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 each column to a value
max salary,
min salary,
count, # Trailing 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 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`)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.
PRQL is being actively developed by a growing community. It's ready to use by the intrepid, either with our supported integrations, or within your own tools, using one of our supported language bindings.
PRQL still has some minor bugs and some missing features, and probably is only ready to be rolled out to non-technical teams for fairly simple queries.
Here's our current Roadmap and our Milestones.
Our immediate focus for the code is on:
- Building out the next few big features, including types and modules.
- Ensuring our supported features feel extremely robust; resolving any priority bugs.
We're also spending time thinking about:
- Making it really easy to start using PRQL. We're doing that by building integrations with tools that folks already use; for example our VS Code extension & Jupyter integration. If there are tools you're familiar with that you think would be open to integrating with PRQL, please let us know in an issue.
- Making it easier to contribute to the compiler. We have a wide group of contributors to the project, but contributions to the compiler itself are quite concentrated. We're keen to expand this; #1840 for feedback.
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 (send us your use-cases!), or inspiring others to use it.
- See the development documentation for PRQL. 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 VS Code extension
- prql-js — JavaScript bindings for PRQL.
This repo is composed of:
- prql-compiler — the compiler, written in rust, whose
main role is to compile PRQL into SQL. It also includes
prqlc, the CLI. - web — our web content: the Book, Website, and Playground.
- bindings — bindings from various languages to
prql-compiler.
It also contains our testing / CI infrastructure and development tools. Check out our development docs for more details.
Many thanks to those who've made our progress possible: