Comment by trueno
3 hours ago
so this is interesting to me, im in retail i work closely with platforms ive used shopify ive used magento ive used smaller players ive helped implement various pieces of all of them.
and i was excited to get some insight, then i realized that this whole thing was written by AI and im going to guess the idea and implementation were probably very AI driven.
> The solution: SKIP LOCKED > Core idea: one row per unit, bounded by design
cool, thanks claude.
Now I'm wondering what the engineering culture is even like at shopify.
Here's the thing. I like databases, I think there's a lot of shit in this space that went and smoked a shit ton their own good stuff to come up with these pure event driven designs that lock you into event workflows with no isolation and remove the ability to do broader bulk-functions.. and then do something even stupider and say "all you need for the interface is graphql" and such service/platform doesn't give you any other way to reconcile or do reporting for your org you have to warehouse from graphql.. this is crap. So seeing a headline where shopify says they want to kinda get behind a unified database strat behind the scenes even if it's not necessarily customer facing, like that's good imo. SQL is many decades of relational algebra that makes insane computations acrossed vast sets of data pure magic and one of the best query dml interfaces of all time.
..however i dont even agree with the claim their making here that redis isnt the tech for a reservation system. redis when used correctly feels like an insanely awesome way to do a reservation system, i lurv redis for stuff like that.
I'm just gonna go forward with the assumption that current and future shopify updates are pure vibeslop. I already hate their data interfaces, but compared to other saas offerings i appreciate that they do have bulk-features.
I found Shopify’s post very easy to read, and learned about some features of MySQL. On the other hand, I didn’t get any value from reading your comment. You seem to have a bunch of opinions about how things should be done, but haven’t given any details about how you came to these conclusions.
I've done multiple large scale implementations with shopify paired with many flavors of order managmeent systems as well as competing offerings in the space. The only one i haven't touched that i'd like to get my feet wet with is commerce tools.
I really just disagreed with the assessment that redis is not good enough for the job for a reservation system. I use sql database all the time, I prefer them. But I'm seeing a claude written article here that seems to heel turn on a proven technology, it would at most be insightful if there was human content in here from actual engineers at shopify who want to vouch for and explain the challenges they were up against with redis rather than just expect me to take claudes word for it. Anyone who's been dabbling with AI knows damn well that you can convince claude to write up a dissertation on any hill you want to die on.
I found it really hard to read, the llm-isms are just too distracting. Does no one proof blog posts anymore?
You should be able to do these increments/decrements in a database at the rate you can write WAL to the disk. But the problem is in a lot of these databases the transaction will hold locks until the WAL hits the disk which causes a massive serialisation problem when you have lots of writes to the same row.
For example if it takes 20ms to write a batch to the WAL then if you do 5 updates to the same row then that is a minimum of 100ms. But without waiting on locks if you can batch all the WAL writes together then this could be just 20ms.
I don’t think holding locks while waiting for WAL is strictly necessary. There is definitely some anomalies that can happen if you don’t wait for WAL to be durable because transactions that don’t write WAL can observe non-durable writes in some situations. So for example conditional updates that don’t perform work. But I assume this can be fixed by making these wait on the commit for dependent transactions to become durable if they are empty. There is also the problem of failing writes that reveal information about non-durable writes which is more tricky. For example you try to insert into a unique index and it fails, but the duplicate was due to a non-durable write that is lost.
Pure reads should be fine when using MVCC because you just show the latest durable version of the DB. I know some other replication systems will run all transactions including reads through the WAL/replicated log in order to not have anomalies.
They do heavily use AI, but you haven’t refuted their point that if inventory is in SQL, storing reservation in a second storage system increases complexity.
I mean I'm all for everything collapsing into sql. SQL all the things. Not really against it, I'd just rather not-AI write the challenges they were up against. It seems like these are all very behind-the-scenes scaling issues they faced, so it'd be cool to hear from them. Redis has great qualities, I don't use it often but I've also for years now understood why Redis was put in front of these use cases to handle them. Complexity be damned generally you're trying to enforce a first come first served or some level of idempotent behavior, so if sqls doing that now then hell yeah. It's just super off-putting to try and upend an important design pattern with an AI written article.