Building and Deploying Microservices with Event Sourcing, CQRS and Docker

@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

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@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
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
Let’s imagine that you are building a
banking app...

@crichardson
Domain model
Account
balance
open(initialBalance)
debit(amount)
credit(amount)
MoneyTransfer
fromAccountId
toAccountId
amount

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

@crichardson
Problem #1: 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
Solution #1: use a microservice architecture
Banking UI
Account Management Service
MoneyTransfer Management
Service
Account
Database
MoneyTransfer Database
Standalone
services

@crichardson
Problem #2: relational databases
Scalability
Distribution
Schema updates
O/R impedance mismatch
Handling semi-structured data

@crichardson
Solution #2: 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
databases
IEEE Software Sept/October 2010 - Debasish Ghosh / Twitter @debasishg

@crichardson
But now we have problems with data
consistency!

@crichardson
Problem #3: Microservices = distributed
data management
Each microservice has it’s own database
Business transactions must update data owned by multiple services,
e.g. Update MoneyTransfer and from/to Accounts
Some data is replicated and must be kept in sync
Tricky to implement reliably without 2PC

@crichardson
Problem #4: NoSQL =
ACID-free, denormalized databases
Limited transactions, i.e. no ACID transactions
Tricky to implement business transactions that update multiple rows,
e.g. Update MoneyTransfer and from/to Accounts
e.g. http://bit.ly/mongo2pc
Limited querying capabilities
Requires denormalized/materialized views that must be synchronized
Multiple datastores (e.g. DynamoDB + Cloud Search ) that need to be kept in sync

@crichardson
Solution to #3/#4: Event-based
architecture to the rescue
Microservices publish events when state changes
Microservices subscribe to events
Maintains eventual consistency across multiple aggregates (in multiple
datastores)
Synchronize replicated data

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

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

How to reliably generate events
whenever state changes?
Database triggers, Hibernate event
listener, ...
Reliable BUT
Not with NoSQL
Disconnected from the business
level event
Limited applicability
Ad hoc event publishing code mixed
into business logic
Publishes business level events
BUT
Tangled code, poor separation of
concerns
Unreliable, e.g. too easy to forget to
publish an event

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, AccountCreditedEvent
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
Persisting events
Ideally use a cross platform format
Use weak serialization:
enables event evolution, eg. add memo field to transfer
missing field provide default value
unknown field ignore
JSON is a good choice

@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
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
Example of an eventual consistency
problem
Scenario:
1. Create the user
2. Create shopping cart
3. Update the user with the shopping cart’s id
The user temporarily does not have a shopping cart id!
Client might need to retry their request at a later point
Server should return status code 418??

@crichardson
Handling duplicate events
Idempotent operations
e.g. add item to shopping cart
Duplicate detection:
e.g. track most recently seen event and discard earlier ones

@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 Separation

@crichardson
Command Query Responsibility
Separation (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
Transfers that update the
account
The sequence of debits and
credits
Current balance

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

Dealing with eventually consistent views
Scenario:
Client creates/updates aggregate
Client requests view of aggregate
Problem:
The view might not yet have been
updated
Solution:
Create/Update response contains
aggregate version
Query request contains desired version
Out of date view wait or return “out
of date view” error code
Alternatively:
“Fake it” in the UI until the view is
updated

@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
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

@crichardson
Smoke testing docker images
Smoke test
Docker daemon
Service
container
GET /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

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

@crichardson
Updating 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,
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
Docker is a great way to package microservices

@crichardson
Questions? Let’s talk at the Open Space
@crichardson chris@chrisrichardson.net
http://plainoldobjects.com http://microservices.io

Watch the video with slide synchronization on
InfoQ.com!
http://www.infoq.com/presentations/microservices
-docker-cqrs

Building and Deploying Microservices with Event Sourcing, CQRS and Docker

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