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 Polars: Extremely fast Query Engine for DataFrames and SQL

Polars is an analytical query engine written in Rust. Write your queries with the DataFrame API, in
SQL, or mix both: they run through the same optimizer and the same engine. It is designed to be
fast, easy to use and expressive. Key features are:

- Fast: written from the ground up in Rust with multi-threaded, vectorized (SIMD) execution
- Spill to disk: Operations that exceed available RAM can spill to disk
- Expressive API: compose complex queries with powerful expressions
- Extensible: extend Polars natively with custom code through
  I/O and Expression plugins
- GPU support: optionally accelerate queries on NVIDIA GPUs
- Interoperable: uses the
  Apache Arrow Columnar Format for zero-copy
  data sharing

To learn more, read the user guide.

 Polars in action

 DataFrame API

Queries are composed from expressions or SQL. This lazy query gets optimized out of the box and runs
in parallel across all available cores:

python
import polars as pl

df = (
    pl.scan_parquet("orders.parquet")
    .filter(pl.col("status") == "shipped")
    .group_by("customer_id")
    .agg(
        pl.col("amount").sum().alias("total"),
        pl.len().alias("n_orders"),
    )
    .sort("total", descending=True)
    .collect()
)


 SQL

python
import polars as pl

df = pl.sql("""
    WITH totals AS (
        SELECT customer_id, SUM(amount) AS total, COUNT() AS n_orders
        FROM read_parquet('orders.parquet')
        WHERE status = 'shipped'
        GROUP BY customer_id
    )
    SELECT c.name, t.total, t.n_orders, RANK() OVER (ORDER BY t.total DESC) AS rank
    FROM totals t
    JOIN read_parquet('customers.parquet') c USING (customer_id)
    ORDER BY t.total DESC
""").collect()


 Mix both

DataFrames and LazyFrames in scope can be queried by name, and the result of a SQL query is a
regular LazyFrame, so you can switch between SQL and expressions freely:

python
orders = pl.scan_parquet("orders.parquet")

df = (
    pl.sql("SELECT customer_id, SUM(amount) AS total FROM orders GROUP BY customer_id")
    .filter(pl.col("total")  1_000)
    .collect()
)


Read more in the SQL user guide.

 Performance

Polars is very fast. In fact, it is one of the best performing query engines available, for both
DataFrame and SQL workloads. See the PDS-H benchmarks
results.

 Installation

 Python

Install the latest Polars version with:

sh
pip install polars


See the User Guide for more details
on optional dependencies


Compile Polars from source

If you want a bleeding edge release you should compile Polars from source. Advanced users can also
compile for maximum performance for their architecture.

This can be done by going through the following steps in sequence:

1. Install the latest Rust compiler
2. Install maturin: pip install maturin
3. cd py-polars and choose one of the follo

Top issues

Latest release

💥 Breaking changes

- Disallow mixed string \ numeric arithmetic in eager (30071)

 🚀 Performance improvements

- Limit the memory of buckets sorted ahead in the sort flush (30297)
- Make file readers wait for spills while over the memory budget (30296)
- Count unspilled frames in the memory estimate right away (30295)
- Simplify more boolean expressions and drop redundant null checks (30293)
- Wait for spills while over the out-of-core memory budget (30294)
- Lower the default out-of-core prefetch fraction to 0.5 (30292)
- Aggregate unique and n\_unique in the streaming group-by itself (30281