TeamCity vs TFS

TeamCity has different installation packages for different operating systems. All the user needs to do is download the correct one and run it.

The fact that it is based on Java does not hinder TeamCity's ability to support different build environments. TeamCity in fact supports a large number of languages and tools for each of those languages (build runners and test frameworks).

Some of the languages/platforms that are supported include: Ruby, .NET, Java.

The user interface of TeamCity is clear, well thought out and the dashboard is highly customizable.

The user can easily compose dependencies between builds by adding snapshot and artifact dependencies, all on the one screen. All output of upstream builds is available to downstream builds. Triggering sets off the entire build chain and supports re-running of the portions of the chain that failed.

TeamCity offers well defined APIs for extending, as well as a REST interface.

TeamCity supports both MSTest and NUnit (which is open source) to run tests.

Seems to be the best choice for .NET applications, but to be honest: if you stray from the default settings you will be in a lot of pain most of the time.

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.

It was never planned to be Redox-only.

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.

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.

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.

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.

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

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.

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

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

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

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.

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

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.

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