Coder Legion - Recent posts tagged struct

Is Your Game Choking on GC Allocations?

Fri, 03 Jul 2026 21:43:02 +0000

Is Your Game Choking on GC Allocations? A Guide to Zero-Allocation Data Structures

Introduction

You’ve optimized your shaders, painstakingly batched draw calls, and even dipped your toes into Unity’s Burst Compiler and C# Job System. Yet, your game still experiences inexplicable hitches and stutters, particularly during intense moments. The silent assassin in many Unity games isn’t the GPU struggling with pixels, but the Garbage Collector (GC) reclaiming memory from incessant, hidden allocations. Specifically, insidious GC.Alloc calls, often for temporary class-based data structures, can disrupt your carefully crafted frame budget, leading to frustrating performance spikes.

This tutorial will illuminate this common pitfall and provide a robust architectural solution: embracing struct types combined with Span<T> for transient data. By understanding and applying these principles, you can virtually eliminate many GC.Alloc calls from your high-frequency game logic, leading to buttery-smooth frame rates and a fundamentally cleaner, faster codebase.

The Culprit: Unnecessary Class Allocations

The core problem stems from the fundamental difference between class and struct in C#. A class is a reference type, always allocated on the managed heap. When you create an instance of a class using new, memory is allocated on the heap, and a reference to that memory is returned. When that instance is no longer reachable, it becomes garbage, waiting for the GC to collect it – an operation that can cause noticeable stalls.

Consider a common scenario: processing temporary query results or calculating scores for nearby entities every frame. If you use a class to represent these temporary data points and store them in a List<T>, you’re incurring a double hit:

List<T> itself is a class, though often its internal array can be reused.
Each T instance (if T is a class) added to the list is a separate heap allocation.

Let’s illustrate with a typical (and problematic) pattern for calculating and processing scores for nearby interactable objects:

// PROBLEM: Class-based data structure
public class InteractableScore
{
    public GameObject Target;
    public float Score;

    // Constructor or properties often implicitly lead to heap allocations
    public InteractableScore(GameObject target, float score)
    {
        Target = target;
        Score = score;
    }
}

public class ScoreCalculator : MonoBehaviour
{
    private List<InteractableScore> _currentScores = new List<InteractableScore>();

    void Update()
    {
        _currentScores.Clear(); // Clears references, but allocated objects are now garbage
        
        // Imagine GetNearbyInteractables() yields many results
        foreach (var interactable in GetNearbyInteractables()) 
        {
            float score = CalculateScore(interactable);
            _currentScores.Add(new InteractableScore(interactable, score)); // !!! EACH 'new' is a GC.Alloc !!!
        }

        ProcessScores(_currentScores); 
    }

    private IEnumerable<GameObject> GetNearbyInteractables() { /* ... implementation ... */ return new List<GameObject>(); }
    private float CalculateScore(GameObject obj) { /* ... implementation ... */ return 0f; }
    private void ProcessScores(List<InteractableScore> scores) { /* ... implementation ... */ }
}

In this Update loop, if GetNearbyInteractables() returns 10 objects, you’ve just made 10 new InteractableScore() heap allocations every single frame. Over time, this rapid allocation and deallocation will trigger frequent GC cycles, causing noticeable stutter.

The Solution: Embrace Structs and Span

The solution lies in leveraging value types (struct) and efficient memory slicing (Span<T>). A struct is a value type, meaning its instances are typically allocated directly on the stack (for local variables) or inline within its containing type (if part of an array or another object). When a struct is copied, its entire data is copied, not just a reference. This means no heap allocation for the struct itself, and thus, no GC pressure from its creation.

For temporary collections of struct data, we combine struct with a pre-allocated array and Span<T>. An array, even of structs, is a reference type allocated on the heap once. By pre-allocating an array large enough to hold your maximum expected temporary data, you perform a single heap allocation at startup. Then, you fill this array with struct instances. Since structs are value types, assigning them to array elements does not create new heap allocations for the struct data itself. Finally, Span<T> provides a lightweight, zero-allocation “view” into a portion of this array, allowing you to process only the relevant data without copying or allocating new collections.

