> Instead of one row per item with a quantity column, we use one row per sellable unit. An item with 10 units has 10 rows.
> But one row per unit for all inventory would break down at scale—an item with 50,000 units across 10 locations would mean 500,000 rows, and the reserve query would slow as it scans through them. Instead, we maintain a bounded pool of available rows, capped at 1,000 per item/location combination. Reservations consume rows from this pool; a replenishment process refills it from the inventory ledger.
Shouldn't I feel uncomfortable with such approach? It seems to create a backoff (pool) for lowering the chance of having a synchronization issue.
Comes down to type of items, when you have physical inventory the number is limited so more manageable and interestingly enough the problem only applies to physical inventory.
You are just spending some more disk space to avoid synchronization issues. Denormalization for performance is a really common pattern, just that people do not start with it in the first place itself
Can you explain how that works? With the row-per-item I can see how you’d use locking primitives etc easily to deal with multiple concurrent shopping carts claiming available inventory.. but how does your solution solve contention? There’d need to be some “number of items in inventory” row, wouldn’t there be contention on that?
The point of one row per item is that thousands of concurrent shoppers don’t need to block each other as they can each claim as many free rows as they need for themselves?
1. Deduct the reservation from the inventory when the user starts to order, but in the same txn also maintain a separate row for the in progress order flow.
2. If the order flow is aborted or times out have a background process that returns these to the inventory.
That seems simpler than this approach and involves no locking. Though their presented approach is also reasonable, there must be some reason not to choose a simpler flow. It is not that difficult to have a gc service that scales, but may be they didn't want to separate that.
I was investigating Durable Objects (DO) and had Fable walk me through where in my app they might be appropriate. One place had a dependency with billing (where I use a transaction now) and the proposed re-work to allow for concurrent editing with DO looked very much like this, reservations with idempotency keys. And if you add hierarchical allotments then it scales pretty well.
I disagree with the other posters about the bg process, if you have any bg processing already you should be able to handle the few edge cases without too much trouble.
My understanding is: your proposal is not very different from what Shopify is doing except they are tracking 'reserved units' (one per row) and you are proposing tracking 'orders' as the temporary state to then reconcile back with inventory quantities.
Yes, at a high level. It doesn't rely on skip locked, which is not cheap at DB level. DB has to still typically run query and keep going until it finds an unlocked item. Deducting and checking inventory counts are simpler ops inside the DB.
This seems like what triggers are for and how we do similar type things. Update trigger on order does select for update on the inventory and increases/decreases it as appropriate.
I don't think you really need that even. An indexed lookup is fast and you don't need to store a computed quantity generally.
Two concurrent deductions of inventory do contend but only during the actual DB update. That is just normal DB locking for SQL isolation levels. The blog refers to explicit locking by the app, which is where skip locked comes in.
not the best design to have 1000 rows for each shop*SKU combination. If a candidate proposed this solution during Shopify's System Design interview, i doubt he would be vetted for Senior+ position.
Instead of having 1000 rows per shop*SKU, why not just have one row per shopping cart*SKU?
That way a single row would represent a single cart, and will hold info of multiple items of the same SKU.
No need a cludge with 1000 rows limit and replenishment process. Instead of dealing with N rows, you always deal with a single row.
per my reading of the article, the protection is only needed for a few seconds, while payment is being processed by the payment system.
so the row is inserted when Payment is initiated, and row is deleted when Payment succeeds
What is oversell protection?
Reserve: When payment starts, we mark items as reserved (a short hold, e.g. several minutes).
Claim: When payment succeeds, we permanently deduct quantity from the inventory ledger (source of truth).
but that system could be easily improved to reserve item when user Adds item to a cart, to prevent scenario when user adds item to a cart, goes through checkout, and after initiating payment gets "soldout error":
1. Let user add item to a cart by default (happy path)
2. Initiate async check in the background for SKU and quantity
2a. The check sums up rows for all SKUs and compares to Inventory table (very cheap check since its done to only active shopping carts)
3. After few seconds the check comes back, and we let user know that item is soldout, before/the moment user goes to Checkout.
Ok, but before inserting you must ensure that inventory is not depleted, which means you need to know the count and you need to lock the row. So you still have contention on that item. Them having a 1k buffer allows not to take a lock on a single row every time, and only do it when buffer is empty
there is no need to lock the row, since you a dealing with a shopping cart, not individual item piece. when you run aggregate functions, lock is no needed, it is actually better to run it with SET TRANSACTION ISOLATION LEVEL READ UNCOMMITTED; for aggregation
the check for oversold items is extremely cheap:
with current_order as (
select $SKU1, $q2 as quantity
union
select $SKU2, $q2 as quantity
),
with carts as (
select sku, sum(quantity) as reserved
from active_carts
group by sku
),
with warehouse as (
select sku, available_units
from inventory
group by sku
)
select * from current_order
inner join carts using (sku)
inner join warehouse using (sku)
where warehouse.available_units - carts.reserved < current_order.quantity
assuming there are indexes on sku field in both, results in efficient index seek and agg over 2 tables
I don’t understand how this should prevent oversold. You have a check that reports empty or oversold inventory. But how does that check prevent 2 concurrent actors fighting for the last item from inserting 2 rows?
how does current design resolve concurrent actors fighting for the last item ?
there is ultimately needs to be some global mechanism resolving this conflict. Currently it is an order in which db engine processes transactions by locking rows for a transaction, whoever got the first lock, wins the last remaining items.
my design is the same, except it does not need this dance with moving rows between tables, locking them, and the cludge with replenishment process.
in the simplest form, run the sum() over active non-finished orders and compare to inventory. you get the same result: whoever got the first to run sum() and get positive answer will get the last remaining items.
but the problem as formulated, imho, is not even correctly defined.
Shopify incorrectly formulated the very problem they are trying to solve.
Trying to solve it at the payment time is too late, its better to resolve it earlier, before the checkout.
the "PAY" button should only do one thing: deduct money from cc and that's it. Resolving inventory availability must be solved way earlier, the moment user clicks Checkout, not when user clicks Pay.
So ideally, the error for oversold items should be shown to a user when he clicks Checkout, not when he click PAY
Clearly this is for high concurrency cases where there are many people racing to get all the available items. It's not clear that it's in shopifys or the sellers interest to let items get sequestered in people's shopping carts, which is a spot where there isn't a strong commitment to complete the purchase. At payment time, you can be more assured that the item will actually be purchased.
Still I think their solution is a bit weird. I'd want to commit the reservation transaction with inventory decrement along with a payment key and then use a different transaction to drop the reservation when the transaction completes. If the transaction does not complete in a timely manner you probably need to query external systems anyway to resolve whether the payment actually occurred or not.
They talk about lock contention in this case, but I also wonder about latch contention since these rows are adjacent. If it's a small transaction that's not interactive, does mysql resolve it with just the latches on the needed tables?
> how does current design resolve concurrent actors fighting for the last item ?
It resolves with skip locked. Assuming we have only 1 item left. First query scans the buffer table, locks as many rows as needed (1 in our case), and moves rows to another table. Second query scans the table, finds no rows (even if first one hasn’t finished yet, the row is locked and ignored), checks if it can increase buffer, finds out that it’s fully sold and aborts. Db guarantees that you can’t oversold.
> my design is the same, except it does not need this dance with moving rows between tables, locking them, and the cludge with replenishment process.
I can’t evaluate whether it’s the same or not, because you still haven’t clarified when exactly you’re going to insert the row. In the article they’re inserting in the same transaction. Would you also do it in the transaction? Because if you’ll introduce a separate global mechanism to resolve conflicts, on a high level it would be the same as their approach with redis (you need to have 2 systems)
This is absolutely fascinating. I enjoy real life stories like this. I went to a Node meetup in 2013 when Target had just switched to Node from PHP and it was a similar experience to see their metrics and hear their strategy.
Who knows. I wish ant harshly penalized speaking litotically because it’s essentially reward hacking as it can often be read multiple ways.
It’s also annoying as a human because Claude et al rate their own writing very highly, putting human<>LLM interactions at a disadvantage to human->LLM<>LLM interactions.
Makes sense... if you are counting something in MySQL and now your counter is in Redis that's already strange
But I guess the point is that even in the MySQL scenario the 'reserved_quantities' is almost like a temporary table so either way is not the 'Real' inventory
They were really so proud of that AI image that they just had to tack it on at the end? Did nothing but make the blog post feel like cheap mass produced slop
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.
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.
Shopify’s founder and their coo both fund far-right extremism, and its founder thinks only rich people should be able to vote. But anyway, they switched databases.
The link you shared is just a podcast and does not contain even contain “far right”. Can you provide specific concerns, otherwise I don’t see anything wrong with a CEO of the most successful tech company should not be concerned about a horribly performing country from a GDP perspective.
Yeah it's an awful place to work unless you're a far-right bro. My old director used to use slurs and vape in the office. The founder hires pro gamers with no technical expertise because he thinks they're cool.
Because "slurs" is vague and covers a wide variety of utterances that run the gamut from unprofessional to unemployable and I had a tingle of Spidey sense that OP might have chosen the vague phrasing specifically to inflate the sins of the nameless Shopify director in the mind of the reader.
Great question. It's a vague term and a moving target that not everyone agrees on.
For instance, retarded is now considered a slur. If OP is saying "my director said the R word," then I would question whether OP spared the gory details out of concern for polite company or if they're being oblique in service of their point.
And if managers at Shopify are dropping N-bombs, I want to know that too.
> Instead of one row per item with a quantity column, we use one row per sellable unit. An item with 10 units has 10 rows.
> But one row per unit for all inventory would break down at scale—an item with 50,000 units across 10 locations would mean 500,000 rows, and the reserve query would slow as it scans through them. Instead, we maintain a bounded pool of available rows, capped at 1,000 per item/location combination. Reservations consume rows from this pool; a replenishment process refills it from the inventory ledger.
Shouldn't I feel uncomfortable with such approach? It seems to create a backoff (pool) for lowering the chance of having a synchronization issue.
Comes down to type of items, when you have physical inventory the number is limited so more manageable and interestingly enough the problem only applies to physical inventory.
You are just spending some more disk space to avoid synchronization issues. Denormalization for performance is a really common pattern, just that people do not start with it in the first place itself
you should, their design is not the best. There is middle ground between "one row per SKU" and "1000 rows per SKU".
Its called one row per shopping cart*SKU combo.
if two people order 100 and 500 items of the same SKU, respectively, the table should have only two rows: for order1 and order2. Not 600 rows.
Can you explain how that works? With the row-per-item I can see how you’d use locking primitives etc easily to deal with multiple concurrent shopping carts claiming available inventory.. but how does your solution solve contention? There’d need to be some “number of items in inventory” row, wouldn’t there be contention on that?
The point of one row per item is that thousands of concurrent shoppers don’t need to block each other as they can each claim as many free rows as they need for themselves?
explained below in https://news.ycombinator.com/item?id=49228432
I guess it depends on how the replenishment process works. Unless you're ordering over 1000 of an item, I doubt it would be a problem.
replenishment is an unnecessary cludge that only exists due to poor design. an "algorithmical smell" if you wish
Depends on the scale. Most companies don't approach the scale where this matters.
It seems there could be a simpler solution.
1. Deduct the reservation from the inventory when the user starts to order, but in the same txn also maintain a separate row for the in progress order flow. 2. If the order flow is aborted or times out have a background process that returns these to the inventory.
That seems simpler than this approach and involves no locking. Though their presented approach is also reasonable, there must be some reason not to choose a simpler flow. It is not that difficult to have a gc service that scales, but may be they didn't want to separate that.
I was investigating Durable Objects (DO) and had Fable walk me through where in my app they might be appropriate. One place had a dependency with billing (where I use a transaction now) and the proposed re-work to allow for concurrent editing with DO looked very much like this, reservations with idempotency keys. And if you add hierarchical allotments then it scales pretty well.
I disagree with the other posters about the bg process, if you have any bg processing already you should be able to handle the few edge cases without too much trouble.
My understanding is: your proposal is not very different from what Shopify is doing except they are tracking 'reserved units' (one per row) and you are proposing tracking 'orders' as the temporary state to then reconcile back with inventory quantities.
Yes, at a high level. It doesn't rely on skip locked, which is not cheap at DB level. DB has to still typically run query and keep going until it finds an unlocked item. Deducting and checking inventory counts are simpler ops inside the DB.
This seems like what triggers are for and how we do similar type things. Update trigger on order does select for update on the inventory and increases/decreases it as appropriate.
I don't think you really need that even. An indexed lookup is fast and you don't need to store a computed quantity generally.
The moment you added a background process you just replaced the complexity.
