शैडोइंग अभ्यास: Why Shopify Moved Inventory Reservations from Redis to MySQL - वीडियो के साथ अंग्रेजी बोलना सीखें
पाठ बनाया जा रहा है...
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Hey everyone, today I want to break down one of Shopify engineering's latest blog posts, where they talk about how they replaced Redis with MySQL in their inventory reservation system.
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I thought this post when I read it was super interesting, not only because it overlaps with a lot of the patterns that we teach, it's similar to the Ticketmaster breakdown that we have, but because it moves in the opposite direction of maybe what's most common,
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which is that teams start on MySQL or Postgres, they realize that there's some table or some subsection of their system which doesn't scale to the throughput that they're after,
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and they move that part out into Redis.
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In this case, Shopify ended up taking the opposite approach.
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And it's good evidence of how much MySQL can really handle scale, MySQL or Postgres, of course.
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And they did some interesting things in the process.
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So without further ado, let's break it down.
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Let me start up by quickly defining the problem that Shopify needed to solve, at least from a product perspective.
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So when a buyer goes to purchase an item on Shopify, Shopify has to guarantee that that item is actually available for purchase.
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If they get this wrong, it can fail in one of two different directions.
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The first failure scenario is that two buyers could think that they both purchased the same last unit.
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And so now both of them think that they have an item coming, but one of them is going to have to have a cancellation and a refund put back on their card later, as well as maybe an apology email.
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The other direction is that you have a buyer who thinks that an item is sold out, but it actually isn't.
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And so now the merchant is losing money because they could have sold that item, but Shopify is incorrectly telling you that it's unavailable.
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And at Shopify scale, while these things don't happen often, they can compound really quickly.
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Now the product solution here is one that you all know, you've all used it.
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It's these reservation systems in a transaction at checkout.
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Basically, you're going to put something in your cart and then Shopify is going to hold that aside.
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It's going to reserve it for you for some period of time
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while you continue to type in your credit card information and try to make your payment.
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If you complete your payment, then that unit is going to come off the merchant's inventory for good.
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It's yours now.
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But if the payment fails or if the clock runs out, you use too much time, then the inventory is going to go back into the pool so that somebody else can have it.
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That whole thing is a reservation system.
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And from a technical perspective, for years, Shopify had been using Redis to do this.
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They had a Redis instance for which every single Redis key was a single piece of inventory.
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So the blue hoodie.
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And then the count or the value of that key was how much was currently available to sell.
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So if a user needed to reserve an item, you would decrement that count.
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And then if they didn't end up purchasing, you would need to increment that count back.
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The whole time, the source of truth still lives in MySQL.
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And so let me walk you through what this would look like.
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Imagine there are 10 hoodies available.
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So both Redis and MySQL agree on 10.
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Then a user wants to check out, so they put a hoodie in their cart, which triggers a reservation, and it decrements Redis down to nine.
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But MySQL stays at 10.
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Once they complete their purchase, MySQL also decrements down to nine, and the two are in sync again.
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That part's pretty straightforward.
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Now, what I found interesting when reading it is
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that the blog doesn't actually touch on what I think is the most interesting part, which is how do you handle the case where the user doesn't complete their payment.
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Maybe time expired, maybe they shut their laptop, walked away, you know, whatever it may be.
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In these cases, you need something that's gonna trigger that increment back to Redis, basically to put this thing back in the pool.
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Like I said, the blog doesn't cover how, so this is just my guess.
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But let me walk you through how I think this would happen and what is most likely happening under the hood.
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And that's that in Redis, they probably have two data structures.
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They've got the raw current of the remaining inventory that we're incrementing and decrementing.
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But then alongside it, they probably also keep a sorted set, which is basically just a heap for those who aren't familiar with the sorted set.
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Now, every outstanding reservation, two things are gonna happen.
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It's going to decrement the count, but it's also going to add that reservation to the sorted set where the score is the time that it expires.
