{"id":2184,"date":"2026-04-03T11:42:00","date_gmt":"2026-04-03T03:42:00","guid":{"rendered":"http:\/\/www.son-nagano.com\/blog\/?p=2184"},"modified":"2026-04-03T11:42:00","modified_gmt":"2026-04-03T03:42:00","slug":"how-to-debug-a-reactor-based-application-4701-867a77","status":"publish","type":"post","link":"http:\/\/www.son-nagano.com\/blog\/2026\/04\/03\/how-to-debug-a-reactor-based-application-4701-867a77\/","title":{"rendered":"How to debug a Reactor – based application?"},"content":{"rendered":"

Hey there! I’m from a reactor supplier, and I’ve seen my fair share of challenges when it comes to debugging reactor – based applications. In this blog, I’m gonna share some tips and tricks that can help you navigate through the debugging process like a pro. Reactor<\/a><\/p>\n

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Understanding the Basics of Reactor – based Applications<\/h3>\n

First off, let’s get a clear picture of what a reactor – based application is. Reactor is a reactive programming library for Java. It uses the Reactive Streams specification, which is all about asynchronous data processing with non – blocking backpressure. In simple terms, it allows your application to handle a large number of concurrent requests without getting overwhelmed.<\/p>\n

When you’re building a reactor – based application, you’re dealing with things like Flux and Mono. Flux represents a stream of 0 to N elements, while Mono represents a single element or an empty result. These are the building blocks of your application, and understanding how they work is crucial for debugging.<\/p>\n

Common Issues in Reactor – based Applications<\/h3>\n

1. Backpressure Problems<\/h4>\n

Backpressure is a key concept in reactive programming. It’s all about managing the flow of data between different parts of your application. If the producer is generating data faster than the consumer can handle, you’ll run into backpressure issues.<\/p>\n

For example, let’s say you have a Flux that’s emitting a large number of elements very quickly, and the subscriber can’t process them fast enough. This can lead to memory issues, as the elements pile up in buffers. You might see errors like MissingBackpressureException<\/code> in your logs.<\/p>\n

2. Error Handling<\/h4>\n

In a reactor – based application, errors can propagate through the reactive pipeline. If you don’t handle errors properly, they can cause your application to crash. For instance, if a Mono fails to complete due to an exception, and you haven’t added any error handling logic, the whole pipeline might break.<\/p>\n

3. Concurrency Issues<\/h4>\n

Reactor applications are designed to handle concurrency, but that doesn’t mean you’re immune to concurrency issues. If multiple threads are accessing and modifying shared resources, you might end up with race conditions. This can lead to unpredictable behavior in your application.<\/p>\n

Debugging Techniques<\/h3>\n

1. Logging<\/h4>\n

Logging is one of the simplest yet most effective debugging techniques. You can use logging statements to track the flow of data through your reactive pipeline. For example, you can log the elements emitted by a Flux or Mono at different stages of the pipeline.<\/p>\n

In Java, you can use a logging framework like SLF4J. Here’s an example of how you can log the elements of a Flux:<\/p>\n

import org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\nimport reactor.core.publisher.Flux;\n\npublic class ReactorLoggingExample {\n    private static final Logger logger = LoggerFactory.getLogger(ReactorLoggingExample.class);\n\n    public static void main(String[] args) {\n        Flux<Integer> flux = Flux.just(1, 2, 3, 4, 5);\n        flux.log()\n           .subscribe();\n    }\n}\n<\/code><\/pre>\n
The log()<\/code> operator adds logging statements at different stages of the pipeline, showing when elements are subscribed to, emitted, and completed.<\/p>\n
2. Debugging with Breakpoints<\/h4>\n
If you’re using an IDE like IntelliJ IDEA or Eclipse, you can use breakpoints to pause the execution of your application at specific points. This allows you to inspect the state of your reactive objects, such as the values of variables and the current position in the pipeline.<\/p>\n
For example, you can set a breakpoint inside a map<\/code> operator to see how the elements are being transformed. This can help you identify if there are any issues with the transformation logic.<\/p>\n
3. Using Reactor Debugging Tools<\/h4>\n
Reactor provides some built – in debugging tools. For example, you can use the checkpoint()<\/code> operator. This operator adds a checkpoint in the reactive pipeline, and if an error occurs downstream, it will provide a stack trace that includes the checkpoint information.<\/p>\n
import reactor.core.publisher.Flux;\n\npublic class ReactorCheckpointExample {\n    public static void main(String[] args) {\n        Flux<Integer> flux = Flux.just(1, 2, 3, 4, 5)\n                                 .checkpoint("Before map")\n                                 .map(i -> i \/ 0)\n                                 .checkpoint("After map");\n        flux.subscribe();\n    }\n}\n<\/code><\/pre>\n
In this example, if an error occurs when dividing by zero, the stack trace will show the checkpoints, which can help you pinpoint where the problem originated.<\/p>\n
4. Monitoring and Metrics<\/h4>\n
Monitoring and metrics can give you valuable insights into the performance of your reactor – based application. You can use tools like Micrometer to collect and display metrics such as the number of elements emitted, the processing time, and the error rate.<\/p>\n
For example, you can use Micrometer to measure the time it takes to process a Flux:<\/p>\n
import io.micrometer.core.instrument.MeterRegistry;\nimport io.micrometer.core.instrument.Timer;\nimport reactor.core.publisher.Flux;\n\nimport java.time.Duration;\n\npublic class ReactorMetricsExample {\n    private final MeterRegistry meterRegistry;\n\n    public ReactorMetricsExample(MeterRegistry meterRegistry) {\n        this.meterRegistry = meterRegistry;\n    }\n\n    public void processFlux() {\n        Timer timer = meterRegistry.timer("flux.processing.time");\n        Flux<Integer> flux = Flux.just(1, 2, 3, 4, 5);\n        timer.record(() -> {\n            flux.subscribe();\n        });\n    }\n}\n<\/code><\/pre>\n
Tips for Effective Debugging<\/h3>\n