Building and deploying microservices with event sourcing, CQRS and Docker (Berlin microxchg, Munich microservices meetup)

@crichardson
Building and deploying
microservices with event
sourcing, CQRS and Docker
Chris Richardson
Author of POJOs in Action
Founder of the original CloudFoundry.com
@crichardson
chris@chrisrichardson.net
http://plainoldobjects.com
http://microservices.io

@crichardson
Presentation goal
Share my experiences with building and
deploying an application using Scala, functional
domain models, microservices, event sourcing,
CQRS, and Docker

@crichardson
About Chris
Founder of a buzzword compliant (stealthy, social, mobile, big
data, machine learning, ...) startup
Consultant helping organizations improve how they architect
and deploy applications using cloud, micro services, polyglot
applications, NoSQL, ...
Creator of http://microservices.io

@crichardson
For more information
https://github.com/cer/event-sourcing-examples
http://microservices.io
http://plainoldobjects.com/
https://twitter.com/crichardson

@crichardson
Agenda
Why build event-driven microservices?
Overview of event sourcing
Designing microservices with event sourcing
Implementing queries in an event sourced application
Building and deploying microservices

@crichardson
Tomcat
Traditional application
architecture
Browser/
Client
WAR/EAR
RDBMS
Customers
Accounts
Transfers
Banking
develop
test
deploy
Simple
Load
balancer
scale
Spring MVC
Spring
Hibernate
...
HTML
REST/JSON
ACID

@crichardson
Limitations of the monolithic
architecture
Intimidates developers
Obstacle to frequent deployments
Overloads your IDE and container
Obstacle to scaling development
Modules having conﬂicting scaling requirements
Requires long-term commitment to a technology stack

@crichardson
Apply the scale cube
X axis
- horizontal duplication
Z
axis
-data
partitioning
Y axis -
functional
decomposition
Scale
by
splitting
sim
ilar
things
Scale by
splitting
different things

@crichardson
Use a microservice architecture
Banking UI
Account Management
Service
MoneyTransfer
Management Service
Account
Database
MoneyTransfer
Database
Standalone
services

@crichardson
Limitations of a single
relational database
Scalability
Distribution
Schema updates
O/R impedance mismatch
Handling semi-structured data

@crichardson
Use a sharded relational
database

@crichardson
Use NoSQL databases
Avoids the limitations of RDBMS
For example,
text search Solr/Cloud Search
social (graph) data Neo4J
highly distributed/available database Cassandra
...

@crichardson
Different modules use
different types of databases
IEEE Software Sept/October 2010 - Debasish Ghosh / Twitter @debasishg

@crichardson
But this results in distributed
data management problems

@crichardson
Example #1 - SQL + Text
Search engine
Application
MySQL ElasticSearch
How to maintain consistency without 2PC?
Product #1 Product #1

@crichardson
Example #2 - Cassandra main
table <=> index table
Application
Cassandra
Main Table
Denormalized
view
Index Table

@crichardson
Example #3: Money transfer
Account Management
Service
MoneyTransfer
Management Service
Account
Database
MoneyTransfer
Database
Account #2
Account #1 Money Transfer

@crichardson
Event-based architecture to
the rescue
Components (e.g. services) publish events when state
changes
Components subscribe to events
Maintains eventual consistency across multiple aggregates
(in multiple datastores)
Synchronize replicated data

@crichardson
Event-driven synchronization:
SQL + Text Search engine
Catalog Service
MySQL ElasticSearch
Product #1 Product #1
Search Service
Message Bus
Insert
Product
Created
Product
Created
Index Doc
create product

@crichardson
MoneyTransferService
MoneyTransfer
fromAccountId = 101
toAccountId = 202
amount = 55
state = INITIAL
MoneyTransfer
fromAccountId = 101
toAccountId = 202
amount = 55
state = DEBITED
MoneyTransfer
fromAccountId = 101
toAccountId = 202
amount = 55
state = COMPLETED
Eventually consistent money transfer
Message Bus
AccountService
transferMoney()
Publishes:
Subscribes to:
Subscribes to:
publishes:
MoneyTransferCreatedEvent
AccountDebitedEvent
DebitRecordedEvent
AccountCreditedEvent
MoneyTransferCreatedEvent
DebitRecordedEvent
AccountDebitedEvent
Account
id = 101
balance = 250
Account
id = 202
balance = 125
Account
id = 101
balance = 195
Account
id = 202
balance = 180

@crichardson
To maintain consistency
a service must
atomically publish an event
whenever
a domain object changes

