Against SQL

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  • Preql

    An interpreted relational query language that compiles to SQL.

    I share the author's point of view, which led me to start a new relational programming language that compiles to SQL. If that sounds interesting, you can find it here:

  • opaleye

    The only way out that I can see is to design embedded domain specific languages (EDSLs) that inherit the expressiveness, composability and type safety from the host language. That's what Opaleye and Rel8 (Postgres EDSLs for Haskell do. Haskell is particularly good for this. The query language can be just a monad and therefore users can carry all of their knowledge of monadic programming to writing database queries.

    This approach doesn't resolve all of the author's complaints but it does solve many.

    Disclaimer: I'm the author of Opaleye. Rel8 is built on Opaleye. Other relational query EDSLs are available.


  • Sonar

    Write Clean Python Code. Always.. Sonar helps you commit clean code every time. With over 225 unique rules to find Python bugs, code smells & vulnerabilities, Sonar finds the issues while you focus on the work.

  • rel8

    Hey! Hey! Can u rel8?

  • prosto

    Prosto is a data processing toolkit radically changing how data is processed by heavily relying on functions and operations with functions - an alternative to map-reduce and join-groupby

    One alternative to SQL (type of thinking) is Column-SQL [1] which is based on a new data model. This model is relies on two equal constructs: sets (tables) and functions (columns). It is opposed to the relational algebra which is based on only sets and set operations. One benefit of Column-SQL is that it does not use joins and group-by for connectivity and aggregation, respectively, which are known to be quite difficult to understand and error prone in use. Instead, many typical data processing patterns are implemented by defining new columns: link columns instead of join, and aggregate columns instead of group-by.

    More details about "Why functions and column-orientation" (as opposed to sets) can be found in [2]. Shortly, problems with set-orientation and SQL are because producing sets is not what we frequently need - we need new columns and not new table. And hence applying set operations is a kind of workaround due the absence of column operations.

    This approach is implemented in the Prosto data processing toolkit [0] and Column-SQL[1] is a syntactic way to define its operations.

    [0] Prosto is a data processing toolkit - an alternative to map-reduce and join-groupby

    [1] Column-SQL (work in progress)

    [2] Why functions and column-orientation?

  • PostgreSQL

    Mirror of the official PostgreSQL GIT repository. Note that this is just a *mirror* - we don't work with pull requests on github. To contribute, please see

  • jrutil

    I've also tried to rewrite some of my challenging queries [0] in my hypothetical syntax and while I think your observation about pipeline length is correct, the result still came out much better than SQL. Frankly, even in F#, most of my pipelines are around 5 functions too. In my view, pipelines are just a convenient mental model. I'd love to see your sketches, here are my (very WIP) concepts: [1]

    [0]: for example this monstrosity:

  • snippets

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  • fquery

    A graph query engine (by adsharma)

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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