Here are some of the most common questions we hear. Have something else you’d like to ask? Pop by our Discord and ask away!

Cool story Hansel, but what can I actually do with PRQL now?

PRQL is ready to use by the intrepid, either with our supported integrations, or within your own tools, using one of our supported language bindings. The easiest way is with our integrations:

  • Prototype your PRQL queries in the Playground or the VS Code extension and copy/paste the resulting SQL into your database. It’s not the perfect workflow, but it’s easy to get started.
  • Jupyter allows writing PRQL in a Jupyter notebook or IPython repl, with a %%prql magic. As well as connecting to existing DBs, our integration with DuckDB enables querying pandas dataframes, CSVs & Parquet files, and writing the output to a dataframe.
  • DuckDB extension — is a DuckDB extension which allows querying a DuckDB instance with PRQL.

It’s also possible to query PRQL from your code with our bindings for R, Rust, Python & JS. For an example of using PRQL with DuckDB, check out Querying with PRQL.

Something here reminds me of another project, did you take the idea from them?

Yes, probably. We’re standing on the shoulders of giants:

  • 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, similar to PRQL. But it can only use Kusto-compatible DBs.
  • 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. There are other similar pieces out there.
  • Julia’s DataPipes.jl & Chain.jl demonstrate how effective point-free pipelines can be, and how line breaks can work as pipes.
  • OCaml’s elegant and simple syntax serves as inspiration.

And there are many projects similar to PRQL:

  • 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.
  • EdgeDB is an alternative to SQL focused on traditional transactional workloads (as opposed to PRQL’s focus on analytical workloads). Their post We can do better than SQL contains many of the criticisms of SQL that inspired PRQL.
  • 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.

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

How is PRQL different from all the projects that SQL has defeated?

Many languages have attempted to replace SQL, and yet SQL has massively grown in usage and importance in the past decade. There are lots of reasonable critiques on these attempts. So a reasonable question is “Why are y’all building something that many others have failed at?”. Some thoughts:

  • PRQL is open. It’s not designed for a specific database. PRQL will always be fully open-source. There will never be a commercial product. We’ll never have to balance profitability against compatibility, or try and expand up the stack to justify a valuation. Whether someone is building a new tool or writing a simple query — PRQL can be more compatible across DBs than SQL.
  • PRQL is analytical. The biggest growth in SQL usage over the past decade has been from querying large amounts of data, often from analytical DBs that are specifically designed for this — with columnar storage and wide denormalized tables. SQL carries a lot of unrelated baggage, and focusing on the analytical use-case lets us make a better language.
  • PRQL is simple. There’s often a tradeoff between power and accessibility — e.g. rust is powerful vs. Excel is accessible — but there are also instances where we can expand the frontier. PRQL’s orthogonality is an example of synthesizing this tradeoff — have a single filter rather than WHERE & HAVING & QUALIFY brings both more power and more accessibility.

In the same way that “SQL was invented in the 1970s and therefore must be bad” is questionable logic, “n languages have tried and failed so therefore SQL cannot be improved.” suffers a similar fallacy. SQL isn’t bad because it’s old. It’s bad because — in some cases — it’s bad.

Which databases does PRQL work with?

PRQL compiles to SQL, so it’s compatible with any database that accepts SQL.

A query’s dialect can be explicitly specified, allowing for dialect-specific SQL to be generated. See the Dialect docs for more info; note that there is currently very limited implementation of this, and most dialects’ implementation are identical to a generic implementation.

What’s this aggregate function?

…and why not just use SELECT & GROUP BY?

SQL uses SELECT for all of these:

  • Selecting or computing columns, without changing the shape of the data:

    SELECT x * 2 FROM y as two_x
  • Reducing a column into a single row, with a reduction function:

  • Reducing a column into groups, with a reduction function and a GROUP BY function:


These are not orthogonal — SELECT does lots of different things depending on the context. It’s difficult for both people and machines to evaluate the shape of the output. It’s easy to mix meanings and raise an error (e.g. SELECT x, MIN(y) FROM z).

PRQL clearly delineates two operations with two transforms:

  • select — picking & calculating columns. These calculations always produce exactly one output row for every input row.

    from employees
    select name = f"{first_name} {last_name}"
  • aggregate — reducing multiple rows to a single row, with a reduction function like sum or min.

    from employees
    aggregate [total_salary = sum salary]

aggregate can then be used in a group transform, where it has exactly the same semantics on the group as it would on a whole table — another example of PRQL’s orthogonality.

from employees
group department (
  aggregate [total_salary = sum salary]

While you should be skeptical of new claims from new entrants Hadley Wickham, the developer of Tidyverse commented in a discussion on PRQL:

FWIW the separate group_by() is one of my greatest design regrets with dplyr — I wish I had made by a parameter of summarise(), mutate(), filter() etc.

For more detail, check out the docs in the PRQL Book.

Can PRQL write to databases?

PRQL is focused on analytical queries, so we don’t currently support writing or modifying data in databases. However, PRQL queries can be used to generate SQL statements that write to databases. For example, surround the SQL output of a PRQL query in CREATE OR REPLACE TABLE foo AS (...).

Is it ‘PRQL’ or ‘prql’ or ‘Prql’?

It’s PRQL, since it’s a backronym! We name the repo and some libraries prql because of a strong convention around lowercase, but everywhere else we use PRQL.

Where can I find the logos?

See our press materials.