The Domain Model pattern is a great way to develop complex business logic. Unfortunately, a typical domain model is a tangled, birds nest of classes. It can’t be decomposed into microservices. Moreover, business logic often relies on ACID transactions to maintain consistency. Fortunately, there is a solution to this problem: aggregates. An aggregate is an often overlooked modeling concept from the must read book Domain Driven Design. In this talk you will learn how aggregates enable you to develop business logic for the modern world of microservices and NoSQL. We will describe how to use aggregates to design modular business logic that can be partitioned into microservices. You will learn how aggregates enable you to use eventual consistency instead of ACID. We will describe the design of a microservice that is built using aggregates, and Spring Cloud.

1. @crichardson
Developing Microservices
with Aggregates
Chris Richardson
Founder of Eventuate.io
Founder of the original CloudFoundry.com
Author of POJOs in Action
@crichardson
chris@chrisrichardson.net
http://eventuate.io

2. @crichardson
Presentation goal
Show how
Domain Driven Design Aggregates
and Microservices
are a perfect match

3. @crichardson
About Chris

4. @crichardson
About Chris
Consultant and trainer
focusing on modern
application architectures
including microservices
(http://www.chrisrichardson.net/)

5. @crichardson
About Chris
Founder of a startup that is creating
a platform that makes it easier for
developers to write transactional
microservices
(http://eventuate.io)

6. @crichardson
For more information
http://learnmicroservices.io

7. @crichardson
Agenda
The problem with Domain Models and microservices
Overview of aggregates
Maintaining consistency between aggregates
Using event sourcing with Aggregates
Example application

8. @crichardson
The Microservice architecture
tackles complexity through
modularization

9. @crichardson
Microservice
=
Business capability

10. @crichardson
Microservice architecture
Browser
Mobile
Device
Store
Front UI
API
Gateway
Catalog
Service
Review
Service
Order
Service
…
Service
Catalog
Database
Review
Database
Order
Database
…
Database
HTML
REST
REST
Apply X-axis and Z-axis
scaling to each service
independently

11. @crichardson
Service boundaries
enforce modularity

12. @crichardson
But there are challenges…

13. @crichardson
Product service
Customer serviceOrder service
Domain model = tangled web of
classes
Order
OrderLine
Item
quantity
…
Address
street
city
…
Customer
Product
name
price
?
?
creditLimit

14. @crichardson
Reliance on ACID transactions
to enforce invariants
BEGIN TRANSACTION
…
SELECT ORDER_TOTAL
FROM ORDERS WHERE CUSTOMER_ID = ?
…
SELECT CREDIT_LIMIT
FROM CUSTOMERS WHERE CUSTOMER_ID = ?
…
INSERT INTO ORDERS …
…
COMMIT TRANSACTION
Simple and
ACID

15. @crichardson
But it violates encapsulation…
BEGIN TRANSACTION
…
SELECT ORDER_TOTAL
FROM ORDERS WHERE CUSTOMER_ID = ?
…
SELECT CREDIT_LIMIT
FROM CUSTOMERS WHERE CUSTOMER_ID = ?
…
INSERT INTO ORDERS …
…
COMMIT TRANSACTION
Private to the
Order Service
Private to the
Customer Service

16. @crichardson
.. and requires 2PC
BEGIN TRANSACTION
…
SELECT ORDER_TOTAL
FROM ORDERS WHERE CUSTOMER_ID = ?
…
SELECT CREDIT_LIMIT
FROM CUSTOMERS WHERE CUSTOMER_ID = ?
…
INSERT INTO ORDERS …
…
COMMIT TRANSACTION

17. @crichardson
2PC is not a viable option
Guarantees consistency
BUT
2PC is best avoided
Not supported by many NoSQL databases etc.
CAP theorem 2PC impacts availability
….

18. @crichardson
Doesn’t fit with the
NoSQL DB “transaction” model

19. @crichardson
Agenda
The problem with Domain Models and microservices
Overview of aggregates
Maintaining consistency between aggregates
Using event sourcing with Aggregates
Example application

20. @crichardson
Domain Driven Design:
read it!

21. @crichardson
Domain Driven Design -
building blocks
Entity
Value object
Services
Repositories
Aggregates
Adopted
Ignored
(at least by me)

22. About Aggregates
Cluster of objects that can
be treated as a unit
Graph consisting of a root
entity and one or more
other entities and value
objects
Typically business entities
are Aggregates, e.g.
customer, Account, Order,
Product, ….
Order
OrderLine
Item
quantity
productId
productName
productPrice
customerId
Address
street
city
…

23. Aggregate: rule #1
Reference other
aggregate roots via
identity
(primary key)
Order
OrderLine
Item
quantity
productId
productName
productPrice
customerId
Address
street
city
…

24. @crichardson
Foreign keys in a
Domain Model?!?

