Epoll vs. io_uring: A pragmatic choice for high-performance services

Choosing between Linux's established epoll and the newer, more powerful io_uring involves a trade-off between simplicity and raw performance. This review breaks down the decision for founders building networked services.

THE ANSWER UP FRONT

For most new projects needing high I/O, epoll is the default, battle-tested choice, especially when using mature runtimes like Go or Rust's Tokio. Its performance is more than sufficient for the vast majority of networked applications. You should skip io_uring unless you are building foundational infrastructure like a database, proxy, or storage engine where benchmark data proves syscall overhead is your primary bottleneck. Bottom line: epoll is the pragmatic default for application developers; io_uring is the high-leverage, high-cost option for systems engineers operating at the performance frontier.

METHODOLOGY

This v0 review synthesizes the well-established technical trade-offs between two Linux kernel I/O interfaces: epoll (available since kernel 2.5.44) and io_uring (available since kernel 5.1). The analysis is based on the technical consensus from numerous engineering blogs, kernel documentation, and public discussions, prompted by a signal from Hacker News on June 22, 2026. The original source URL was an opaque Google News redirect, so this review addresses the general topic rather than a single post.

This review covers the architectural differences, claimed performance characteristics, and the practical implications of developer complexity and ecosystem support. It does not include our own independent benchmarks, a deep analysis of specific kernel version features, or a security vulnerability assessment of each subsystem. Our position is based on public claims and widespread developer experience. We will update this review with direct benchmarks when we test a specific, non-trivial workload.

WHAT IT DOES

Both epoll and io_uring solve the problem of efficiently handling many I/O operations at once, but they use fundamentally different models.

Epoll: The established standard for multiplexed I/O

epoll is an event notification system. A developer provides the kernel with a list of file descriptors (e.g., network sockets, files) to monitor. The application then makes a single blocking call, epoll_wait(), which returns when one or more of those descriptors are ready for an I/O operation (like reading or writing). This is a readiness-based model. It's the foundation of nearly every modern asynchronous networking library, including Node.js's libuv, Go's netpoller, and Rust's Tokio. Its API is relatively simple and extremely well-understood.

Io_uring: True asynchronous I/O

io_uring is a true asynchronous interface. Instead of asking the kernel which sockets are ready, the application submits I/O requests directly to the kernel without waiting. It does this by placing commands (e.g., read this socket, write to this file) into a shared memory ring buffer called the Submission Queue (SQ). The kernel processes these requests in the background and places completion events into another shared ring buffer, the Completion Queue (CQ). This completion-based model can dramatically reduce the number of syscalls required, as many operations can be submitted at once and their results collected in batches. It also supports buffered and non-buffered file I/O, not just networking.

WHAT'S INTERESTING / WHAT'S NOT

Performance claims require context

The primary advantage of io_uring is performance, achieved by minimizing costly kernel-to-userspace transitions. Benchmarks in ideal conditions, like a simple echo server, often show dramatic throughput gains over epoll. The founder of io_uring, Jens Axboe, has published benchmarks demonstrating these benefits. However, these gains are only realized if syscall overhead is the actual application bottleneck. In many real-world services, the limiting factor is business logic, data serialization, or memory copies, not the I/O notification mechanism. Adopting io_uring may not yield significant improvements if the rest of the application isn't optimized.

Complexity is the real cost

The epoll API is small and conceptually simple. In contrast, io_uring is notoriously complex. Developers must correctly manage two shared memory ring buffers, handle memory ordering, chain operations, and interact with a large and evolving API. While libraries like liburing provide a C-level abstraction, the underlying model is far more demanding than epoll. This complexity translates directly to higher engineering cost, a steeper learning curve, and a greater potential for subtle, hard-to-debug bugs.

Ecosystem support is maturing, but not universal

Support for io_uring is growing. Rust has excellent libraries like tokio-uring and glommio. Go has experimental support. However, it is rarely the default, batteries-included option. Opting for io_uring often means leaving the well-supported path of a language's standard library or dominant async framework. This can create challenges for hiring, onboarding, and long-term project maintenance.

PRICING

Pricing as of June 2026:

Both epoll and io_uring are features of the Linux kernel and are available at no direct monetary cost. The true cost is measured in engineering hours for implementation, debugging, and maintenance. The complexity of io_uring means its total cost of ownership is significantly higher than that of epoll.

VERDICT

For founders and engineering teams building typical networked services, epoll remains the correct choice. The mature, ergonomic, and highly-performant ecosystems built on top of it (like Go's networking stack and Rust's Tokio) provide more than enough performance while minimizing complexity. The productivity benefits of these ecosystems are immense.

Choose io_uring only when you have a clear, benchmark-driven reason. This applies to teams building performance-critical infrastructure where I/O syscall overhead is the primary system bottleneck. If you are building a database, a high-performance proxy, a storage engine, or a virtualization platform, the performance ceiling of io_uring is a compelling and necessary advantage. For everyone else, it is a premature optimization that trades developer velocity for performance gains you may never realize.

WHAT WE'D TEST NEXT

A v2 of this review would require building and benchmarking two versions of a non-trivial application, such as a TLS-terminating reverse proxy that applies a simple routing rule. We would measure requests per second, p99 latency, and CPU utilization under varying levels of concurrency. We would also compare the implementations directly, analyzing the lines of code and the number of unsafe or complex operations required for each. Finally, we would test across several recent Linux kernel versions to quantify how io_uring's performance and feature set have evolved.

The investor read

io_uring represents the frontier of systems programming on Linux. Companies building foundational infrastructure (databases, message queues, proxies) that successfully adopt io_uring are signaling deep technical expertise and a focus on extreme performance, which can be a moat. ScyllaDB is a prime example. For investors, a startup choosing io_uring for a standard web application is a potential red flag for premature optimization and 'not-invented-here' syndrome. However, a company whose core value proposition is performance (e.g., a faster alternative to an existing database) and can back it up with io_uring benchmarks is a strong positive signal. The trend suggests that over the next 5 years, high-level runtimes will abstract away io_uring's complexity. The companies that build those valuable abstractions are also interesting investment targets.