The Edge & Databases: Everything Everywhere All at Once


Cloudflare Workers and Edge Functions bring the Serverless model to the next level by letting developers deploy code instantly globally to give it exceptional performance, reliability, and scale.

Having server-side applications execute close to where their users are located brings greater performance and drastically improves the user experience of an app. However, due to their limited runtime environment, working with your favorite traditional database is challenging since it can’t be accessed in CloudFlare Workers directly. Prisma solves this problem in multiple ways.

The goal of the talk is for developers to understand what the Edge really means, how it works, and how to work with your favorite traditional database on the Edge.


Hello everyone. It's good to see you here and also put a face to the names of the people that I usually interact with on Twitter and hopefully also after the conference. So I, Ryan Dahl mentioned in the previous talk, you know, when you're submitting a talk, you usually just submit something. And then I came up with a better title, which is Edge and Databases, Everything, Everywhere and All at Once. In this talk, we'll talk about what it means to work with databases on the edge and how exactly you can use them if you choose to. Yeah. So my name is Alex and I'm a javascript addict. I'm sorry, wrong, wrong session. I am a developer advocate at prisma, where as Kevin gracefully mentioned that I work on teaching how to make working with databases easy for developers. As a company, we have doubled down our efforts in understanding the serverless ecosystem, especially when it comes to working with databases so that we can provide tools that offer the best experience for you as developers. And one of them is Accelerate, which we launched a couple months ago that allows you to cache your data, your database query responses at the edge or globally. So this talk will be split into four sections. In the first section, we'll walk down memory lane and understand how we got here to the edge. And then in the second segment, I'll talk about the edge, what it is, the limitations and how you can get around those limitations in relation to databases. And then in the third bit, I'll walk you, I'll show you a demo of a tool that I built, show you what it would be like with the solution tool that we have built. And then we'll, I'll give, we'll leave with a few final thoughts. And if you have your questions, feel free to ask them after the conclusion. So let's get started. How did you get here? Really? Well, application development, application deployment has come a really long way. And sometimes when I talk about this technologies, I may look young, but I have the mind of a 56 year old. So I hope you also don't feel old, really. Back in the day, eons ago, even before I started learning how to use computers, most teams and companies used to deploy their applications on a computer that say, used to live in the basement, for example. The basement is just an example. And this worked fine. And developers and or teams and devops would be responsible for managing the entire thing. And your application server lived together with your database, which was ideal. And then still in 2006, Amazon or infrastructure as a service came to be. And this shifted responsibility from having to think about the computers and just leave it all to Amazon. And then a few years later, we started seeing the rise of platform as a service, which shifted more responsibility where you had to worry less about the computer that your application run on and just leave that to your cloud provider. And then a few years later, again, we saw the rise of functions as a service with aws, lambda, cloud, I can't think of any other examples at the moment. But it was great because we worried less about our servers. And we only focused on building the code that runs our applications. And this was great because you could scale your app almost infinitely because you can in the event there was a surge of traffic in your application, you can scale up to say 1000 instances of functions running at the same time. And then in the event there was no traffic, it would scale down to zero and you would only pay for exactly what you use instead of a long running server. But this came with a few challenges especially when it came to working with relational databases. And in this talk, I'll talk about three concrete challenges that well, developers and teams experienced. And the biggest one of them all was connection management and handling how you manage the connections to your database. So let's take an example of a function which is denoted by this lovely UFO here. If your function had to interact with a database, and you're using query builder or database connector, if and you go with the default configuration, it would likely open multiple connections. And this is okay, if you have a single lambda that runs once in a long time, because your database would be pretty much relaxed. But the main challenge comes in when you have a surge of traffic, and you have an entire swarm of lambdas, and not one or two, three, so many, four, but an entire swarm. And in this case, your database is in a state of panic, because it usually has a limited number of connections at any given point in time. And before you know it, you're you'll be out of connections, and your data and your