A translation of a Z3 example from Python to Rust This post is a translation of a Z3 example from Python to Rust. It is not very inspired: Please read the original Python article 1 for more background. However, at the bottom of this page, I provide a few more links and resources with a few notes. The Z3 crate does not have any examples, so it makes it a bit tough to get started.

A short update: I just published my second crate (a Rust package): https://crates.io/crates/macos-perf I hope this can be used to accurately measure Rust code on M1 Apple devices. Please try it, and provide feedback. Example Running the example code I provide gives the following results: sudo cargo run --quiet --example main PerformanceCounters { cycles: 46148.1, branches: 23053.4, missed_branches: 2.4, instructions: 162342.9, } We see the missed branches, cycles, branches taken and instructions.

Serde is an amazing collection of Rust libraries for Serialization and Deserialization with many useful supported formats. For example, I can convert a YAML file into a native Rust struct by just slapping a #[derive(Deserialize)] above the struct. One challenge that I faced was reading YAML files of different schema versions. One option is to use Option<T> for all fields which changed between versions. Generally speaking, this is probably the easiest and most suitable solution.

A while back, I posted my first Rust Video. In it, I am showing how to write a simple command line application in Rust – including useful libraries. The CLI is mimicking the GNU shuf utility: It randomly shuffles files of an input file. Libraries used: structopt eyre & color-eyre rand Along the way we also see the usefulness of the Rust Analyzer. I hope you enjoy it.

Practical Notes on Advanced Git Bisect This is an adaptation from my Twitter thread: Git bisect ✂️ a thread 🧵. — Christoph 🇺🇦 🌻🕊 (@chsiedentop) November 20, 2020 Recap of Bisect I assume you’ve used or heard of git bisect. If you’ve neither heard off nor used git bisect before, then I encourage you to give it a try. Franziska Hinkelmann provides a good intro and a test repo to find a bug with git bisect.

Yesterday’s post showed that the original C++ version is around 13 times slower than my non-optimized Rust version. Next, I’ll show how to get easily equivalent performance to Rust. One problem that I immediately made out when first seeing the C++ version is that it uses std::regex. std::regex is known to be horribly slow 1. Just replacing the checking of the file name extension with the in-built e.path().extension() != ".txt" speeds up from 373 ms to 52ms!

Yesterday, I wrote about implementing word-count in Rust. Having never written Swift before1, I wanted to try my hand at the same problem. This is certainly not comparable to my Rust version or the C++ version 2. Doing this in Swift just consisted of constant googling and the result is certainly not what a proficient Swifter would write. Getting started I created a new Swift package, and built it in the command line.

Last week there were two posts on the reputation of C++. In a noble effort, Jussi Pakkanen showcased modern C++. Nothing fancy, just clean and readable code. Unfortunately, around ten years after C++11, the news that simple (and safe) C++ code is now the default has not spread everywhere. Jason Moiron answered with Go code doing the same (article). Both programs find the ten most common words in all txt files, which can be recursively found in the current directory.

Crash Reporting If your program is running on more than one machine or is used by others, you probably want to get some insight into when things fail. I believe this is commonly called “Crash Reporting”. Such a crash reporting system will consist of a component to collect individual incidents and of a component to aggregate individual reports. For example, it will group common incidents by stack trace. One public example of this, is Mozilla’s system (Socorro).

After last week’s post on how Nushell can be used to convert unstructured data to structured data, I wondered how I would approach the task using traditional tooling. I wrote ‘Bash’ in the title, but what I precicesley mean is POSIX pipes and command line utilities typically found on a Unix-like environment (Linux, macOS, etc). Again, the task is to convert weirdly structured text output (from sshping) to JSON for further processing or long term storage.