Here’s the refactored, performance-optimized approach:

// SOLUTION: Struct-based data structure
public struct InteractableScoreData // It's a struct!
{
    public GameObject Target;
    public float Score;
}

public class ZeroAllocScoreCalculator : MonoBehaviour
{
    // Pre-allocate the buffer ONCE at startup. This is a single heap allocation.
    // Choose a size appropriate for your maximum expected interactables.
    private InteractableScoreData[] _scoreBuffer = new InteractableScoreData[50]; 

    void Update()
    {
        int currentScoreCount = 0; // Tracks how many valid scores we've added to the buffer
        
        foreach (var interactable in GetNearbyInteractables())
        {
            if (currentScoreCount < _scoreBuffer.Length) // Prevent out-of-bounds
            {
                float score = CalculateScore(interactable);
                // Assigning a struct to an array element does NOT allocate on the heap!
                _scoreBuffer[currentScoreCount] = new InteractableScoreData 
                { 
                    Target = interactable, 
                    Score = score 
                };
                currentScoreCount++;
            }
        }

        // Use Span<T> to create a zero-allocation view of the valid data in our buffer
        Span<InteractableScoreData> currentScores = _scoreBuffer.AsSpan(0, currentScoreCount);
        
        // Process the scores using the Span
        ProcessScores(currentScores); 
    }

    private IEnumerable<GameObject> GetNearbyInteractables() { /* ... implementation ... */ return new List<GameObject>(); }
    private float CalculateScore(GameObject obj) { /* ... implementation ... */ return 0f; }
    
    // Updated to accept Span<T>
    private void ProcessScores(Span<InteractableScoreData> scores) 
    { 
        foreach (ref readonly var scoreData in scores) // Using 'ref readonly' for even more efficiency
        {
            // Process scoreData directly from the buffer without copying
            Debug.Log($"Target: {scoreData.Target.name}, Score: {scoreData.Score}");
        }
    }
}

In this refined code, the _scoreBuffer is allocated only once when the MonoBehaviour starts. In Update, no new heap memory is allocated for InteractableScoreData instances. Instead, the struct values are directly assigned into pre-existing slots in the array. Span<T> then provides an incredibly efficient way to iterate over the valid portion of this array without any additional memory allocations.

This approach not only prevents GC spikes but also significantly improves cache locality. Because struct instances in an array are laid out contiguously in memory, the CPU can process them much faster, leveraging its cache more effectively – a critical win for data-oriented design.

Conclusion

Eliminating insidious GC.Alloc calls is paramount for achieving consistent, high frame rates in Unity. By consciously choosing struct over class for transient, high-frequency data and combining this with pre-allocated buffers viewed through Span<T>, you can drastically reduce GC pressure. This isn’t just about micro-optimizations; it’s about adopting a fundamentally cleaner and faster architectural pattern for your game logic. Start treating memory as a finite resource, and your game’s performance will thank you.

Optimizing Memory Layout in Go: A Deep Dive into Struct Design

Thu, 16 Apr 2026 10:59:45 +0000

Reorder the fields in a Go struct and the size changes — without changing the data it holds. A bool next to an int64 wastes 7 bytes of padding. Across 10 million allocations, that's 67MB of memory you're paying for but never using.

This matters in high-throughput services where struct slices dominate heap usage: event pipelines, in-memory caches, analytics collectors.

