Understanding Heap and Stack in Memory Management

Introduction

For my first blog post, I chose a subject that we rarely worry about in everyday programming, but that, at some point, will make all the difference, especially to reduce bottlenecks in an application. Yes, let's talk about memory allocation, more specifically about how heap and stack memory work.

I promise to explain these concepts in a simple way. Heap and stack are two distinct areas of a process's memory layout in the operating system. In short, in a very simplified way, they are different "areas" of your computer's memory, each with a specific function and storing different types of data.

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Stack

The stack is basically a consecutive block of memory, whose allocation and release are automatic. It operates in the format LIFO (last-in-first-out), which means that the last element inserted is the first to be removed. When a function's execution scope ends, the associated stack frame is automatically freed, avoiding problems like memory leaks (unless you insert an infinite loop or something similar).

In addition, access to the stack is faster because the data is stored sequentially, which makes reading and writing easier. However, it has limitations in terms of size and is intended for temporary data, such as local variables and function parameters.

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Heap

The heap, on the other hand, is an area of ​​memory dedicated to the allocation of dynamic data. It is managed by the garbage collector (in the case of languages ​​like Go). Unlike the stack, the heap is a space shared between threads or goroutines and is used to store long-running data.

Heap management is more complex because it requires the garbage collector to monitor the allocated data and identify which is no longer needed. Furthermore, data on the heap may be randomly scattered in RAM, making access slower.

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How to best use the stack and heap

In terms of performance, the ideal is to use the stack as much as possible. Because it is more efficient and does not burden the garbage collector, the stack should be the first choice. When it is necessary to use the heap, it is important to do so in an intelligent and minimized way, as when using buffers.

In the case of Go, the compiler allocates local variables on the stack whenever possible. However, if the compiler identifies that the variable can be referenced after the function returns, it will allocate it on the heap to avoid dangling pointer errors. Very large variables can also be moved to the heap to avoid compromising limited stack space.

If a variable has its address accessed, it is a candidate to be allocated on the heap. However, more sophisticated analyzes performed by the compiler may allow some of these variables to remain on the stack, as long as they do not survive the function's return.

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How to reduce the burden of the garbage collector?

To minimize the impact of the garbage collector, we can adopt the following practices:

• Avoid using pointers unnecessarily: Data referenced by pointers are allocated on the heap and can be spread across memory in a disorganized way. Use pointers only when really necessary.
• Prefer primitive types: Data such as numbers, booleans, strings and runes are generally allocated on the stack, reducing the need for management by the garbage collector.
• Optimize heap usage: Whenever possible, use structures that minimize dynamic allocations and focus on reusing already allocated resources, such as buffers and object pools.
• Make the most of the stack: Temporary or local variables should be allocated to the stack whenever possible, ensuring greater efficiency and performance.

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Conclusion

Understanding the difference between heap and stack, as well as memory management in Go, is fundamental to optimizing the performance of your applications. By using the stack whenever possible and being careful with your use of pointers, you can significantly reduce the load on the garbage collector, resulting in faster, more efficient programs. Over time, these practices will contribute to building more scalable and better performing systems.

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