[huntaub]

Respect to your work on ZeroFS, but I find it kind of off-putting for you to come in and immediately put down JuiceFS, especially with benchmark results that don't make a ton of sense, and are likely making apples-to-oranges comparisons with how JuiceFS works or mount options.

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.

[selfhoster1312]

I have very limited experiences with object storage, but my humble benchmarks with juicefs + minio/garage [1] showed very bad performance (i.e. total collapse within a few hours) when running lots of small operations (torrents).

I wouldn't be surprised if there's a lot of tuning that can be achieved, but after days of reading docs and experimenting with different settings i just assumed JuiceFS was a very bad fit for archives shared through Bittorrent. I hope to be proven wrong, but in the meantime i'm very glad zerofs was mentioned as an alternative for small files/operations. I'll try to find the time to benchmark it too.

[1] https://git.deuxfleurs.fr/Deuxfleurs/garage/issues/1021

[Eikon]

> but I find it kind of off-putting for you to come in and immediately put down JuiceFS, especially with benchmark results that don't make a ton of sense, and are likely making apples-to-oranges comparisons with how JuiceFS works or mount options.

The benchmark suite is trivial and opensource [1].

Is performing benchmarks “putting down” these days?

If you believe that the benchmarks are unfair to juicefs for a reason or for another, please put up a PR with a better methodology or corrected numbers. I’d happily merge it.

EDIT: From your profile, it seems like you are running a VC backed competitor, would be fair to mention that…

[1] https://github.com/Barre/ZeroFS/tree/main/bench

[wgjordan]

> The benchmark suite is trivial and opensource.

The actual code being benchmarked is trivial and open-source, but I don't see the actual JuiceFS setup anywhere in the ZeroFS repository. This means the self-published results don't seem to be reproducible by anyone looking to externally validate the stated claims in more detail. Given the very large performance differences, I have a hard time believing it's an actual apples-to-apples production-quality setup. It seems much more likely that some simple tuning is needed to make them more comparable, in which case the takeaway may be that JuiceFS may have more fiddly configuration without well-rounded defaults, not that it's actually hundreds of times slower when properly tuned for the workload.

(That said, I'd love to be wrong and confidently discover that ZeroFS is indeed that much faster!)

[huntaub]

Yes, I'm working in the space too. I think it's fine to do benchmarks, I don't think it's necessary to immediately post them any time a competitor comes up on HN.

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.

[eYrKEC2]

I'm always curious about the of the option space. I appreciate folks talking about the alternative s. What's yours?

[huntaub]

Our product is Archil [1], and we are building our service on top of a durable, distributed SSD storage layer. As a result, we have the ability to: (a) store and use data in S3 in its native format [not a block based format like the other solutions in this thread], (b) durably commit writes to our storage layer with lower latency than products which operate as installable OSS libraries and communicate with S3 directly, and (c) handle multiple writers from different instances like NFS.

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.

[1] https://archil.com