connector-x
datafusion-ballista
connector-x | datafusion-ballista | |
---|---|---|
11 | 12 | |
1,786 | 1,288 | |
2.5% | 4.6% | |
9.1 | 8.2 | |
5 days ago | 5 days ago | |
Rust | Rust | |
MIT License | 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.
connector-x
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How moving from Pandas to Polars made me write better code without writing better code
This was originally a blocker, however, we managed to set up a multi-stage Docker build to build from source. Here is the Github issue where we, along with community members, managed to solve it.
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I used multiprocessing and multithreading at the same time to drop the execution time of my code from 155+ seconds to just over 2+ seconds
There's packages like connector-x and polars that do a lot of what you're mentioning out of the box. I used these two to massively speed up an SQLalchemy + Pandas based ETL in the past as well.
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Rust in Data Science?
Thanks for sharing connector-x, I will also start to use it. I wonder if there are a list of tools like that. I know Ruff, Polars, pydantic-core.
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Querying Postgres Tables Directly from DuckDB
I was trying https://github.com/sfu-db/connector-x and hacking around with this https://github.com/spitz-dan-l/postgres-binary-parser but it turned out that a COPY to csv using asyncpg and then converting to parquet was the fastest.
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An alternativt to TradingView ?
if you store the OHLC data in a relational database, use connector-x to load the data into pandas dataframe
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Python and ETL
For SQL reading I'd really recommend connector-x, they do a great job preventing unneeded serialization and don't have to go through python.
- Fastest library to load data from DB to DataFrames
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Waiting for your data loading from database to dataframes?
Indeed, currently we do not support persistent connections among different queries. We target more on the bulk loading scenario where the bottleneck is caused by the data size and the connection construction overhead is negligible. However, one possible solution to the problem is to expose our connection pool object that we use inside Rust to users, so the next call could reuse the same pool. We do not plan for this yet, but happy to see whether this is a common need! Feel free to open an issue in our github repo: https://github.com/sfu-db/connector-x
Feel free to ask any questions here or open an issue in our github repo: https://github.com/sfu-db/connector-x . You can also join our discord community: https://discord.com/invite/xwbkFNk and ask question under connector channel!
- ConnectorX: The fastest tool to load data from databases to dataframes
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
Rudderstack - Privacy and Security focused Segment-alternative, in Golang and React
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..
lightweight-charts - Performant financial charts built with HTML5 canvas
seafowl - Analytical database for data-driven Web applications 🪶
mmr - Python based algorithmic trading platform for Interactive Brokers
opteryx - 🦖 A SQL-on-everything Query Engine you can execute over multiple databases and file formats. Query your data, where it lives.
postgres-binary-parser - Cython implementation of a parser for PostgreSQL's COPY WITH BINARY format
sqlglot - Python SQL Parser and Transpiler
datafusion - Apache DataFusion SQL Query Engine