Implementing Retry With Backoff & Jitter In Java

Hey guys! Let's dive into a super important topic in software development: implementing retries with exponential backoff and jitter using the power of Resilience4j in Java. This is a crucial strategy for building resilient applications that can gracefully handle transient failures. We'll explore how to design a robust retry mechanism, configure it with exponential backoff and jitter, and integrate it with a circuit breaker for even more resilience. Plus, we'll talk about how to validate that it works as expected. Let's get started!

Understanding the Need for Retries

So, why do we even need retries in the first place? Well, in the real world of distributed systems, things can go wrong. Networks can be flaky, servers can be overloaded, and services can temporarily become unavailable. Instead of letting these transient failures bring your application to its knees, retries provide a way to automatically attempt the operation again. This can be a lifesaver when dealing with intermittent issues.

Now, while simple retries are better than nothing, they can be pretty aggressive and might overwhelm a failing service. This is where exponential backoff comes into play. It gradually increases the delay between retry attempts, giving the service a chance to recover. Jitter, on the other hand, introduces a bit of randomness to the backoff, preventing all retries from happening at the exact same time, which could potentially overload the service after it recovers. Resilience4j offers a clean and efficient way to implement retries with both backoff and jitter.

Building the RetryFactory

Let's start by creating a RetryFactory. This guy is responsible for creating and configuring Retry instances. We want our Retry to use exponential backoff and be configurable. I have got you! Here's how a basic RetryFactory might look:

import io.github.resilience4j.retry.Retry;
import io.github.resilience4j.retry.RetryConfig;
import io.github.resilience4j.retry.RetryRegistry;
import java.time.Duration;
import java.util.Random;
import java.util.function.Supplier;

public class RetryFactory {

    private final RetryRegistry retryRegistry;
    private final Random random;

    public RetryFactory(RetryRegistry retryRegistry) {
        this.retryRegistry = retryRegistry;
        this.random = new Random();
    }

    public Retry createRetry(String name, int maxAttempts, Duration initialInterval, double multiplier, Duration maxInterval, double jitterFactor) {
        RetryConfig config = RetryConfig.custom()
                .maxAttempts(maxAttempts)
                .intervalFunction(interval -> {
                    long base = initialInterval.toMillis() * (long) Math.pow(multiplier, interval - 1);
                    long jitter = (long) (jitterFactor * base * random.nextDouble());
                    long intervalMillis = Math.min(base + jitter, maxInterval.toMillis());
                    return Duration.ofMillis(intervalMillis);
                })
                .build();
        return retryRegistry.retry(name, config);
    }

    public Retry createRetry(String name, int maxAttempts, Duration initialInterval, double multiplier, Duration maxInterval) {
        return createRetry(name, maxAttempts, initialInterval, multiplier, maxInterval, 0.2);
    }

}

In this example, the createRetry method takes parameters for the retry name, maximum attempts, initial interval, multiplier for exponential backoff, maximum interval, and a jitterFactor. The intervalFunction calculates the delay between retries based on the exponential backoff formula and applies jitter. The jitter adds a random element to the delay, ensuring that retries are not synchronized. The RetryRegistry is used to manage and create Retry instances.

The RetryingClient and Composition with CircuitBreaker

Now, let's build a RetryingClient. This client will use the Retry instance to retry operations on a FlakyService. We'll also compose it with a CircuitBreaker for added protection. The CircuitBreaker will automatically trip and prevent further calls if the service is consistently failing. This is a crucial element for preventing cascading failures.

Here's an example RetryingClient class:

import io.github.resilience4j.circuitbreaker.CircuitBreaker;
import io.github.resilience4j.circuitbreaker.CircuitBreakerRegistry;
import io.github.resilience4j.retry.Retry;
import java.util.concurrent.Callable;
import java.util.concurrent.atomic.AtomicInteger;
import java.util.function.Supplier;

public class RetryingClient {

    private final Retry retry;
    private final CircuitBreaker circuitBreaker;
    private final FlakyService flakyService;
    private final AtomicInteger attemptCount = new AtomicInteger(0);

    public RetryingClient(Retry retry, CircuitBreaker circuitBreaker, FlakyService flakyService) {
        this.retry = retry;
        this.circuitBreaker = circuitBreaker;
        this.flakyService = flakyService;
    }

    public <T> T execute(Callable<T> operation) {
        return CircuitBreaker.decorateCallable(circuitBreaker, Retry.decorateCallable(retry, () -> {
            attemptCount.incrementAndGet();
            try {
                return operation.call();
            } catch (Exception e) {
                throw new RuntimeException(e);
            }
        }));
    }

    public int getAttemptCount() {
        return attemptCount.get();
    }

}

In this code, we have a RetryingClient that takes a Retry, a CircuitBreaker, and a FlakyService as input. The execute method wraps the operation with both the Retry and the CircuitBreaker. The attemptCount is used to track the number of retry attempts. The CircuitBreaker will protect the service by stopping calls if the service is failing, and the Retry will handle transient failures by retrying the operation.

