duckdf

🦆 SQL for R dataframes, with ducks (by phillc73)

Duckdf Alternatives

Similar projects and alternatives to duckdf

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a better duckdf alternative or higher similarity.

duckdf reviews and mentions

Posts with mentions or reviews of duckdf. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-02-10.
  • DuckDB – in-process SQL OLAP database management system
    4 projects | news.ycombinator.com | 10 Feb 2023
    Quite a while ago, when duckdb was just a duckling, I wrote an R package that supported direct manipulation of R dataframes using SQL.[1] duckdb was the engine for this.

    The approach was never as fast as data.table but did approach the speed of dplyr for more complex queries.

    Life had other things in store for me and I haven’t touched this library for a while now.

    At the time there was no Julia connector for duckdb, but now that there is, I’d like to try this approach in that language.

    [1] https://github.com/phillc73/duckdf

  • ClickHouse as an alternative to Elasticsearch for log storage and analysis
    13 projects | news.ycombinator.com | 2 Mar 2021
    Yeah, I agree sqldf is quite slow. Fair point.

    As you've seen, duckdb registers an "R data frame as a virtual table." I'm not sure what they mean by "yet" either.

    Of course it is possible to write an R dataframe to an on-disk duckdb table, if that's what you want to do.

    There are some simple benchmarks on the bottom of the duckdf README[1]. Essentially I found for basic SQL SELECT queries, dplyr is quicker, but for much more complex queries, the duckdf/duckdb combination performs better.

    If you really want speed of course, just use data.table.

    [1] https://github.com/phillc73/duckdf

  • Julia 1.6: what has changed since Julia 1.0?
    9 projects | news.ycombinator.com | 14 Feb 2021
    That's a really good point that I'd not really thought about. I'd never really considered the difference between calling just functions versus macros.

    Thinking about Query.jl and DataFramesMeta.jl, and I am for sure not an expert in either, I can't specifically speak to your `head` example, but other base functions can be combined with macros. For example, see the LINQ examples from DataFramesMeta.jl[1] where `mean` is being used. Or again the LINQ style examples in Query.jl[2], where `descending` is used in the first example, or `length` later in the Grouping examples.

    Is that the kind of thing you meant?

    For whatever reason, with the way my brain is wired, the LINQ style of query just works for me. I have never directly used LINQ, but do have some SQL experience. In fact, I wrote some dinky little wrapper functions[3] around duckdb[4] so I could directly query R dataframes and datatables with SQL using that backend, rather than sqldf[5].

    [1] https://juliadata.github.io/DataFramesMeta.jl/stable/#@linq-...

    [2] https://www.queryverse.org/Query.jl/stable/linqquerycommands...

    [3] https://github.com/phillc73/duckdf

    [4] https://duckdb.org/

    [5] https://cran.r-project.org/web/packages/sqldf/index.html

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