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In doing that, now whatever is sitting at the top of that heap, the top of that sorted set, is always the reservation that's due to expire next.
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It can pull off the top of the heap, make sure that that thing is already expired.
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If it is, get rid of it off the heap
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and then go and increment that count of how many things are available to be reserved.
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Now, this cron job is going to work great for the majority of cases.
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But if you've read any of our contents, then something might be coming into your mind, which is that if something's selling really, really, really fast, then you don't have to wait for that cron job to add things back to the pool.
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Even if the cron job was running every 10 seconds, you would have a user who's told that there are no hoodies available, when in reality, there should be a hoodie that's back in the pool.
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The cron job just hasn't run yet, right?
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There's a 10 second lag there.
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So what you could do to fix this, and this is probably what I would do, is that you could tie this all together in a single atomic transaction.
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Basically, before you take a unit out of the reserve pool, you would look at the head of the heap for anything that's already expired, remove those from the heap, increment them back, and then run your own decrement.
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So say the user comes in and they reserve a hoodie.
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The count would then go from 10 to nine and they would add their reservation to that heap.
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Now imagine that some time goes by and their time expires.
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Then another user comes to check out and what they do is that they go first check the head of that heap.
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They see that there is a reservation that's already expired.
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So they remove it from the heap and they run the increment.
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So we're back up to 10.
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And then they run their own decrement.
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And all of this can be part of what's called a Lua script in Redis.
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So that full chunk happens atomically, just like a transaction in a database.
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If you do it this way, then you never have the problem where you're turning away a buyer because of a reservation that's already dead, right?
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The expired stuff gets cleaned up right at the moment somebody actually wants it.
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And the cron job is just there maybe as a backstop to clean things up periodically in the background.
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Okay, so back to Shopify.
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This is what was running in production, or at least our best guess of what was running in production.
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and it's a totally reasonable design.
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The reason that you reach for Redis in the first place is that it can handle really high
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read and write throughput on a single key.
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If that count were just a row in MySQL, then every checkout for that item would be fighting over the same row.
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And at scale, that's where things start to really slow down and back up.
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But the problem with this introduced, and this was the motivation for why they wanted to evolve into this new system we'll go into in a second, is that you now have two systems that have to be kept in sync, Redis and MySQL.
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And so when a payment succeeds, you deduct that inventory from MySQL, but then you also have to go over to Redis and you have to pull that reservation off of the heap
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because if you were to leave it sitting there, they would eventually expire and then the cron job
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or whoever came next would increment it back to the pool of units that are still available, right?
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So that's two writes in two different system and there's no easy way to wrap those in a single atomic step.
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And so if there's ever a crash or a failure between those two things, then you can be left in an inconsistent state where the two disagree on how many things can be sold.
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So in an ideal world, you would love for all of this to exist in just a single data store as part of a single transaction.
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And in fact, this is exactly what Shopify had originally tried.
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The post is a bit terse about this, but it says that the earlier attempts would fail and that the single row with a quantity column couldn't handle the contention.
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So if you could imagine that in MySQL, they have two different tables or two different rows, one that has the reservation count and one that has the actual total inventory count.
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Take what was in Redis and put it in MySQL.
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Now every single checkout for that hoodie is an update competing for the same row.
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And MySQL hands out a row lock to one transaction at a time.
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So your buyers just end up in a single file line
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waiting for whoever is in front of them to end up committing.
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So the next natural thing that people tend to do is that you try to take that single row, maybe it's a row of quantity 10, and you would turn it into 10 distinct rows.
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This is a really logical thing to do because it's breaking up the thing that you're competing for.
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Now not everybody is competing for a single row.
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They're competing against the 10 rows.
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But the issue is what happens when you actually go to write that query.
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So you end up doing a select for update statement to lock the row yourself.
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But then every one of those queries is still grabbing just the first available row.
