db-benchmark
TypedTables.jl
db-benchmark | TypedTables.jl | |
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12 | 2 | |
126 | 144 | |
2.4% | 0.7% | |
8.0 | 5.2 | |
5 months ago | 4 months ago | |
R | Julia | |
Mozilla Public License 2.0 | GNU General Public License v3.0 or later |
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db-benchmark
- Database-Like Ops Benchmark
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Polars
DuckDB maintains a benchmark of open source database-like tools, including Polars and Pandas
https://duckdblabs.github.io/db-benchmark/
- Planning a New Benchmarking for Comparing Filter2Groupby for 3,000 Files (100,000 Rows/Files)
- Pandas vs. Julia – cheat sheet and comparison
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Polars supports SQL statement in Python Plus CLI Verion (Polars.exe 24.4MB)
DuckDB is also a SQL/Python app, refer to this benchmark, seem it run very fast https://duckdblabs.github.io/db-benchmark/
- The Return of the H2o.ai Database-Like Ops Benchmark
- I discovered that the fastest way to create a Pandas DataFrame from a CSV file is to actually use Polars
TypedTables.jl
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Pandas vs. Julia – cheat sheet and comparison
Indeed DataFrames.jl isn't and won't be the fastest way to do many things. It makes a lot of trade offs in performance for flexibility. The columns of the dataframe can be any indexable array, so while most examples use 64-bit floating point numbers, strings, and categorical arrays, the nice thing about DataFrames.jl is that using arbitrary precision floats, pointers to binaries, etc. are all fine inside of a DataFrame without any modification. This is compared to things like the Pandas allowed datatypes (https://pbpython.com/pandas_dtypes.html). I'm quite impressed by the DataFrames.jl developers given how they've kept it dynamic yet seem to have achieved pretty good performance. Most of it is smart use of function barriers to avoid the dynamism in the core algorithms. But from that knowledge it's very clear that systems should be able to exist that outperform it even with the same algorithms, in some cases just by tens of nanoseconds but in theory that bump is always there.
In the Julia world the one which optimizes to be fully non-dynamic is TypedTables (https://github.com/JuliaData/TypedTables.jl) where all column types are known at compile time, removing the dynamic dispatch overhead. But in Julia the minor performance gain of using TypedTables vs the major flexibility loss is the reason why you pretty much never hear about it. Probably not even worth mentioning but it's a fun tidbit.
> For what it's worth, data.table is my favourite to use and I believe it has the nicest ergonomics of the three I spoke about.
I would be interested to hear what about the ergonomics of data.table you find useful. if there are some ideas that would be helpful for DataFrames.jl to learn from data.table directly I'd be happy to share it with the devs. Generally when I hear about R people talk about tidyverse. Tidier (https://github.com/TidierOrg/Tidier.jl) is making some big strides in bringing a tidy syntax to Julia and I hear that it has had some rapid adoption and happy users, so there are some ongoing efforts to use the learnings of R API's but I'm not sure if someone is looking directly at the data.table parts.
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I wrote one of the fastest DataFrame libraries
Not that I am a heavy DataFrame user, but I have felt more at home with the comparatively light-weight TypeTables [1]. My understanding is that the rather complicated DataFrame ecosystem in Julia [2] mostly stems from whether tables should be immutable and/or typed. As far as I am aware there has not been any major push at the compiler level to speed up untyped code yet – although there should be plenty of room for improvements – which I suspect would benefit DataFrames greatly.
[1]: https://github.com/JuliaData/TypedTables.jl
[2]: https://typedtables.juliadata.org/stable/man/table/#datafram...
What are some alternatives?
Tidier.jl - Meta-package for data analysis in Julia, modeled after the R tidyverse.
vaex - Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀
DataFramesMeta.jl - Metaprogramming tools for DataFrames
data.table - R's data.table package extends data.frame:
db-benchmark - reproducible benchmark of database-like ops
rust-dataframe - A Rust DataFrame implementation, built on Apache Arrow
ballista - Distributed compute platform implemented in Rust, and powered by Apache Arrow.