PRQL

Pipelined Relational Query Language, pronounced "Prequel".

PRQL is a modern language for transforming data — a simpler and more powerful SQL. 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 transpiles to SQL.

PRQL was discussed on Hacker News and Lobsters earlier this year when it was just a proposal.

Current status

PRQL just hit 0.1! This means:

• It works™, for basic transformations such as filter, select, aggregate, take, sort, & join. Variables (derive), functions (func) and CTEs (table) work.
  • More advanced language features are forthcoming, like better inline pipelines, window clauses, and arrays.
• It's not friendly at the moment:
  • It runs from a CLI only, taking input from a file or stdin and writing to a file or stdout.
  • Error messages are bad.
  • For an interactive experience, combine with a tool like Up.
• The documentation is lacking.
  • Our current top priority is to have some decent documentation #233.
• It doesn't support changing the dialect.
• It has bugs. Please report them!
• It has sharp corners. Please report grazes!
• We'll release backward-incompatible changes. The versioning system for the language is not yet implemented.

Installation

PRQL can be installed with cargo, or built from source.

cargo install prql

Usage

$ echo "from employees | filter has_dog" | prql compile

SELECT
  *
FROM
  employees
WHERE
  has_dog

See below for fuller examples of PRQL.

Python implementation

There is a python implementation at qorrect/PyPrql, which can be installed with pip install pyprql. It has some great features, including a native interactive console with auto-complete for column names.

An example

Here's a fairly simple SQL query:

SELECT TOP 20
    title,
    country,
    AVG(salary) AS average_salary,
    SUM(salary) AS sum_salary,
    AVG(salary + payroll_tax) AS average_gross_salary,
    SUM(salary + payroll_tax) AS sum_gross_salary,
    AVG(salary + payroll_tax + benefits_cost) AS average_gross_cost,
    SUM(salary + payroll_tax + benefits_cost) AS sum_gross_cost,
    COUNT(*) as count
FROM employees
WHERE salary + payroll_tax + benefits_cost > 0 AND country = 'USA'
GROUP BY title, country
ORDER BY sum_gross_cost
HAVING count > 200

Even this simple query demonstrates some of the problems with SQL's lack of abstractions:

• Unnecessary repetition — the calculations for each measure are repeated, despite deriving from a previous measure. The repetition in the WHERE clause obfuscates the meaning of the expression.
• Functions have multiple operators — HAVING & WHERE are fundamentally similar operations applied at different stages of the pipeline but SQL's lack of pipeline-based precedence requires it to have two different operators.
• Operators have multiple functions — the SELECT operator both creates new aggregations, and selects which columns to include.
• Awkward syntax — when developing the query, commenting out the final line of the SELECT list causes a syntax error because of how commas are handled, and we need to repeat the columns in the GROUP BY clause in the SELECT list.

Here's the same query with PRQL:

from employees
filter country = "USA"                           # Each line transforms the previous result.
derive [                                         # This adds columns / variables.
  gross_salary: salary + payroll_tax,
  gross_cost:   gross_salary + benefits_cost     # Variables can use other variables.
]
filter gross_cost > 0
aggregate by:[title, country] [                  # `by` are the columns to group by.
    average salary,                              # These are aggregation calcs run on each group.
    sum     salary,
    average gross_salary,
    sum     gross_salary,
    average gross_cost,
    sum_gross_cost: sum gross_cost,
    ct: count,
]
sort sum_gross_cost
filter ct > 200
take 20

As well as using variables to reduce unnecessary repetition, the query is also more readable — it flows from top to bottom, each line representing a transformation of the previous line's result. For example, TOP 20 / take 20 modify the final result in both queries — but only PRQL represents it as the final transformation. And context is localized — the aggregate function contains both the calculations and the columns to group by.

While PRQL is designed for reading & writing by people, it's also much simpler for code to construct or edit PRQL queries. In SQL, adding a filter to a query involves parsing the query to find and then modify the WHERE statement, or wrapping the existing query in a CTE. In PRQL, adding a filter just involves adding a filter transformation to the final line.

An example using Functions

Here's another SQL query, which calculates returns from prices on days with valid prices.