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And so the second buyer locks up behind the first one, the third one locks up behind them, and you haven't actually solved anything.
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There's all these rows under that they could go grab, but they don't know that, right?
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Now the big breakthrough here, and this is maybe you know, one of the top three most interesting things from the Shopify blog is
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that this all changed in 2018 when MySQL 8 shipped a new feature called Skip Locked.
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Now, when your query is scanning for a row to lock and it hits one that somebody already has, instead of just waiting there like it did before for the transaction to finish,
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it just steps right over it and it keeps looking for the next free one.
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So now two buyers who are going for the same hoodie, they can land on those two different rows like we wanted and both of them can commit at the same time.
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Now, the new simple flow for five hoodies is going to look something like this.
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Instead of one row with five on it, you've got five rows sitting in a reservation table and one row for each hoodie.
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Now when a buyer goes to checkout, they're going to open a transaction.
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They're going to grab one of these rows with four update skip locked this time.
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They're going to pull it out of that pool.
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They're going to write a hold saying that that unit belongs to them at checkout and they're going to commit.
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So now you only have four left.
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If the payment does go through, then you deduct that hoodie from the merchant's inventory and you can retire the hold.
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And both of those two things can happen as one transaction in the same database.
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If the payment fails, you delete the hold and you put the row back into the pool and you're back to five.
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But very interestingly, this introduces a new problem, right?
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When you're talking about millions of merchants who themselves have thousands of items, each of which have hundreds of thousands or tens of thousands of units of inventory per item,
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then the row count just got huge.
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We used to have a single row per item, even if there were 10,000 units.
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Now that's 10,000 rows.
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Multiply that by all these different merchants and you're looking at millions of millions of millions of rows.
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What's worse is that almost all of these rows are just sitting there doing nothing.
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A merchant might have 10,000 hoodies in the warehouse, but they're never going to have 10,000 people checking out at the exact same moment.
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And so you're carrying all these millions of idle rows per merchant to serve maybe a few dozen reservations
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that could be competing at the exact same time, which just means that you have a bigger database, which needs to be replicated, backed up, maintained, and of course paid for.
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So what Shopify ended up doing was something I thought was pretty clever.
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They capped it.
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So now no matter how much inventory a merchant actually has, there are never more than a thousand rows in that pool at any given time for that given item, right?
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And then it just gets refilled out of the merchant's real inventory as it starts to drain.
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So you have a thousand there.
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If a bunch of people reserve and buy things and it ends up going down to 500, whatever, then we can come in, fill it back up to a thousand.
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That thousand in effect just ends up being how many hoodies can be reserved at once.
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That's like your contention number.
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We'll be able to survive a blast of a thousand people trying to buy all at once.
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But the real count still lives in the inventory table where it always has.
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And the pool is just sized for a concurrency that's reasonable as opposed to millions, right?
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As for the refill, that ends up just happening on the reserve path.
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And so during a big enough flash sale, you could drain that pool completely, but then a checkout goes to grab a row and there's nothing there.
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And that request does the refill itself before then retrying to pull something from the pool, right?
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That thousand number seems a little arbitrary, but they chose it just by looking at peak reservation rates.
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So they saw the most that ever comes in was maybe something like a couple hundred.
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Let's add a buffer, make it a thousand.
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That's how they ended up at that number.
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So what happens now when a reservation expires though?
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The post doesn't say this in detail, but it gives us enough to be able to reverse engineer probably what's happening
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because it does tell us that the replenishment process refills the pool from the inventory ledger.
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So when that replenishment runs, it has to answer one question.
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How many units should be reservable right now?
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And that number is just the merchant's inventory
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that hasn't sold yet minus whatever is still held by a reservation that hasn't expired.
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And then of course, cap it at a thousand.
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So let's put some numbers on it again with an example.
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Say a merchant had 800 hoodies that haven't been sold and 200 buyers are sitting in checkout with live reservations.
