Spring cloud stream
A new rick & morty adventure
i want to welcome you
to the microverse
spring cloud stream
- framework for creating message driven microservices
- builds upon Spring Boot
- based on Spring Integration
- opinionated configuration of message brokers
- persistent pub/sub semantics
- supports different middleware vendors:
- Kafka
- RabbitMQ
- ActiveMQ
- Redis
- AWS Kinesis
Basic example
@SpringBootApplication
@EnableBinding(Sink.class)
public class VoteRecordingSinkApplication {
public static void main(String[] args) {
SpringApplication.run(VoteRecordingSinkApplication.class, args);
}
@StreamListener(Sink.INPUT)
public void processVote(Vote vote) {
votingService.recordVote(vote);
}
}spring cloud stream
- Has reactive programming support through Reactor or RxJava
@EnableBinding(Processor.class)
@EnableAutoConfiguration
public static class UppercaseTransformer {
@StreamListener
@Output(Processor.OUTPUT)
public Flux<String> receive(@Input(Processor.INPUT) Flux<String> input) {
return input.map(s -> s.toUpperCase());
}
}
example: Web application
with a RabbitMQ output binder
public interface MyChannels {
@Output("some-output")
MessageChannel output();
}spring.jms.pub-sub-domain=true<dependency>
<groupId>org.springframework.cloud</groupId>
<artifactId>spring-cloud-starter-stream-rabbit</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>
When using JMS:
@RestController
@SpringBootApplication
@EnableBinding(MyChannels.class)
public class SpringCloudStreamDemoApplication {
public static void main(String[] args) {
SpringApplication.run(SpringCloudStreamDemoApplication.class, args);
}
@Autowired
private MyChannels myChannels;
@GetMapping(params = "message")
public void sendMessage(@RequestParam("message") String message) {
this.myChannels.output().send(MessageBuilder.withPayload(message).build());
}
@StreamListener("some-output")
public void receiveMessage(String message) {
System.out.println(message);
}
}Web application
with a RabbitMQ output binder
Advantages
of pub-sub style communication
- data is broadcast through shared topics
- reduces complexity across producer and consumer
- new consumer can easily be added without disturbing others
- reduces coupling between microservices
Consumer groups
splitting up workload
- similar to Kafka consumer groups
- all groups which subscribe to a given destination receive a copy of published data
- only one member of that group receives actual message
- default behaviour: every instance is anonymous, single-member consumer group
use case: szechuan sauce
RICk
Genius. Grandpa. Asshole.
- morty's grandfather
- inventor of
- the microverse
- interdimensional travel
- a butter-passing robot
- wants more szechuan sauce
- too lazy to look for it himself
RICk
Genius. Grandpa. Asshole.
Rick Microservice
RabbitMQ Binder
RabbitMQ
- microverse
- meeseeks
- rick
Meeseeks
Existence is pain.
- invented by Rick
- summoned by Meeseeks Box
- magical creature
- spawned for a single purpose
- disappears once task is complete
MEEseeks
Existence is pain.
Meeseeks microservice
RabbitMQ Binder
RabbitMQ
- microverse
- rick
- mcdonalds
- meeseeks
mcdonalds
One in five hundred chance to win.
McDonalds microservice
RabbitMQ Binder
RabbitMQ
- meeseeks
- mcdonalds
MORTY
The perfect sidekick.
Morty microservice
RabbitMQ Binder
RabbitMQ
- rick
- meeseeks
- mcdonalds
- microverse
architecture
RabbitMQ
Scale up/down
The Microverse
TIME FOR ACTION
SPring Cloud stream 2.0
WHAts new?
POLLED CONSUmers
consume messages when you feel like it
public interface PolledConsumer {
@Input
PollableMessageSource destIn();
@Output
MessageChannel destOut();
}Micrometer support
export all metrics to any compatible registry like prometheus
ACTUATOR BINDING CONTROLS
allow both the VISUALISATION AND CONTROL of the Bindings lifecycle.
ACTUATOR AND WEB DEPENDENCY ARE NOW OPTIONAL
run your streaming microservice simply as a process
ENHANCED SUPPORT FOR KAFKA STREAMS
- enhanced interop between Kafka Streams and regular Kafka bindings
- multiple Kafka Streams types (KStream, KTable)
- bindable components, and interactive-query support
@SpringBootApplication
@EnableBinding(KStreamProcessor.class)
public class WordCountProcessorApplication {
@StreamListener("input")
@SendTo("output")
public KStream<?, WordCount> process(KStream<?, String> input) {
return input
.flatMapValues(value -> Arrays.asList(value.toLowerCase().split("\\W+")))
.groupBy((key, value) -> value)
.windowedBy(TimeWindows.of(5000))
.count(Materialized.as("WordCounts-multi"))
.toStream()
.map((key, value) -> new KeyValue<>(null, new WordCount(key.key(), value, new Date(key.window().start()), new Date(key.window().end()))));
}
public static void main(String[] args) {
SpringApplication.run(WordCountProcessorApplication.class, args);
}links
QUEstions?
Spring Cloud Stream - A new Rick & Morty adventure
By Dieter Hubau
Spring Cloud Stream - A new Rick & Morty adventure
My presentation on Spring Cloud Stream for Ordina JOIN event
