dolt
dvc
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dolt | dvc | |
---|---|---|
93 | 109 | |
16,971 | 13,116 | |
2.9% | 1.4% | |
10.0 | 9.7 | |
5 days ago | 5 days ago | |
Go | Python | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
dolt
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A MySQL compatible database engine written in pure Go
Hi, this is my project :)
For us this package is most important as the query engine that powers Dolt:
https://github.com/dolthub/dolt
We aren't the original authors but have contributed the vast majority of its code at this point. Here's the origin story if you're interested:
https://www.dolthub.com/blog/2020-05-04-adopting-go-mysql-se...
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The Great Migration from MongoDB to PostgreSQL
It's a pretty good default stance, yeah.
We have been trying to convince people to use our new database [1] for several years and it's an uphill battle, because Postgres really is the best choice for most people. They really have to need our unique feature (version control) to even consider it over Postgres, and I don't blame them.
[1] https://github.com/dolthub/dolt
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What I Talk About When I Talk About Query Optimizer (Part 1): IR Design
We implemented a query optimizer with a flexible intermediate representation in pure Go:
https://github.com/dolthub/go-mysql-server
Getting the IR correct so that it's both easy to use and flexible enough to be useful is a really interesting design challenge. Our primary abstraction in the query plan is called a Node, and is way more general than the IR type described in the article from OP. This has probably hurt us: we only recently separated the responsibility to fetch rows into its own part of the runtime, out of the IR -- originally row fetching was coupled to the Node type directly.
This is also the query engine that Dolt uses:
https://github.com/dolthub/dolt
But it has a plug-in architecture, so you can use the engine on any data source that implements a handful of Go interface.
- Dolt – Git for Data
- Dolt: A version-controlled SQL database
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Show HN: DoltgreSQL – Version-Controlled Database, Like Git and PostgreSQL
Just want to point out that we're announcing development on the project. It's absolutely not ready for mainstream use yet! We have Dolt (https://github.com/dolthub/dolt) which is production-ready and widely in use, but it uses MySQL's syntax and wire protocol. We are building the Dolt equivalent for PostgreSQL, which is DoltgreSQL, but it's only pre-alpha.
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Pg_branch: Pre-alpha Postgres extension brings Neon-like branching
Interesting that branching is now better supported and almost free. I wonder if merging can be simplified or whether it already is as simple and as fast as it can be?
I guess I am inspired by Dolt’s ability to branch and merge: https://github.com/dolthub/dolt
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SQLedge: Replicate Postgres to SQLite on the Edge
#. SQLite WAL mode
From https://www.sqlite.org/isolation.html https://news.ycombinator.com/item?id=32247085 :
> [sqlite] WAL mode permits simultaneous readers and writers. It can do this because changes do not overwrite the original database file, but rather go into the separate write-ahead log file. That means that readers can continue to read the old, original, unaltered content from the original database file at the same time that the writer is appending to the write-ahead log
#. superfly/litefs: aFUSE-based file system for replicating SQLite https://github.com/superfly/litefs
#. sqldiff: https://www.sqlite.org/sqldiff.html https://news.ycombinator.com/item?id=31265005
#. dolthub/dolt: https://github.com/dolthub/dolt
> Dolt can be set up as a replica of your existing MySQL or MariaDB database using standard MySQL binlog replication. Every write becomes a Dolt commit. This is a great way to get the version control benefits of Dolt and keep an existing MySQL or MariaDB database.
#. pganalyze/libpg_query: https://github.com/pganalyze/libpg_query :
> C library for accessing the PostgreSQL parser outside of the server environment
#. Ibis + Substrait [ + DuckDB ]
> ibis strives to provide a consistent interface for interacting with a multitude of different analytical execution engines, most of which (but not all) speak some dialect of SQL.
> Today, Ibis accomplishes this with a lot of help from `sqlalchemy` and `sqlglot` to handle differences in dialect, or we interact directly with available Python bindings (for instance with the pandas, datafusion, and polars backends).
