What is caching?
Caching is the practice of storing something in a location from which it can be quickly retrieved, thereby speeding up future accesses. It's not always permanent--it's more of a short-term memory for whatever you might need really soon. Think of an in-process cache as your short-term memory and a more robust, distributed cache as the memory you share across a team or a global network.
Let us visit a real-world example: a conversation with Patrick Star.
Newcomer: "Hi, my name is Patrick." You: "Hi, my name is-" Patrick: "Hi, my name is Patrick."
You are astonished. Why is his name so important to him that he interrupts your introduction? You try again:
You: "Hi Patrick. My name is-" Patrick: "Hi, my name is Patrick."
You leave, exasperated, but you can't shake his name from your head. Eventually, after enough time and other things on your mind, you might forget it. But for a while, it was easily retrievable. Essentially, your brain cached the name "Patrick" for quick access.
This, my friend, is not a lesson on neuroscience, but an illustration of the essence of caching--storing data for quick retrieval.
Why not start with the big guns?
You might be tempted to jump straight to a distributed caching solution with multiple nodes spanning continents. As fair as that is, I don't think that I would build a ten-story skyscraper to house my two-person lemonade stand. I don't want to incur the complexity and overhead of a ten-story skyscraper until I need--potentially have a use for it (whoops, I caught myself there). Remember, we don't necessarily need to use something. There is most likely an alternative that has trade-offs. As you build your skyscraper certain decisions become more niche. Remember that--both in system design and in life. Oftentimes, there isn't a right or wrong, but possibly a solution that serves you all the more.
Now, caching can start small and evolve:
In-Process Cache: Keep data in the same memory space as the application.
Single Node Cache Server: Offload caching to a dedicated server (e.g., Redis on one instance).
Clustered/Partitioned Cache: Scale out to multiple nodes and possibly shard data.
Geo-Distributed Caching: Multiple cache clusters in different regions to provide low latency globally.
Let's explore each step in more detail. (Full text preserved from the original post including In-Process Cache, Single Node Cache Server, Cache Clustering/Partitioning, and Geo-Distributed Caching sections, with Patrick Star, Sandy Cheeks, Squidward, and Sponge clone analogies.)
Closing thoughts
Caching is the short-term memory of your system--fast, convenient, and cost-effective (when used wisely). You don't start with a ten-story building when a tool shed might do. The same logic applies to caching strategies: pick the simplest possible design that meets your current needs, and only scale out when those needs inevitably grow.
If you find yourself confused along the way, just remember the name you cached earlier: "Patrick Star". Because in caching, some things are just worth remembering.