SELECT
  date,
  -- Can't use a `WHERE` clause, as it would affect the row that the `LAG` function referenced.
  IF(is_valid_price, price_adjusted / LAG(price_adjusted, 1) OVER
    (PARTITION BY sec_id ORDER BY date) - 1 + dividend_return, NULL) AS return_total,
  IF(is_valid_price, price_adjusted_usd / LAG(price_adjusted_usd, 1) OVER
    (PARTITION BY sec_id ORDER BY date) - 1 + dividend_return, NULL) AS return_usd,
  IF(is_valid_price, price_adjusted / LAG(price_adjusted, 1) OVER
    (PARTITION BY sec_id ORDER BY date) - 1 + dividend_return, NULL)
    - interest_rate / 252 AS return_excess,
  IF(is_valid_price, price_adjusted_usd / LAG(price_adjusted_usd, 1) OVER
    (PARTITION BY sec_id ORDER BY date) - 1 + dividend_return, NULL)
    - interest_rate / 252 AS return_usd_excess
FROM prices

This might seem like a convoluted example, but it's taken from a real query. Indeed, it's also simpler and smaller than the full logic — note that it starts from price_adjusted, whose logic had to be split into a previous query to avoid the SQL becoming even less readable.

Here's the same query with PRQL:

prql version:0.1 db:snowflake                         # Version number & database name.

func lag_day x = (
  window x
  by sec_id
  sort date
  lag 1
)
func ret x = x / (x | lag_day) - 1 + dividend_return
func excess x = (x - interest_rate) / 252
func if_valid x = is_valid_price ? x : null

from prices
derive [
  return_total:      prices_adj   | ret | if_valid  # `|` can be used rather than newlines.
  return_usd:        prices_usd   | ret | if_valid
  return_excess:     return_total | excess
  return_usd_excess: return_usd   | excess
]
select [
  date,
  sec_id,
  return_total,
  return_usd,
  return_excess,
  return_usd_excess,
]

Because we define the functions once rather than copying & pasting the code, we get all the benefits of encapsulation and extensibility — we can have reliable & tested functions, whose purpose is explicit, which we can share across queries and colleagues.

Principles

PRQL is intended to be a modern, simple, declarative language for transforming data, with abstractions such as variables & functions. It's intended to replace SQL, but doesn't have ambitions as a general-purpose programming language. While it's at a pre-alpha stage, it has some immutable principles:

• Pipelined — PRQL is a linear pipeline of transformations — each line of the query is a transformation of the previous line's result. This makes it easy to read, and simple to write. This is also known as "point-free style".
• Simple — PRQL serves both sophisticated engineers and analysts without coding experience. By providing simple, clean abstractions, the language can be both powerful and easy to use.
• Compatible — PRQL transpiles to SQL, so it can be used with any database that uses SQL, and with any existing tools or programming languages that manage SQL. PRQL should allow for a gradual onramp — it should be practical to mix SQL into a PRQL query where PRQL doesn't yet have an implementation. Where possible PRQL can unify syntax across databases.
• Analytical — PRQL's focus is analytical queries; we de-emphasize other SQL features such as inserting data or transactions.
• Extensible — PRQL can be extended through its abstractions, and can evolve without breaking backward-compatibility, because its queries can specify their PRQL version.

Roadmap

I'm excited and inspired by the level of enthusiasm behind the project, both from individual contributors and the broader community of users who are unsatisfied with SQL. We currently have an initial working version for the intrepid early user.

I'm hoping we can build a beautiful language, an app that's approachable & powerful, and a vibrant community. Many projects have reached the current stage and fallen, so this requires compounding on what we've done so far.

Language design

Already since becoming public, the language has improved dramatically, thanks to the feedback of dozens of contributors. The current state of the basics is now stable and while we'll hit corner-cases, I expect we'll only make small changes to the existing features — even as we continue adding features.

Feel free to post questions or continue discussions on Language Design Issues.

Documentation

Currently the documentation exists in tests, examples, docs.rs and some Notes below.

If you're up for contributing and don't have a preference for writing code or not, this is the area that would most benefit from your contribution. Issues are tagged with documentation.

Friendliness

Currently the language is not friendly, as described in Current status. We'd like to make error messages better, sand off sharp corners, etc.

