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.
PRQL can be as simple as:
from employees
filter country == "USA" # Each line transforms the previous result
aggregate [ # `aggregate` reduces column to a value
max salary,
min salary,
count, # Closing commas are allowed :)
]Here's a fuller example of the language;
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 row
average gross_salary,
sum_gross_cost = sum gross_cost, # `=` sets a column name
]
)
filter sum_gross_cost > 100000 # Identical syntax for 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`)For more on the language, more examples & comparisons with SQL, visit prql-lang.org. To experiment with PRQL in the browser, check out PRQL Playground.
After several months of building, PRQL is ready to use! Check out the 0.2 Release Notes!
To stay in touch with PRQL:
- Follow us on Twitter
- Join us on Discord
- Star this repo
- Contribute — join us in building PRQL, through writing code or inspiring others to use it. It's easy to get started — the project can be built in a couple of commands, and we're a really friendly community!
- PRQL Playground — experiment with PRQL in the browser.
- PRQL Book — the language documentation.
- dbt-prql — write PRQL in dbt models.
- Jupyter magic — run PRQL in Jupyter, either against a DB, or a Pandas DataFrame / CSV / Parquet file through DuckDB.
- PyPRQL Docs — the PyPRQL documentation, the python bindings to PRQL, including Jupyter magic.
- PRQL VSCode Extension
- PRQL-js — JavaScript bindings for PRQL.
Many thanks to those who've made our progress possible:
We have core developers who are responsible for reviewing code, making decisions on the direction of the language, and project administration:
- @aljazerzen — Aljaž Mur Eržen
- @max-sixty — Maximilian Roos
- @charlie-sanders — Charlie Sanders
We welcome others to join who have a track record of contributions.