> [...] `Substrait` is a new cross-language serialization format for communicating (among other things) query plans. It's still in its early days, but there is already nascent support for Substrait in Apache Arrow, DuckDB, and Velox.
#. benbjohnson/postlite: https://github.com/benbjohnson/postlite
> postlite is a network proxy to allow access to remote SQLite databases over the Postgres wire protocol. This allows GUI tools to be used on remote SQLite databases which can make administration easier.
> The proxy works by translating Postgres frontend wire messages into SQLite transactions and converting results back into Postgres response wire messages. Many Postgres clients also inspect the pg_catalog to determine system information so Postlite mirrors this catalog by using an attached in-memory database with virtual tables. The proxy also performs minor rewriting on these system queries to convert them to usable SQLite syntax.
> Note: This software is in alpha. Please report bugs. Postlite doesn't alter your database unless you issue INSERT, UPDATE, DELETE commands so it's probably safe. If anything, the Postlite process may die but it shouldn't affect your database.
#. > "Hosting SQLite Databases on GitHub Pages" (2021) re: sql.js-httpvfs, DuckDB https://news.ycombinator.com/item?id=28021766
#. awesome-db-tools https://github.com/mgramin/awesome-db-tools
- How do you sync dev databases across multiple devices?
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Ask HN: Data Management for AI Training
If you are just looking for data versioning there is Dolt:
https://github.com/dolthub/dolt
And that has a user-friendly UI in DoltHub:
https://www.dolthub.com/
You wouldn't store the images themselves in Dolt, those would likely be links to S3 but al the labels and surrounding metadata could be stored in Dolt?
DISCLAIMER: I'm the CEO of DoltHub so this is self-promotion.
dvc
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My Favorite DevTools to Build AI/ML Applications!
Collaboration and version control are crucial in AI/ML development projects due to the iterative nature of model development and the need for reproducibility. GitHub is the leading platform for source code management, allowing teams to collaborate on code, track issues, and manage project milestones. DVC (Data Version Control) complements Git by handling large data files, data sets, and machine learning models that Git can't manage effectively, enabling version control for the data and model files used in AI projects.
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Why bad scientific code beats code following "best practices"
What you’re describing sounds like DVC (at a higher-ish—80%-solution level).
https://dvc.org/
See pachyderm too.
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First 15 Open Source Advent projects
10. DVC by Iterative | Github | tutorial
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Platforms such as MLflow monitor the development stages of machine learning models. In parallel, Data Version Control (DVC) brings version control system-like functions to the realm of data sets and models.
- ML Experiments Management with Git
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Git Version Controlled Datasets in S3
I was using DVC (https://dvc.org/) for some time to help solve this but it was getting hard to manage the storage connections and I would run into cache issues a lot, but this solves it using git-lfs itself.
- Ask HN: How do your ML teams version datasets and models?
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Exploring MLOps Tools and Frameworks: Enhancing Machine Learning Operations
DVC (Data Version Control):
- Evaluate and Track Your LLM Experiments: Introducing TruLens for LLMs
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[D] Is there a tool to keep track of my ML experiments?
I have been using DVC and MLflow since then DVC had only data tracking and MLflow only model tracking. I can say both are awesome now and maybe the only factor I would like to mention is that IMO, MLflow is a bit harder to learn while DVC is just a git practically.
What are some alternatives?
liquibase - Main Liquibase Source
MLflow - Open source platform for the machine learning lifecycle
absurd-sql - sqlite3 in ur indexeddb (hopefully a better backend soon)
lakeFS - lakeFS - Data version control for your data lake | Git for data
noms - The versioned, forkable, syncable database
Activeloop Hub - Data Lake for Deep Learning. Build, manage, query, version, & visualize datasets. Stream data real-time to PyTorch/TensorFlow. https://activeloop.ai [Moved to: https://github.com/activeloopai/deeplake]
TimescaleDB - An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension.
delta - An open-source storage framework that enables building a Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, and Hive and APIs
vitess - Vitess is a database clustering system for horizontal scaling of MySQL.
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
temporal_tables - Temporal Tables PostgreSQL Extension
aim - Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.