Both bug reports of unfriendliness, and code contributions to improve them are welcome; there's a friendliness label.

Fast feedback

As well as a command-line tool that transpiles queries, we'd like to make developing in PRQL a wonderful experience, where it feels like it's on your side:

• Syntax highlighting in editors.
• A live transpiler in a browser, including compiling to wasm #175.
• Initial type-inference, where it's possible without connecting to the DB, e.g. #55.
• (I'm sure there's more, ideas welcome)

Database cohesion

One benefit of PRQL over SQL is that auto-complete, type-inference, and error checking can be much more powerful.

This is harder to build, since it requires a connection to the database in order to understand the schema of the table.

Not in focus

We should focus on solving a distinct problem really well. PRQL's goal is to make reading and writing analytical queries easier, and so for the moment that means putting some things out of scope:

• Building infrastructure outside of queries, like lineage. dbt is excellent at that! (#13).
• Writing DDL / index / schema manipulation / inserting data (#16).
• Add typing into the syntax (#15) (though type inference is a goal above, and this could be a useful extension at some point).

Interested in joining?

If you're interested in joining the community to build a better SQL, there are lots of ways of contributing; big and small:

• Star this repo.
• Send the repo to a couple of people whose opinion you respect.
• Subscribe to Issue #1 for updates.
• Join the Discord.
• Contribute towards the code. There are many ways of contributing, for any level of experience with rust. And if you have rust questions, there are lots of people on the Discord who will patiently help you.
  • Find an issue labeled help wanted or good first issue and try to fix it. Feel free to PR partial solutions, or ask any questions on the Issue or Discord.
  • Build the code, find examples that yield incorrect results, and post a bug report.
  • Start with something tiny! Write a test / write a docstring / make some rust nicer — it's a great way to get started in 30 minutes.

Any of these will inspire others to spend more time developing this; thank you in advance.

Development environment

Setting up a local dev environment is simple, thanks to the rust ecosystem:

• Install rustup & cargo.
• That's it! Running cargo test should complete successfully.
• For more advanced development; e.g. adjusting insta outputs or compiling for web, run the commands in Taskfile.yml, either with copy & paste or by installing Task and running task install-dev-tools.
• For quick contributions, hit . in GitHub to launch a github.dev instance.
• Any problems: post an issue and we'll help.

Inspired by

• dplyr is a beautiful language for manipulating data, in R. It's very similar to PRQL. It only works on in-memory R data.
  • There's also dbplyr which compiles a subset of dplyr to SQL, though requires an R runtime.
• Kusto is also a beautiful pipelined language, very similar to PRQL. But it can only use Kusto-compatible DBs.
  • A Kusto-to-SQL transpiler would be a legitimate alternative to PRQL, though there would be some impedance mismatch in some areas. My central criticism of Kusto is that it gives up broad compatibility without getting that much in return.
• Against SQL gives a fairly complete description of SQL's weaknesses, both for analytical and transactional queries. @jamii consistently writes insightful pieces, and it's worth sponsoring him for his updates.
• Julia's DataPipes.jl & Chain.jl, which demonstrate how effective point-free pipelines can be, and how line-breaks can work as pipes.
• OCaml, for its elegant and simple syntax.

Similar projects

• Ecto is a sophisticated ORM library in Elixir which has pipelined queries as well as more traditional ORM features.
• Morel is a functional language for data, also with a pipeline concept. It doesn't compile to SQL but states that it can access external data.
• Malloy from Looker & @lloydtabb in a new language which combines a declarative syntax for querying with a modelling layer.
• FunSQL.jl is a library in Julia which compiles a nice query syntax to SQL. It requires a Julia runtime.
• LINQ, is a pipelined language for the .NET ecosystem which can (mostly) compile to SQL. It was one of the first languages to take this approach.
• Sift is an experimental language which heavily uses pipes and relational algebra.
• After writing this proposal (including the name!), I found Preql. Despite the similar name and compiling to SQL, it seems to focus more on making the language python-like, which is very different to this proposal.

If any of these descriptions can be improved, please feel free to PR changes.

Our contributors

Many thanks to those who've made our progress possible: