arrow2
datafusion-ballista
| arrow2 | datafusion-ballista | |
|---|---|---|
| 25 | 12 | |
| 1,071 | 1,288 | |
| - | 4.6% | |
| 0.0 | 8.2 | |
| 3 months ago | 5 days ago | |
| Rust | Rust | |
| 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.
arrow2
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Polars: Company Formation Announcement
One of the interesting components of Polars that I've been watching is the use of the Apache Arrow memory format, which is a standard layout for data in memory that enables processing (querying, iterating, calculating, etc) in a language agnostic way, in particular without having to copy/convert it into the local object format first. This enables cross-language data access by mmaping or transferring a single buffer, with zero [de]serialization overhead.
For some history, there's has been a bit of contention between the official arrow-rs implementation and the arrow2 implementation created by the polars team which includes some extra features that they find important. I think the current status is that everyone agrees that having two crates that implement the same standard is not ideal, and they are working to port any necessary features to the arrow-rs crate and plan on eventually switching to it and deprecating arrow2. But that's not easy.
https://github.com/apache/arrow-rs/issues/1176
https://github.com/jorgecarleitao/arrow2/pull/1476
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Data Engineering with Rust
https://github.com/jorgecarleitao/arrow2 https://github.com/apache/arrow-datafusion https://github.com/apache/arrow-ballista https://github.com/pola-rs/polars https://github.com/duckdb/duckdb
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Polars[Query Engine/ DataFrame] 0.28.0 released :)
Currently datafusion and polars aren't directly operable iirc because they use different underlying arrows implementations, but there seems to be work being done on that here https://github.com/jorgecarleitao/arrow2/issues/1429
- Arrow2 0.15 has been released. Happy festivities everyone =)
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Rust is showing a lot of promise in the DataFrame / tabular data space
[arrow2](https://github.com/jorgecarleitao/arrow2) and [parquet2](https://github.com/jorgecarleitao/parquet2) are great foundational libraries for and DataFrame libs in Rust.
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Matano - Open source security lake built with Arrow2 + Rust
[1] https://github.com/jorgecarleitao/arrow2
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Polars 0.23.0 released
In lockstep with arrow2's 0.13 release, we have published polars 0.23.0.
- Arrow2 v0.13.0, now with support to read Apache ORC and COW semantics!
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::lending-iterator — Lending/streaming Iterators on Stable Rust (and a pinch of HKT)
This is so freaking life-saving! - we have been using StreamingIterator and FallibleStreamingIterator in libraries (arrow2 and parquet2) and the existing landscape is quite confusing for new users!
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Mssql :(
arrow2 has support for mssql via ODBC (which microsoft has first class support to). Here are the integration tests we have (both read and write) against mssql specifically.
datafusion-ballista
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Polars
Not super on topic because this is all immature and not integrated with one another yet, but there is a scaled-out rust data-frames-on-arrow implementation called ballista that could maybe? form the backend of a polars scale out approach: https://github.com/apache/arrow-ballista
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Rust vs. Go in 2023
> Is Rust's compile-time GC about something other than performance somehow?
AFAIK, memory safety and language features as RAII is also available in C++, for instance. About the reasons for slow compilation, take a look at https://www.reddit.com/r/rust/comments/xna9mb/why_are_rust_p...
Not having a GC is also about not having a runtime as you mention (e.g. nice for creating Python extensions and embedded systems programming) and also more runtime deterministic performance: on that, if I'm not mistaken that was the reason for Discourse switching to Rust and also, e.g.: "the choice of Rust as the main execution language avoids the overhead of GC pauses and results in deterministic processing times" https://github.com/apache/arrow-ballista/blob/main/README.md
- Ballista (Rust) vs Apache Spark. A Tale of Woe.
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Evolution and Trends of Data Engineering 2022/23
Ballista (Arrow-Rust), which is largely inspired by Apache Spark, there are some interesting differences.
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Data Engineering with Rust
https://github.com/jorgecarleitao/arrow2 https://github.com/apache/arrow-datafusion https://github.com/apache/arrow-ballista https://github.com/pola-rs/polars https://github.com/duckdb/duckdb
- Any job processing framework like Spark but in Rust?
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Is Apache Arrow DataFusion and Ballista the future of big data engineering/science?
Source: https://github.com/apache/arrow-ballista
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Pure Python Distributed SQL Engine
Can you explain how this might differ from something like https://github.com/apache/arrow-ballista
I've seen several variants of "next-gen" spark, but nowhere have I really seen the different tradeoffs/advantages/disadvantages between them.
- Scala or Rust? which one will rule in future?
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Welcome to Comprehensive Rust
Rust has amazing integration with Python through PyO3 [1] so see it like a safe alternative for high performance calculations. The ecosystem itself is starting to come together exciting projects like Polars [2] (Pandas alternative), nalgebra [3], Datafusion [4] and Ballista [5]
[1] https://github.com/PyO3/pyo3
[2] https://github.com/pola-rs/polars/
[3] https://docs.rs/nalgebra/latest/nalgebra/
[4] https://github.com/apache/arrow-datafusion
[5] https://github.com/apache/arrow-ballista
What are some alternatives?
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
duckdb - DuckDB is an in-process SQL OLAP Database Management System
datafusion - Apache DataFusion SQL Query Engine
lance - Modern columnar data format for ML and LLMs implemented in Rust. Convert from parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, with more integrations coming..
db-benchmark - reproducible benchmark of database-like ops
seafowl - Analytical database for data-driven Web applications 🪶
arrow-rs - Official Rust implementation of Apache Arrow
connector-x - Fastest library to load data from DB to DataFrames in Rust and Python
pyodide - Pyodide is a Python distribution for the browser and Node.js based on WebAssembly
opteryx - 🦖 A SQL-on-everything Query Engine you can execute over multiple databases and file formats. Query your data, where it lives.
explorer - Series (one-dimensional) and dataframes (two-dimensional) for fast and elegant data exploration in Elixir
sqlglot - Python SQL Parser and Transpiler