PyCall.jl
Slick
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PyCall.jl | Slick | |
---|---|---|
28 | 17 | |
1,438 | 2,637 | |
1.2% | 0.2% | |
6.1 | 8.7 | |
about 2 months ago | 7 days ago | |
Julia | Scala | |
MIT License | BSD 2-clause "Simplified" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
PyCall.jl
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I just started into Julia for ML
For point 3 you can use https://github.com/cjdoris/PythonCall.jl or https://github.com/JuliaPy/PyCall.jl (and their respective Python sister packages).
- The Mojo Programming Language: A Python Superset Drawing from Rust's Strengths
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Calling Chapel, Carbon, and zig code in Julia
PyCall.jl is really handy. Are there any similar projects for calling Chapel code, or Carbon/zig?
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Am I dumb in thinking I can use Rust as a Fast Python and leave it at that?
Julia and Python interop should not be a problem at all. Actually Julia has one of the best interops I’ve ever seen, so much that swift copied it. https://github.com/JuliaPy/PyCall.jl
- Which tools do you use for python + Data Science?
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I don't want to abandon Rust for Julia
One small note, julia also has great python interop via PyCall.jl
- Faster Python calculations with Numba: 2 lines of code, 13× speed-up
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Interoperability in Julia
It is possible to call Python from Julia using PyCall. Then to install PyCall, run the command in the Julia REPL.
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Why is Python so used in the machine learning?
That said, you can run python modules in Julia. So you can just export your code as a module and then use it in Julia via the PyCall package. short description here github here <— you’d just add the pacakge via the really nice package manager built into julia, but for link for more detailed documentation
- Use rust code in Python with pyo3
Slick
- How many people/companies are fully on Scala 3?
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First Slick prerelease for Scala 3!
Made a PR on slick to document this https://github.com/slick/slick/pull/2760 (workaround is quite easy, you can just define def tupled = (apply _).tupled in the companion object of the case class and it will also compile for all Scala versions).
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Sketch of a Post-ORM
The Scala ecosystem has a few ways to do composable type-safe query building, e.g. Slick[0] or more recently Quill[1]. . I believe both also have ways to do compile-time string interpolation (e.g. sql"""select * from users where id = ${user.id}""") which generate prepared statements (I know Slick does prepared statements. Quill has similar macros but I haven't looked into how safe they are to use).
[0] https://scala-slick.org/
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Slick 3.5.0-M3 has been released
Release notes at https://github.com/slick/slick/releases/tag/v3.5.0-M3
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Database abstraction library which allows a clean domain model
With all this in mind, I landed at the first candidate: slick from https://scala-slick.org/ that you all probably know.
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Scala 3 migration: 7 benefits that outweigh the risks
I think Slick's current priority is also getting in Scala 3 support: https://github.com/slick/slick/issues/2177
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Slick 3.4.x is here!
Future releases might not be announced here. To get notified, go to https://github.com/slick/slick, click the Watch dropdown button at the top, select Custom, check Releases, and click Apply.
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Is there any good resource for learning Slick (3.x)?
https://github.com/slick/slick/pull/2097 now I use slightly lower version of slick so this might be an upgrade that resolves (I do recall using it in 21 and it was still buggy and I filed a ticket, which I cannot find at the moment), but given a complex enough query (we have one in PROD which has tons of flexibility in terms of filters that can be passed in) but it also makes for complex code.
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Slick 3.4.0 is imminent
I started writing a reply but then I realized it would be long and depends on exactly what you mean, so maybe it's better to post the question in https://github.com/slick/slick/discussions/categories/questions?
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Scala: A Love Story
I purchased the very entertaining book Seven Languages in Seven Weeks. Although I found Haskell fascinating and tempting, I knew it was unrealistic to introduce it in our company. Scala on the other hand looked like it could be the holy grail: All the characteristics I was looking for, no need to abandon the JVM and its cornucopia of tools and libraries, and the possibility for coexistence with Java and therefore incremental adoption. After implementing some simple programs to identify any immediate risks of committing to the language and its ecosystem, I started to introduce Scala in customer projects. Luckily, I was fortunate enough to work with open-minded, curious, and ambitious team members who were also experienced enough to appreciate the benefits of the language. We immediately applied our experience with functional programming, and embraced immutability. Libraries like Slick and Akka HTTP (we actually started out with its predecessor, Spray) made building database-backed REST services a breeze. And the resulting code was robust and highly maintainable. Scala's expressive type system and type inference made it easy to build a restrictive, consistent domain model without bloating the code. There was virtually no overhead. Any boilerplate could be easily abstracted out. In the end, the application code felt natural, concise and elegant. Programming was fun again.
What are some alternatives?
py2many - Transpiler of Python to many other languages
doobie - Functional JDBC layer for Scala.
Revise.jl - Automatically update function definitions in a running Julia session
Quill - Compile-time Language Integrated Queries for Scala
julia - The Julia Programming Language
ScalikeJDBC - A tidy SQL-based DB access library for Scala developers. This library naturally wraps JDBC APIs and provides you easy-to-use APIs.
Genie.jl - 🧞The highly productive Julia web framework
Squeryl - A Scala DSL for talking with databases with minimum verbosity and maximum type safety
are-we-fast-yet - Are We Fast Yet? Comparing Language Implementations with Objects, Closures, and Arrays
Clickhouse-scala-client - Clickhouse Scala Client with Reactive Streams support
fast-ruby - :dash: Writing Fast Ruby :heart_eyes: -- Collect Common Ruby idioms.
Sorm - A functional boilerplate-free Scala ORM