polars

GitHub describes it as Extremely fast Query Engine for DataFrames, written in Rust. The repository metadata lists Rust as its primary language. The metadata lists the MIT license. This article stays within the project description and details documented in the GitHub repository README.

GitHub describes it as Extremely fast Query Engine for DataFrames, written in Rust. The repository metadata lists Rust as its primary language. The metadata lists the MIT license. The README describes the project this way: Polars is an analytical query engine for DataFrames, written in Rust. It is designed to be fast, easy to use and expressive. Key features are:

The README section "Polars: Extremely fast Query Engine for DataFrames" states: - Fast : written from the ground up in Rust with multi-threaded, vectorized (SIMD) execution - Lazy & eager execution : with query optimization out of the box - Larger-than-RAM : the streaming engine processes datasets that don't fit in memory - Expressive API : compose complex queries with powerful expressions - Extensible : extend Polars natively with custom code through - Multi-language : bindings for Python, Rust, Node.js, R, and SQL - GPU support : optionally accelerate queries on NVIDIA GPUs - Interoperable : uses the Apache Arrow Columnar Format for zero-copy data sharing

The README section "Polars in action" states: Queries are composed from expressions. This lazy query gets optimized out of the box and runs in parallel across all available cores:

The README section "Performance" states: Polars is very fast. In fact, it is one of the best performing Dataframe solutions available. See the PDS-H benchmarks results.

The repository README is the source for this review. It does not replace a local installation or an independent test.

• Source 1 · Project repository
• Source 2 · README