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I was tired of pulling in 500KB just to retry a HTTP call — so I built my own
Amine Canina · 2026-04-24 · via DEV Community

Your microservice calls an external API. It fails.

You add Resilience4j. Now you have 10 transitive dependencies, a 500KB JAR, 3 config classes, and a @Bean method just to say "try 3 times, wait 1 second".

I hit that wall one too many times. So I built RetryKit — a zero-dependency Java 17 retry & circuit breaker library. No Spring required. No transitive pulls. JAR under 50KB.

<dependency>
  <groupId>io.github.caninaam</groupId>
  <artifactId>retrykit</artifactId>
  <version>1.0.1</version>
</dependency>

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Fixed delay retry with fallback — the most common case:

String result = RetryKit.<String>retry()
    .maxAttempts(3)
    .waitDuration(Duration.ofSeconds(1))
    .fallback(ctx -> "default")
    .call(() -> myService.call());

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Exponential backoff with jitter — for avoiding thundering herd under load:

RetryKit.<String>retry()
    .maxAttempts(4)
    .exponentialBackoff(Duration.ofMillis(500), 2.0, Duration.ofSeconds(10))
    .withJitter(0.2)
    .call(() -> myService.call());

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Retry only on specific exceptions — don't retry a 400 Bad Request:

RetryKit.<String>retry()
    .maxAttempts(3)
    .retryOn(IOException.class, HttpServerErrorException.class)
    .call(() -> myService.call());

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Most retry libraries make you choose: retry OR circuit breaker OR timeout. In real production systems you need all three, composed in the right order. RetryKit lets you express that as a single string:

RetryKit.<String>retry()
    .pipeline("TIMEOUT(3s) > RETRY(3) > CB(50%)")
    .call(() -> myService.call());

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Read it left to right — outermost wrapper first. TIMEOUT(3s) — each attempt must complete within 3 seconds. RETRY(3) — retry up to 3 times on failure. CB(50%) — open the circuit if 50%+ of calls fail.

The full DSL syntax:

TIMEOUT(5s)
TIMEOUT(500ms)

RETRY(3)
RETRY(maxAttempts:5, waitDuration:500ms)
RETRY(maxAttempts:4, waitDuration:1s, backoff:2.0, maxWait:10s, jitter:0.2)

CB(50%)
CB(failureRate:50%, minCalls:5, wait:1m, halfOpen:2, timeout:2s)

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A realistic production pipeline in one line:

.pipeline("TIMEOUT(2s) > RETRY(maxAttempts:4, waitDuration:500ms, backoff:2.0, jitter:0.2) > CB(failureRate:60%, minCalls:10, wait:30s)")

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This used to take 40+ lines of config. Now it's one.

For teams that want to tune retry behavior without redeploying:

production:
  mode: RETRY_FIRST
  maxAttempts: 3
  waitDuration: PT1S
  circuitBreaker:
    failureRateThreshold: 50
    minimumNumberOfCalls: 5
    waitDurationInOpenState: PT1M

aggressive:
  mode: PIPELINE
  pipeline: "TIMEOUT(2s) > RETRY(3) > CB(50%)"

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Load it in your app:

RetryKit.<String>fromYaml("/etc/myapp/retrykit.yaml")
    .profile("production")
    .<String>as()
    .withHotReload(Duration.ofSeconds(10))
    .fallback(ctx -> "fallback")
    .build();

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Change maxAttempts or failureRateThreshold in the file — the running service picks it up in 10 seconds. No restart, no redeploy.

Two workflow modes depending on your use case. RETRY_FIRST — retry exhausts attempts, CB accumulates failures across retries. CB_FIRST — circuit breaker is checked before any retry attempt, if OPEN fail immediately — no retries wasted. Use CB_FIRST when the downstream service is known to be down.

Two distinct exceptions so you always know what happened:

try {
    kit.call(() -> myService.call());
} catch (RetryException e) {
    // service WAS called — all e.attempts() failed
} catch (CircuitBreakerOpenException e) {
    // service was NOT called — CB is open, we already know it's down
}

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How does RetryKit compare?

RetryKit Resilience4j
Dependencies 0 ~10
JAR size < 50 KB ~500 KB
Pipeline DSL yes no
YAML hot reload yes no
Java version 17+ 8+
Setup time 5 min 30 min+

Most enterprise retry libraries are built for every possible use case — you pay that cost even when you need none of it. RetryKit does one thing well.

GitHub: https://github.com/caninaam/retry-kit
Maven Central: https://central.sonatype.com/artifact/io.github.caninaam/retrykit

Next post — how the circuit breaker state machine works under the hood, and why compareAndSet matters in concurrent systems.