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Well, 800 minus 200 is 600 units that could be reserved right now, which is under the cap of a thousand.
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So 600 rows is what belongs in that pool.
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If there are 400 rows, already in the pool at that moment, then the replenishment would just insert 200 more rolls.
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Now imagine that 50 of those buyers just shut their laptop and their reservations end up expiring.
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Well, the nice thing with the system is
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that nothing has to go clean that up because the next time the replenishment runs, only 150 reservations are still live.
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And so the number it lands on is 650 and those 50 units are back in the pool.
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And that's basically just the same inline trick that we were talking about in the Redis scenario, where the buyer who needs the inventory is the one who goes and reclaims it,
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who refills the pool, as opposed to waiting for some external cron job to do it on a schedule.
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Okay, so just to wrap up, a couple things here stood out to me that I want to make sure I emphasize as your takeaways.
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First, most teams assume that MySQL and Postgres can't handle their scale, so they pull the hot paths out into an in-memory store to shed that load.
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And I want to be clear, that is a completely reasonable thing to do.
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Redis was the right call for Shopify for years, and their post says outright that it handled their throughput great.
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What ended up pushing Shopify over the edge was not the scale.
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It was at the reservation and the inventory lived in two different systems.
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And so whenever the payment succeeded, they had to do two writes that couldn't commit together.
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One lesson for you to take away is that if two things have to be atomic, then they really have to live in the same data store.
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The other big takeaway is that MySQL and Postgres can handle way more throughput than people think nowadays.
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The chances are your site, whatever you're building, is not doing the same volume as Shopify.
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And so if they can handle this sort of volume, there's a really high chance you can too.
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And it's a totally reasonable place for you to start.
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The last thing I'll emphasize is that Shopify first ruled out MySQL and they were reasonable or right to do so.
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Skip locked didn't exist yet.
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The database then changed, not their reasoning.
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And so if you're a team that made decisions a long time ago, it's worth coming back and revisiting the technologies to see how much they've evolved
