does it Help Developers Solve
Complex Application and Integration Problems?
Must Integrate Multiple Bounded Contexts
Inside Bounded Contexts Are Aggregate Components
Aggregates Produce Facts: Domain Events
In App: Cross-Aggregates are Reactive
new
One Transaction Saves Aggregate and Event
Other Bounded Contexts react to Domain Events
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?
Time-to-Market is Reduced Through Modeling with Business Knowledge
Software Quality is Enhanced and Business Goals Achieved Through the Bounded Context and Ubiquitous Language
Software is More Scalable Through Smaller Focused Bounded Contexts
How Can In-Memory Data Grids Like Open Source Apache Geode and Pivotal GemFire (Based on Apache Geode) Fit With These
Modern Concepts?
Pivotal Software
Wes WilliamsPivotal Data Engineering
Largest startup in history of Silicon Valley
Helping enterprises scale withProcess + OS Software + Data Engineering
Reactor & DDD - Demo
High-speedPrice 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
Reactive Kafka – Why?
High-speedstreaming transactions
Pivotal GemFire
High-speedstreaming 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
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.
GemFire – Eventing ArchitectureApplicatio
nApplicatio
n
DB or other Data Store
Orders
Update!Continuous QuerySelect * from /EventStore where type = “Buy Order”Cache
Write
Algorithmic Orders
EventStore
Listening for new orders
Built-in Event-Driven Architecture
GemFire – Fast Calculations
High-speedcalculations
Algorithmic Trading System
Pivotal GemFire
Flight Scheduler Target Marketing
<Your company’s analytic here>
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 filledUsing DDD & Reactor technology with an In-memory Data Grid
Demo – Convert Prices to …
High-speedPrice 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
Demo - Analytics
Volume-Weighted Average Price
Execute trade when VWAP < target price
open
close
Calculate VWAP Evaluate Trade
Σ (price * volume)________________________________________________________
Σ (volume)VWAP =
GemFire – Eventing ArchitectureApplicatio
nApplicatio
n
Orders
Update!
Continuous QuerySelect * 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
Demo
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
Next Steps
My Best Selling Books
@VaughnVernon
http://idddworkshop.com
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_NYSEhttps://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
PacificMeeting URL: https://pivotal.zoom.us/j/540483502
