septum VS ripgrep

https://github.com/pyjarrett/septum

The hardest part about getting code search right imo is grabbing the right amount of surrounding context, which septum is aimed at solving on a per-file basis.

Another one I'm surprised hasn't been mentioned is stack-graphs (https://github.com/github/stack-graphs), which tries to incrementally resolve symbolic relationships across the whole codebase. It powers github's cross-file precise indexing and conceptually makes a lot of sense, though I've struggled to get the open source version to work

septum - interactive searching for contexts matching and excluding parameters

I do a lot of weird and experimental work in Ada. Some of it works, whereas a lot of it doesn't. While I have done this sort of work in Python, Ruby, Rust, C or C++ in the past, when I do it in Ada, I end up saving time later on since the language forces many "good practices."

I'd appreciate any issues or suggestions you want to report on GitHub to help me improve this.

Ada is another option without a GC. I wrote a search tool for large codebases with it (https://github.com/pyjarrett/septum), and the easy multitasking and pinning to CPUs allows you to easily go wide if the problem you're solving supports it.

There's very little allocation since it supports returning VLAs (like strings) from functions via a secondary stack. Its Alire tool does the toolchain install and provides package management, so trying the language out is super easy. I've done a few bindings to things in C with it, which is ridiculously easy.

I mentioned my project Septum in a HackerNews comment, which caused it to pick up over 200 GitHub stars. That seemed to give Ada some publicity since it's a general purpose tool, so I'll also publish a new up-to-date version (0.0.6) here soon.

I work on code bases with millions of lines, so I wrote a tool called Septum to help me (https://github.com/pyjarrett/septum/). This isn't to replace grep or ripgrep or silver searcher, those are all great tools you should have!

Septum is neighborhood based (context-based) search, so you can find contiguous groups of lines which contain specific things, but exclude other things. It's also interactive so you can add/remove filters as needed. This makes it useful for those cases where terms change based on their context so you can exclude terms related to the contexts you don't want to keep. It reads .septum/config which contains its normal commands to load directories and settings, so you can have different configs per project you're working on.

Here's a short demo video of his Septum tool mentioned in the article: https://asciinema.org/a/459292

I also did a few things: - Wrote an online e-book about Ada - Septum - context-based source code search for multi-million line codebases (I use this nearly every day at work. It's being submitted as my Ada crate of the year. - dir_iterators - library similar to the incredible walkdir. - project_indicators - library for spinners and progress bars. - trendy_terminal - library for cross-platform terminal setup, VT100 support, and GNU readline-like behavior. - trendy_test - library for simple unit testing, which runs tests in parallel. - Ada Ray Tracer - an Ada port of Ray Tracing in One Weekend. - dirs_to_graphviz - Make graphviz files from directory trees. - rst_tables - a tool to draw RST table outlines.

If you want to integrate fzf with rg, fd, bat to fuzzy find files, directories or ripgrep the content of a file and preview using bat, but the fzf document only has commands for Linux shell (bash,...), and you want to achieve that on your Windows Machine using Powershell, this post may be for you.

There were quite some occurrences of this component in the entire project, therefore just searching for base-input was not going to cut it for me. Instead, I decided to use regular expressions resp. regex with ripgrep. After installing ripgrep it provides a rg command line tool.

ripgrep - https://github.com/BurntSushi/ripgrep

Basic code searching skills seems like something new developers are never explicitly taught, but which is an absolutely crucial skill to build early on.

I guess the knowledge progression I would recommend would look something kind this:

- Learning about Ctrl+F, which works basically everywhere.

- Transitioning to ripgrep https://github.com/BurntSushi/ripgrep - I wouldn't even call this optional, it's truly an incredible and very discoverable tool. Requires keeping a terminal open, but that's a good thing for a newbie!

- Optional, but highly recommended: Learning one of the powerhouse command line editors. Teenage me recommended Emacs; current me recommends vanilla vim, purely because some flavor of it is installed almost everywhere. This is so that you can grep around and edit in the same window.

- In the same vein, moving back from ripgrep and learning about good old fashioned grep, with a few flags rg uses by default: `grep -r` for recursive search, `grep -ri` for case insensitive recursive search, and `grep -ril` for case insensitive recursive "just show me which files this string is found in" search. Some others too, season to taste.

- Finally hitting the wall with what ripgrep can do for you and switching to an actual indexed, dedicated code search tool.

live grep: ripgrep

The world has moved on though to opinionated tools, and Rust isn't even the furthest in that direction (That would be Go). The equivalent of those two lines in Cargo.toml would be this example of a basic configuration from the jacoco-maven-plugin: https://www.jacoco.org/jacoco/trunk/doc/examples/build/pom.x... - That's 40 lines in the section to do the "defaults".

Yes, you could add a load of config for files to include/exclude from coverage and so on, but the idea that that's a norm is way more common in Java projects than other languages. Like here's some example Cargo.toml files from complicated Rust projects:

Servo: https://github.com/servo/servo/blob/main/Cargo.toml

rust-gdext: https://github.com/godot-rust/gdext/blob/master/godot-core/C...

ripgrep: https://github.com/BurntSushi/ripgrep/blob/master/Cargo.toml

socketio: https://github.com/1c3t3a/rust-socketio/blob/main/socketio/C...

I'm not clear on why you're seeing the results you are. It could be because your haystack is so small that you're mostly just measuring noise. ripgrep 14 did introduce some optimizations in workloads like this by reducing match overhead, but I don't think it's anything huge in this case. (And I just tried ripgrep 13 on the same commands above and the timings are similar if a tiny bit slower.)

[1]: https://github.com/radare/ired

[2]: https://github.com/BurntSushi/ripgrep/discussions/2597