TFS was replaced by RedoxFS and is no longer maintained, most of the features of TFS have been incorporated into RedoxFS

TFS: Next-generation file system

TFS is a modular, fast, and feature rich next-gen file system, employing modern techniques for high performance, high space efficiency, and high scalability.

TFS was created out of the need for a modern file system for Redox OS, as a replacement for ZFS, which proved to be slow to implement because of its monolithic design.

TFS is inspired by the ideas behind ZFS, but at the same time it aims to be modular and easier to implement.

TFS is not related to the file system of the same name by terminalcloud.

While many components are complete, TFS itself is not ready for use.

Design goals

TFS is designed with the following goals in mind:

• Concurrent

TFS contains very few locks and aims to be as suitable for multithreaded systems as possible. It makes use of multiple truly concurrent structures to manage the data, and scales linearly by the number of cores. This is perhaps the most important feature of TFS.

• Asynchronous

TFS is asynchronous: operations can happen independently; writes and reads from the disk need not block.

• Full-disk compression

TFS is the first file system to incorporate complete full-disk compression through a scheme we call RACC (random-access cluster compression). This means that every cluster is compressed only affecting performance slightly. It is estimated that you get 60-120% more usable space.

• Revision history

TFS stores a revision history of every file without imposing extra overhead. This means that you can revert any file into an earlier version, backing up the system automatically and without imposed overhead from copying.

• Data integrity

TFS, like ZFS, stores full checksums of the file (not just metadata), and on top of that, it is done in the parent block. That means that almost all data corruption will be detected upon read.

• Copy-on-write semantics

Similarly to Btrfs and ZFS, TFS uses CoW semantics, meaning that no cluster is ever overwritten directly, but instead it is copied and written to a new cluster.

• O(1) recursive copies

Like some other file systems, TFS can do recursive copies in constant time, but there is an unique addition: TFS doesn't copy even after it is mutated. How? It maintains segments of the file individually, such that only the updated segment needs copying.

• Guaranteed atomicity

The system will never enter an inconsistent state (unless there is hardware failure), meaning that unexpected power-off won't ever damage the system.

• Improved caching

TFS puts a lot of effort into caching the disk to speed up disk accesses. It uses machine learning to learn patterns and predict future uses to reduce the number of cache misses. TFS also compresses the in-memory cache, reducing the amount of memory needed.

• Better file monitoring

CoW is very suitable for high-performance, scalable file monitoring, but unfortunately only few file systems incorporate that. TFS is one of those.

• All memory safe

TFS uses only components written in Rust. As such, memory unsafety is only possible in code marked unsafe, which is checked extra carefully.

• Full coverage testing

TFS aims to be full coverage with respect to testing. This gives relatively strong guarantees on correctness by instantly revealing large classes of bugs.

• SSD friendly

TFS tries to avoid the write limitation in SSD by repositioning dead sectors.

• Improved garbage collection

TFS uses Bloom filters for space-efficient and fast garbage collection. TFS allows the FS garbage collector to run in the background without blocking the rest of the file system.

FAQ

Why do you use SPECK as the default cipher?

• SPECK is a relatively young cipher, yet it has been subject to a lot of (ineffective) cryptanalysis, so it is relatively secure. It has really good performance and a simple implementation. Portability is an important part of the TFS design, and truly portable AES implementations without side-channel attacks is harder than many think (particularly, there are issues with SubBytes in most portable implementations). SPECK does not have this issue, and can thus be securely implemented portably with minimal effort.

How similar is TFS and ZFS?

• Not that similar, actually. They share many of the basic ideas, but otherwise they are essentially unconnected. But ZFS' design has shaped TFS' a lot.

Is TFS Redox-only?

• No, and it was never planned to be Redox-only.

How does whole-disk compression work?

• Whole-disk compression is -- to my knowledge -- exclusive to TFS. It works by collecting as many "pages" (virtual data blocks) into a "cluster" (allocation unit). By doing this, the pages can be read by simply decompressing the respective cluster.

Why is ZMicro so slow? Will it affect the performance of TFS?

• The reason ZMicro is so slow is because it works on a bit level, giving excellent compression ratio on the cost of performance. This horribly slow performance is paid back by the reduced number of writes. In fact, more than 50% of the allocations with ZMicro will only write one sector, as opposed to 3. Secondly, no matter how fast your disk is, it will not get anywhere near the performance of ZMicro because disk operations are inherently slow, and when put in perspective, the performance of the compression is really unimportant.

Extendible hashing or B+ trees?

• Neither. TFS uses a combination of trees and hash tables: Nested hash tables, a form of hash trees. The idea is that instead of reallocating, a new subtable is created in the bucket.

Resources on design

I've written a number of pieces on the design of TFS:

• SeaHash: Explained - This describes the default checksum algorithm designed for TFS.
• On Random-Access Compression - This post describes the algorithm used for random-access compression.
• Ternary as a prediction residue code - The use of this is related to creating a good adaptive (headerless) entropy compressor.
• How LZ4 works - This describes how the LZ4 compression algorithm works.
• Collision Resolution with Nested Hash Tables - This describes the method of nested hash tables we use for the directory structure.
• An Atomic Hash Table - This describes the concurrent, in-memory hash table/key-value store.

Specification

The full specification can be found in specification.tex, to render it install texlive or another distribution with XeTeX, and run:

xelatex --shell-escape specification.tex

Then open the file named specification.pdf

lib.rs:

MLCR: Machine-Learning-based Cache Replacement

MLCR trains a neural network to "guess" how long time will pass before the cache block is accessed again. In other words, it provides a qualified guess to approximate the ideal Bélády's algorithm without a time machine.

MLCR is slow, because it needs to train a neural network, but in many cases, the added precision pays off by greatly reducing the number of cache misses. As such, it should only be used when the cached medium is significantly slower than training the network (e.g. hard disks or internet downloads).