In this lesson, you will learn how to leverage Unreal Insights and memory tracking tools to isolate the exact moment and source of memory allocation spikes in your project.
Understanding the Allocation Spike
A memory spike occurs when your game suddenly requests a large block of heap memory, often leading to frame stutters, hitches, or "Out of Memory" crashes on console hardware. Unlike baseline memory usage, which grows predictably, spikes are usually tied to specific gameplay events—such as spawning a massive Niagara particle system, loading high-resolution textures, or instantiating complex Actor hierarchies.
Tracking with Unreal Insights
To hunt these spikes, you must capture a session using the Memory Insights trace channel. By launching your project with -trace=memory, you generate a data file that records every FMemory::Malloc and FMemory::Free call.
Once loaded into the Insights browser, navigate to the Memory Profiler tab. Look for the "Live Objects" or "Allocations" timeline. You are looking for a steep, vertical climb in the graph. By selecting a specific timeframe where the memory jumped, you can inspect the Call Stack view.
Identifying the Culprit
The Call Stack reveals exactly which function requested the allocation. You will often see patterns like this:
// Example of a problematic allocation spike
void AEnemySpawner::SpawnWave() {
for(int32 i = 0; i < 100; ++i) {
// Spawning 100 actors in one frame causes an immediate heap spike
GetWorld()->SpawnActor<AEnemy>(EnemyClass);
}
}
If the profiler shows the SpawnActor call stack consuming a massive chunk of your memory budget, you’ve identified your spike. In this case, the solution would be to spread the spawning logic over several frames (using a timer or a simple counter) rather than batching them into a single update.
By comparing the "Live" versus "Total" allocated memory in Insights, you can determine if the spike is caused by persistent assets (leaks) or temporary data (spikes). Focus on the "Peak" allocation columns to see which assets or systems are the heaviest contributors.
Key Takeaway: Use Memory Insights to correlate sudden heap growth with specific call stacks, then refactor those high-frequency spikes into spread-out, incremental operations.