Daft
quokka
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Daft | quokka | |
---|---|---|
7 | 23 | |
1,684 | 1,081 | |
38.2% | - | |
9.8 | 8.3 | |
3 days ago | 7 months ago | |
Rust | 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.
Daft
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Daft: Distributed DataFrame for Python
There are benchmarks here - https://github.com/Eventual-Inc/Daft?tab=readme-ov-file#benc.... Seems to outperform Dask by a fair bit.
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Daft: A High-Performance Distributed Dataframe Library for Multimodal Data
Hi (one of the maintainers here), that is a good suggestion! I wasn't aware of that project. I went ahead and made an issue to add `export DO_NOT_TRACK=1` as one of the variables we track! https://github.com/Eventual-Inc/Daft/issues/1015
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Daft: The Distributed Python Dataframe
We are looking at supporting other distributed backends as well - please drop by our discussion forums (https://github.com/Eventual-Inc/Daft/discussions) and drop us a message if you have any suggestions! We’d love to hear from you :)
quokka
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How Query Engines Work
An awesome read!
Something related that I found out about from HN a few months back is another engine called quokka. It's particularly interesting and applicable how quokka schedules distributed queries to outperform Spark https://github.com/marsupialtail/quokka/blob/master/blog/why...
- Quokka – Distributed Polars on Ray
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Algorithmic Trading with Go
Hi Justin, you might be interested in my blog: https://github.com/marsupialtail/quokka/blob/master/blog/bac... advocating a cloud based approach.
You don't have to use the system I am building, but it's worth thinking about that design.
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Daft: A High-Performance Distributed Dataframe Library for Multimodal Data
SQL support is very challenging.
I work on Quokka (https://github.com/marsupialtail/quokka). I support Iceberg reads. Recently we are adding SQL support from just parsing the DuckDB logical plan, though that is very challenging as well.
The Python world lacks a standard for a plug and play SQL query optimizer. Apache Calcite is good for the JVM world, but not great if you are trying to cut out the JVM.
- Why your dataframe library needs to understand vector embeddings
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The Inner Workings of Distributed Databases
In case people are interested, I wrote a post about fault tolerance strategies of data systems like Spark and Flink: https://github.com/marsupialtail/quokka/blob/master/blog/fau...
The key difference here is that these systems don't store data, so fault tolerance means recovering within a query instead of not losing data.
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Launch HN: DAGWorks – ML platform for data science teams
would love to collaborate on an integration with pyquokka (https://github.com/marsupialtail/quokka) once I put out a stable release end of this month :-)
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is spark always your go to solution ?
Then you should keep an eye on quokka. This may become the "Spark" for Polars/DuckDB. It seems to be under active development though I'm not sure how stable it is.
- Distributed fault tolerance made simple
- Fault tolerance for distributed data systems is quite simple
What are some alternatives?
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
opteryx - 🦖 A SQL-on-everything Query Engine you can execute over multiple databases and file formats. Query your data, where it lives.