How to atomically update the
datastore and publish event(s)?
Use 2PC
Guaranteed atomicity BUT
Need a distributed transaction
manager
Database and message broker must
support 2PC
Impacts reliability
Not fashionable
2PC is best avoided
Use datastore as a message queue
1. Update database: new entity state
& event
2. Consume event & mark event as
consumed
Eventually consistent mechanism
See BASE: An Acid Alternative,
http://bit.ly/ebaybase
• BUT Tangled business logic and
event publishing code
• Difﬁcult to implement when using a
NoSQL database :-(

@crichardson
Event sourcing
For each aggregate:
Identify (state-changing) domain events
Deﬁne Event classes
For example,
Account: AccountOpenedEvent, AccountDebitedEvent,
ShoppingCart: ItemAddedEvent, ItemRemovedEvent,
OrderPlacedEvent

@crichardson
Persists events
NOT current state
Account
balance
open(initial)
debit(amount)
credit(amount)
AccountOpened
Event table
AccountCredited
AccountDebited
101 450
Account table
X
101
101
101
901
902
903
500
250
300

@crichardson
Replay events to recreate
state
Account
balance
AccountOpenedEvent(balance)
AccountDebitedEvent(amount)
AccountCreditedEvent(amount)
Events

@crichardson
Before: update state + publish
events
Two actions that must be atomic
Single action that can
be done atomically
Now: persist (and publish)
events

@crichardson
Aggregate traits
Map Command to Events
Apply event returning
updated Aggregate

@crichardson
Account - command processing
Prevent
overdraft

@crichardson
Account - applying events
Immutable

@crichardson
Request handling in an event-sourced application
HTTP
Handler
Event
Store
pastEvents = ﬁndEvents(entityId)
Account
new()
applyEvents(pastEvents)
newEvents = processCmd(SomeCmd)
saveEvents(newEvents)
Microservice A

@crichardson
Event Store publishes events -
consumed by other services
Event
Store
Event
Subscriber
subscribe(EventTypes)
publish(event)
publish(event)
Aggregate
NoSQL
materialized
view
update()
update()
Microservice B

@crichardson
Optimizing using snapshots
Most aggregates have relatively few events
BUT consider a 10-year old Account many transactions
Therefore, use snapshots:
Periodically save snapshot of aggregate state
Typically serialize a memento of the aggregate
Load latest snapshot + subsequent events

@crichardson
Event Store API
trait EventStore {
def save[T <: Aggregate[T]](entity: T, events: Seq[Event],
assignedId : Option[EntityId] = None): Future[EntityWithIdAndVersion[T]]
def update[T <: Aggregate[T]](entityIdAndVersion : EntityIdAndVersion,
entity: T, events: Seq[Event]): Future[EntityWithIdAndVersion[T]]
def find[T <: Aggregate[T] : ClassTag](entityId: EntityId) :
Future[EntityWithIdAndVersion[T]]
def findOptional[T <: Aggregate[T] : ClassTag](entityId: EntityId)
Future[Option[EntityWithIdAndVersion[T]]]
def subscribe(subscriptionId: SubscriptionId):
Future[AcknowledgableEventStream]
}

@crichardson
Event store implementations
Home-grown/DIY
geteventstore.com by Greg Young
Talk to me about my project :-)

@crichardson
Business beneﬁts of event
sourcing
Built-in, reliable audit log
Enables temporal queries
Publishes events needed by big data/predictive analytics etc.
Preserved history More easily implement future
requirements

@crichardson
Technical beneﬁts of event
sourcing
Solves data consistency issues in a Microservice/NoSQL-
based architecture:
Atomically save and publish events
Event subscribers update other aggregates ensuring
eventual consistency
Event subscribers update materialized views in SQL and
NoSQL databases (more on that later)
Eliminates O/R mapping problem

@crichardson
Drawbacks of event sourcing
Weird and unfamiliar
Events = a historical record of your bad design decisions
Handling duplicate events can be tricky
Application must handle eventually consistent data
Event store only directly supports PK-based lookup (more on
that later)

@crichardson
The anatomy of a microservice
Event Store
HTTP Request
HTTP Adapter
Aggregate
Event Adapter
Cmd
Cmd
Events
Events
Xyz Adapter
Xyz Request
microservice

@crichardson
Asynchronous Spring MVC
controller

@crichardson
MoneyTransferService
DSL concisely speciﬁes:
1.Creates MoneyTransfer aggregate
2.Processes command
3.Applies events
4.Persists events

@crichardson
MoneyTransfer Aggregate

@crichardson
Handling events published by
Accounts
1.Load MoneyTransfer aggregate
2.Processes command
3.Applies events
4.Persists events

@crichardson
Let’s imagine that you want to
display an account and it’s
recent transactions...

