JVM Advent

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Resilient applications with Spring and Resilience4J

Resilience is a fundamental property of software applications, especially in distributed systems like microservices and cloud native applications.

A resilient application keeps providing its services even in the presence of faults. Errors can and will happen, so it’s crucial to build fault-tolerant applications. A graceful degradation should be provided in the worst-case scenario, for example, by implementing a fallback logic.

Resilience4J is a library implementing the most common resilience patterns for Java applications, including time limiters, bulkheads, circuit breakers, rate limiters, retries, and cache. This article will show you how to use Resilience4J to include retries, bulkheads, and rate limiters in your Spring applications. Resilience4J provides integration with Spring Boot, including auto-configuration and metrics exported through Spring Boot Actuator.

You can find the full source code for the examples shown in this article on GitHub.

Setup

To use Resilience4J in your Spring Boot application, you need to include the following dependencies. In this example, I’m using Resilience4J 1.6.1 and Spring Boot 2.3.6.

implementation 'org.springframework.boot:spring-boot-starter-actuator'
implementation 'org.springframework.boot:spring-boot-starter-aop'
implementation 'io.github.resilience4j:resilience4j-reactor:1.6.1'
implementation 'io.github.resilience4j:resilience4j-spring-boot2:1.6.1'

If your application is using Project Reactor, for example, when working with Spring Web Flux, then you need an extra dependency to support objects like Mono and Flux.

implementation 'io.github.resilience4j:resilience4j-reactor:1.6.1'

The resilience patterns I’ll show you in the next sections will be applied to a REST call to a service downstream that returns holiday greetings. The reactive WebClient object is used for the purpose.

public Mono<String> getHolidayGreetings(String url) {
    return webClient.get().uri(url)
            .retrieve()
            .bodyToMono(String.class);
}

For all the patterns, Resilience4J lets you define a fallback method to run whenever an operation is unrecoverable. In this case, the fallback

behavior is logging an error message and return a default holiday greeting.
public Mono<String> fallback(String url, Throwable throwable) {
    log.error("Fallback executed for {} with {}", url, throwable.toString());
    return Mono.just("Happy holidays!");
}

Retry

When your client doesn’t receive a response from a service downstream within a specific time window or replies with an error, you can use the retry pattern to attempt the request again. It’s a useful pattern for when the called service is experiencing some temporary issues, perhaps it’s overloaded, and it’s momentarily unable to process new requests. In that scenario, start an immediate sequence of retry attempts would only make things worse.

A better solution is to increasingly add some delay between retry attempts to guarantee the service downstream some time to recover, without being flooded with requests.

Resilience4J provides a Retry component that lets you retry an operation. You can configure it either programmatically or in your application.yml file. Exponential backoff is a common strategy for increasing the delay between retry attempts, and Resilience4J comes with an implementation for it.

resilience4j.retry:
  instances:
    holidayClient:
      # The maximum number of retry attempt
      maxRetryAttempts: 3
      # Initial interval between retry attempts
      waitDuration: 1s
      # Use exponential backoff strategy
      enableExponentialBackoff: true
      # Multiplier for the exponential backoff
      exponentialBackoffMultiplier: 2

The Retry configuration is applied to a holidayClient instance. The delay is computed as the waitDuration multiplied by the exponentialBackoffMultiplier.

When configuring the Retry component through properties, you can leverage the Resilience4J @Retry annotation to apply the pattern to a specific operation. The name parameter has to be the same as the instance name defined in the configuration. Optionally, you can pass the name of a fallback method. After the third and last attempt, an exception is thrown. If a fallback method is defined, it’s executed; otherwise, the exception is sent to the caller.

@Retry(name = "holidayClient", fallbackMethod = "fallback")
public Mono<String> getHolidayGreetingWithRetries(String url) {
  return getHolidayGreetings(url);
}

Retries are a useful tool in case of temporarily unavailable services, but it might be that it’s not a good fit in other scenarios. By default, a retry is attempted if the operation throws any exception. Not all the exceptions are the same. For example, getting back a 404 response is probably acceptable, so you don’t want to keep retrying the request. Resilience4J lets you further configure the Retry component by defining which exceptions should trigger a retry and which ones should be ignored.

You should also be careful with the type of operation that will be retried. Retrying an idempotent operation like a GET request is fine, but it’s not a recommended pattern for non-idempotent operations. The fact you haven’t received response doesn’t mean that the operation has not been executed. Maybe the response got lost on its way back to the caller. Or perhaps it took too long, and the caller timeout expired before getting any response.