1. Backgrounds process can back up
2. They need context of the user and need to switch context per user
3. What if they fail, you create some DLQ or another process to handle the failure
4. Who looks on those failure and how do they act
TLDR; there is always a cost
The design in the shoppify post already had a background process for the item replenishment.
Can you clarify why this involves no locking? There can still be 2 actors fighting for the same row.
Two concurrent deductions of inventory do contend but only during the actual DB update. That is just normal DB locking for SQL isolation levels. The blog refers to explicit locking by the app, which is where skip locked comes in.
Yes, the point is to spread contention across multiple rows. They also mention this in the beginning of the article
now you have two problems. what happens when your reservation system backs up?
not the best design to have 1000 rows for each shop*SKU combination. If a candidate proposed this solution during Shopify's System Design interview, i doubt he would be vetted for Senior+ position.
Instead of having 1000 rows per shop*SKU, why not just have one row per shopping cart*SKU?
That way a single row would represent a single cart, and will hold info of multiple items of the same SKU.
No need a cludge with 1000 rows limit and replenishment process. Instead of dealing with N rows, you always deal with a single row.
I have never worked anywhere where describing how their system actually works would pass the company's own system design interview
> Instead of having 1000 rows per shopSKU, why not just have one row per shopping cartSKU?
At what point that row is inserted?
per my reading of the article, the protection is only needed for a few seconds, while payment is being processed by the payment system.
so the row is inserted when Payment is initiated, and row is deleted when Payment succeeds
but that system could be easily improved to reserve item when user Adds item to a cart, to prevent scenario when user adds item to a cart, goes through checkout, and after initiating payment gets "soldout error":Ok, but before inserting you must ensure that inventory is not depleted, which means you need to know the count and you need to lock the row. So you still have contention on that item. Them having a 1k buffer allows not to take a lock on a single row every time, and only do it when buffer is empty
there is no need to lock the row, since you a dealing with a shopping cart, not individual item piece. when you run aggregate functions, lock is no needed, it is actually better to run it with SET TRANSACTION ISOLATION LEVEL READ UNCOMMITTED; for aggregation
the check for oversold items is extremely cheap:
assuming there are indexes on sku field in both, results in efficient index seek and agg over 2 tablesThanks! I don't uSe 'with' enough
I don’t understand how this should prevent oversold. You have a check that reports empty or oversold inventory. But how does that check prevent 2 concurrent actors fighting for the last item from inserting 2 rows?
how does current design resolve concurrent actors fighting for the last item ?
there is ultimately needs to be some global mechanism resolving this conflict. Currently it is an order in which db engine processes transactions by locking rows for a transaction, whoever got the first lock, wins the last remaining items.
my design is the same, except it does not need this dance with moving rows between tables, locking them, and the cludge with replenishment process.
in the simplest form, run the sum() over active non-finished orders and compare to inventory. you get the same result: whoever got the first to run sum() and get positive answer will get the last remaining items.
but the problem as formulated, imho, is not even correctly defined.
Shopify incorrectly formulated the very problem they are trying to solve.
Trying to solve it at the payment time is too late, its better to resolve it earlier, before the checkout.
the "PAY" button should only do one thing: deduct money from cc and that's it. Resolving inventory availability must be solved way earlier, the moment user clicks Checkout, not when user clicks Pay.
So ideally, the error for oversold items should be shown to a user when he clicks Checkout, not when he click PAY
Clearly this is for high concurrency cases where there are many people racing to get all the available items. It's not clear that it's in shopifys or the sellers interest to let items get sequestered in people's shopping carts, which is a spot where there isn't a strong commitment to complete the purchase. At payment time, you can be more assured that the item will actually be purchased.
Still I think their solution is a bit weird. I'd want to commit the reservation transaction with inventory decrement along with a payment key and then use a different transaction to drop the reservation when the transaction completes. If the transaction does not complete in a timely manner you probably need to query external systems anyway to resolve whether the payment actually occurred or not.
They talk about lock contention in this case, but I also wonder about latch contention since these rows are adjacent. If it's a small transaction that's not interactive, does mysql resolve it with just the latches on the needed tables?
> how does current design resolve concurrent actors fighting for the last item ?
It resolves with skip locked. Assuming we have only 1 item left. First query scans the buffer table, locks as many rows as needed (1 in our case), and moves rows to another table. Second query scans the table, finds no rows (even if first one hasn’t finished yet, the row is locked and ignored), checks if it can increase buffer, finds out that it’s fully sold and aborts. Db guarantees that you can’t oversold.
