Building Scalable Applications Using io_uring for Async Operations

Written By:
Founder & CTO
June 20, 2025

In today’s world of modern computing, performance, scalability, and resource efficiency are paramount. The increasing demand for real-time applications, high-throughput data pipelines, web-scale services, and low-latency platforms necessitates a new approach to input/output (I/O) operations on Linux. Traditional I/O models such as select, poll, epoll, and even POSIX AIO have begun to show their age, especially in environments with massive concurrency, high-performance networking, and asynchronous workflows. This is where io_uring comes into play, a modern, fast, and efficient interface that revolutionizes asynchronous I/O on Linux.

io_uring provides developers with a flexible and powerful tool to build scalable, asynchronous systems without incurring the overhead and complexity of traditional I/O APIs. This blog will serve as a deep dive into how to leverage io_uring to build scalable applications using async operations. We will explore what io_uring is, how it works, the architectural concepts behind it, the benefits over traditional models, and how to practically implement it for various use cases.

This blog is crafted specifically for developers looking to implement high-performance, asynchronous I/O in their applications with a deep technical understanding of system-level programming on Linux.

What Is io_uring?
A Brief Background

io_uring is a Linux kernel feature introduced in version 5.1. Designed by Jens Axboe, io_uring is a new asynchronous I/O interface that overcomes many of the limitations found in older I/O models like epoll, select, and libaio. At its core, io_uring eliminates the need for repeated syscalls per I/O event, thus significantly improving performance in high-load systems.

It enables applications to submit I/O operations in a non-blocking, asynchronous, and batch-oriented manner via shared memory regions known as rings. This reduces the need for context switching between user space and kernel space, enabling extremely low-latency and high-throughput applications.

Why io_uring Matters for Async Operations

Traditional asynchronous I/O in Linux has always been complex. epoll only supports readiness-based I/O and is unsuitable for file operations. POSIX AIO suffers from inconsistent behavior and poor performance. io_uring simplifies and unifies async programming across different file types, sockets, and metadata operations, making it ideal for applications requiring robust and scalable async support.

Core Architectural Concepts of io_uring
The Submission and Completion Ring Buffers

The architecture of io_uring is based on two memory-mapped ring buffers:

  • Submission Queue (SQ): Where user space submits I/O operations.

  • Completion Queue (CQ): Where the kernel posts the completion of those operations.

These queues are mapped into user space, allowing the application to prepare and collect I/O events without involving the kernel unless necessary. This shared memory approach is one of the fundamental reasons io_uring is incredibly efficient and suitable for high-performance asynchronous I/O.

SQEs and CQEs

The I/O operations are represented as Submission Queue Entries (SQEs). Each SQE can encode operations like read, write, accept, connect, recv, send, fsync, and many more. Once the kernel completes the operation, a corresponding Completion Queue Entry (CQE) appears in the CQ, indicating the status and result.

This model allows the user space to operate in a decoupled and highly asynchronous manner, reducing latency and increasing the overall I/O throughput significantly.

Batching and Linking Operations

One of the most powerful features of io_uring is batching multiple I/O requests and submitting them together using a single syscall. Additionally, you can link multiple SQEs using flags like IOSQE_IO_LINK or IOSQE_IO_HARDLINK, allowing for complex, dependent operation chains such as read → process → write to be executed efficiently and in order.

Advantages of io_uring over Traditional I/O Models
Low-Latency, High-Throughput

Since io_uring minimizes syscall overhead by utilizing shared memory ring buffers, the latency per operation is drastically lower than traditional I/O models. This makes it especially beneficial in real-time, streaming, and interactive systems.

True Async for All I/O Types

Unlike epoll, which only supports readiness-based I/O for sockets, io_uring supports full async for both file I/O and socket I/O. This unification simplifies application architecture and improves consistency across different I/O sources.

Zero-Copy Data Transfers

io_uring supports registered memory buffers, enabling zero-copy I/O. This avoids expensive memory duplication between user space and kernel space, leading to significant performance improvements, particularly for applications dealing with large data payloads or streaming media.

Efficient Polling Mode

io_uring offers an efficient polling mode that allows the kernel to monitor the submission queue without the need for io_uring_enter() syscalls. This is useful for ultra-low-latency systems such as high-frequency trading engines, gaming servers, and database engines.

