empirical-lang
IndexedTables.jl
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empirical-lang | IndexedTables.jl | |
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6 | 2 | |
167 | 119 | |
0.0% | 1.7% | |
1.8 | 5.9 | |
about 3 years ago | 27 days ago | |
C++ | Julia | |
GNU General Public License v3.0 or later | MIT License |
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empirical-lang
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Code in Database vs. Code in Application
I'm interested in languages experimenting with integrating tables/dataframes and the relational model as native parts of the language
With how much of the code I've written that's been around representing entities and doing CRUD on collections of them it seems a bit wild not to have native "Table" types.
There's an HN user who has a neat language called "Empirical" based on this, and there's an active research language called "Flix" that has this as well. APL/K and other array langs feature a similar concept as I understand it.
https://www.empirical-soft.com/
https://flix.dev/
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Tell HN: Stop Accepting Shitty Interviews
The most used in finance is a few functions in pandas, like merge_asof(). It's pretty industry standard now.
https://pandas.pydata.org/docs/reference/api/pandas.merge_as...
The biggest open-source thing I've ever written is Empirical, a language for time-series analysis.
https://www.empirical-soft.com
I'm also top 1% on Stack Overflow. A lot of companies have found me through there.
https://stackoverflow.com/users/478288/chrisaycock
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Table Oriented Programming (2002)
I started Empirical with the goal of "q like Haskell". The end result went in a radically different direction, but the guiding light has always been to have a statically typed language where tables and queries are a first-class operation.
The source code is publicly available under AGPL with the Commons Clause:
https://github.com/empirical-soft/empirical-lang
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Scripting Languages of the Future
Some of the OP's observations are why I wrote Empirical [1].
- I wanted static types that felt dynamic, which meant being able to infer types from a file as long as the file path can be determined at compile time.
- I wanted Dataframes to be a first-class container since the high-level of abstraction leads to great performance when doing analytics.
Therefore, I created a language with statically typed Dataframes. Empirical can infer a CSV's type at compile time, namely because performs compile-time function evaluation automatically. The result is a language that runs easily in a REPL but can be used for long-running scripts without worrying about common programming errors.
[1] https://www.empirical-soft.com
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APL Compiler Based on Tail (Typed Array Intermediate Language)
I wanted to hit the runtime repeatedly with as little overhead as possible. So VVM has no type look-up, multiple operands per instruction, and a cache-efficient IR. The sum() operand for the above example is invoked directly in a loop almost as fast as hand-written C++.
VVM has its own assembly language [2]. I have blog post that explains some of the design choices [3].
[1] https://github.com/empirical-soft/empirical-lang/tree/master...
[2] https://github.com/empirical-soft/empirical-lang/tree/master...
[3] https://www.empirical-soft.com/2020/09/03/a-tour-of-the-vect...
IndexedTables.jl
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Table Oriented Programming (2002)
unfortunately, I don't have access to that code anymore, I wrote a number of loaders for different data set types including CSV. The time series were all modeled as forward iterating stream of tuples, so there is no specific table abstraction. There is an implicit assumption that the stream is ordered by the join key, in a time series this being the timestamp, though nothing in the implementation enforced that.
Joins are always n-way merge joins, so you can write something like y = 2x^2 - 3z and fold that into a single streaming operation y = f( x, z ) where y, x and z are time streams.
When rendered to screen they looked very similar to your examples. With plugins in the IDE you could directly plot and array of time series as a chart.
Since the time I wrote NamedTuples the Julia core team folded the functionality into the core of Julia https://docs.julialang.org/en/v1/manual/types/#Named-Tuple-T.... This is the core of https://juliadb.org/ all credit to the Julia core team
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How do I access a specific column/row based on the column name and/or row value with an indexed table?
I am talking about https://github.com/JuliaData/IndexedTables.jl. I’m just getting started with Julia so I might not really know what I’m doing right now lol
What are some alternatives?
passerine - A small extensible programming language designed for concise expression with little code.
Julia-DataFrames-Tutorial - A tutorial on Julia DataFrames package
Apache Geode - Apache Geode
Chain.jl - A Julia package for piping a value through a series of transformation expressions using a more convenient syntax than Julia's native piping functionality.
piccolo - An experimental stackless Lua VM implemented in pure Rust
julia - The Julia Programming Language
apltail - APL Compiler targeting a typed array intermediate language
Linq-in-Rust - Language Integrated Query in Rust.
smlsharp - SML# compiler
tail2futhark
xonsh - :shell: Python-powered, cross-platform, Unix-gazing shell.
Beef - Beef Programming Language