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Retry and Backoff Strategies

Retry and Backoff Strategies

Retry and Backoff Strategies

Retry and Backoff Strategies

Retry and Backoff Strategies

Retry and Backoff Strategies

Retry and Backoff Strategies

Retry and Backoff Strategies

In distributed systems, failures are inevitable. Networks drop packets, services restart, databases time out. Retry and backoff strategies are essential for building systems that gracefully handle transient failures without overwhelming downstream services.

When to Retry

Not all failures deserve a retry. Distinguish between transient and permanent failures:

Transient failures (retry): Network timeouts, connection resets, 503 Service Unavailable, 429 Too Many Requests. These indicate temporary conditions that may resolve on their own.

Permanent failures (do not retry): 400 Bad Request, 401 Unauthorized, 404 Not Found, 403 Forbidden. Retrying these will never succeed and wastes resources.

Always inspect the error type or status code before deciding to retry.

Idempotency Is Required

Never retry an operation unless it is idempotent. If a request succeeds on the server but the response is lost, a retry will create a duplicate. This is catastrophic for operations like charging a credit card or creating an order.

The solution is idempotency keys. Clients generate a unique key for each operation and include it in the request header:

POST /payments

Idempotency-Key: 550e8400-e29b-41d4-a716-446655440000

{

"amount": 1000,

"currency": "USD"

}

The server stores the result keyed by the idempotency key. If the same key is received again, the server returns the stored result instead of executing the operation again. Stripe's API is a canonical example of this pattern.

Fixed Retry

The simplest strategy: wait N seconds between each retry, up to a maximum number of attempts.

max_retries = 3

delay = 1 # second

for attempt in range(max_retries):

try:

return make_request()

except TransientError:

if attempt == max_retries - 1:

raise

time.sleep(delay)

Pros: Simple to implement and understand. Cons: If the service is still recovering, all clients retry simultaneously, potentially causing a thundering herd.

Exponential Backoff

Increase the delay exponentially between each retry. If the first retry waits 1 second, the second waits 2, the third waits 4, then 8, 16, and so on.

max_retries = 5

base_delay = 1

for attempt in range(max_retries):

try:

return make_request()

except TransientError:

if attempt == max_retries - 1:

raise

delay = base_delay * (2 ** attempt)

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