# Analysis of Async vs io_uring
The use of an **io_uring-based runtime** is highly likely to provide a **significant benefit** over a regular **Tokio** runtime for your specific goal: a high-performance, block device storage access library aiming for **sub-10 microsecond latencies** on Linux.

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## 🚀 io_uring vs. Regular Tokio for Low Latency

The core difference lies in how they handle I/O and interact with the kernel:

### 1. io_uring (Optimal for Low Latency & High Throughput)
* **Mechanism:** `io_uring` is a modern Linux I/O interface that uses **ring buffers** for communication between user space and the kernel. It's a **completion-based** I/O model.
    * **Benefit:** The primary gain is **syscall batching** and **reduced context switching**. Multiple I/O requests are placed in the submission queue (SQ) in user space, and the kernel processes them, placing results in the completion queue (CQ). Often, the entire submission-completion cycle can occur **without a single syscall** (especially with the `SQPOLL` feature).
* **Latency Impact:** Eliminating per-operation syscalls and context switches directly tackles the overhead that plagues traditional I/O, which is crucial for achieving **sub-10 $\mu s$ latencies**. For file/disk I/O, where traditional async on Linux is poor, `io_uring` provides true asynchronous, non-blocking access.
* **Caveat:** It is a **Linux-only** solution.

### 2. Regular Tokio (Traditional Cross-Platform Async)
* **Mechanism:** Standard Tokio uses a **readiness-based** I/O model (like `epoll` for networking) and delegates **file/disk I/O** to a thread pool via `tokio::fs` or `tokio::runtime::Handle::spawn_blocking`.
* **Latency Impact:**
    * **Network I/O:** Can be very fast, but still involves one syscall per readiness event.
    * **Disk I/O (Your Use Case):** Relying on `spawn_blocking` means your async task waits for a synchronous I/O call on another thread. This introduces **context switching overhead** and **synchronization cost** with the thread pool, which can easily push latencies well above your $10 \mu s$ target, especially under high concurrency. One benchmark showed native Tokio being **44 times slower** than an optimized alternative for random reads.

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## ⚙️ Optimal Rust Crates (Fall 2025)

For a high-performance, Linux-only block device storage library, the optimal set of crates focuses on exploiting `io_uring` directly.

### 1. io_uring-Native Runtimes (Recommended)

To achieve the lowest latency, you should use an entire runtime built around `io_uring` for true zero-syscall asynchronous disk I/O.

* **`glommio`**: A high-performance, **thread-per-core** async runtime built from the ground up on `io_uring`. This model is often used in storage systems and databases (like ScyllaDB) and is designed to eliminate cross-thread coordination overhead, which is a major benefit for tail latency. It's a strong contender for your specific, highly-optimized goal.
* **`monoio`**: Another purpose-built, efficient `io_uring`-based runtime. It is known for its lean design and strong performance profile, especially for high-throughput scenarios.

### 2. io_uring Wrappers (Alternative)

These can be used as building blocks or potentially with the standard Tokio ecosystem, though they often require more manual buffer management or runtime workarounds to be safe and performant.

* **`io-uring`**: This is the low-level, unsafe wrapper for the raw `io_uring` system call interface. It's stable (`~0.7` as of late 2025) and necessary if you want to implement your own highly-specialized I/O logic.
* **`tokio-uring`**: This crate attempts to bridge the gap by providing `io_uring` I/O within the Tokio ecosystem. **Maturity is a concern**; search results suggest it has seen less recent development and can be challenging to integrate perfectly with the main Tokio scheduler, sometimes incurring unexpected overhead compared to the purpose-built runtimes. For absolute low latency, a native `io_uring` runtime is generally preferred.

| Feature | io_uring Runtimes (`glommio`, `monoio`) | Regular Tokio (`tokio::fs`) |
| :--- | :--- | :--- |
| **I/O Model** | Completion-Based (True Async Disk I/O) | Readiness-Based (`epoll`) + **Blocking Thread Pool for Disk I/O** |
| **Latency** | **Significantly Lower** (Bypasses syscalls & context switches) | Higher (Overhead from `spawn_blocking` and cross-thread sync) |
| **Throughput** | High, especially with batching | High, but disk I/O is bottlenecked by the thread pool |
| **Platform** | **Linux-only** (Requires a recent kernel) | Cross-platform |

### 3. Streams and Channels

While **streams** and **channels** (such as those from the **`futures`** or **`tokio`** crates) are essential for structuring the *data flow* and *inter-task communication* in your library, they are **not a substitute** for the underlying I/O mechanism.

* They will abstract *how* you handle the data (e.g., streaming chunks to a consumer), but the I/O **performance bottleneck** (Tokio vs. io_uring) remains in the layer below them.
* You will still use channels and streams extensively regardless of which runtime you choose.

# References
Here are some other references:

 - [https://tonbo.io/blog/exploring-better-async-rust-disk-io](https://tonbo.io/blog/exploring-better-async-rust-disk-io) tonbo.i blog async-rust
 - [https://github.com/tonbo-io/fusio](https://github.com/tonbo-io/fusio) Github Repository



# ✅ Conclusion and Recommendation

Given your strict **sub-10 $\mu s$ latency** requirement for a **block device storage access library** on **Linux**, an `io_uring`-native approach is the optimal choice.

* **Optimal Crates:** Use a purpose-built runtime like **`glommio`** or **`monoio`** over standard Tokio.

This path maximizes performance by leveraging the kernel's most efficient I/O method, but it forces you to be Linux-exclusive. 

Would you like me to look for specific examples or tutorials on using `glommio` or `monoio` for high-performance file I/O?