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1. Confirm that repeating the operation is safe
A failed response does not prove that the server failed to perform the operation. The server might have completed a write and the response might have been lost; retrying could then create a duplicate side effect.
RFC 9110 says a client “SHOULD NOT automatically retry a request with a non-idempotent method” unless it knows the operation is idempotent or can detect that the original request was never applied. See RFC 9110 §9.2.2. HTTP method alone is not a complete safety check: determine what the particular operation does.
- Retry an operation that is idempotent by its documented semantics only if the API’s guidance also permits retrying the failure.
- For a non-idempotent operation, retry only when the API provides a supported mechanism—such as an idempotency key or operation-specific deduplication—or you can establish that the original request was not applied.
- If neither condition holds, return the error rather than risking a duplicate action.
Do not assume an idempotency key is supported or works for every endpoint; verify its scope and behavior in the target API’s documentation.
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2. Classify failures using the API contract
Retry only failures the service identifies as potentially temporary. Transient network or server failures and throttling can be retryable, but there is no universal list of status codes that applies to every API. Google Cloud Storage’s guidance warns against retrying unretryable errors and against unconditional retries of non-idempotent operations: Cloud Storage retry strategy.
- Potentially transient: failures the service documents as temporary, including some server-side or network errors.
- Throttling: apply the service’s documented handling, including any retry timing guidance.
- Usually not fixed by repetition: authentication, authorization, malformed input, and other request errors that require a corrected credential, permission, or request.
Make the decision operation-aware as well as error-aware: a retryable-looking response does not make an unsafe operation safe to repeat. Avoid hard-coding a generic status-code list in place of the service’s error guidance.
3. Choose a backoff and jitter policy
A capped exponential window grows after each failed attempt. One common policy is full jitter: calculate a maximum window, then choose a random delay from zero through that window.
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window_n = min(max_backoff, base_delay × 2^n)
delay_n = uniform_random(0, window_n)
Here, n is the retry index, starting at zero for the first retry wait. “Full jitter” specifically means sampling across the whole interval from zero to the calculated window. Other randomized policies use different ranges or add randomness after calculating a delay; name the policy accurately rather than calling all jitter full jitter.
Randomized waits spread clients out instead of having them all retry at the same increasing intervals. Set the base and cap to suit the API, the expected transient failures, and the caller’s latency budget. A larger cap can reduce rapid repeat traffic but can also make a request take too long to be useful.
Published examples are not universal defaults
| Documented example | What it specifies | Scope |
|---|---|---|
| Google Cloud IAM | min(2^n + random_fraction, maximum_backoff) seconds; n starts at zero, and each retry gets a new random fraction no greater than one second. The algorithm stops at a configured deadline. |
IAM guidance, not a default for all APIs. Google Cloud IAM retry strategy |
| AWS SDK standard mode reference | Full jitter: random(0, 1) × min(20,000 ms, base_delay × 2^retry). The reference gives a 50 ms base for transient non-throttling errors and 1,000 ms for throttling errors, plus a retry quota. |
The cited AWS SDK reference’s documented behavior; not an HTTP standard or a guarantee for every language SDK, service, or configuration. AWS SDK retry behavior |
| Azure architecture guidance | Exponential backoff with jitter is a general guideline for background operations; interactive operations may instead need immediate or regular-interval retries. | General guidance illustrating how user experience and latency affect policy. Azure transient-fault handling |
These examples use different schedules and contexts. Select a policy based on your service’s error rules, retry hints, and deadline rather than copying a provider’s numbers without its surrounding assumptions.
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4. Bound attempts and total elapsed time
Set both a maximum number of attempts and an overall deadline. An attempt limit constrains the extra load your client can create; a deadline prevents retries from continuing after the caller no longer has time to use the result. Count attempts explicitly: in the pseudocode below, max_retries excludes the initial request, so the largest possible total is max_retries + 1.
Google Cloud IAM’s example stops after a configured deadline. AWS Well-Architected guidance also cautions that retries can create backlogs and recommends limiting retries. See AWS REL05-BP03: Limit retries.
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5. Apply server retry guidance deliberately
HTTP Retry-After can be either an HTTP date or a non-negative integer delay in seconds, as specified in RFC 9110 §10.2.3. If your client supports this field, parse both forms and follow the target API’s contract for how it interacts with your local backoff, the deadline, and any maximum wait.
Do not assume there is one universal formula for combining the server’s requested wait with a locally generated jitter delay. The AWS SDK reference also documents service-specific handling for x-amz-retry-after; that proprietary header and its behavior should not be generalized to unrelated APIs. Implement server hints according to the specific API or SDK documentation.
6. Put the decisions into the retry loop
This pseudocode makes the policy boundaries visible. It is an implementation outline, not tested code; adapt the error classifier, operation-safety check, timing behavior, and response handling to your API and runtime.
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for retry_index in 0..max_retries:
response = send(request)
if response succeeded:
return response
if not retryable(response):
return or raise response
if not operation_is_safe_to_repeat(request):
return or raise response
if retry_index == max_retries or deadline_exceeded():
return or raise response
window = min(max_backoff, base_delay × 2^retry_index)
delay = uniform_random(0, window) # full jitter
delay = apply_api_retry_after_per_contract(delay, response)
if delay_would_exceed_deadline(delay):
return or raise response
sleep(delay)
The retry-hint function is intentionally API-specific: do not substitute an undocumented local rule for the service’s instructions. In production code, also ensure that cancellation interrupts waiting and that the request timeout respects the remaining caller deadline.
7. Check SDK retries and observe the final behavior
Before adding a custom retry loop, check whether the SDK already retries, which failures it considers retryable, how it interprets server hints, and what attempt or deadline limits apply. Wrapping an SDK retry loop with another loop can multiply requests: if both layers retry independently, the number of actual sends can exceed either layer’s configured limit.
Choose one deliberate retry owner where possible. If retries must exist at multiple layers, account for their combined attempt limits and timing rather than treating each layer in isolation. AWS Well-Architected guidance identifies layered retries and observability as reliability concerns: AWS REL05-BP03.
Quick Recap
- Record attempt counts, retry reasons, delays, and final errors without logging secrets or sensitive request data.
- Monitor repeated failures and throttling so a retry policy does not hide a persistent outage or amplify load.
- Verify the effective behavior at the layer that sends requests, including SDK defaults and any wrapper or proxy retries.
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