Introduction

This book is an introduction to the Polars DataFrame library. Its goal is to introduce you to Polars by going through examples and comparing it to other solutions. Some design choices are introduced here. The guide will also introduce you to optimal usage of Polars.

Even though Polars is completely written in Rust (no runtime overhead!) and uses Arrow -- the native arrow2 Rust implementation -- as its foundation, the examples presented in this guide will be mostly using its higher-level language bindings. Higher-level bindings only serve as a thin wrapper for functionality implemented in the core library.

For Pandas users, our Python package will offer the easiest way to get started with Polars.

Goals and non-goals

The goal of Polars is to provide a lightning fast DataFrame library that utilizes all available cores on your machine. Unlike tools such as dask -- which tries to parallelize existing single-threaded libraries like NumPy and Pandas -- Polars is written from the ground up, designed for parallelization of queries on DataFrames.

Polars goes to great lengths to:

• Reduce redundant copies
• Traverse memory cache efficiently
• Minimize contention in parallelism

Polars is lazy and semi-lazy. It allows you to do most of your work eagerly, similar to Pandas, but it also provides a powerful expression syntax that will be optimized and executed on within the query engine.

In lazy Polars we are able to do query optimization on the entire query, further improving performance and memory pressure.

Polars keeps track of your query in a logical plan. This plan is optimized and reordered before running it. When a result is requested, Polars distributes the available work to different executors that use the algorithms available in the eager API to produce a result. Because the whole query context is known to the optimizer and executors of the logical plan, processes dependent on separate data sources can be parallelized on the fly.

Performance 🚀🚀

Polars is very fast, and in fact is one of the best performing solutions available. See the results in h2oai's db-benchmark. The image below shows the biggest datasets yielding a result.

Current status

Below a concise list of the features allowing Polars to meet its goals:

• Copy-on-write (COW) semantics
  • "Free" clones
  • Cheap appends
• Appending without clones
• Column oriented data storage
  • No block manager (i.e. predictable performance)
• Missing values indicated with bitmask
  • NaN are different from missing
  • Bitmask optimizations
• Efficient algorithms
• Very fast IO
  • Its csv and parquet readers are among the fastest in existence
• Query optimizations
  • Predicate pushdown
    • Filtering at scan level
  • Projection pushdown
    • Projection at scan level
  • Aggregate pushdown
    • Aggregations at scan level
  • Simplify expressions
  • Parallel execution of physical plan
  • Cardinality based groupby dispatch
    • Different groupby strategies based on data cardinality
• SIMD vectorization
• NumPy universal functions

Acknowledgements

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