Memoization vs. Caching. Not cache, let’s call it kashe. Memoization is a software cache technique in which the results of functions are saved in a cache. Long story short, but memoization is not a cache, not a persistent cache. It's based on the Latin word memorandum, meaning "to be remembered". Memoization and caching to me are almost the same thing, both involving storing precomputed results under a key, unless by caching you get concerned about the physical CPU caches and worry about cache alignment. Buffer vs. cache. There are many types of cache (hardware caches, network caches, software caches…), with different applications and performances. 1 Memoization vs Caching. Following best practices, memoization should be implemented on pure functions. You can think of it as a cache for method results. Memoization vs Caching. A cache can store data that is computed on demand rather than retrieved from a backing store. Memoization is used to avoid recomputing expensive things and make rendering faster. Caching stores recent past work. Using a cache that by default can create leaks is thus not recommended. It's not a misspelling of the word memorization, though in a way it has something in common. Memoization with Decorators Definition of Memoization. In software, this takes two forms: Memoizing stores all past work. As memoization trades space for speed, memoization should be used in functions that have a limited input range so as to aid faster checkups. It can change the asymptotic complexity of some algorithms. Memoization effectively refers to remembering ("memoization" → "memorandum" → to be remembered) results of method calls based on the method inputs and then returning the remembered result rather than computing the result again. Memoization. It might be quite a long term on a server side, but cannot and should not be a real long term cache on a client side. When to memoize your functions Although it might look like memoization can be used with all functions, it actually has limited use cases: This is a top-down approach, and it has extensive recursive calls. While caching can refer in general to any storing technique (like HTTP caching) for future use, memoizing specifically involves caching the return values of a function. Both techniques attempt to increase efficiency by reducing the number of calls to computationally expensive code. In Dynamic Programming (Dynamic Tables), you break the complex problem into smaller problems and solve each of the problems once. Just a bit different. Caching the results of some operation is so prevalent in computer science that the world would slow down considerably without it. I would argue that in a frontend application the best limit for a memoize cache is just one value: the latest computed one. Memoization is like caching. In Memoization, you store the expensive function calls in a cache and call back from there if exist when needed again. The semantics of a "buffer" and a "cache" are not totally different; even so, there are fundamental differences in intent between the process of caching and the process of buffering. The term "memoization" was introduced by Donald Michie in the year 1968. You could configure lodash cache to limit the number of saved values. Memoization even sounds like a moment. Every computer has a number of caches built into the hardware and operating system to optimize memory access, speedup … Memoization is a technique of caching function results “in” the function itself to make the function have memory, and the callers won’t need to know if the function is memoized or not. In this post I will discuss one type of software cache: memoization. Memoization. The core concept of caching is keeping in a high-speed data structure the results of past work in hopes that you’ll use that data again soon. Reasons that pure functions produce an output which depends on the input without changing the program's state (side effects). Memoization is similar to caching with regards to memory storage.