functions will start running into errors, and they start failing. And this is not ideal, because sadly, your database is unable to scale up with your functions in serverless. Pause. A few other problems that we still experience with serverless include function timeouts, which doesn't make it ideal for working with, say, long running processes. So if you have like a batch operation that takes an hour to perform, and most cloud providers usually have like a set time of how long your function should run, which doesn't make it ideal if you have a process that runs really long. And another challenge that we still run into is cold starts, where this affects the latency and of your function, which makes it doesn't give an optimal experience for your users. But it's not all that bad, because we found solutions. And that's great, because it just pushes innovation forward. And one of them was setting the connection pool size when connecting to your database to one. So instead of having multiple connections, you can say, in this case, limit it to only one. However, this is not ideal, because if you have, for example, the batch operation that's inserting 1000 records, then there would be run sequentially instead of in parallel, which makes it a little slow. That's okay. We have another possibility, which we could define a concurrency limit. So in the event, as I mentioned, if you have a surge of traffic, your cloud provider usually sets how many lambdas that you can run at any given point in time. So in this case, you can go to your aws console, for example, and then you can limit instead of having 100 lambdas running concurrently, you can have say 10 or 20 at any point in time. But the most robust solution of them all is by using an external connection pool like pgBouncer, which will is responsible for managing all the connections that go to your database. So in this case, you would, you wouldn't run out of connections, because it has like a log of how many that it can actually give at any given point in time. So with all this innovation, it brought us to the edge, which is kind of like a new era in a way, because edge, in this case is a form of serverless compute that allows you to run your applications even closer to your as close as possible to your users. So in this case, once you deploy application, say it would be instantly deployed across the globe. And if your user is somewhere in the east coast of the US, then that the application that's deployed, or the data center that is closest to them would be responsible for returning the data that is theirs. And if you have another user that's also somewhere, say in East Asia, the data center that's closest to them would also respond to them. Now, this comes with a couple of benefits, by the way, when deploying to the edge. And by the way, when referring to the edge, I'm referring to cloudflare workers and Dino edge functions. But you can also accomplish a similar setup using virtual machines as well, but it would require a little more fiddling on your on your own. Well, you can scale infinitely because your application is all over the globe. And this means that you have lower latency. And on cloudflare workers, by the way, there are no cold starts because it runs on a different runtime, which I'll mention, I'll talk about in just a few. And it reduces the cost of how you are running your application. However, when it comes to working with databases, it comes with a few paper cuts that you have to be aware of. And that's that one, you have a constrained runtime, because when you're running your application on lambdas, you usually have the full access to the entire node api. However, when you're using cloudflare workers, in particular, you are limited to a V8 isolate, which is a significantly smaller runtime that has a limited surface in terms of APIs that you can use at the point at that to deploy your application. And then another challenge that cloudflare workers or Edge has is that you have a limited compute resources. For example, you have less CPU, you have less memory, and you also have like a significantly smaller allowance in how big your application should be. And I think for cloudflare workers is somewhere around five megabytes. And this gets even harder when it comes to work with databases. And since we have a limited api surface, there are no TCP connections, which makes it difficult to talk to databases. And since most database deployments are usually in a single region, having to serve your Edge functions, which are global becomes a little bit difficult because of the latency and the round trips connecting to your database. Another pause. Come on, cheer up, liven up. However, it's not all that bad. There are solutions and you can work around them. And for the problem on working with TCP connections, one way that you can get around it is by using a proxy connection using either HTTP or WebSockets. And this means that you have a proxy that would sit in between your Edge function and your database. And your Edge function would communicate to your data, to the proxy using either HTTP or WebSockets. And then the proxy would use TCP to communicate with the database. And then it would respond to your application with the data that you requested for. And a couple of examples of these tools include the Neon serverless package, aws RDS proxy, and the prisma data proxy. The second challenge that I mentioned is the latency and