ESBs would look different if built today. Large monolithic applications would be decomposed into microservices with bounded contexts. Services would be independently deployable and designed for failure. An ESB centralized integration but microservices use decentralized approaches like service discovery. While challenging, microservices evolve legacy systems towards modular, scalable architectures. It's a learning process, and the industry is still evolving effective patterns.

1. 1
How would ESBs look like,
if they were done today?
Markus Eisele, @myfear
Developer Advocate
markus@jboss.org
October, 2015

2. “What’s right isn’t always popular.
What’s popular isn’t always right”
Howard Cosell

3. Application Server
Large Java EE / J2EE based applications
Application Server
EAR EAR EAR
WAR
JAR JAR JAR JAR
JAR JAR WAR
JAR JAR
EAM <?>
LoadBalancer

4. • Monolithic application – everything is package into a single .ear
• Reuse primarily by sharing .jars
• A “big” push to production once or twice a year
• Single database schema for the entire application
• >= 500k loc
• >= Heavyweight Infrastructure
• Thousands of Testcases
• Barely New Testcases
TechnicalImplications

5. 5
• >= 20 Team Member
• The single .ear requiring a multi-month test cycle /
• Huge bug and feature databases
• User Acceptance Undefined
• Technical Design Approach
• Barely Business Components or Domains
• Requiring multiple team involvement & significant
oversight
TeamandQAImplications

6. 6
• Still changing requirements.
• New features tend to be HUGE!
• Cross-cutting concerns nearly impossible to
implement.
Andevennow…

7. 7
Why?

8. 8
Technical Dept!
We’re lazy!
Inexperienced!
No education! Outdated Infrastructure!
We always did it like that.
Outdated Designpattern?
Grown application Outdated Runtimes!

9. 9
Where did we go
from here?

10. 10
We treated
everything as a
legacy system and
try to solve
integration
problems.

11. 11
ENTERPRISE!
CENTRALIZE!
STANDARDS!
LICENSES!
INTEGRATION?
Enterprise Service Bus
Orchestration

12. 12
• Still very large codebases
• Overloaded IDEs
• Hard to understand and modify
• Hard to test
• Complex dependencies
• Small Changes generate big Impact
• Difficult to scale
• Mostly not rewritten but “re-wired”
• Data Segmentation not defined
• Scaling difficult
TechnicalImplications

13. 13
Hmmm … and
where are we
today?

14. 14

15. 15
Name it whatever you
like.

16. 16
We’re decomposing
monoliths
and evolve them into
microservices
architectures.

17. 17
Reduce Impact of Change by
Encapsulating Source of
Change
http://martinfowler.com/articles/microservices.html

18. 18
How to find the
Right
Services?

19. 19
Domain Driven Design
Bounded contexts
Designed For Automation
Designed for Failure
Independently Deployable
FromScratch

20. 20
Verb or Use Case
e.g. Checkout UI
Noun
e.g. Catalog product service
Single Responsible Principle e.g.
Unix utilities
EvolutionFromExisting

21. 21
What did ESBs do?

22. 22
• Monitor and control routing of message exchange
between services
• Resolve contention between communicating service
components
• Control deployment and versioning of services
• Marshal use of redundant services
• Cater for commodity services like
• event handling,
• data transformation and mapping,
• message and event queuing and sequencing,
• security or exception handling,
• protocol conversion and
• enforcing proper quality of communication
service

23. 23
Let’s deconstruct the
$hit.

24. 24
”Monitor and control routing of
message exchange between services”

25. 25
• Not really anymore.
• “Services do one thing well”
• Bunch of different approaches to
service design and interaction.
• No centralized point of “control”

26. 26
Aggregator Pattern
w or w/o Proxy
Chained Pattern Branch Pattern …..