> my design is the same, except it does not need this dance with moving rows between tables, locking them, and the cludge with replenishment process.
I can’t evaluate whether it’s the same or not, because you still haven’t clarified when exactly you’re going to insert the row. In the article they’re inserting in the same transaction. Would you also do it in the transaction? Because if you’ll introduce a separate global mechanism to resolve conflicts, on a high level it would be the same as their approach with redis (you need to have 2 systems)
EDIT: wording
This is absolutely fascinating. I enjoy real life stories like this. I went to a Node meetup in 2013 when Target had just switched to Node from PHP and it was a similar experience to see their metrics and hear their strategy.
"But the hardest lesson wasn't about database design. It was discovering that the real bottleneck wasn’t what we were observing and measuring."
But was it load bearing?
Even better.
It's web-scale.
load = bearing
gun = smoking
insight = key
gap = closed
summary = executived
belt = suspended
boot = strapped
It's honestly weird Claude converges on this language because it's incredibly wordy and hard to parse.
One would think semantic density would win out in training.
Why? This is a common transition that people use in speech and text.
Close out previous paragraph. Segue to completely different topic.
How else are you supposed to go on a tangent?
Who knows. I wish ant harshly penalized speaking litotically because it’s essentially reward hacking as it can often be read multiple ways.
It’s also annoying as a human because Claude et al rate their own writing very highly, putting human<>LLM interactions at a disadvantage to human->LLM<>LLM interactions.
Makes sense... if you are counting something in MySQL and now your counter is in Redis that's already strange
But I guess the point is that even in the MySQL scenario the 'reserved_quantities' is almost like a temporary table so either way is not the 'Real' inventory
It’s fascinating that in order to do this, they had to remove 50% of reads and 33% of transactions from the main DB.
They were really so proud of that AI image that they just had to tack it on at the end? Did nothing but make the blog post feel like cheap mass produced slop
This is Shopify, the leadership is full steam ahead on AI in a big way and they review employee performance based on AI usage.
And Lutke is a fascist.
All the biggest proponents of AI seem to be fascists.
Weird.
The blog probably was.shopify was pretty early and publicly all in on using AI for everything
The lengths companies will go to avoid running different pieces of software...
It can be easier and cheaper to solve problems via technology changes than operations and people
Now you only need MySQL expertise and maintenance rather than Redis and MySQL
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.
Example of their culture: https://x.com/tobi/status/1909251946235437514
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.
outside the slop, i liked this post that was linked on innodb locking: https://jahfer.com/posts/innodb-locks/
Shopify’s founder and their coo both fund far-right extremism, and its founder thinks only rich people should be able to vote. But anyway, they switched databases.
https://www.techwontsave.us/episode/340_shopifys_leaders_are...
The link you shared is just a podcast and does not contain even contain “far right”. Can you provide specific concerns, otherwise I don’t see anything wrong with a CEO of the most successful tech company should not be concerned about a horribly performing country from a GDP perspective.
You should be concerned what they spot as the problem and what they propose as the solution
https://www.ctvnews.ca/business/article/shopify-ceo-draws-cr...
Yeah it's an awful place to work unless you're a far-right bro. My old director used to use slurs and vape in the office. The founder hires pro gamers with no technical expertise because he thinks they're cool.
Don't get shy now. Which slurs?
Why are you trying to get someone to repeat slurs?
Because "slurs" is vague and covers a wide variety of utterances that run the gamut from unprofessional to unemployable and I had a tingle of Spidey sense that OP might have chosen the vague phrasing specifically to inflate the sins of the nameless Shopify director in the mind of the reader.
Which slurs are ok, and which aren't?
Great question. It's a vague term and a moving target that not everyone agrees on.
For instance, retarded is now considered a slur. If OP is saying "my director said the R word," then I would question whether OP spared the gory details out of concern for polite company or if they're being oblique in service of their point.
And if managers at Shopify are dropping N-bombs, I want to know that too.
I... guess that's possible? Summarization doesn't really seem suspicious to me.
Are you implying that vaping is far right?
I think that was part of the “bro” bit, not the far right bit
Sounded to me like they were saying people vape in the office, which would make a bad work environment on top of the far right bros.