the round trips when connecting to your database. And one way to get around this is by using a globally replicated data store. And by this, I mean your database provider or you as well as a developer could be responsible for creating replicas of your data and distributing them across the globe. And I know this sounds easy, sort of ideal, but the moment you start talking about replicating your data, we find ourselves in the distributed systems territory. And it's not that easy because you have to start thinking about consistency. For example, what happens, how do you synchronize your data across your replicas and which replica or node is responsible for handling write requests. This is not really an easy problem to fix. And as much as companies like Fauna, cloudflare, Cockroach and aws are doing a great job with these tools, we also have to come to the terms that perhaps our database is not likely to move to the edge. And that's okay. Now, one final solution or suggestion, because this is just like a rough draft and I would like your opinion on it, is by using something called a specialized data store or cache, meaning you only store the data that your user needs per region. And this comes with a disclaimer that you have to have your application or some parts of your application has to have some tolerance for stale data. And that's okay. So as Lee Robinson quoted, wrote in his article on state of databases and the edge in 2023, perhaps what we're looking for when it comes to working with applications on the edge is not a replicated data store, but a specialized data api. So here's what it would look like. Well, we would have your application deployed all over the globe and your database sitting somewhere in the East Coast because that's where most well, all the aws US East jokes come from. And then if a user somewhere from the US East would make a request, then the first request, of course, would be forwarded to your database. And then after that, your database would respond. Of course, the first request would take a while. However, that data that the user requested for could be cached at that data center. And then that data would be then served to the user. And then in the event the user makes a second request, then the response time for that particular function would be significantly faster. And that's the same data that will be served. Now, how long you serve this data would be completely up to you. Could be 10 seconds, 60 seconds, two months. But again, as I mentioned before, it's completely based on how much tolerance your application has for stale data. And stale data is not wrong. It's also good because it serves you. Sorry. Because with stale data, you're able to reduce the load on your database. So this is an example of what it would look like in a cloudflare worker. Sorry for the misquoting. In this case, I'm using prisma, which is a tool for querying, interacting with databases. And in this case, I am requesting for posts in my database. And I'm setting a cache. Sorry, the red can't be seen, but I'm highlighting the cache strategy. And the feed that I'm requesting for has a time to live for 60 seconds. And this means that the data will be stored in the edge for 60 seconds. And then the SWR stands for Stale While Revalidate, meaning that you will be able to serve the stale data for 60 seconds before your worker is responsible for refetching the data and revalidating it for you. So yeah, that's what it would look like. So a quick demo of an app that I built. This is a beautiful blog. And if I refresh this page hoping that the Wi-Fi is working. Yes. So we have some data from cloudflare, and we can see that the data center that responded to our request is based in Berlin. And the code for the data center is FRA. And FRA is like an IATA code, which is like the airport codes, but for data centers. And for our particular database request, we can see that the database, the data center that responded to it is the same as our data center. And in this case, it was a cache miss. And that's why perhaps the refresh took a while. And the data was last updated in GMT. This is about two hours ago. And if I refresh this data, we can see that response was significantly faster. And we can see that it was at the cache status at time to live, meaning it has already been cached and any other subsequent request would be significantly faster. The same goes for the individual posts. We can see that transition to the different pages is significantly slow. And those are cache miss and the same data center responded to our data, which is the code FRA. So what does this look like in the code? So in the, I'll only show you one query. In this case, I have a query that for the all posts and I'm filtering for posts that have been published. And in this case, I set a time to live for about 10 minutes and the stale while revalidate is 60 seconds. In this case, I'm also getting the cloudflare information from the request that CF. Sadly, that's not typed in remix, by the way. This is a familiar, a simple application using remix. Anywho, that's all for the demo. In case this is the kind of thing that you'd be interested in trying out, just feel free to reach out and talk to me or Flo over there. Say, hi Flo. So in conclusion, the big question is, should you migrate to the edge? Well, it depends. And I don't have the answer for you. It comes with a few considerations, meaning that if your application