27. 27
”Resolve contention between
communicating service components”

28. 28
“Smart endpoints and dumb pipes”
- Martin Fowler
http://martinfowler.com/articles/microservices.html

29. 29
“Control deployment and
versioning of services”

30. 30
- Deployment
- Configuration
- Profiles / App Packaging
- Service Discovery
- Versions
- Monitoring
- Governance

31. 31
“Marshal use of redundant services”

32. 32
“Decentralized Governance”
- Martin Fowler
http://martinfowler.com/articles/microservices.html#DecentralizedGovernanc
e

33. 33
“Cater for commodity services”

34. 34
• a lightweight service runtime
• Cross – Service Security
• Transaction Management
• Service Scaling
• Load Balancing
• Deployment
• Configuration
• Profiles / App Packaging
• Service Discovery
• Versions
• Monitoring
• Governance
• Failure Handling
• Asynchronous vs. Synchronous
• Cross – Service Logging
• ...

35. 35
An approach.

36. 36
Container
Container
Load
Balancer
ServiceAA DBClient Cache
APIGateway
Security
Service
Registry
ContainerContainer
Apossiblesolution...

37. 37
The Pieces

38. 38
http://undertow.io/

39. 39
http://www.apiman.io/

40. 40

41. 41

42. 42
• Implemented with Docker and
Kubernetes
• Use any JVM (or any technology)
• Docker images, encourage static, well-
defined, well-tested deployments
• Provides networking, JVM isolation,
orchestration, auto-scaling, health
checks, cloud deployments
• Still in community!
• Supports OpenShift v3
Fabric8 V2

43. 43
And keep in mind….

44. 44
 Operations and development are skills, not
roles. Delivery teams are composed of
people with all the necessary skills.
 Delivery teams run software products - not
projects - that run from inception to
retirement
DevOps .. Is a culture.

45. 45
Let’s take one
step at a time and
not solve
everything at
once.
aka “Evolutionary Design”

46. 46
Easy As That?

47. 47
• No silver bullet; distributed systems are *hard*
• Dependency hell, custom shared libraries
• Fragmented and inconsistent management
• Team communication challenges
• Health checking, monitoring, liveness
• Over architecting, performance concerns, things
spiraling out of control fast
WARNING: Challenges ahead!

48. 48
 Complex Runtime: many moving parts
 Distributed Systems are inherently complex
 Services are deployed on multiple instances
 Decentralized Data (Distributed Transactions vs
eventual consistency)
 Communication between services (Network and
Configuration)
 Synchronous vs. Asynchronous vs. Messaging
Communication
 Communication overhead (n2n)
 Failure Handling (Circuit Breaker)
 Service-/Metadata Registry
WARNING: Challenges ahead!

49. 49
?
Load Balancing
API Management
Configuration
Deployment
Monitoring
SecurityGovernance
Nodes
Cartridges
Versions Patches
Changes

50. 50
And this is only the
beginning...
The industry is still
learning a lot.

51. 51

52. 52
Correct functional decomposition is
crucial for microservices:
• pretty hard to get right from the
start
• a modular system can evolve to
microservices
• balance the needs with the costs
• work on it evolutionary
Takeaway:

53. 53
Are they here to stay?

54. 54
Nobody knows.

55. 55
Take with you today:

56. 56
• There is no single successor to ESBs.
• The whole turned into pieces.
• We’re still evolving them.

57. 57
http://bit.ly/virtualJBUG
@vJBUG

58. 58
http://developers.redhat.com/promotions/distributed-javaee-architecture

59. 59

61. 61
http://www.lordofthejars.com/2014/07/rxjava-java8-java-ee-7-arquillian-bliss.html
http://www.lordofthejars.com/2014/09/defend-your-application-with-hystrix.html
http://techblog.netflix.com/2012/02/fault-tolerance-in-high-volume.html
http://martinfowler.com/articles/microservices.html
http://microservices.io/patterns/microservices.html
http://techblog.netflix.com/2013/01/optimizing-netflix-api.html
http://www.infoq.com/articles/microservices-intro
https://sites.google.com/a/jezhumble.net/devops-manifesto/