@crichardson
Displaying balance + recent
credits and debits
We need to do a “join: between the Account and the
corresponding MoneyTransfers
(Assuming Debit/Credit events don’t include other account, ...)
BUT
Event Store = primary key lookup of individual aggregates, ...
Use Command Query Responsibility Segregation

@crichardson
Command Query Responsibility
Segregation (CQRS)
Command-side
Commands
Aggregate
Event Store
Events
Query-side
Queries
(Denormalized)
View
Events

@crichardson
Query-side microservices
Event Store
Updater - microservice
View Updater
Service
Events
Reader - microservice
HTTP GET
Request
View Query
Service
View
Store
e.g.
MongoDB
Neo4J
CloudSearch
update query

@crichardson
Persisting account balance and
recent transactions in MongoDB
{
id: "298993498",
balance: 100000,
transfers : [
{"transferId" : "4552840948484",
"fromAccountId" : 298993498,
"toAccountId" : 3483948934,
"amount" : 5000}, ...
],
changes: [
{"changeId" : "93843948934",
"transferId" : "4552840948484",
"transactionType" : "AccountDebited",
"amount" : 5000}, ...
]
}
Denormalized = efficient lookup
MoneyTransfers that
update the account
The debits and credits
Current
balance

@crichardson
Persisting account info using
MongoDB...
class AccountInfoUpdateService
(accountInfoRepository : AccountInfoRepository, mongoTemplate : MongoTemplate)
extends CompoundEventHandler {
@EventHandlerMethod
def created(de: DispatchedEvent[AccountOpenedEvent]) = …
@EventHandlerMethod
def recordDebit(de: DispatchedEvent[AccountDebitedEvent]) = …
@EventHandlerMethod
def recordCredit(de: DispatchedEvent[AccountCreditedEvent]) = …
@EventHandlerMethod
def recordTransfer(de: DispatchedEvent[MoneyTransferCreatedEvent]) = …
}

Other kinds of views
AWS Cloud Search
Text search as-a-Service
View updater batches
aggregates to index
View query service does
text search
AWS DynamoDB
NoSQL as-a-Service
On-demand scalable -
specify desired read/write
capacity
Document and key-value
data models
Useful for denormalized,
UI oriented views

Beneﬁts and drawbacks of
CQRS
Beneﬁts
Necessary in an event-sourced
architecture
Separation of concerns =
simpler command and query
models
Supports multiple denormalized
views
Improved scalability and
performance
Drawbacks
Complexity
Potential code duplication
Replication lag/eventually
consistent views

@crichardson
My application architecture
API
gateway Event
Store
Service 1
Service 2
Service ...
Event
Archiver
Indexer
AWS
Cloud
Search
S3
NodeJS Scala/Spring Boot

@crichardson
Spring Boot based micro
services
Makes it easy to create stand-alone, production ready
microservices
Automatically configures Spring using Convention over
Configuration
Externalizes configuration
Built-in health checks and (Codahale) metrics
Generates standalone executable JARs with embedded
web server

@crichardson
Jenkins-based deployment
pipeline
Build & Test
microservice
Build & Test
Docker
image
Deploy
Docker
image
to registry
One pipeline per microservice

@crichardson
Building Docker images
cp ../build/libs/service.${1}.jar build/service.jar
docker build -t service-${VERSION} .
docker/build.sh
Building only takes 5
seconds!

@crichardson
Smoke testing docker images
Smoke test
Docker
daemon
Service
containerGET /health
POST /containers/create
creates
POST /containers/{id}/start
Docker daemon must listen on
TCP port

@crichardson
Publishing Docker images
docker tag service-${VERSION}:latest
${REGISTRY_HOST_AND_PORT}/service-${VERSION}
docker push ${REGISTRY_HOST_AND_PORT}/service-${VERSION}
docker/publish.sh
Pushing only takes 25
seconds!

@crichardson
CI environment runs on
Docker
EC2 Instance
Jenkins
Container
Artifactory
container
EBS volume
/jenkins-
home
/gradle-home
/artifactory-
home

@crichardson
Updating the production
environment
Large EC2 instance running Docker
Deployment tool:
1. Compares running containers with what’s been built by Jenkins
2. Pulls latest images from Docker registry
3. Stops old versions
4. Launches new versions
One day: use Docker clustering solution and a service discovery mechanism,
Most likely, AWS container service
Mesos and Marathon + Zookeeper, Kubernetes or ???

@crichardson
Summary
Event sourcing solves key data consistency issues with:
Microservices
Partitioned SQL/NoSQL databases
Use CQRS to implement materialized views for queries
Spring Boot is a great foundation for microservices
Docker is a great way to package microservices

@crichardson
@crichardson chris@chrisrichardson.net
http://plainoldobjects.com http://microservices.io

Building and deploying microservices with event sourcing, CQRS and Docker (Berlin microxchg, Munich microservices meetup)

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