Bulkhead

Bulkheads are partitions in a ship containing water in case of a breach, preventing the whole boat from sinking. Within an application, bulkheads can be implemented as partitions of threads preventing problems in one area to affect the entire service.

In practice, it relies on an isolated resource pool and sets a limit on the number of concurrent requests that the threads can execute in that pool. Resilience4J provides a Bulkhead component that lets you isolate an operation inside a bulkhead that you can configure either programmatically or in your application.yml file.

resilience4j.bulkhead:
  instances:
    holidayClient:
      # Max amount of time a thread should be blocked
      # when attempting to enter a saturated bulkhead.
      maxWaitDuration: 100ms
      # Max amount of parallel executions allowed by the bulkhead.
      maxConcurrentCalls: 10

The Bulkhead configuration is applied to a holidayClient instance. There can only be 10 concurrent calls. When the bulkhead is saturated, a thread is allowed to block for a maximum of 100ms before the request is dropped.

When configuring the Bulkhead component through properties, you can leverage the Resilience4J @Bulkhead annotation to apply the pattern to a specific operation. The name parameter has to be the same as the instance name defined in the configuration. Optionally, you can pass the name of a fallback method.

When the bulkhead is saturated and the wait duration expired, the fallback method is executed if present, otherwise the request is dropped, and an exception is thrown.

@Bulkhead(name = "holidayClient", fallbackMethod = "fallback")
public Mono<String> getHolidayGreetingWithBulkhead(String url) {
  return getHolidayGreetings(url);
}

In reactive applications, for example, when using Spring Web Flux and Project Reactor, the risk of exhausting and blocking all threads available is lower, but it’s still a good pattern in your toolbox for building resilient applications.

Rate Limiter

Rate limiters constraint the number of requests you can make in a given time interval. If you expose an API, rate limiters help to protect your application against request flooding. In a client, you can leverage rate limiters to control the number of requests done to a service downstream, useful to don’t DDoS other applications in your system, or to limit the usage of an API in case of pay-per-use services.

Resilience4J provides a RateLimiter component that lets you apply a constraint on how many requests can be done for a given operation in a specific interval of time. You can configure it either programmatically or in your application.yml file.

resilience4j.ratelimiter:
  instances:
    holidayClient:
      # The number of permissions available during one limit refresh period.
      limitForPeriod: 10
      # After each period, the rate limiter sets its permissions count
      # back to the limitForPeriod value.
      limitRefreshPeriod: 1s
      # The default wait time a thread waits for permission.
      timeoutDuration: 500ms

The RateLimiter configuration is applied to a holidayClient instance. For each period, only 10 requests can be processed. Every second, the RateLimiter resets the permissions count to 10, and a new period is started. Each request will wait 500ms at most to get permission to execute the operation; otherwise, it’s rejected.

Optionally, you can pass the name of a fallback method. If a request is rejected because it exceeds the rate-limiting and a fallback method is defined, it’s executed; otherwise, the exception is sent to the caller.

@RateLimiter(name = "holidayClient", fallbackMethod = "fallback")
public Mono<String> getHolidayGreetingWithRateLimiter(String url) {
  return getHolidayGreetings(url);
}

Conclusion

In distributed systems, change is the constant, and failure can and will happen. A resilient application keeps providing its service in the face of adversity. In the worst-case scenario, it keeps doing so with graceful degradation of its functionality.

Resilient patterns like retries, bulkheads, and rate limiters can help your applications being resilient to faults. Resilience4J is a Java library implementing the most common resilience patterns, and provides integration with Spring Boot and Spring Cloud.

If you’re interested in this subject, I recommend reading “Release It! Second Edition -> Design and Deploy Production-Ready Software” by Michael T. Nygard.

For more information about Resilience4J, you can refer to the project website. And if you work with Spring and need to use circuit breakers in your architecture, then check out the Spring Cloud Circuit Breaker project.

Author: ThomasVitale

Thomas is a Senior Software Engineer specialized in building modern, cloud native, robust, and secure enterprise applications. He’s the author of “Cloud Native Spring in Action”, published by Manning.

He designs and develops software solutions at Systematic, Denmark, supporting home care, social services, and help to citizens.

Thomas has an MSc in computer engineering specializing in software, is a Red Hat Certified Enterprise Application Developer and Pivotal Certified Spring Professional. He likes contributing to open source projects like Spring Security, Spring Cloud, and Keycloak. When he doesn’t develop software, Thomas loves reading, traveling, playing the piano, and composing music.

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