PRQL language book

Pipelined Relational Query Language, pronounced “Prequel”.

PRQL is a modern language for transforming data — a simple, powerful, pipelined SQL replacement. Like SQL, it’s readable, explicit and declarative. Unlike SQL, it forms a logical pipeline of transformations, and supports abstractions such as variables and functions. It can be used with any database that uses SQL, since it compiles to SQL.

This book serves as a tutorial and reference guide on the language and the broader project. It currently has three sections, navigated by links on the left:

  • Tutorial — A friendly & accessible guide for learning PRQL. It has a gradual increase of difficulty and requires only basic understanding of programming languages. Knowledge of SQL is beneficial, because of many comparisons to SQL, but not required.
  • Reference — In-depth information about the PRQL language. Includes justifications for language design decisions and formal specifications for parts of the language.
  • Project — General information about the project, tooling and development.

To lead with a couple of examples, with a comparison to SQL: the language can be as simple as:

PRQL

from tracks
filter artist == "Bob Marley"                 # Each line transforms the previous result
aggregate {                                   # `aggregate` reduces each column to a value
  plays    = sum plays,
  longest  = max length,
  shortest = min length,                      # Trailing commas are allowed
}

SQL

SELECT
  COALESCE(SUM(plays), 0) AS plays,
  MAX(length) AS longest,
  MIN(length) AS shortest
FROM
  tracks
WHERE
  artist = 'Bob Marley'

…and here’s a fuller example:

PRQL

from employees
filter start_date > @2021-01-01               # Clear date syntax
derive {                                      # `derive` adds columns / variables
  gross_salary = salary + (tax ?? 0),         # Terse coalesce
  gross_cost = gross_salary + benefits_cost,  # Variables can use other variables
}
filter gross_cost > 0
group {title, country} (                      # `group` runs a pipeline over each group
  aggregate {                                 # `aggregate` reduces each group to a value
    average gross_salary,
    sum_gross_cost = sum gross_cost,          # `=` sets a column name
  }
)
filter sum_gross_cost > 100_000               # `filter` replaces both of SQL's `WHERE` & `HAVING`
derive id = f"{title}_{country}"              # F-strings like Python
derive country_code = s"LEFT(country, 2)"     # S-strings allow using SQL as an escape hatch
sort {sum_gross_cost, -country}               # `-country` means descending order
take 1..20                                    # Range expressions (also valid here as `take 20`)

SQL

WITH table_1 AS (
  SELECT
    title,
    country,
    salary + COALESCE(tax, 0) + benefits_cost AS _expr_1,
    salary + COALESCE(tax, 0) AS _expr_2
  FROM
    employees
  WHERE
    start_date > DATE '2021-01-01'
),
table_0 AS (
  SELECT
    title,
    country,
    AVG(_expr_2) AS _expr_0,
    COALESCE(SUM(_expr_1), 0) AS sum_gross_cost
  FROM
    table_1
  WHERE
    _expr_1 > 0
  GROUP BY
    title,
    country
)
SELECT
  title,
  country,
  _expr_0,
  sum_gross_cost,
  CONCAT(title, '_', country) AS id,
  LEFT(country, 2) AS country_code
FROM
  table_0
WHERE
  sum_gross_cost > 100000
ORDER BY
  sum_gross_cost,
  country DESC
LIMIT
  20