is dependent on a central data store, then it's probably not that great because latency and when you have multiple requests, one after another in a single function would take a while. And perhaps what we should do is understand the needs, the data needs of our application, which is, and because some parts of our application, as I mentioned, can tolerate stale data and that's okay. And finally, let's just remember that all these are tools and you don't have to migrate from whatever you're using if it's running perfectly. So thank you, by the way. I hope you enjoyed the talk and I'd like to give a shout out to our former colleague, Martina Vellander, who let me use the beautiful illustrations on my slides. Thank you. Thank you ever so much. First question is, can I use cache strategy for prisma raw queries? At the moment, not yet, but that's in our plan to include caching for your raw queries. Great. Next question. If the data is stored or cached next to the users, what happens when the users work within one account or domain from different locations? What happens when people work between locations? Will there end up being an inconsistency? So if you work from different locations, I think, well, one, we're relying on cloudflare for accelerate in this case, and the closest data center to the user will be the one that will respond to the user's request. And if the data is not cached in that particular data center, then it will forward the request to the database and then cache the data in the data center that received the request. So even if they move around, it will always be seeing whether it's cached on the data center that's closest to them. Yes. Great. Next question. How is SWR different from, there's a TEL, and I don't know if this is an acronym. TTL. TTL. It was TEL here. Thank you so much. How is SWR different from TTL on the Edge server? Oh, that's a tough one. So time to live just specifies how long you should cache the data, and then stale while revalidates specifies how long you can cache the, you can serve the stale data before it can go and refetch the data again from your database. I hope that's clear. I'm not sure I fully understand. I mean, that's totally cool. Perhaps this is something we can go and have a chat with in the break. Can you maybe describe it in another way if there's another way you have kind of up your sleeve now, or would you rather we do this in the break and you have a moment to think about this one? So I can give it one more try. So if you have decided to cache your data for 60 seconds, then the TTL will apply first. So the first request will for the request to the database, and then it will be cached in the database. Now, once the 60 seconds is up, the SWR now comes in place, and then it specifies how long you can continue serving the stale data before you can refetch it again from the database. So yeah, it's just, yeah. So they play together, but at different points in that journey of storing and serving up potentially stale data. Yes. Awesome. Thank you. We've got, we seem to have quite a few, but like quick fire, quick fiery kind of questions. Let's go with this question next. Is cloudflare Workers the only edge solution that would support this caching strategy? No. So Accelerate Edge is just one of the platforms that you could use with Accelerate. You can still use it with serverless as well, or you can still use it when working with a traditional server as well. So it's not the only platform that you can use cache strategies with. Cool. And that means you can bring your own cloud providers to this. You can use the strategy with your own cloud provider too? Yes. You can deploy applications. Yes. Cool. Next question is, I mean, I think this one might be a repeat, asked in a different way, but semantically I'm going to ask it just in case. On what platforms can we use prisma caching? I'd say I'd recommend it particularly for serverless and the edge if you're using those particularly, but if your application is in the same network as your database, then perhaps it would be faster to request the data from the database instead of using the cache in this case. Yes. I just have a couple generally. What do you think, so someone went away and tried to implement this now, what are the common mistakes, misconceptions, missteps that you commonly see people? That's a tough one. I mean, the product has only been out for about two months. So none yet, but we are hoping to look for, well, people could try it and then so that we can also learn from them as well. Interesting. Where can people go to learn more? slash accelerate should be the right place as well. Are there any other resources people may want to check out? The URL that I just mentioned is should work. It also has a link to a wait list and it's still in early access, meaning it's like a private beta. So once you get access, then we'll share with you more resources like the docs, but you can also get that as well if you're interested in reading more about how it works. Yeah. Which I don't have at the top of my mind right now. That's all good. People can always come and ask you questions should they have them. I would like us all to give a massive, massive round of applause for just a really fascinating talk. I'm really interested to learn more and thank you for introducing us to these concepts. Thanks for having me.
27 min
14 Apr, 2023

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