Implementing a FlakyService

For testing, we'll need a FlakyService. This service will simulate transient failures. I have got you covered! Here's how you might implement a FlakyService:

import java.util.concurrent.atomic.AtomicInteger;

public class FlakyService {

    private final int maxFailures;
    private final AtomicInteger failureCount = new AtomicInteger(0);

    public FlakyService(int maxFailures) {
        this.maxFailures = maxFailures;
    }

    public String call() {
        int count = failureCount.incrementAndGet();
        if (count <= maxFailures) {
            throw new RuntimeException("Simulated failure");
        } else {
            return "Success";
        }
    }

}

The FlakyService throws an exception for a specified number of times before succeeding. This helps us test our retry mechanism thoroughly.

Testing the Retry Mechanism

Now, let's write some tests to verify that our retry mechanism is working correctly. The key things to test are:

• The number of retry attempts: Does the operation retry the expected number of times before succeeding or failing?
• The total elapsed time: Does the total time taken for the operation align with the backoff schedule and jitter?

Here are some sample JUnit tests for the RetryingClient:

import io.github.resilience4j.circuitbreaker.CircuitBreaker; 
import io.github.resilience4j.circuitbreaker.CircuitBreakerConfig; 
import io.github.resilience4j.circuitbreaker.CircuitBreakerRegistry; 
import io.github.resilience4j.retry.Retry; 
import io.github.resilience4j.retry.RetryConfig; 
import io.github.resilience4j.retry.RetryRegistry; 
import java.time.Duration; 
import org.junit.jupiter.api.BeforeEach; 
import org.junit.jupiter.api.Test; 
import static org.junit.jupiter.api.Assertions.assertEquals; 
import static org.junit.jupiter.api.Assertions.assertTrue; 
import static org.mockito.Mockito.mock; 
import static org.mockito.Mockito.when; 

public class RetryingClientTest {

    private RetryFactory retryFactory;
    private CircuitBreakerRegistry circuitBreakerRegistry;
    private RetryRegistry retryRegistry;

    @BeforeEach
    void setUp() {
        retryRegistry = RetryRegistry.ofDefaults();
        circuitBreakerRegistry = CircuitBreakerRegistry.ofDefaults();
        retryFactory = new RetryFactory(retryRegistry);
    }

    @Test
    void testRetrySuccess() {
        // Given
        int maxFailures = 2;
        FlakyService flakyService = new FlakyService(maxFailures);
        CircuitBreaker circuitBreaker = circuitBreakerRegistry.circuitBreaker("test");
        Retry retry = retryFactory.createRetry("test", 3, Duration.ofMillis(10), 2.0, Duration.ofMillis(100));
        RetryingClient retryingClient = new RetryingClient(retry, circuitBreaker, flakyService);

        // When
        String result = retryingClient.execute(flakyService::call);

        // Then
        assertEquals("Success", result);
        assertEquals(maxFailures + 1, retryingClient.getAttemptCount());
    }

    @Test
    void testRetryFailure() {
        // Given
        int maxFailures = 3;
        FlakyService flakyService = new FlakyService(maxFailures + 1); // Fail more times than retries
        CircuitBreaker circuitBreaker = circuitBreakerRegistry.circuitBreaker("test");
        Retry retry = retryFactory.createRetry("test", 3, Duration.ofMillis(10), 2.0, Duration.ofMillis(100));
        RetryingClient retryingClient = new RetryingClient(retry, circuitBreaker, flakyService);

        // When & Then
        try {
            retryingClient.execute(flakyService::call);
        } catch (Exception e) {
            // Expected
            assertEquals(4, retryingClient.getAttemptCount()); // 3 retries + 1 initial attempt
        }
    }

}

These tests cover both success and failure scenarios, ensuring that the retry mechanism works as expected. The tests use the FlakyService to simulate failures and verify the number of retries. They are also checking to see if the success case retry mechanism can get the job done.

Validating Elapsed Time

For validating the elapsed time, especially with the jitter, the tests will have to be more sophisticated. With jitter, the exact elapsed time cannot be predicted, but it can be bounded. The elapsed time will be between a minimum and maximum total time based on the retry's configuration (initial interval, multiplier, max interval, jitter factor, and number of attempts).

Here’s how to calculate the minimum and maximum expected elapsed times:

• Minimum Time: Sum of all backoff intervals, with the jitter set to the minimum value (0). The elapsed time will only consider the intervals.
• Maximum Time: Sum of all backoff intervals, with the jitter set to the maximum value (jitter factor * base). The elapsed time will consider the intervals plus jitter.

I won't give you code, since this is a test implementation, but here is a sample of how it would be structured:

1. Calculate Expected Intervals: Use the Retry configuration (initial interval, multiplier, max interval, and number of attempts) to determine the backoff intervals. Make sure you are accounting for the jitterFactor in the calculation.
2. Calculate Min and Max Times: Calculate the minimum and maximum total elapsed times based on the intervals. Sum the intervals for the min time. Sum the intervals plus jitter for the max time. The max is a sum that considers jitter, meaning that the jitter is accounted for in all the attempts.
3. Run the Test: Run the test with the RetryingClient and measure the actual elapsed time. Get the start time before the call and the end time after.
4. Assert the result: Validate that the actual elapsed time falls within the calculated min and max bounds.

Conclusion

And that's it, guys! We've covered how to implement a retry mechanism with exponential backoff and jitter in Java using Resilience4j. Remember, this is a crucial step in building robust, resilient applications. By combining retries, exponential backoff, jitter, and circuit breakers, you can build systems that are much more tolerant of failures and much more reliable. Happy coding!