3/30/2023 0 Comments Immutable method map javascript![]() The peak of micro-optimizations can somewhat conflict, but those are usually reserved for the smallest and most critical sections of code.Ĭoming from the low-level standpoint, if we x-ray concepts like objects and strings and so forth, at the heart of it is just bits and bytes in various forms of memory with different speed/size characteristics (speed and size of memory hardware typically being mutually exclusive). Efficiency is often a function of elegance and simplicity. We typically don't find horrific-to-maintain codebases tripping over race conditions as being the most efficient, even if we disregard the bugs. Performance is still largely a productivity metric in any non-trivial codebase. Hardware does best when it's not sporadically allocating memory and can just modify existing memory instead (why we have concepts like temporal locality). At this kind of very technical conceptual level, immutability could only makes things slower. I'm very excited by immutable designs, even coming from a C perspective, and from the perspective of finding new ways to effectively program this beastly hardware we have these days.Īs to the question of whether it makes things slower, a robotic answer would be yes. Late-comer to this Q&A with already great answers, but I wanted to intrude as a foreigner used to looking at things from the lower-level standpoint of bits and bytes in memory. This often leads to comparable if not lower levels of garbage, depending on your algorithm. Just for some reason people don't notice it anymore.īy encouraging sharing and discouraging creating variables until you have a valid value to put in them, immutability tends to encourage cleaner coding practices and longer lived data structures. ![]() I've had people remark in functional programming classes I teach about how much garbage an algorithm creates, then I show the typical imperative version of the same algorithm that creates just as much. I think you'll be surprised at the ratio. Sit down sometime and really look at how many temporary objects and defensive copies you create compared to truly long-lived data structures. Perhaps this is due to the popularity of memory-managed languages. Second, I find a lot of people have a fairly inaccurate idea of the typical lifetime of objects in typical imperative programs. The performance is close enough to mutable data structures that functional programmers generally consider it negligible. Most implementations are able to take advantage of persistent data structures most of the time. It is called a persistent data structure. In general, most of a data structure is not copied, but shared, and only the changed portions are copied. But YMMV.įirst of all, your characterization of immutable data structures is imprecise. If you want to design a data type where arrays with large (or undefined) size, random access and changing contents are involved, use mutability.įor situations between these two extremes, use your judgement.If you design a data structure with only a few attributes based on primitive or other immutable types, try immutability first.My personal rule of thumb for this situation is: Then you build a lot of code upon that, and several weeks or months later you will see if the decision was a good or a bad one. For some cases immutability will hurt performance, for some the opposite might be true, for lots of cases it will depend on how smart your implementation is, and for even more cases the difference will be negligible.Ī final note: a real world problem you might encounter is you need to decide early for or against immutability for some basic data structures. So when you look at cases like this one from an older SO post, the answer to your questions becomes probably clear - it depends. I worked last month on a program where we stumbled over exactly that problem because we initially decided against using an immutable data structure, and hesitated to refactor this later because it did not seem worth the hassle. My example, however, is not so hypothetical as it might sound. ![]() ![]() What this demonstrates is - if you create the right hypothetical scenario, you can prove anything, especially when it comes to performance. That will leave a lot more garbage than in your case. So to avoid unwanted side effects, you start creating a full copy of the object "just in case" and pass that copy around, even if it turns out no property has to be changed at all. Without immutability, you might have to pass an object around between different scopes, and you do not know beforehand if and when the object will be changed. For example, if you need to change a single property of an object, better to just create a whole new object with the new property, and just copy over all the other properties from the old object, and let the old object be garbage collected. ![]()
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