25. @crichardson
Domain model = collection of
loosely connected aggregates
Order
OrderLine
Item
quantity
…
Address
street
city
…
Customer
Product
name
price

26. @crichardson
Product service
Customer serviceOrder service
Easily partition into microservices
Order
OrderLine
Item
quantity
…
Address
street
city
…
Customer
Product
name
price

27. @crichardson
Aggregate rule #2
Transaction
=
processing one command by
one aggregate

28. @crichardson
Product service
Customer serviceOrder service
Transaction scope = service
Order
OrderLine
Item
quantity
…
Address
street
city
…
Customer
Product
name
price

29. @crichardson
Transaction scope
=
NoSQL database
“transaction”

30. @crichardson
Aggregate granularity
If an update must be atomic then it must be handled by a
single aggregate
Therefore
Aggregate granularity is important

31. @crichardson
Aggregate granularity
Consistency
Scalability/
User experience
Customer
Order
Product
Customer
Order
Product
Customer
Order
Product

32. @crichardson
Agenda
The problem with Domain Models and microservices
Overview of aggregates
Maintaining consistency between aggregates
Using event sourcing with Aggregates
Example application

33. @crichardson
Customer management
How to maintain data consistency
between aggregates?
Order management
Order Service
placeOrder()
Customer Service
updateCreditLimit()
Customer
creditLimit
...
has ordersbelongs toOrder
total
Invariant:
sum(open order.total) <= customer.creditLimit
?

34. @crichardson
Event-driven architecture

35. @crichardson
Use event-driven, eventually
consistent order processing
Order
Service
Customer
Service
Order created
Credit Reserved
Credit Check Failed
Create Order
OR
Customer
creditLimit
creditReservations
...
Order
state
total
…
create()
reserveCredit()
approve()/reject()

36. @crichardson
Order Management
Order
id : 4567
total: 343
state = CREATED
Customer Management
Customer
creditLimit : 12000
creditReservations: {}
Customer
creditLimit : 12000
creditReservations: { 4567 -> 343}
Order
id : 4567
total: 343
state = APPROVED
Eventually consistent credit checking
Message Bus
createOrder()
Publishes:
Subscribes to:
Subscribes to:
publishes:
OrderCreatedEvent
CreditReservedEvent
OrderCreatedEvent CreditReservedEvent

37. @crichardson
Complexity of compensating
transactions
ACID transactions can simply rollback
BUT
Developer must write application logic to “rollback” eventually
consistent transactions
For example:
CreditCheckFailed => cancel Order
Money transfer destination account closed => credit source account
Careful design required!

38. @crichardson
How atomically update
database and publish an event
Order Service
Order
Database
Message Broker
insert Order
publish
OrderCreatedEvent
dual write problem
?

39. @crichardson
Failure = inconsistent system
Order Service
Order
Database
Message Broker
insert Order
publish
OrderCreatedEvent
X

40. @crichardson
2PC is not an option

41. @crichardson
Reliably publish events when
state changes

42. @crichardson
Agenda
The problem with Domain Models and microservices
Overview of aggregates
Maintaining consistency between aggregates
Using event sourcing with Aggregates
Example application

43. @crichardson
Event sourcing = event-centric
persistence
Application
Database
Event store
update
publish
X

44. @crichardson
Event sourcing
For each domain object (i.e. DDD aggregate):
Identify (state changing) domain events, e.g. use Event
Storming
Define Event classes
For example, Order events: OrderCreated, OrderCancelled,
OrderApproved, OrderRejected, OrderShipped

45. @crichardson
Persists events
NOT current state
Order
status
….
101 ACCEPTED
Order table
X

46. @crichardson
Persists events
NOT current state
Event table
Entity type
Event
id
Entity
id
Event
data
Order 902101 …OrderApproved
Order 903101 …OrderShipped
Event
type
Order 901101 …OrderCreated

47. @crichardson
Replay events to recreate
state
Order
state
OrderCreated(…)
OrderAccepted(…)
OrderShipped(…)
Events
Periodically snapshot to avoid loading all events

48. @crichardson
The present is a fold over
history
currentState = foldl(applyEvent, initialState, events)

