dud
sqldb-logger
| dud | sqldb-logger | |
|---|---|---|
| 14 | 3 | |
| 166 | 339 | |
| - | - | |
| 6.0 | 0.0 | |
| 5 days ago | 4 months ago | |
| Go | Go | |
| BSD 3-clause "New" or "Revised" License | MIT License |
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dud
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Ask HN: How do your ML teams version datasets and models?
I've used DVC in the past and generally liked its approach. That said, I wholeheartedly agree that it's clunky. It does a lot of things implicitly, which can make it hard to reason about. It was also extremely slow for medium-sized dataset (low 10s of GBs).
In response, I created a command-line tool that addresses these issues[0]. To reduce the comparison to an analogy: Dud : DVC :: Flask : Django.
[0]: https://github.com/kevin-hanselman/dud
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🐂 🌾 Oxen.ai - Blazing Fast Unstructured Data Version Control, built in Rust
There is also https://github.com/kevin-hanselman/dud
- Data Version Control
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Tup – an instrumenting file-based build system
I very much agree with you about DVC's feature creep. The other issue I have with it is speed. DVC has left me scratching my head at its sluggishness many times. Because of these factors, I've been working on an alternative that focuses on simplicity and speed[0]. My tool is often five to ten times faster than DVC[1]. I'd love to hear what you think.
[0]: https://github.com/kevin-hanselman/dud
[1]: https://kevin-hanselman.github.io/dud/benchmarks/
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Non-Obvious Docker Uses
I don't know about replacing Make with Docker, but I use the two together to good effect. One of my favorite hacks is adding a 'docker-%' rule in my Makefile to run make commands in a Docker image[1]. It's a bit mind-bending, and there's a few gotchas, but it works surprisingly well for simple rules.
[1]: https://github.com/kevin-hanselman/dud/blob/e98de8fcdf7ad564...
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Git-annex – Managing large files with Git
Thanks for sharing your experience. It's non-trivial and surprising behavior like this that drove me to build a custom system[0] myself. When I started researching version control tools for large files, I remember feeling like git-annex and Git LFS were awkwardly bolted onto Git; Git simply wasn't designed for large files. Then I found DVC[1], and its approach rang true for me. However, after using DVC for a year or so, I grew tired of DVC's many puzzling behaviors (most of which are outlined in the README at [0]). In the end, I built the tool I wanted for the job -- one that is exceptionally simple and fast.
[0]: https://github.com/kevin-hanselman/dud
- Alternative to Git LFS or DVC
- Show HN: A small and simple alternative to Git LFS or DVC
- Dud: a lightweight tool for versioning data alongside source code and building data pipelines.
- Dud: a tool for versioning data alongside source code. A faster and simpler alternative to DVC.
sqldb-logger
- A logger for Go SQL database driver without modifying existing stdlib usage
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DB Question: Can I get the final sql query from db.Exec?
You can use this to get instant query logging for sql.DB. https://github.com/simukti/sqldb-logger
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Ask HN: What are some tools / libraries you built yourself?
sqldb-logger: https://github.com/simukti/sqldb-logger
It's a thin layer that wrap Go (Golang) SQL database driver, it does not change sql.DB usage in any existing Go app.
It is because I prefer to use vanilla Go sql.DB in my web service and I could not find any SQL logger for Go SQL database standard library that can use structured JSON logging.
What are some alternatives?
dvc - 🦉 ML Experiments and Data Management with Git
logxi - A 12-factor app logger built for performance and happy development
scalar - Scalar: A set of tools and extensions for Git to allow very large monorepos to run on Git without a virtualization layer
logutils - Utilities for slightly better logging in Go (Golang).
docker-merge - Docker images as git repositories, so you can merge them.
go-logger - Simple logger for Go programs. Allows custom formats for messages.
Task - A task runner / simpler Make alternative written in Go
lumberjack - lumberjack is a log rolling package for Go
oxen-release - Lightning fast data version control system for structured and unstructured machine learning datasets. We aim to make versioning datasets as easy as versioning code.
phuslu/log - Fastest structured logging
pachyderm - Data-Centric Pipelines and Data Versioning
glog - Leveled execution logs for Go