Yeah, I'd be uncomfortable with that approach. One of their key design goals was to minimize underselling, recognizing that it results in lost revenue. But if a seller has 5k inventory in one location, has a spike of 2k orders, but only 1k of the orders can successfully reserve inventory, then isn't that an argument that you lost the revenue of the 2nd 1k orders that error out before the replenishment process succeeds?
I'm skeptical of this approach. Sure, row contention means that you cannot have a database transaction per customer order attempting to decrease inventory count by 1 each time. But you can have a batch transaction whereby the transaction decreases inventory by 100 (thus touching the high-contention inventory row once) and credits each of 100 different customer cart database rows (which are not under heavy contention and can be on a different disk entirely). Attempted customer orders are submitted to a reservation system put in charge of assembling the batches. Customers wait some short period of time - say, 15 seconds - for the reservation attempt to be batched and to be notified that they successfully locked a reservation. Arguing that "slow reservations trigger throttling and a worse buyer experience", without an actual number for what counts as "slow" to serve as an SLO and as a design target, is a cop-out inviting over-engineering.
> But if a seller has 5k inventory in one location, has a spike of 2k orders, but only 1k of the orders can successfully reserve inventory, then isn't that an argument that you lost the revenue of the 2nd 1k orders that error out before the replenishment process succeeds?
They explicitly cover this in the article, saying they do reservation inline. It does increase latency for these orders, but it doesn’t result in an error
> But you can have a batch transaction whereby the transaction decreases inventory by 100 (thus touching the high-contention inventory row once) and credits each of 100 different customer cart database rows (which are not under heavy contention and can be on a different disk entirely).
They mention this as well, checkout batching increased implementation complexity.
> Arguing that "slow reservations trigger throttling and a worse buyer experience", without an actual number for what counts as "slow" to serve as an SLO and as a design target, is a cop-out inviting over-engineering.
True. The article would’ve been better if they included such numbers. However the fact that they didn’t mention this doesn’t imply they haven’t done research. I haven’t found anything related to checkout specifically, however there are in general articles, indicating that increased latency correlates with revenue drop.
I'm skeptical of this approach. Sure, row contention means that you cannot have a database transaction per customer order attempting to decrease inventory count by 1 each time. But you can have a batch transaction whereby the transaction decreases inventory by 100 (thus touching the high-contention inventory row once) and credits each of 100 different customer cart database rows (which are not under heavy contention and can be on a different disk entirely). Attempted customer orders are submitted to a reservation system put in charge of assembling the batches. Customers wait some short period of time - say, 15 seconds - for the reservation attempt to be batched and to be notified that they successfully locked a reservation. Arguing that "slow reservations trigger throttling and a worse buyer experience", without an actual number for what counts as "slow" to serve as an SLO and as a design target, is a cop-out inviting over-engineering.