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.
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.
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.
The architecture of io_uring is based on two memory-mapped ring buffers:
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.
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.
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.
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.
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.
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.
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.
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);
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.
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);
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.
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.
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.
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.
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 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.
While io_uring is extremely powerful, it must be used responsibly:
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.