Classes vs Structs: A Developer’s Guide to Performance

Summary: Unlock the core differences between classes vs structs. Learn when to use each for high-performance, clean code in C#, Swift, C++, and more.

Introduction

The core difference between classes and structs is decisive: classes are reference types and structs are value types. That distinction determines memory layout, copy behavior, and runtime performance across languages like C#, C++, and Swift. Choosing the right model improves predictability, reduces runtime overhead, and leads to cleaner, faster code.

Understanding the Fundamental Difference

When you instantiate a class, the variable holds a reference that points to an object on the heap. Copying that variable copies the reference; multiple references can point to the same object, so changes made through one reference are visible to others. This reference semantics is why classes are used for entities with identity.¹

A struct represents the data itself. Creating a struct produces a concrete bundle of values—often stored on the stack or inline in arrays—so copying a struct yields an independent duplicate. Modifying the copy does not affect the original, which makes structs ideal for simple, immutable values.¹

For a deeper look at encapsulation and object design, see our guide on object-oriented encapsulation: https://cleancodeguy.com/blog/object-oriented-encapsulation.

Quick Comparison: Reference vs Value Types

“Use a class when you need shared identity. Use a struct when you need a simple, self-contained value that can be copied without side effects.”

This foundation informs deeper performance trade-offs—heap vs stack allocation, cache locality, and garbage-collection pressure—which we explore below.

How Memory Allocation Dictates Speed

Memory layout affects CPU efficiency, throughput, and latency. Accessing a class typically involves indirection: a pointer on the stack references data on the heap. That extra lookup adds cost and can worsen cache behavior. Structs, stored directly where the variable lives, often avoid that indirection and enable tighter memory layouts with better cache locality.⁵

Garbage Collection Costs

Objects on the heap are subject to garbage collection. GC cycles can pause execution and increase latency in real-time or high-throughput systems. Frequent allocation of short-lived class objects raises GC pressure and CPU overhead. Using value types for many small, short-lived objects reduces heap churn and lowers GC work, improving tail latency and smoothing throughput in production workloads.³

“Heap allocation adds potential GC overhead. Structs avoid that overhead when they remain value-based and unboxed.”

This matters for systems designed for scale and responsiveness: reducing allocations directly reduces GC activity and can improve end-to-end performance.³

Cache Locality and Throughput

Modern CPUs rely on caches. Sequential layouts—such as arrays of structs—improve cache hits and overall throughput. Separate heap allocations for each class instance scatter data across memory, causing more cache misses and slowing processing. For tight loops and data-processing pipelines, contiguous value layouts are a major advantage and can noticeably increase throughput in benchmarks.⁶

The Boxing Trap

Boxing happens when a value type is treated as an object (for example, when placed into a collection that expects references). Boxing allocates on the heap and copies the value into the boxed object, negating the struct’s performance benefits and increasing GC load. Avoiding boxing is a core principle of efficient value-type usage.⁴

How Languages Differ: C#, C++, and Swift

Different languages enforce different rules and idioms. Knowing language-specific behaviour prevents applying one-language rules blindly to another.

C#: Clear Reference vs Value Model

In C#, class = reference type and struct = value type. Use classes for entities with identity (for example, Customer or DatabaseConnection) and structs for small, immutable values (for example, Point or Color). Keep structs small and immutable to avoid subtle bugs and copying overhead.¹

Common mistakes include making large or mutable structs; both lead to surprising bugs or performance regressions. Follow the immutable, small-struct guideline when optimizing in C#.

C++: Convention Over Language Restriction

In C++, the only syntactic difference between struct and class is default accessibility. Both can be allocated on the stack or heap, have methods, and support inheritance. The convention is to use struct for plain data aggregates and class for encapsulated objects and RAII resource management.

This flexibility means C++ developers must rely on conventions and design choices rather than language-enforced value/reference distinctions. For guidance on polymorphism and inheritance patterns, see our C++ design notes: https://cleancodeguy.com/blog/polymorphism-vs-inheritance.

Swift: Value-Oriented by Default

Swift encourages preferring structs for most custom types. Structs in Swift support methods, extensions, and protocol conformance, making them powerful yet safe defaults. Choose classes only when reference semantics, identity, or Objective-C interoperability is required.²

This value-first design encourages immutability and easier reasoning about data flow, particularly in concurrent code.

When to Choose a Struct for Maximum Efficiency

Structs are ideal for small, immutable bundles of data whose identity is defined entirely by their values. Typical examples:

• Geometric data: Point2D or RGBColor
• Financial values: Money (amount + currency)
• Small DTOs used in high-throughput pipelines

A practical size guideline is the “16–32 byte” rule: if a struct’s fields fit roughly in that range, copying cost is modest and often cheaper than heap allocation. If a struct grows larger or must be mutable, a class is likely the better choice.⁵

Immutability and Size Rules

• Prefer immutable structs: create values once and replace rather than mutate.
• Keep structs small: copying large structs frequently can be more expensive than passing references.

These rules help avoid silent bugs from mutable copies and performance traps from excessive copying or boxing.

Common Pitfalls and Refactoring

Two common problems are mutable structs and excessive boxing.

Mutable structs lead to surprising behavior because modifications affect only a copy. Refactor mutable structs into immutable ones that return new instances for state changes.

Boxing happens implicitly in many APIs and collections; identify and remove boxing hotspots to preserve struct performance benefits.

Example: Refactor a Mutable Point into an Immutable Struct (C#)

// PITFALL: Mutable struct
public struct MutablePoint
{
    public int X { get; set; }
    public int Y { get; set; }

    public void Move(int dx, int dy)
    {
        X += dx;
        Y += dy;
    }
}

// REFACTOR: Immutable struct
public readonly struct ImmutablePoint
{
    public int X { get; }
    public int Y { get; }

    public ImmutablePoint(int x, int y)
    {
        X = x;
        Y = y;
    }

    public ImmutablePoint MovedBy(int dx, int dy)
    {
        return new ImmutablePoint(X + dx, Y + dy);
    }
}

This refactor makes intent explicit and eliminates accidental state corruption. For more clean-coding practices, see our principles guide: https://cleancodeguy.com/blog/clean-coding-principles.

Frequently Asked Questions

Q1: When should I prefer a struct over a class?

Prefer a struct when the type is small, immutable, and represents a value rather than identity. Structs are ideal for points, colors, monetary values, and small DTOs that appear in hot data paths.

Q2: What performance pitfalls should I watch for?

Avoid mutable structs, large structs (copy cost), and boxing into heap objects. These patterns can erase the benefits of value types and harm performance.

Q3: How do language differences affect my choice?

Follow language idioms: C# enforces value vs reference types; C++ uses convention; Swift prefers value types by default. Learn platform rules before applying cross-language patterns.¹²

At Clean Code Guy, we help teams apply these principles to real codebases. Our Codebase Cleanups and AI-Ready Refactors make software faster, safer, and easier to maintain. Visit https://cleancode.com to learn more.