755 karma · joined January 19, 2012
hleath [at] archil.com
Secondly, I think that a lot of companies publish these "tech blogs" as a way to boost recruiting (look at the cool stuff that we're doing, don't you want to join us?). Amazon, of course, doesn't have a recruiting problem. If you want to work on the largest-scale systems, it's already a top destination for you.
The purpose of S3 isn't to be cheap, it's to be simple.
This means that all of the non-atomic operations that you might want to do on S3 (including edits to the middle of files, renames, etc) are run on the machine running S3fs. As a result, if your machine crashes, it's not clear what's going to show up in your S3 bucket or if would corrupt things.
As a result, S3fs is also slow because it means that the next stop after your machine is S3, which isn't suitable for many file-based applications.
What AWS has built here is different, using EFS as the middle layer means that there's a safe, durable place for your file system operations to go while they're being assembled in object operations. It also means that the performance should be much better than s3fs (it's talking to ssds where data is 1ms away instead of hdds where data is 30ms away).
1. we're not NFS, we wrote our own protocol to get much better performance
2. we're planning on coming out with native branching this month, which should make these kinds of workloads much easier to build!
There's also some really great projects like SlateDB in this space, which could be more like what you're looking for (~RocksDB like API that runs on S3).
Check it out here: https://www.npmjs.com/package/@archildata/just-bash
That doesn't normally happen to platforms of this size.
Our team spent years working on NFS+Lustre products at Amazon (EFS and FSx for Lustre), so we understand the performance problems that these storage products have traditionally had.
We've built a custom protocol that allows our users to achieve high-performance for small file operations (git -- perfect for coding agents) and highly-parallel HPC workloads (model training, inference).
Obviously, there are tons of storage products because everyone makes different tradeoffs around durability, file size optimizations, etc. We're excited to have an approach that we think can flex around these properties dynamically, while providing best-in-class performance when compared to "true" storage systems like VAST, Weka, and Pure.
My expectations are that in 2026 we will see more and more developers attempt to build custom FUSE file systems and then run into the long tail of compatibility pain.
If you're running a FUSE adapter provided by a third party (Mountpoint, GCS FUSE), odds are that you aren't going to get great performance because it's going to have to run across a network super far away to work with your data. To improve performance, these adapters need to be sure to set fiddly settings (like using Kernel-side writeback caching) to avoid the penalty of hitting the disk for operations like write.
If you're trying to write a FUSE adapter, it's up to you to implement as much of the POSIX spec that you need for the programs that you want to run. The requirements per-program are often surprising. Want to run "git clone", then you need to support the ability to unlink a file from the file system and keep its data around. Want to run "vim", you need the ability to do renames and hard links. All of this work needs to happen in-memory in order to get the performance that applications expect from their file system, which often isn't how these things are built.
Regarding agents in particular, I'm hopeful that someone (which is quite possibly us), builds a FUSE-as-a-service primitive that's simple enough to use that the vast majority of developers don't have to worry about these things.
I want to go ahead and nominate this for the understatement of the year. I expect that 2026 is going to be filled with people finding this out the hard way as they pivot towards FUSE for agents.
I don't want to see the cloud storage sector turn as bitter as the cloud database sector.
I've previously looked through the benchmarking code, and I still have some serious concerns about the way that you're presenting things on your page.
Unfortunately, NFSv4 also has the silly rename semantics...
For example, it doesn't really make sense that "92% of data modification operations" would fail on JuiceFS, which makes me question a lot of the methodology in these tests.
This actually makes a ton of sense to me in lots of the LLM contexts (e.g. seeing how we are starting to prefer having LLMs write one-off scripts to do API calls rather than just pointing them at problems and having them try it directly).
Thanks!
How do you see the product evolving as agents become better and better?
You could achieve this today using one of the many adapters that turn S3 into a file system, without needing to wait for any SQLite buy in.