TL;DR
Researchers evaluated Linux's io_uring for modern DBMS I/O, finding that simply swapping I/O layers does not guarantee gains. They benchmarked two real-world use cases, analyzed advanced io_uring features, produced design guidelines, and validated them by improving PostgreSQL performance by 14%.
What happened
A research team examined how database systems can effectively use Linux's io_uring interface to reduce I/O overhead and unify storage and network operations. io_uring is an asynchronous system-call batching API that addresses limitations of traditional Linux I/O interfaces, but the authors found that a naive replacement of existing I/O stacks with io_uring often fails to produce system-level improvements. To clarify where io_uring helps, they evaluated two concrete scenarios: integrating io_uring into a storage-bound buffer manager and using it for high-throughput data shuffling in network-bound analytical workloads. The study also measured the effects of advanced io_uring capabilities such as registered buffers and passthrough I/O. From these experiments the authors derived practical guidelines for designing I/O-intensive systems and applied those rules to PostgreSQL’s io_uring integration, reporting a 14% end-to-end performance increase when following their recommendations.
Why it matters
- io_uring provides a unified, asynchronous interface for both storage and network I/O, which can simplify DBMS I/O paths.
- Performance gains from io_uring depend on how it is integrated; naive substitutions can fail to improve throughput or latency.
- The paper gives actionable design guidance that system builders can apply to reduce I/O overhead in DBMSs.
- A validated case study on PostgreSQL indicates these guidelines can produce measurable end-to-end improvements.
Key facts
- io_uring is an asynchronous system-call batching interface in Linux that unifies storage and network operations.
- The authors evaluated io_uring in two use cases: a storage-bound buffer manager and network-bound analytical data shuffling.
- Advanced io_uring features analyzed include registered buffers and passthrough I/O and their impact on end-to-end performance.
- The study finds that simply replacing traditional Linux I/O interfaces with io_uring does not automatically yield performance benefits.
- Architectural choices in a DBMS determine whether low-level io_uring optimizations translate into system-wide gains.
- The authors distilled practical guidelines for designing I/O-intensive systems to effectively use io_uring.
- Applying the paper’s guidelines to PostgreSQL’s io_uring integration produced a reported 14% performance improvement.
- The paper is available on arXiv (arXiv:2512.04859) and lists Matthias Jasny, Muhammad El-Hindi, Tobias Ziegler, Viktor Leis, and Carsten Binnig as authors.
What to watch next
- Whether similar guideline-driven improvements appear across other DBMS implementations (not confirmed in the source).
- How future io_uring kernel enhancements or additional features affect the trade-offs observed in this study (not confirmed in the source).
Quick glossary
- io_uring: A Linux kernel interface that provides asynchronous, batched submission and completion of I/O operations for storage and networking.
- Registered buffers: A mechanism that pins or registers memory with the kernel or device to reduce per-operation overhead during repeated I/O.
- Passthrough I/O: I/O operations that bypass certain layers of the kernel stack to reduce copying and context-switch overhead.
- Buffer manager: A DBMS component responsible for caching, reading, and writing pages between memory and persistent storage.
- Data shuffling: Network-intensive redistribution of data among nodes or processes, common in analytical query processing and distributed joins.
Reader FAQ
What is the main takeaway about io_uring for DBMSs?
io_uring can reduce I/O overhead and unify storage and network I/O, but benefits depend on careful architectural integration rather than a simple replacement of existing interfaces.
Which workloads did the researchers test?
They tested a storage-bound buffer manager integration and a network-bound high-throughput data shuffling scenario.
How much improvement did PostgreSQL see when following the paper's guidelines?
The authors report a 14% performance improvement in PostgreSQL after applying their guidelines.
Is source code or a wider validation suite provided?
not confirmed in the source
Computer Science > Databases [Submitted on 4 Dec 2025 (v1), last revised 12 Dec 2025 (this version, v2)] High-Performance DBMSs with io_uring: When and How to use it Matthias Jasny,…
Sources
- High-Performance DBMSs with io_uring: When and How to use it
- io_uring for High-Performance DBMSs: When and How to …
- (PDF) High-Performance DBMSs with io_uring: When and …
- High-Performance DBMSs with io_uring: When and How to …
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