Immutable Arrays: Evaluating Copy-by-Change Methods

Abstract

In programming, immutability is now one of the key principles, which is in line with the general trend toward producing safer, more predictable, and concurrent-friendly code. Immutable arrays non-alterable data structuresare at the core of the concept of referential transparency and the prohibition of unintended side effects. Still, to be strictly immutable comes at a cost, especially when there is a need to update an array, hence the reason behind the writing of this paper that debates the performance of different copy-by-change methods. This paper assesses the effectiveness of such approaches, whereby changes in an immutable array are reflected in the newly created version that contains only the modified elements. First of all, the researchers want to know how the implementations of copy-by-change differently affect the performance, the amount of memory used, and also the scalability. Secondly, the researchers want to know the most reserving strategies for balancing immutability with computational efficiency. As to the methodology, the research is empirically performed by measuring the real costs of operations in different programming languages and the costs of the tests in real-world scenarios. The experiment results of journaling and benchmarking in Java, C#, and functional languages such as Haskell and Scala reveal that immutable arrays are less time and memory effective than their mutable counterparts; however, to a great extent, the penalty is softened by structural sharing, persistent data structures, and copy-on-write techniques. Moreover, the study points out that the compromise between immutability and performance depends on the case. Therefore, the copy-by-change method is a good choice when the main focus of an application is safety and concurrency rather than being fast. This research draws attention to the rise of immutability as the mainstay of designing systems that can scale and sets out avenues for enhancing operations on immutable arrays by mixing approaches and dispatching compiler-level tricks.