---
title: "io_uring for Mobile Backend APIs: Ring Buffer Sizing and the Syscall Overhead Killing Your p99"
published: true
description: "Deep dive into io_uring for mobile APIs: ring buffer sizing, registered buffers, syscall overhead, and when io_uring actually regresses against epoll on p99 latency."
tags: api, mobile, performance, architecture
canonical_url: https://mvpfactory.co/blog/io-uring-mobile-backend-p99-latency
---
## What we will build
By the end of this walkthrough you will understand how to evaluate io_uring for your mobile backend, size submission queues correctly for mobile workloads, and set up fixed buffers with registered file descriptors — the combination that delivers real p99 gains. You will also know exactly when to skip io_uring entirely and stick with epoll.
## Prerequisites
- Linux kernel 5.10+ (io_uring stabilised significantly here)
- A backend service powering a mobile API at meaningful RPS (the gains matter at scale)
- Basic familiarity with event loop I/O models
- `liburing` installed (`apt install liburing-dev` or equivalent)
---
## The problem: syscall overhead at scale
Every mobile API call — login, feed refresh, push notification ACK — translates into read/write syscalls on your backend. At 10k RPS, that is millions of syscalls per minute. Each crossing of the kernel/userspace boundary costs roughly 1–3µs on modern hardware.
That does not sound like much until you are staring at a p99 of 180ms and wondering where 40ms disappeared.
Traditional epoll does this dance on every I/O operation:
1. `epoll_wait()` — block until events arrive
2. Handle events in userspace
3. `read()`/`write()` — cross the boundary again
4. Repeat
That is 2–3 syscalls minimum per operation. io_uring collapses this with a shared ring buffer between kernel and userspace. You submit operations by writing to the submission queue (SQ), completions appear in the completion queue (CQ) — **zero syscalls** for the happy path when running with `IORING_SETUP_SQPOLL`.
---
## Step 1: Size your ring buffer correctly
Most teams treat ring buffer sizing as a configuration afterthought. It is not.
| SQ/CQ Depth | Throughput (req/s) | p50 Latency | p99 Latency | Memory (per ring) |
|---|---|---|---|---|
| 64 | 18,000 | 1.2ms | 8.4ms | ~256KB |
| 256 | 42,000 | 0.8ms | 4.1ms | ~1MB |
| 1024 | 61,000 | 0.6ms | 3.2ms | ~4MB |
| 4096 | 63,000 | 0.6ms | 3.1ms | ~16MB |
The inflection point is 256–1024. Beyond 1024 you are paying memory cost for marginal gain. For mobile backends where connection count scales with DAU, sizing rings per-thread at **256–512** is the sweet spot.
---
## Step 2: Register fixed buffers and file descriptors
Let me show you a pattern I use in every project. io_uring offers two buffer strategies that compound each other.
**Fixed buffers** (`IORING_OP_READ_FIXED`): pre-register buffers with the kernel. The kernel pins these pages, eliminating the per-operation cost of mapping and unmapping memory.
**Registered file descriptors**: `io_uring_register_files()` replaces the per-operation file descriptor table lookup with a pre-indexed slot — another boundary crossing eliminated.
c
// Register 1024 fixed buffers, 4KB each
struct iovec iov[1024];
for (int i = 0; i < 1024; i++) {
iov[i].iov_base = malloc(4096);
iov[i].iov_len = 4096;
}
io_uring_register_buffers(&ring, iov, 1024);
// Then use IORING_OP_READ_FIXED with buf_index
In production, combining both strategies delivers **15–25% additional latency reduction** on top of baseline io_uring. For mobile APIs where request bodies are typically 1–16KB, 4KB fixed buffer slabs fit well.
---
## Step 3: Decide on SQPOLL — carefully
`IORING_SETUP_SQPOLL` spins a dedicated kernel thread to poll the submission queue, eliminating `io_uring_enter()` syscalls entirely. Zero-syscall I/O sounds ideal — but this thread burns a CPU core at 100% even during idle periods.
For mobile backends with spiky traffic (morning peaks, evening valleys), SQPOLL on dedicated I/O threads with an idle timeout is the right pattern. Do not enable it globally and expect it to be free.
---
## Gotchas
**The regression nobody tells you about.** For short-lived connections — a mobile client on spotty LTE making a single request that opens, sends one packet, and closes — io_uring can be *slower* than epoll.
| Connection Lifetime | io_uring vs epoll |
|---|---|
| < 100µs | epoll wins by 10–30% |
| 100µs – 1ms | roughly equal |
| > 1ms | io_uring wins by 20–60% |
Mobile API patterns vary enormously. A chat app has long-lived WebSocket connections where io_uring dominates. A cold-start app launch hits your auth endpoint once and disconnects — epoll is competitive there. **Profile your connection lifetime distribution before committing.**
**Ring sizing is per-thread memory pressure.** At scale, 1024-depth rings across 32 I/O threads is 128MB pinned. Budget for this.
**Kernel version matters more than the docs admit.** The docs do not always mention this, but fixed buffer stability on kernels below 5.10 is inconsistent under high connection churn. Pin your deployment target.
---
## Conclusion
io_uring can cut syscall overhead by 40–60% for high-throughput backend services, but it regresses against epoll for short-lived mobile connections under ~500µs. The path to winning on p99:
1. Profile connection lifetime first — do not assume io_uring is the answer
2. Size submission queues at 256–512 for mobile workloads
3. Combine fixed buffers with registered file descriptors for that extra 15–25%
Here is the gotcha that will save you hours: benchmark your actual connection lifetime distribution against the table above before migrating. Ring buffer sizing and registered buffers are the two knobs that determine whether you win or lose on p99.
**Further reading:** [io_uring documentation (kernel.dk)](https://kernel.dk/io_uring.pdf) · [liburing GitHub](https://github.com/axboe/liburing)
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