Spring 5 added support for reactive programming with the Spring WebFlux module, which has been improved upon ever since. Get started with the Reactor project basics and reactive programming in Spring Boot:
Mocking is an essential part of unit testing, and the Mockito library makes it easy to write clean and intuitive unit tests for your Java code.
Get started with mocking and improve your application tests using our Mockito guide:
Handling concurrency in an application can be a tricky process with many potential pitfalls. A solid grasp of the fundamentals will go a long way to help minimize these issues.
Get started with understanding multi-threaded applications with our Java Concurrency guide:
Spring 5 added support for reactive programming with the Spring WebFlux module, which has been improved upon ever since. Get started with the Reactor project basics and reactive programming in Spring Boot:
Since its introduction in Java 8, the Stream API has become a staple of Java development. The basic operations like iterating, filtering, mapping sequences of elements are deceptively simple to use.
But these can also be overused and fall into some common pitfalls.
To get a better understanding on how Streams work and how to combine them with other language features, check out our guide to Java Streams:
Get started with Spring and Spring Boot, through the Learn Spring course:
>> LEARN SPRINGExplore Spring Boot 3 and Spring 6 in-depth through building a full REST API with the framework:
Yes, Spring Security can be complex, from the more advanced functionality within the Core to the deep OAuth support in the framework.
I built the security material as two full courses - Core and OAuth, to get practical with these more complex scenarios. We explore when and how to use each feature and code through it on the backing project.
You can explore the course here:
Spring Data JPA is a great way to handle the complexity of JPA with the powerful simplicity of Spring Boot.
Get started with Spring Data JPA through the guided reference course:
Refactor Java code safely β and automatically β with OpenRewrite.
Refactoring big codebases by hand is slow, risky, and easy to put off. Thatβs where OpenRewrite comes in. The open-source framework for large-scale, automated code transformations helps teams modernize safely and consistently.
Each month, the creators and maintainers of OpenRewrite at Moderne run live, hands-on training sessions β one for newcomers and one for experienced users. Youβll see how recipes work, how to apply them across projects, and how to modernize code with confidence.
Join the next session, bring your questions, and learn how to automate the kind of work that usually eats your sprint time.
1. Overview
With the introduction of Spring WebFlux, we got another powerful tool to write reactive, non-blocking applications. While using this technology is now way easier than before, debugging reactive sequences in Spring WebFlux can be quite cumbersome.
In this quick tutorial, weβll see how to easily log events in asynchronous sequences and how to avoid some simple mistakes.
2. Maven Dependency
Letβs add the Spring WebFlux dependency to our project so we can create reactive streams:
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-webflux</artifactId>
</dependency>
We can get the latest spring-boot-starter-webflux dependency from Maven Central.
3. Creating a Reactive Stream
To begin letβs create a reactive stream using Flux and use the log() method to enable logging:
Flux<Integer> reactiveStream = Flux.range(1, 5).log();
Next, we will subscribe to it to consume generated values:
reactiveStream.subscribe();
4. Logging Reactive Stream
After running the above application we see our logger in action:
2018-11-11 22:37:04 INFO | onSubscribe([Synchronous Fuseable] FluxRange.RangeSubscription)
2018-11-11 22:37:04 INFO | request(unbounded)
2018-11-11 22:37:04 INFO | onNext(1)
2018-11-11 22:37:04 INFO | onNext(2)
2018-11-11 22:37:04 INFO | onNext(3)
2018-11-11 22:37:04 INFO | onNext(4)
2018-11-11 22:37:04 INFO | onNext(5)
2018-11-11 22:37:04 INFO | onComplete()
We see every event that occurred on our stream. Five values were emitted and then stream closed with an onComplete() event.
5. Advanced Logging Scenario
We can modify our application to see a more interesting scenario. Letβs add take() to Flux which will instruct the stream to provide only a specific number of events:
Flux<Integer> reactiveStream = Flux.range(1, 5).log().take(3);
After executing the code, weβll see the following output:
2018-11-11 22:45:35 INFO | onSubscribe([Synchronous Fuseable] FluxRange.RangeSubscription)
2018-11-11 22:45:35 INFO | request(unbounded)
2018-11-11 22:45:35 INFO | onNext(1)
2018-11-11 22:45:35 INFO | onNext(2)
2018-11-11 22:45:35 INFO | onNext(3)
2018-11-11 22:45:35 INFO | cancel()
As we can see, take() caused the stream to cancel after emitting three events.
The placement of log() in your stream is crucial. Letβs see how placing log() after take() will produce different output:
Flux<Integer> reactiveStream = Flux.range(1, 5).take(3).log();
And the output:
2018-11-11 22:49:23 INFO | onSubscribe([Fuseable] FluxTake.TakeFuseableSubscriber)
2018-11-11 22:49:23 INFO | request(unbounded)
2018-11-11 22:49:23 INFO | onNext(1)
2018-11-11 22:49:23 INFO | onNext(2)
2018-11-11 22:49:23 INFO | onNext(3)
2018-11-11 22:49:23 INFO | onComplete()
As we can see changing the point of observation changed the output. Now the stream produced three events, but instead of cancel(), we see onComplete(). This is because we observe the output of using take() instead of what was requested by this method.
6. Conclusion
In this quick article, we saw how to log reactive streams using built-in log() method.
