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To reduce allocations in a high-throughput .NET API, match the technique to the buffer’s lifetime: use Span<T> for synchronous borrowed views, Memory<T> when a buffer must cross an asynchronous boundary, stackalloc for small bounded scratch space, and pools when measurements show repeated creation is causing meaningful garbage-collection pressure. None is an automatic latency upgrade: each trades allocation work for lifetime, ownership, or retained-memory complexity.
Choose by lifetime, ownership, and allocation shape
Start by identifying what is being allocated, how long it must live, and who owns it. A span is a view into existing storage, not an owner. Stack allocation is for short-lived scratch data. A memory wrapper can travel through asynchronous work, but does not itself own the backing buffer. Pools reuse storage or objects across calls, adding return, reset, and retention responsibilities.
| Option | Best fit | Lifetime and ownership | Main caution |
|---|---|---|---|
Span<T> |
Operations on a contiguous region during synchronous work | A borrowed view over storage such as an array or stack allocation | Cannot be stored in an ordinary class field or carried across an async suspension |
Memory<T> |
A contiguous region needed by asynchronous or longer-lived code | A view that can be stored; backing storage still needs a clear owner and lifetime | It does not own or automatically dispose the backing storage |
stackalloc |
Small, bounded scratch buffers used within a method | Stack storage reclaimed when the method returns | Contents start undefined, stack space varies, and allocation inside loops can exhaust stack space |
ArrayPool<T> |
Repeated array creation and disposal that contributes to GC pressure | Rent, use, then return the array to the pool | Rented arrays can be larger than requested and must be returned exactly once |
ObjectPool<T> |
Expensive-to-initialize, constrained, predictable objects reused frequently | Borrow and return reusable instances, resetting state as needed | Limits retained objects, not necessarily how many objects are allocated |
The .NET 10 JIT adds another possible optimization: it can stack-allocate certain small fixed-size arrays when escape analysis proves they do not leave the creating method. The documented expansion includes arrays of value and reference types, with escape analysis for local struct fields and delegates. This is an optimization opportunity, not a guarantee for every array expression. See Microsoft’s .NET 10 runtime updates.
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Span<T> describes a contiguous region of memory. It can refer to an array, stack-allocated storage, or other compatible buffers, letting an API operate on a slice without requiring a new array for that slice. Use it when the operation can finish synchronously and the view does not need to outlive the storage.
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A span is not a way to extend a buffer’s lifetime. Its restrictions prevent storing it in an ordinary class field or carrying it across an await suspension. If work must continue asynchronously, pass or retain a suitable Memory<T> view instead, with the backing storage’s owner responsible for keeping it alive.
Use Memory<T> when work must outlive the current call
Memory<T> can be stored and used in asynchronous flows, making it appropriate when a buffer must survive beyond a synchronous method call. Like a span, it is a view; it does not establish ownership of the underlying array or other storage. The component that owns the storage must ensure it remains valid for every use.
When an API accepts IMemoryOwner<T>, it is accepting ownership responsibility as well: it must dispose the owner or explicitly transfer ownership onward. With rented memory, failing to dispose the owner can cause leaks or other performance degradation. Make the ownership handoff visible in the API contract and in every exit path. Microsoft’s guidance covers memory and spans in .NET.
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Use stackalloc only for small, bounded scratch buffers
stackalloc reserves memory on the current method’s stack. It is useful for a small temporary buffer when its maximum size is known and conservative, and its contents are initialized before use. Uninitialized stack memory must never be treated as zero-filled input.
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- Keep the requested size small and bounded; available stack space varies by environment and call depth.
- Initialize every element that will be read.
- Do not put
stackallocinside a loop. Microsoft’s C# reference says to allocate outside the loop and reuse the block inside it, because the stack space is reclaimed when the method returns, not on each iteration. - Use another strategy for larger or variable buffers rather than risking stack exhaustion.
See the C# stackalloc reference for the language’s allocation and loop guidance.
Use ArrayPool<T> for repeated array churn, with a guaranteed return
If repeated creation and destruction of arrays is producing material garbage-collection pressure, ArrayPool<T> can let callers reuse arrays. Renting transfers temporary use, not permanent ownership. A rented array may have more elements than requested, so code should track the requested usable length rather than assuming the entire array is the logical buffer.
using System.Buffers;
T[] buffer = ArrayPool<T>.Shared.Rent(requestedLength);
try
{
// Use only the range the operation needs.
Process(buffer.AsSpan(0, requestedLength));
}
finally
{
ArrayPool<T>.Shared.Return(buffer);
}Return each rented array exactly once, including when processing throws. Do not use it after returning it. Pooling can reduce repeated array churn, but its bookkeeping and lifecycle constraints mean the benefit must be verified on the actual workload.
Use ObjectPool<T> for costly reusable objects, not as a general cache
ObjectPool<T> is aimed at objects that are expensive to initialize, frequently reused, and suitable for constrained reuse. Objects need an explicit reset policy so one caller’s state does not leak into another operation. ASP.NET Core’s documented example uses IResettable for that purpose; it is an illustration, not a recommended pool size or benchmark.
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An object pool limits how many objects it retains; it does not necessarily cap how many it creates under demand. Objects that are cheap to construct, rarely reused, or retain large amounts of state may not be good candidates. Microsoft advises: “Use object pooling only after collecting performance data using realistic scenarios for your app or library.” See Object reuse with ObjectPool in ASP.NET Core.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.ASP.NET Core 10 also evicts idle framework memory blocks
ASP.NET Core 10 automatically evicts memory blocks in the pools used by Kestrel, IIS, and HTTP.sys when they are idle or under less load. It also adds metrics under Microsoft.AspNetCore.MemoryPool and an injectable memory-pool factory. A custom factory does not gain automatic eviction merely by being plugged in; it needs comparable eviction logic. These framework changes concern those server memory pools, not every application-defined ArrayPool<T> or ObjectPool<T>. Details are in the ASP.NET Core 10 release notes.
Measure the workload before and after changing allocation strategy
There is no universal allocation or latency reduction figure for these techniques. Compare the same realistic workload on the target runtime and inspect allocation rate, garbage-collection pressure, retained memory, throughput, and tail latency. A change that reduces allocations can still worsen latency or memory use if it adds contention, reset work, or long-lived retained buffers.
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- If a small local array is the concern, check whether .NET 10’s JIT can prove it does not escape before replacing clear code with manual stack allocation.
- If a synchronous API only needs to inspect or transform a region, consider accepting
Span<T>rather than allocating a slice. - If work crosses asynchronous boundaries, use
Memory<T>with an explicit backing-store owner. - If repeated array allocation is a measured source of GC pressure, test renting and returning arrays on all success and failure paths.
- If objects are costly to initialize and predictably reused, test an object pool with a defined reset policy and observe retained memory as well as latency.
Benchmark the actual target runtime and workload rather than inferring heap behavior from source syntax: JIT escape analysis depends on the allocation’s shape and lifetime, and pooling outcomes depend on real usage patterns.
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