49. @crichardson
Event Store
Application architecture
Order 123 Customer 456
OrderCreated
OrderApproved
…
CustomerCreated
CustomerCreditReserved
…
CreateOrder
UpdateOrder
GetOrder
Subscribe
Order
Service
CreateCustomer
UpdateCustomer
GetCustomer
Subscribe
Customer
Service

50. @crichardson
Request handling in an event sourced application
HTTP
Handler
Event
Store
pastEvents = findEvents(entityId)
Order
new()
applyEvents(pastEvents)
newEvents = processCmd(someCmd)
saveEvents(newEvents) (optimistic locking)
Order Service
applyEvents(newEvents)

51. @crichardson
Event Store publishes events
consumed by other services
Event
Store
Event
Subscriber
subscribe(EventTypes)
publish(event)
publish(event)
Customer
update()
Customer Service

52. @crichardson
Event Store publishes events
consumed by other services
Event
Store
Event
Subscriber
subscribe(EventTypes)
publish(event)
publish(event)
CQRS View
update()
Service Xyz
send notifications
…

53. Event store = database + message
broker
Hybrid database and
message broker
Implementations:
Home grown/DIY
geteventstore.com by
Greg Young
http://eventuate.io
(mine)
Event Store
Save
aggregate
events
Get
aggregate
events
Subscribe
to events

54. @crichardson
Benefits of event sourcing
Solves data consistency issues in a Microservice/NoSQL based
architecture
Reliable event publishing: publishes events needed by predictive
analytics etc, user notifications,…
Eliminates O/R mapping problem (mostly)
Reifies state changes:
Built in, reliable audit log
temporal queries
Preserved history More easily implement future requirements

55. @crichardson
Drawbacks of event
sourcing…
Requires application rewrite
Weird and unfamiliar style of programming
Events = a historical record of your bad design decisions
Must detect and ignore duplicate events
Idempotent event handlers
Track most recent event and ignore older ones
…

56. @crichardson
… Drawbacks of event
sourcing
Querying the event store can be challenging
Some queries might be complex/inefficient, e.g. accounts with
a balance > X
Event store might only support lookup of events by entity id
Must use Command Query Responsibility Segregation (CQRS)
to handle queries application must handle eventually
consistent data

57. @crichardson
Agenda
The problem with Domain Models and microservices
Overview of aggregates
Maintaining consistency between aggregates
Using event sourcing with Aggregates
Example application

58. @crichardson
Orders and Customers
example
Kafka
Rest API
Event Store
Event
Store
Adapter
Customer
Service
Rest API
Order Service
Customer
Aggregate
Order
Aggregate
Rest API
Order
History
Service
MongoDB
CQRS View
Event
Store
Adapter
Event
Store
Adapter

59. @crichardson
Customer microservice
Customer
Service
Customer
Controller
Spring
Cloud
Stream
Kafka
Customer
Aggregate
Rest API
Customer
Event Handlers
Event
Store
Event
Store
Adapter

60. @crichardson
The Customer aggregate
Money creditLimit
Map<OrderId, Money> creditReservations
Customer
List<Event> process(CreateCustomerCommand cmd) { … }
List<Event> process(ReserveCreditCommand cmd) { … }
…
void apply(CustomerCreatedEvent anEvent) { … }
void apply(CreditReservedEvent anEvent) { … }
…
State
Behavior

61. @crichardson
Familiar concepts restructured
class Customer {
public void reserveCredit(
orderId : String,
amount : Money) {
// verify
// update state
this.xyz = …
}
public List<Event> process(
ReserveCreditCommand cmd) {
// verify
…
return singletonList(
new CreditReservedEvent(…);
)
}
public void apply(
CreditReservedEvent event) {
// update state
this.xyz = event.xyz
}

62. @crichardson
Customer command processing

63. @crichardson
Customer applying events

64. @crichardson
Customer Controller

65. @crichardson
Creating a Customer
save() concisely specifies:
1.Creates Customer aggregate
2.Processes command
3.Applies events
4.Persists events

66. @crichardson
Event handling in Customers
Durable subscription name
1. Load Customer aggregate
2. Processes command
3. Applies events
4. Persists events
Triggers BeanPostProcessor
Publish message to Spring Cloud
Stream (Kafka) when Customer not
found

67. @crichardson
Summary
Aggregates are the building blocks of microservices
Use events to maintain consistency between aggregates
Event sourcing is a good way to implement a event-driven
architecture

68. @crichardson
@crichardson chris@chrisrichardson.net
http://learnmicroservices.io
Questions?