5f524dc534
* Fix compatibility after Java DSL migration to the SI Core * Also upgrade all the JMS sample to the JMS 2.0 * Fix default `connectionFactory` bean reference to the current `jmsConnectionFactory` * Remove unsupported JPA-1.0 Spec via `OpenJpaAutoConfiguration`, since the minimum now is JPA-2.0 * Remove non-lambda sample since minimum now is Java 8 * Rename `PromiseTest` to `MonoTest` and fix it to Reactor 3.0 * Fix `stomp-server.xml` to use proper Reactor 3.0 `eactor.util.function.Tuples` for SpEL * Regenerate POMs
Café Sample Application
Overview
The Café Sample emulates the simple operation of a coffee shop that is modeled using Enterprise Integration Patterns (EIP). The sample is inspired by one of the samples featured in Gregor Hohpe's ramblings. For more details, please see: Starbucks Does Not Use Two-Phase Commit. The domain is that of a café, and the basic flow is depicted in the following diagram:
Barista
hotDrinks ____________________
|==========| -->| |
orders drinks / | prepareHotDrink() |
Place Order ->Cafe->|======|->OrderSplitter->|======|->DrinkRouter | |
\ coldDrinks | prepareColdDrink() |
|==========| -->| |
|____________________|
Legend: |====| - channels
The Order object may contain multiple OrderItems. Once the order is placed, a Splitter will break the composite order message into a single message per drink. Each of these is then processed by a Router that determines whether the drink is hot or cold (checking the OrderItem object's 'isIced' property). The Barista prepares each drink, but hot and cold drink preparation are handled by two distinct methods:
- prepareHotDrink
- prepareColdDrink
The prepared drinks are then sent to the Waiter where they are aggregated into a Delivery object.
Café Sample Implementations
There are currently three implementations of the Café Sample that use:
- Spring Integration channels and components
- AMQP (RabbitMQ) to demonstrate a distributed architecture
- JMS (ActiveMQ) to demonstrate JMS-backed queues and a distributed architecture
All three implementations follow the same flow described above. See each one's README.md file for more details regarding the respective implementations.
Upon running any of the alternatives, you should see the output similar to this:
INFO : Barista - task-scheduler-1 prepared cold drink #1 for order #1: iced 3 shot MOCHA
INFO : Barista - task-scheduler-1 prepared cold drink #2 for order #2: iced 3 shot MOCHA
INFO : Barista - task-scheduler-1 prepared cold drink #3 for order #3: iced 3 shot MOCHA
INFO : Barista - task-scheduler-1 prepared cold drink #4 for order #4: iced 3 shot MOCHA
INFO : Barista - task-scheduler-2 prepared hot drink #1 for order #1: hot 2 shot LATTE
-----------------------
Order #1
Iced MOCHA, 3 shots.
Hot LATTE, 2 shots.
-----------------------
INFO : Barista - task-scheduler-1 prepared cold drink #5 for order #5: iced 3 shot MOCHA
INFO : Barista - task-scheduler-1 prepared cold drink #6 for order #6: iced 3 shot MOCHA
INFO : Barista - task-scheduler-1 prepared cold drink #7 for order #7: iced 3 shot MOCHA
INFO : Barista - task-scheduler-1 prepared cold drink #8 for order #8: iced 3 shot MOCHA
INFO : Barista - task-scheduler-1 prepared cold drink #9 for order #9: iced 3 shot MOCHA
INFO : Barista - task-scheduler-2 prepared hot drink #2 for order #2: hot 2 shot LATTE
-----------------------
Order #2
Iced MOCHA, 3 shots.
Hot LATTE, 2 shots.
Happy integration :-)