How alignment and padding work

Go stores struct fields in a contiguous block of memory. Each field must be aligned to a memory address that's a multiple of its own size — int64 aligns to 8 bytes, int32 to 4, bool to 1. When a smaller field is followed by a larger one, the compiler inserts invisible padding bytes to satisfy the alignment requirement.

type Bad struct {
    Active  bool    // 1 byte
    // 7 bytes padding
    Balance float64 // 8 bytes
    Age     uint8   // 1 byte
    // 7 bytes padding
}
// Total: 24 bytes (only 10 bytes of actual data)

Reorder from largest to smallest:

type Good struct {
    Balance float64 // 8 bytes
    Active  bool    // 1 byte
    Age     uint8   // 1 byte
    // 6 bytes padding (struct itself aligns to 8)
}
// Total: 16 bytes (same 10 bytes of data, 8 bytes less waste)

That's a 33% reduction per struct, just by reordering fields.

Measuring the difference

Use reflect.TypeOf and unsafe.Sizeof to check struct sizes at runtime:

package main

import (
    "fmt"
    "reflect"
    "unsafe"
)

type Bad struct {
    Active  bool
    Balance float64
    Age     uint8
}

type Good struct {
    Balance float64
    Active  bool
    Age     uint8
}

func main() {
    fmt.Println("Bad:", unsafe.Sizeof(Bad{}), "bytes")   // 24
    fmt.Println("Good:", unsafe.Sizeof(Good{}), "bytes") // 16

    // Field-by-field inspection
    t := reflect.TypeOf(Bad{})
    for i := 0; i < t.NumField(); i++ {
        f := t.Field(i)
        fmt.Printf("  %s: size=%d, offset=%d\n", f.Name, f.Type.Size(), f.Offset)
    }
}

How much memory this saves at scale

Here's the math for a real scenario — an analytics service tracking page view events:

type PageView struct {
    // Unoptimized layout
    IsBot      bool      // 1 + 7 padding
    Timestamp  int64     // 8
    StatusCode uint16    // 2 + 6 padding
    Duration   int64     // 8
    UserID     uint32    // 4 + 4 padding
    PathHash   uint64    // 8
}
// Size: 48 bytes

type PageViewOptimized struct {
    // Sorted by alignment: 8 → 4 → 2 → 1
    Timestamp  int64
    Duration   int64
    PathHash   uint64
    UserID     uint32
    StatusCode uint16
    IsBot      bool
}
// Size: 32 bytes

Struct count	Unoptimized	Optimized	Saved
100K	4.6 MB	3.1 MB	1.5 MB
1M	45.8 MB	30.5 MB	15.3 MB
10M	457 MB	305 MB	152 MB

At 10 million structs, the difference is 152MB — enough to matter for your container memory limits and GC pressure.

Automated detection with fieldalignment

You don't need to manually audit every struct. The fieldalignment analyzer from golang.org/x/tools catches suboptimal layouts automatically:

go install golang.org/x/tools/go/analysis/passes/fieldalignment/cmd/fieldalignment@latest
fieldalignment ./...

It reports every struct that could be smaller and suggests the optimal field order. You can also run it as part of golangci-lint by enabling the govet linter with the fieldalignment check.

The alignment rules

Type	Size	Alignment
`bool`, `byte`, `uint8`, `int8`	1 byte	1
`uint16`, `int16`	2 bytes	2
`uint32`, `int32`, `float32`	4 bytes	4
`uint64`, `int64`, `float64`, pointer, `string`, slice, map, interface	8 bytes	8

The general rule: sort fields from largest alignment to smallest. This minimizes padding because smaller fields can pack together in the leftover space after larger fields.

Structs themselves are padded to a multiple of their largest field's alignment. That's why the Good struct above is 16 bytes (multiple of 8) even though the data only needs 10 bytes.

When not to bother

Field ordering optimization is worth the effort when:

You allocate millions of the same struct (event pipelines, time-series data, game state)
The struct is stored in a large slice that stays in memory
You're hitting container memory limits or seeing heavy GC pauses

It's not worth the effort when:

The struct is allocated once or a handful of times
Readability would suffer from rearranging logically grouped fields
The struct is mostly pointers and strings (already 8-byte aligned)

Run fieldalignment on your codebase as a CI check. Fix the easy wins — the structs that save 8+ bytes per instance — and leave the rest alone. The tool does the thinking for you.