How to design robust and predictable APIs with idempotency?10186 2018-09-12 12:55
How could APIs be un-robust and un-predictable?
- Networks are unreliable.
- Servers are more reliable but may still fail.
How to solve the problem? 3 Principles:
Client retries to ensure consistency.
Retry with idempotency and idempotency keys to allow clients to pass a unique value.
- In RESTful APIs, the PUT and DELETE verbs are idempotent.
- However, POST may cause “double-charge” problem in payment. So we use a idempotency key to identify the request.
- If the failure happens before the server, then there is a retry, and the server will see it for the first time, and process it normally.
- If the failure happens in the server, then ACID database will guarantee the transaction by the idempotency key.
- If the failure happens after the server’s reply, then client retries, and the server simply replies with a cached result of the successful operation.
Retry with exponential backoff and random jitter. Be considerate of the thundering herd problem that servers that may be stuck in a degraded state and a burst of retries may further hurt the system.
For example, Stripe’s client retry calculates the delay like this…
def self.sleep_time(retry_count) # Apply exponential backoff with initial_network_retry_delay on the # number of attempts so far as inputs. Do not allow the number to exceed # max_network_retry_delay. sleep_seconds = [Stripe.initial_network_retry_delay * (2 ** (retry_count - 1)), Stripe.max_network_retry_delay].min # Apply some jitter by randomizing the value in the range of (sleep_seconds # / 2) to (sleep_seconds). sleep_seconds = sleep_seconds * (0.5 * (1 + rand())) # But never sleep less than the base sleep seconds. sleep_seconds = [Stripe.initial_network_retry_delay, sleep_seconds].max sleep_seconds end