Large payload paths can cut CPU by avoiding extra memory copies. See how Node.js, io_uring, and SharedArrayBuffer fit together in production. Read now.
Why Extra Memory Copies Cost You
When a Node.js server moves large payloads — video segments, database dumps, file uploads — the bytes rarely travel in a straight line. A read pulls data from a socket into a kernel buffer, then into a JavaScript buffer, and a write pushes it back the other way. Each hop is a memory copy, and each copy burns CPU cycles and cache bandwidth that scale with payload size, not request count. For small JSON responses this overhead is invisible. For multi-megabyte streams it becomes the dominant cost, and it grows exactly when you least want it to: under load.
Zero-copy networking is the practice of removing those redundant copies from the hot path. Instead of shuttling data through userspace only to hand it back to the kernel, you keep it in one place and let the kernel move it, or you share a single backing buffer across the boundaries that would normally each demand their own.
Where io_uring Fits
io_uring is a Linux interface for submitting I/O operations through a pair of shared ring buffers. Your process places requests on a submission queue, the kernel places results on a completion queue, and both sides read and write the same memory without a system call per operation. That design attacks two problems at once: the per-call syscall overhead that dogs high-throughput servers, and the copying that traditional read/write semantics imply. Because the rings are shared memory, the kernel can complete work while your event loop does other things, and it can be told to move bytes between file descriptors without staging them in userspace at all.
For Node.js this is a natural fit. The runtime is already built around an event loop that batches I/O, so a completion-queue model maps cleanly onto how the platform thinks. The payoff is largest on the paths where copies hurt most — proxying, static file serving, and any pipeline that reads from one descriptor and writes to another.
Sharing Buffers with SharedArrayBuffer
io_uring handles the kernel side; SharedArrayBuffer handles the JavaScript side. A normal ArrayBuffer belongs to one context, so passing data between the main thread and a worker means copying it. A SharedArrayBuffer exposes the same underlying memory to multiple threads, which lets you designate a region that both your I/O layer and your application logic read from directly. Combined with io_uring's shared rings, you can construct a path where a large payload lands in one buffer and is consumed in place, rather than being duplicated at every stage.
Getting this right means being deliberate about ownership and lifetime. Consider these constraints before you build on it:
- A shared buffer that is still being written by the kernel must not be read or reused until its completion arrives.
- Concurrent access across threads needs coordination — typically atomics — to avoid torn reads.
- Buffer pooling helps, but you must track which regions are in flight so you never hand out one that is still owned by an outstanding operation.
Practical Guidance for Production
Treat zero-copy as an optimization for a specific class of traffic, not a default. Profile first: if your workload is many small requests, the copy overhead is negligible and the added complexity of ring management and shared memory is not worth it. Reach for these techniques when payload size is large, throughput is high, and CPU spent on memcpy shows up clearly in your profiles.
Keep the surface small. Isolate the io_uring and SharedArrayBuffer machinery behind a narrow interface so the rest of your application still sees ordinary streams, and lean on Linux's own facilities for correctness rather than reinventing them. Done this way, you gain the CPU headroom on the heavy paths without letting the sharp edges leak into the rest of the codebase.