Cache Warming Time Calculator

When you push out a big site update without pre-warming, there’s a particular type of risk: all of your requests is hitting your origin server, which stays silent for a bit. Then you hit publish, and the users come rushing in, and…oh no! Every request miss the cache, and pounds on your database simultaneously. Your app slows to a crawl, your users have a bad experience. You didn’t warm up the cache first.

Cache warming loads your edge servers full of content before any actual traffic comes. But that involves estimating how much traffic you’ll get and how long it will take. This estimate must take into account latency, bandwidth, and concurrent connection. Because people don’t understand the math behind origin protection, most teams makes guesses that is incorrect.

How to Warm Up Your Cache Correctly

The core problem is not about raw bandwidth; it is about concurrency caps and rate limits, which together determine how fast things happen. When you make a preload request, it goes off to the origin, your app spits out a response and sends some data back. Now, let’s say your average latency is two hundred milliseconds, and you’re capped at eight concurrent requests per point of presence. No matter what the network speed are, you have a limit on how much you can get through. You can use the calculator on this page to enter your latency numbers and object counts to see what your bottleneck will be.

Remember that some of those prefetched items won’t work out in reality. Some items will be updated during the prefetch, others will timeout, and others will hit rate limits. Even if you’re shooting for 95% object coverage, the fact that three percent fail means you’ll miss the mark. This is why the tool assumes a higher total number of requests are required to achieve desired hit ratio. It forces you to realize that, with failure factored in, you will need to issue more requests than there are unique objects to get what you want. This is what makes the difference between a relatively cool cache that still results in loads on origin versus a nice launch.

Another important variable is POP fanout. Warming ten separate edge locations individually will send ten times as much traffic to your origin relative to warming a single shield cache for all users. A lot of teams believe more points of presence equals better user performance… which it does, but those same points also multiplies the origin’s stress during the warmup phase. Should you stagger the warmup across multiple location waves? Or risk overwhelming your backend with parallel fetches? These tradeoffs is broken out in the reference tables here and show how various architectures deals with fanout pressure. Knowing the difference helps you avoid designing a warmup job that seems great on paper but blows up your production database in practice.

More importantly, compression is not as small as some engineers think. Bandwidth estimates can be 50% or even higher wrong if your backend serves an ungzipped HTML, but the edge respond with Brotli compressed responses to the client. When you account for those savings, you may be able to complete your warmup task much quicker then you naively estimated, since less data needs to be transferred, making it less of a bottleneck relative to the time needed to fetch each object. You should of use actual response sizes sampled from your logs rather than disk usage in your file system, as the former usually doesn’t reflect the weight of what will actualy be transferred.

Lastly, you can’t preload everything, but you need to know where to prioritize what you do warm. What if you have a 50k SKU product catalog? It will take hours for it to fully hydrate in all of your global edges. So instead, focus on the head of the distribution. Prioritize warming the homepage, the top ten categories, and the most popular products first. Then the long tail will fill naturaly as people navigate over there. This is a tiered approach which protect your origin from unnecessary strain and gives the majority of your visitors immediate access to stored content.

It’s not just about filling up a disk; it’s about knowing what you’re measuring in the first place. You’re directing a carefully planned burst of traffic that looks like real users but doesn’t exceed the capacity of your infrastructur. You model the number and length of those requests exactly, transforming a dicey release into a predictable process. It’s not about going fast; it’s about controlling what happens. Knowing precisely how many requests to expect and when to expect them ends guesswork about what your servers can handle and begins managing what they can do. That’s the difference between a pro deployment and a wildcat.