Practical Steps to Build Scalable Applications with io_uring
Step 1: Initialize io_uring Instance

To begin, use io_uring_queue_init() or io_uring_queue_init_params() to initialize the ring. The queue size depends on the expected concurrency level, e.g., 256 or 1024 entries for high-volume servers.

struct io_uring ring;

io_uring_queue_init(1024, &ring, 0);

Step 2: Pre-register Buffers and File Descriptors

Use io_uring_register_buffers() and io_uring_register_files() to register memory and file descriptors upfront. This reduces per-operation setup time and allows you to enable zero-copy and fixed-file optimizations.

Step 3: Submit Batched I/O Operations

Prepare multiple SQEs and submit them as a batch. For example, submitting 1000 read operations with a single syscall can yield tremendous savings in context switches and syscall latency.

struct io_uring_sqe *sqe = io_uring_get_sqe(&ring);

io_uring_prep_read(sqe, fd, buf, BUF_SIZE, offset);

io_uring_submit(&ring);

Step 4: Poll or Wait for Completion Events

Use io_uring_wait_cqe() or io_uring_peek_cqe() to retrieve completion results. Polling minimizes latency but consumes CPU; waiting conserves CPU but may add microseconds of delay.

Step 5: Process CQEs and Resubmit as Needed

As you receive results, process them asynchronously and resubmit new SQEs if the workload is ongoing. This keeps your pipeline full and CPU utilization high.

Real-World Use Cases
High-Concurrency HTTP Servers

Modern web servers like those built in Rust (using tokio-uring) or C (custom frameworks) benefit enormously from io_uring’s ability to handle thousands of concurrent connections efficiently. io_uring reduces syscall bottlenecks and allows servers to process requests using fewer threads and lower memory overhead.

Database Storage Engines

Databases rely heavily on random I/O, file syncing, and metadata operations. io_uring supports all these and allows batching, coalescing, and linked operations to reduce fsync cost and enhance query throughput.

Real-Time Media and Streaming Platforms

Streaming workloads benefit from io_uring’s low-latency, zero-copy, and batching capabilities. Applications like video streaming services or audio processing pipelines use io_uring to minimize I/O stalls and maintain stable frame rates.

Message Brokers and Queues

Message brokers like NATS, Kafka, and Redis-like services can use io_uring for rapid enqueue/dequeue operations on sockets or files, achieving high IOPS with low memory use.

Best Practices for Using io_uring in Production
  • Use polling sparingly: Ideal for real-time systems but consumes CPU.

  • Pre-register memory and file descriptors: Minimizes runtime overhead.

  • Monitor kernel version compatibility: Some features only work in newer Linux kernels (5.10+).

  • Scale with multiple urings per thread: Parallelize work on multi-core CPUs.

  • Design for failure handling: Use robust CQE status checking.

  • Avoid over-subscription: Queue sizes must match concurrency expectations.

  • Use linked operations: Express complex flows like read → process → write as atomic sequences.

  • Combine with event-driven designs: Integrate io_uring into state machines or async runtimes.

Security and Kernel Considerations

While io_uring is extremely powerful, it must be used responsibly:

  • Security: Some distributions disable io_uring in sandboxed environments due to syscall and memory mapping concerns.

  • Kernel version: Features evolve rapidly, ensure your kernel version supports the io_uring ops you intend to use.

  • Memory pinning: Registered buffers remain locked in memory, manage resource usage to avoid OOM risks.

When Not to Use io_uring
  • Simple applications: For basic file reads/writes or scripts, traditional syscalls may be simpler and sufficient.

  • Untrusted multi-tenant environments: io_uring expands the attack surface; use with care in cloud-native apps.

  • Very old Linux kernels: Kernel 5.1+ required; some distros do not backport features.

The Future of Async Linux I/O

io_uring is not just another system call, it is a paradigm shift in how Linux handles I/O. As support increases across languages (C, C++, Rust, Go, Python), and as runtimes like tokio-uring or liburing become more mature, io_uring will become the default for performance-critical workloads.

It empowers developers to build applications that are not only scalable and efficient, but also simpler in architecture, thanks to the unified async API across I/O types. Whether you’re building a high-frequency trading platform, a content delivery network, or a distributed database, io_uring is the future of scalable I/O on Linux.