We Replaced MMAP with Io_uring in Our Rust Query Engine. It Got Slower
It was convenient: it let us lazily read huge numbers of Arrow IPC files from disk without managing memory ourselves. It fit our file format perfectly — Arrow IPC’s layout is designed for zero-copy random access, and mmap gives you exactly that. Then we deployed to production, ran real concurrent query loads, and mmap became a real problem.
- ▪It was convenient: it let us lazily read huge numbers of Arrow IPC files from disk without managing memory ourselves.
- ▪It fit our file format perfectly — Arrow IPC’s layout is designed for zero-copy random access, and mmap gives you exactly that.
- ▪Then we deployed to production, ran real concurrent query loads, and mmap became a real problem.
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| Original publisher | Hacker News: Front Page |
| Canonical URL | https://www.conviva.ai/resource/we-replaced-mmap-with-io_uring-in-our-rust-query-engine-it-got-slower/ |
| Publication time | Fri, 11 Sep 2026 05:26:58 +0000 |
| Retrieval time | 2026-09-11T06:54:42.108Z |
| Last seen | 2026-09-11T06:54:42.108Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | lD_1FXGf3M9- · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
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| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
In the beginning, there was mmap. It was convenient: it let us lazily read huge numbers of Arrow IPC files from disk without managing memory ourselves. It fit our file format perfectly — Arrow IPC’s layout is designed for zero-copy random access, and mmap gives you exactly that. Then we deployed to production, ran real concurrent query loads, and mmap became a real problem. Our Workload At Conviva, we analyze trillions of events a day to pinpoint and diagnose end user experience. At the core of our architecture is an event and pattern analysis engine built on DataFusion, Arrow, Rust, Rayon, and Tokio. Raw events get transformed, encoded in a proprietary mostly-numeric format, and stored in the cloud. We copy them to local NVMe and read large (~3–5 GB) Arrow IPC files.
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