symbolicator VS ccache

- it entirely removes a class of discussion of "opinion" on style. Tabs or spaces? Import ordering? Alignment? Doesn't matter, use go fmt. It's built into the toolchain, everyone has it. Might it be slightly more optimal to do X? Sure, but there's no discussion here.

- it hits that sweet spot between python and C - compilation is wicked fast, little to no app startup time, and runtime is closer to C than it is to python.

- interfaces are great and allow for extensions of library types.

- it's readable, not overly terse. Compared to rust, e.g. [0], anyone who has any programming experience can probably figure out most of the syntax.

We've got a few internal services and things in Go,vanr we use them for onboarding. Most of my team have had PR's merged with bugfixes on their first day of work, even with no previous go experience. It lets us care about business logic from the get go.

[0] https://github.com/getsentry/symbolicator/blob/master/crates...

> Aren't they slower or about as slow as C++, which is notorious for being frustratingly slow, especially for local, non-distributed builds?

Yes. Significantly slower. The last rust crate I pulled [0] took as long to build as the unreal engine project I work on.

[0] https://github.com/getsentry/symbolicator/

2022: https://blog.sentry.io/we-just-gave-260-028-dollars-to-open-...

In addition to that, there are contributions to open source done in the form of code that is, open source, such as the symbolication service: https://github.com/getsentry/symbolicator and many others: https://github.com/getsentry/

Digging up the topic, I also found that new framework https://github.com/tokio-rs/axum, which already seems to be popular.

Sentry is rewriting some of their libs from Actix to Axum: https://github.com/getsentry/symbolicator/commit/b6ef7cb00b7...

Disclosure: I work at Sentry.

> My personal term for this sort of "We're OK with little people using the software but we don't want any competition"

Large companies are free to use Sentry. There are Fortune 50 companies running Sentry at scale internally without paying us a cent. That's totally cool.

You're also free to compete with Sentry. You're not free to repackage Sentry for the purposes of competing us. There are lots of competing error and performance monitoring products out there that do perfectly fine without it.

I should also note that many components of Sentry are distributed with OSI-approved licenses that you are free to use to compete with us. For example, our Symbolication service (https://github.com/getsentry/symbolicator) ships with an MIT license, and it's an important part of our business.

# Download and install ccache for faster compilation wget https://github.com/ccache/ccache/releases/download/v4.8.3/ccache-4.8.3.tar.xz tar -xf ccache-4.8.3.tar.xz pushd ccache-4.8.3 cmake . make -j $CPUS make install popd # Install NumPy, a dependency for PyTorch dnf install -y numpy # Install Python typing extensions for better type-checking sudo -u ec2-user pip3 install typing-extensions # Clone PyTorch repository and install from source git clone --recursive https://github.com/pytorch/pytorch.git pushd pytorch python3 setup.py install popd # Refresh the dynamic linker run-time bindings ldconfig # Install additional Python libraries for PyTorch sudo -u ec2-user pip3 install sympy filelock fsspec networkx

> What can I use to cache with MSVC that isn't Incredibuild?

Ccache works, but if you use the Visual Studio C++ compiler you need to configure your build to be cacheable.

https://github.com/ccache/ccache/wiki/MS-Visual-Studio

That ecosystem now is too young. All these things must be configured by an automatic installer. Packages and pods need to be tested before release, etc. Previous was 80 min compilation time of the Firestore package. Solved with https://ccache.dev/. But why Dart developers must know, how to install and configure object cache for C++ compilers?

Related

https://github.com/icecc/icecream - another option that does what distcc does, but aimed at a somewhat different use case.

https://ccache.dev/ - a similar idea but provides caching of build outputs instead of distributing builds. You can use it together with distcc to achieve even better performance.

> All those big changes introduce commits that make git bisect generally slower.

Bisection search is log2(n) so doubling the number of commits should only add one more bisection step, yes?

> Which might be awful if you also have some C code to recompile at every step of bisecting.

That reminds me, I've got to try out ccache (https://ccache.dev/ ) for my project. My full compile is one minute, but the three files that take longest to compiler rarely change.

I worked with the internals of this some 16 years ago, maintaining a customized version at Zeugma Systems. Some change of mine was reworked by someone and upstreamed:

https://github.com/ccache/ccache/commit/e8354384f67bc733bea5...