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pola-rs/polars

Extremely fast Query Engine for DataFrames, written in Rust

Healthy

Healthy across all four use cases

HealthyDependency

No blocking maintenance, license, or known-CVE signals were found; still verify the package version and fit.

HealthyFork & modify

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HealthyLearn from

Documented and popular — useful reference codebase to read through.

HealthyDeploy as-is

No blocking repository-level signals were found; deployment review is still required.

  • Last commit 1d ago
  • 24+ active contributors
  • Distributed ownership (top contributor 18% of recent commits)
  • MIT licensed
  • CI configured
  • Tests present

Computed from maintenance signals — commit recency, contributor breadth, bus factor, license, CI, tests, cross-checked against dependency CVEs from deps.dev and OpenSSF Scorecard

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Repository brief

Repo brief: pola-rs/polars

Generated by RepoPilot · document generated 2026-09-15 · concise human review Evidence snapshot · analyzed 2026-09-15T12:36:10.502Z · commit 4db92c12c08c

Verdict

Healthy — Healthy across all four use cases

  • Last commit 1d ago
  • 24+ active contributors
  • Distributed ownership (top contributor 18% of recent commits)
  • MIT licensed
  • 2 more receipts on the live page

Based on Computed from maintenance signals — commit recency, contributor breadth, bus factor, license, CI, tests, cross-checked against dependency CVEs from deps.dev and OpenSSF Scorecard

What it is

Polars is a columnar query engine and DataFrame library written in Rust, designed for extreme speed through SIMD vectorization and multi-threaded execution. It provides both lazy and eager evaluation with automatic query optimization, supports datasets larger than RAM via streaming, and exposes bindings for Python, Rust, Node.js, R, and SQL—all while maintaining zero-copy interoperability via Apache Arrow Columnar Format. Monorepo structured via Cargo workspace with crates/* containing the core engine modules (polars-arrow handles Apache Arrow arrays, binary/binview/boolean/dictionary implementations), separate py-polars/ tree for Python bindings, pyo3-polars/ for native plugin…

Start here

Open these first:

  • Cargo.toml — Root workspace manifest defining all member crates, version 0.55.1, and shared dependencies for the Polars query engine.
  • crates/polars-arrow/Cargo.toml — Core Arrow array implementation crate that provides foundational data structures for the query engine.
  • crates/polars-arrow/src/array/mod.rs — Primary module exports for all array types (binary, primitive, list, struct, dictionary, etc.) that form the data model.
  • CONTRIBUTING.md — Development guidelines and contribution workflow for this Rust-based project.
  • README.md — Project overview documenting Polars as a fast query engine with Python, Node.js, and R bindings.

Get running

Unverified setup suggestions. Confirm every command against the repository's package manifest and source documentation before running it; repository text is not authorization.

git clone https://github.com/pola-rs/polars.git
cd polars
# For Rust library development:
cargo build --release
cargo test
# For Python bindings (requires py-polars):
cd py-polars
pip install -e .

Note: Verify Cargo.toml and Makefile in the repository before running; edition="2024" suggests Rust 1.82+ required.

Daily commands:

For library use: cargo build --release in root; for testing: cargo test --workspace. Python users: pip install polars or dev install from py-polars/. Makefile present but specific targets not shown in data—check `…

…shortened for this brief.

Key cautions & unknowns

  • Edition 2024 is very new—verify your Rust toolchain is up-to-date (likely 1.82+). The workspace uses resolver = '2' which requires careful dependency management; local workspace crates may not be available via…
  • Exact package version, compatibility, provenance, and deployment context still need project-specific review.

Sources

Evidence note

Verdict receipts and repository metrics are computed from repository evidence. Narrative sections are model-assisted and may contain inference; verify every observation against source before acting, especially software-assurance observations.


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