[ilganeli]

Hello! I am the author. Thank you for the comment.

While Stripe does love to write, I think our core premise here was that being able to reason mathematically about independent distributed systems was a unique application of a ledger. Likewise, we think the data quality offering is a unique extension since it gives us tools to make improvements to our systems over time.

[leviathant]

This must be what it's like when someone overhears me and a colleague talking about enterprise ecommerce integration on our way to get lunch.

[ericyd]

I appreciate the reply. I think I would have appreciated a more clear description of how you are reasoning mathematically about your distributed systems. Simply claiming that you can and do is fine, but as an engineer I would have loved more practical examples. The only part that I found which touched on this was when you described how to find clearing errors by simply searching for clearing accounts with a non zero balance. Are there other examples of mathematically reasoning that you could share? I don't need/expect a reply here, this question is rhetorical to demonstrate how I think this article could have been stronger for me.

[legojoey17]

Not the author, but I worked on the Ledger team at Stripe for 5 years.

Clearing is definitely a key part and a large amount of signal can be derived simply through the zero-balance assertion. To give you some more detailed examples of that:

1. System A bookkeeps +$11 and system B bookkeeps -$10 against the same account, where this is representative of A handing off responsibilities to B during some multi-step process and something was off. In practice, this might look like a Charge being handed off from a product team to the team integrating with card networks for submission. There's plenty of reasons this could have gone awry internally from either team like incorrect fee handling, incorrect FX handling, etc.

2. Pipelines reconciling data may bookkeep $11 and -$10 against the same account, where the two events come from different data sources. This could be a difference in Stripe data vs partner reporting or even a difference between two reports from the same partner (e.g. a transaction-level report against a aggregate we've estimated attributions for). Again, there's plenty of reasons this goes wrong like an error on our side, unexpected partner behaviour, actual partner error, or our incorrect interpretation of a complex partner behaviour.

This approach is general in that we don't need to be concerned about what the actual data models or system interactions are to effectively apply monitoring to everything. Another element is to not create a lot of noise on for non-zero balances while they're in an interim state. Some amount of modelling happens to establish an expected time to clear for accounts, and with the right granularity, like "Charges from product teams to integrating teams usually take 10 minutes", "partner report A and B arrive and are processed 1 day apart", or "we receive money from partner A about 2 days after submitting".

In the end, we're trying to boil down every other Stripe system and partner integrations into a Stripe-wide set of discrete states, transitions, and with dollar value they're for. Teams may have their own system diagrams but they'll also have a parallel Ledger event diagram if they handle money which takes consideration to get right (e.g. failure modes, modelling everything well). I suspect that's what the author is getting at with reasoning more mathematically about distributed systems, as we add a twist on a typical system design here.

[f30e3dfed1c9]

Stripe may love to write, but it appears to hate its readers. The light gray type on that web page is really hard to read.

[15457345234]

I had to read it a few times too, I'm still not entirely convinced that "being able to reason mathematically about independent distributed systems was a unique application of a ledger" actually parses into anything.

Is 'being able to reason' an 'application of a' ?

It looks... off