![]() ![]() Implemented using the stack data structure, meaning that variables are stored in the stack memory.Memory is allocated during compile time, or before program execution.Like the word "static" suggests, statically allocated variables are permanent, meaning they need to be allocated beforehand and last as long as the program runs.Now that you understand what memory allocation is, it is time to familiarize yourself with the two types of memory allocation, namely static and dynamic, and distinguish between the two. Now you may be wondering, how do we then write memory-efficient code if we have so little control over Python's memory management? Before we get into that, we need to further understand some important terms concerning memory management. ![]() One last thing you should know about how Python's heap is managed is that you have zero control over it. This is because computers need different storage requirements and speed tradeoffs for integers as compared to strings. While strings and integers may not be that different considering how much time we take to recognize and memorize them, they are treated very differently by computers. As you may already know, some examples of object types are strings and integers. Look at it as your Python program requesting your operating system for a chunk of memory to work with.Īt the next level, several object-specific allocators operate on the same heap and implement distinct management policies depending on the object type. It does this by interacting with the memory manager of your operating system. Before they can be stored in memory, a chunk of memory must first be allocated or assigned for each of them.Īt the lowest level, Python's raw memory allocator will first make sure that there is available space in the private heap to store these objects. For these objects to be useful, they need to be stored in the memory to be accessed. ![]() Python Memory AllocationĮverything in Python is an object. Also, remember that it is the Python memory manager that handles most of the dirty work related to memory management so that you can just focus on your code. Best Practices for Improving Python Code PerformanceĪccording to the Python documentation (3.9.0) for memory management, Python's memory management involves a private heap that is used to store your program’s objects and data structures.Would you rather choose the app which runs smoothly or the one which noticeably runs slower? This is one good example where two individuals would spend the same amount of time coding and yet have noticeably different code performances. One of them has written a more memory-efficient code which results in a faster-performing app. However, say you have two developers who have used Python in developing the same app and they have completed it within the same amount of time. In the tech world, you may have heard that "done is better than perfect". This is caused by the failure to free used memory after the processes terminate. Another benefit is that it prevents memory leak, a problem which causes RAM usage to continuously increase even when processes are killed, eventually leading to slowed or impaired device performance.The great thing about writing code that is memory-efficient is that it does not necessarily require you to write more lines of code. More available RAM would generally mean more room for cache, which will help speed up disk access. It leads to faster processing and less need for resources, namely random access memory (RAM) usage.So what do we get out of writing memory-efficient code? Ultimately, you can enforce it as a habit that can potentially be adopted in other programming languages that you know. However, having a good understanding of Python memory management is a great start that will enable you to write more efficient code. Due to its simplicity, however, Python does not provide you much freedom in managing memory usage, unlike in languages like C++ where you can manually allocate and free memory. Ever heard of the Python memory manager? It is the manager keeping Python's memory in check, thus enabling you to focus on your code instead of having to worry about memory management. How so? Python memory management is implemented in a way that makes our life easier. Python's memory management plays a role in its popularity, too. While Python is not the fastest language out there, its great readability coupled with unrivaled community support and library availability has made it extremely attractive for getting things done with code. This is largely due to its super friendly syntax and its applicability for just about any purpose. However, according to the 2020 Stack Overflow Developer Survey results, Python is the 2nd most popular programming language behind JavaScript (as you may have guessed). Python is not known to be a "fast" programming language. ![]()
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