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Introduction

While Polars does support writing queries in SQL, it's recommended that users familiarize themselves with the expression syntax for more readable and expressive code. As a primarily DataFrame library, new features will typically be added to the expression API first. However, if you already have an existing SQL codebase or prefer to use SQL, Polars also offers support for SQL queries.

Note

In Polars, there is no separate SQL engine because Polars translates SQL queries into expressions, which are then executed using its built-in execution engine. This approach ensures that Polars maintains its performance and scalability advantages as a native DataFrame library while still providing users with the ability to work with SQL queries.

Context

Polars uses the SQLContext to manage SQL queries . The context contains a dictionary mapping DataFrames and LazyFrames names to their corresponding datasets1. The example below starts a SQLContext:

SQLContext 

ctx = pl.SQLContext()

Register Dataframes

There are 2 ways to register DataFrames in the SQLContext:

  • register all LazyFrames and DataFrames in the global namespace
  • register them one by one

SQLContext 

df = pl.DataFrame({"a": [1, 2, 3]})
lf = pl.LazyFrame({"b": [4, 5, 6]})

# Register all dataframes in the global namespace: registers both df and lf
ctx = pl.SQLContext(register_globals=True)

# Other option: register dataframe df as "df" and lazyframe lf as "lf"
ctx = pl.SQLContext(df=df, lf=lf)

We can also register Pandas DataFrames by converting them to Polars first.

SQLContext 

import pandas as pd

df_pandas = pd.DataFrame({"c": [7, 8, 9]})
ctx = pl.SQLContext(df_pandas=pl.from_pandas(df_pandas))

Note

Converting a Pandas DataFrame backed by Numpy to Polars triggers a conversion to the Arrow format. This conversion has a computation cost. Converting a Pandas DataFrame backed by Arrow on the other hand will be free or almost free.

Once the SQLContext is initialized, we can register additional Dataframes or unregister existing Dataframes with:

  • register
  • register_globals
  • register_many
  • unregister

Execute queries and collect results

SQL queries are always executed in lazy mode to benefit from lazy optimizations, so we have 2 options to collect the result:

  • Set the parameter eager_execution to True in SQLContext. With this parameter, Polars will automatically collect SQL results
  • Set the parameter eager to True when executing a query with execute, or collect the result with collect.

We execute SQL queries by calling execute on a SQLContext.

register · execute 

# For local files use scan_csv instead
pokemon = pl.read_csv(
    "https://gist.githubusercontent.com/ritchie46/cac6b337ea52281aa23c049250a4ff03/raw/89a957ff3919d90e6ef2d34235e6bf22304f3366/pokemon.csv"
)
ctx = pl.SQLContext(register_globals=True, eager_execution=True)
df_small = ctx.execute("SELECT * from pokemon LIMIT 5")
print(df_small)


shape: (5, 13)
┌─────┬───────────────────────┬────────┬────────┬───┬─────────┬───────┬────────────┬───────────┐
│ #   ┆ Name                  ┆ Type 1 ┆ Type 2 ┆ … ┆ Sp. Def ┆ Speed ┆ Generation ┆ Legendary │
│ --- ┆ ---                   ┆ ---    ┆ ---    ┆   ┆ ---     ┆ ---   ┆ ---        ┆ ---       │
│ i64 ┆ str                   ┆ str    ┆ str    ┆   ┆ i64     ┆ i64   ┆ i64        ┆ bool      │
╞═════╪═══════════════════════╪════════╪════════╪═══╪═════════╪═══════╪════════════╪═══════════╡
│ 1   ┆ Bulbasaur             ┆ Grass  ┆ Poison ┆ … ┆ 65      ┆ 45    ┆ 1          ┆ false     │
│ 2   ┆ Ivysaur               ┆ Grass  ┆ Poison ┆ … ┆ 80      ┆ 60    ┆ 1          ┆ false     │
│ 3   ┆ Venusaur              ┆ Grass  ┆ Poison ┆ … ┆ 100     ┆ 80    ┆ 1          ┆ false     │
│ 3   ┆ VenusaurMega Venusaur ┆ Grass  ┆ Poison ┆ … ┆ 120     ┆ 80    ┆ 1          ┆ false     │
│ 4   ┆ Charmander            ┆ Fire   ┆ null   ┆ … ┆ 50      ┆ 65    ┆ 1          ┆ false     │
└─────┴───────────────────────┴────────┴────────┴───┴─────────┴───────┴────────────┴───────────┘

Execute queries from multiple sources

SQL queries can be executed just as easily from multiple sources. In the example below, we register :

  • a CSV file loaded lazily
  • a NDJSON file loaded lazily
  • a Pandas DataFrame

And we join them together with SQL. Lazy reading allows to only load the necessary rows and columns from the files.

In the same way, it's possible to register cloud datalakes (S3, Azure Data Lake). A PyArrow dataset can point to the datalake, then Polars can read it with scan_pyarrow_dataset.

register · execute 

# Input data:
# products_masterdata.csv with schema {'product_id': Int64, 'product_name': Utf8}
# products_categories.json with schema {'product_id': Int64, 'category': Utf8}
# sales_data is a Pandas DataFrame with schema {'product_id': Int64, 'sales': Int64}

ctx = pl.SQLContext(
    products_masterdata=pl.scan_csv("products_masterdata.csv"),
    products_categories=pl.scan_ndjson("products_categories.json"),
    sales_data=pl.from_pandas(sales_data),
    eager_execution=True,
)

query = """
SELECT 
    product_id,
    product_name,
    category,
    sales
FROM 
    products_masterdata
LEFT JOIN products_categories USING (product_id)
LEFT JOIN sales_data USING (product_id)
"""

print(ctx.execute(query))


shape: (5, 4)
┌────────────┬──────────────┬────────────┬───────┐
│ product_id ┆ product_name ┆ category   ┆ sales │
│ ---        ┆ ---          ┆ ---        ┆ ---   │
│ i64        ┆ str          ┆ str        ┆ i64   │
╞════════════╪══════════════╪════════════╪═══════╡
│ 1          ┆ Product A    ┆ Category 1 ┆ 100   │
│ 2          ┆ Product B    ┆ Category 1 ┆ 200   │
│ 3          ┆ Product C    ┆ Category 2 ┆ 150   │
│ 4          ┆ Product D    ┆ Category 2 ┆ 250   │
│ 5          ┆ Product E    ┆ Category 3 ┆ 300   │
└────────────┴──────────────┴────────────┴───────┘

Compatibility

Polars does not support the full SQL language, in Polars you are allowed to:

  • Write a CREATE statements CREATE TABLE xxx AS ...
  • Write a SELECT statements with all generic elements (GROUP BY, WHERE,ORDER,LIMIT,JOIN, ...)
  • Write Common Table Expressions (CTE's) (WITH tablename AS)
  • Show an overview of all tables SHOW TABLES

The following is not yet supported:

  • INSERT, UPDATE or DELETE statements
  • Table aliasing (e.g. SELECT p.Name from pokemon AS p)
  • Meta queries such as ANALYZE, EXPLAIN

In the upcoming sections we will cover each of the statements in more details.

  1. Additionally it also tracks the common table expressions as well. ↩