this post was submitted on 24 Aug 2024
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ext4 is intended for a completely different use case, though? bcachefs is competing with btrfs and ZFS in big storage arrays spanning multiple drives, probably with SSD cache. ext4 is a nice filesystem for client devices, but doesn't support some things which are kinda fundamental at larger scales like data checksumming, snapshots, or transparent compression.
What's cool about bcache is that it can have fully tiered storage. It can move data from a hard drive to a SSD and vis versa. It isn't a cache like in ZFS as ZFS wipes the cache drive on mount and adding a cache doesn't increase capacity
@possiblylinux127 @DaPorkchop_. ZFS has a persistent L2ARC cache now.
There's XFS for larger scale stuff.
XFS still isn't a multi-device filesystem, though... of course you can run it on top of mdraid/LVM, but that still doesn't come close to the flexibility of what these specialized filesystems can do. Being able to simply run
btrfs device add /dev/sdx1 /and immediately having the new space available is far less hassle than adding a device to an md array, then resizing the partition and then resizing the filesystem (and removing a device is even worse). Snapshots are a similar deal - sure, LVM can let you snapshot your entire virtual block device, but your snapshots are block devices themselves which need to be explicitly mounted, while in btrfs/bcachefs a snapshot is just a directory, and can be isolated to a specific subvolume rather than the entire block device.Data checksums are also substantially less useful when the filesystem can't address the underlying devices individually, because it makes repairing the data from a replica impossible. If you have a file on an md RAID1 device and one of the replicas has a bad block, you might be able to detect the bitrot by verifying the checksum, but you can't actually fix it, because even though there is a second copy of the data on another drive, mdadm simply exposes a simple block device and doesn't provide any way to read from "the other copy". mdraid can recover from total drive failure, but not data corruption.
Is XFS still maintained?
One of the best filesystem codebases out there. Really a top notch file system if you don't need to resize it once it's created. It is a write through, not copy on write, so some features such as snapshots are not possible using XFS. If you don't care about features found in btrfs, zfs or bcachefs, and you don't need to resize the partition after creating it, XFS is a solid and very fast choice.
Ext4 codebase is known to be very complex and some people say even scary. It just works because everybody's using it and bugs have been fixed years ago.
xfs_growfs is a thing. I know nothing about xfs. Is this something I should avoid for some reason?
https://docs.redhat.com/en/documentation/red_hat_enterprise_linux/6/html/storage_administration_guide/xfsgrow
No reason to avoid it. Just know that you can't easily shink the filesystem, only grow it. To shrink you'd need to create a new FS then copy the data over manually.
I heard that ext4s best feature was its fsck utils being extremely robust and able to recover from a lot of problems. Which does not shine a great light on the filesystem itself :/ and probably a result of the complex codebase.
It's used in RHEL.
Yes.
Honestly I'm fine with ZFS on larger scale, but on desktop I want a filesystem that can do compression (like NTFS on windows) and snapshots.
I have actually used compression a lot, and it spared me a lot of space. No, srorage is not cheap, or else I'm awaiting your shipment.
Other than that I'm doing differential backups on windows, and from time to time it's very useful that I can grab a file to which something just happened. Snapshots cost much less storage than complete copies, which I couldn't afford, but this way I have daily diffs for a few years back, and it only costs a TB or so.
Sadly I have yet to see a truly compassionate FS 🥲
Yeah, same :D
It was a typo, I have meant compression. Specifically a per-file controlled compression, not per-directory or per-dataset.