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and whether some of the complexity that you added to a system could now be reversed into something much more simple.
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And so if you enjoyed this, check out the other blog posts that we've written about so far, and we'll have a lot more coming soon.
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Take care.
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इस पाठ की शब्दावली और बोलने के सुझाव
यह C1 स्तर का स्पीकिंग पाठ वीडियो “Why Shopify Moved Inventory Reservations from Redis to MySQL” पर आधारित है। सबसे ज़्यादा दोहराए गए शब्द ये हैं: row, reservation, Mysql, Shopify, pool। इस वीडियो में शैडोइंग के लिए 186 वाक्य और 3161 शब्द हैं। बोला गया हिस्सा 13:40 लंबा है। वक्ता तेज़ बोलता है, लगभग 231 शब्द प्रति मिनट, इसलिए जुड़ी हुई और कमज़ोर ध्वनियाँ ज़्यादा मिलेंगी। 84% शब्द अंग्रेज़ी के 3,000 सबसे आम शब्दों में से हैं; बाकी शब्द अभ्यास से पहले देख लेना अच्छा रहेगा।
इस वीडियो की मुख्य शब्दावली
वीडियो के 15 सबसे कठिन शब्द, उच्चारण और अर्थ के साथ:
| शब्द | उच्चारण | अर्थ |
|---|---|---|
| buyer संज्ञा | /ˈbaɪ.əɹ/ | क्रेता, ख़रीदार |
| merchant संज्ञा | /ˈmɝt͡ʃənt/ | व्यापारी, सौदागर |
| atomic विशेषण | /əˈtɑː.mɪk/ | आणविक |
| cart संज्ञा | /kɑɹt/ | ठेला, गाड़ी |
| minus | /ˈmaɪ.nəs/ | कम, घटाकर |
| quantity संज्ञा | /ˈkwɑn(t)ɪti/ | मात्रा, परिमाण |
| drain संज्ञा | /dɹiːn/ | नाली, नाला |
| multiply क्रिया | /ˈmʌltɪplaɪ/ | गुणा करना |
| subsection संज्ञा | उपखंड | |
| replicate क्रिया | /ˈɹɛplɪkeɪt/ | अनुकृति करना, अनुकरण करना |
| unavailable विशेषण | /ˌʌnəˈveɪləbəl/ | अनुपलब्ध |
| arbitrary विशेषण | /ˈɑː.bɪ.tɹə.ɹi/ | यादृच्छिक |
| retire क्रिया | /ɹəˈtaɪɹ/ | सेवानिवृत्ति |
| apology संज्ञा | /əˈpɑ.lə.d͡ʒi/ | क्षमा, क्षमायाचना |
| warehouse संज्ञा | /ˈwɛə(ɹ)haʊs/ | कोठी, मालगोदाम |
वीडियो में आने वाली फ़्रेज़ल वर्ब
| शब्द | अर्थ |
|---|---|
| come back क्रिया | वापस आना |
इस वीडियो का व्याकरण
वक्ता जिन संरचनाओं का सबसे अधिक प्रयोग करता है, वीडियो के असली शब्दों के साथ:
| संरचना | वीडियो में |
|---|---|
| Passive voice be + past participle — ज़ोर इस पर कि क्या होता है, न कि कौन करता है | is sold · is already expired · be reserved |
| Present perfect have/has + past participle — बीता हुआ काम जिसका असर अभी भी है | you've read · hasn't run · haven't actually solved |
| Relative clauses who / which + उपवाक्य — व्यक्ति या वस्तु के बारे में अतिरिक्त जानकारी | part, which is · set, which is · mind, which is |
ध्यान देने योग्य उच्चारण
वक्ता 51 बार संक्षिप्त रूपों का प्रयोग करता है, जैसे you're, they're, doesn't। इन्हें वैसे ही छोटा बोलें जैसा आप सुनते हैं।
- “th” की ध्वनियाँ: throughput /ˈθɹuː.pʊt/, breakthrough /ˈbɹeɪk.θɹuː/
- “sh” और “zh” की ध्वनियाँ: reservation /ˌɹɛz.ɚˈveɪ.ʃən/, contention /kənˈtɛnʃən/, cancellation /kænsəˈleɪʃən/
- लंबे शब्द — बलाघात सही रखें: reservation /ˌɹɛz.ɚˈveɪ.ʃən/, concurrency /kəŋˈkʌɹənsi/, incorrectly /ˌɪnkəˈɹɛk(t).li/, unavailable /ˌʌnəˈveɪləbəl/, cancellation /kænsəˈleɪʃən/
इस वीडियो से अभ्यास कैसे करें
- पहले बिना बोले पूरा वीडियो एक बार सुनें और जो शब्द नहीं आते उन्हें लिख लें।
- 0.75× गति से शुरू करें, एक-एक वाक्य की शैडोइंग करें, और सहज लगने पर सामान्य गति पर लौटें।
- अपनी आवाज़ रिकॉर्ड करें और मूल से तुलना करें, ख़ासकर buyer, merchant, atomic जैसे शब्दों पर ध्यान दें।
शैडोइंग तकनीक क्या है?
शैडोइंग (Shadowing) एक विज्ञान-समर्थित भाषा सीखने की तकनीक है जो मूल रूप से पेशेवर दुभाषिया प्रशिक्षण के लिए विकसित की गई थी। विधि सरल लेकिन शक्तिशाली है: आप मूल अंग्रेज़ी ऑडियो सुनते हैं और तुरंत इसे ज़ोर से दोहराते हैं — जैसे वक्ता की छाया 1-2 सेकंड की देरी से। शोध से पता चलता है कि यह उच्चारण सटीकता, स्वर, लय, जुड़ी हुई ध्वनियाँ, सुनने की समझ और बोलने की प्रवाहशीलता में काफ़ी सुधार करता है।