DaemonMode.jl
ballista
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DaemonMode.jl | ballista | |
---|---|---|
22 | 20 | |
269 | 2,238 | |
- | - | |
4.7 | 9.3 | |
4 months ago | about 3 years ago | |
Julia | Rust | |
MIT License | Apache License 2.0 |
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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.
DaemonMode.jl
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Potential of the Julia programming language for high energy physics computing
Thats for an entry point, you can search `Base.@main` to see a little summary of it. Later it will be able to be callable with `juliax` and `juliac` i.e. `~juliax test.jl` in shell.
DynamicalSystems looks like a heavy project. I don't think you can do much more on your own. There have been recent features in 1.10 that lets you just use the portion you need (just a weak dependency), and there is precompiletools.jl but these are on your side.
You can also look into https://github.com/dmolina/DaemonMode.jl for running a Julia process in the background and do your stuff in the shell without startup time until the standalone binaries are there.
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Julia 1.9.0 lives up to its promise
> If I were to use e.g. Rust with polars, load time would be virtually none.
Because you're compiling...
And if you need to do the same in Julia, you should also pre-compile or some other method like https://github.com/dmolina/DaemonMode.jl (their demo shows loading a database, with subsequent loads after the first one taking roughly ~0.2% of the first)
- Administrative Scripting with Julia
- GNU Octave 8.1
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Ask HN: Why is Julia so underrated?
Well, not nicely certainly, but:
https://github.com/dmolina/DaemonMode.jl
> portable
Neither is python - it just relies on universal availability. Over time…
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Is Julia suitable today as a scripting language?
You can get around a lot of these problems with DaemonMode.jl though.
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Julia performance, startup.jl, and sysimages
You might want DaemonMode.jl
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Can I execute code in Julia REPL if I'm connected to a remote server?
https://github.com/dmolina/DaemonMode.jl can possibly help in the future. Leaving it here so that people know this is planned.
- Ask HN: Why hasn't the Deep Learning community embraced Julia yet?
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Compile for faster execution?
If you strongly prefer to run scripts though, then you can use the package https://github.com/dmolina/DaemonMode.jl in order to re-use a Julia session between multiple scripts, saving you recompilation time.
ballista
- Ballista: Distributed compute platform implemented in Rust using Apache Arrow.
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Open source contributions for a Data Engineer?
His newer project, Ballista, was also donated to Apache Arrow. I hope to get the Rust skills to collaborate with him on open source work someday too. He's also doing really cool work on spark-rapids FYI.
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Best format to use for DataFrames in Rust and Python?
https://github.com/ballista-compute/ballista/blob/main/rust/executor/src/flight_service.rs#L193-L228
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I wrote one of the fastest DataFrame libraries
I'm guessing Polars and Ballista (https://github.com/ballista-compute/ballista) have different goals, but I don't know enough about either to say what those might be. Does anyone know enough about either to explain the differences?
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Introducing Kamu - World's first global collaborative data pipeline
In your article you mention looking for a faster data engine, have you looked at Ballista https://github.com/ballista-compute/ballista? It’s pretty young but it uses the Apache Arrow memory model and the maintainer did a bunch of work on Apache Spark I believe.
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Rust for DE?
https://github.com/ballista-compute/ballista is also a cool project worth checking out.
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Julia: A Post-Mortem
It’s mostly a personal favourite, but once Ballista [1] gets a bit more developed, I expect we’ll tear out our Java/Spark pipelines and replace them with that.
The ML ecosystem in Rust is a bit underdeveloped at the moment, but work is ticking along on packages like Linfa and SmartCore, so maybe it’ll get there? In my field I’m mostly about it’s potential for correct, high-performance data pipelines that are straightforward to write in reasonable time, and hopefully a model-serving framework: I hate that so many of the current tools require annotating and shipping Python when really model-serving shouldn’t really need any Python code.
[1] https://github.com/ballista-compute/ballista
- Ballista 0.4.0
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Why isn't differential dataflow more popular?
I've looked at this and thought it looked amazing, but also haven't used it for anything. Some thoughts...
Rust is a blessing and curse. I seems like the obvious choice for data pipelines, but everything big currently exists in Java and the small stuff is in Javascript, Python or R. Maybe this will slowly change, but it's a big ship to turn. I'm hopeful that tools like this and Balista [1] will eventually get things moving.
Since the Rust community is relatively small, language bindings would be very helpful. Being able to configure pipelines from Java or Typescript(!) would be great.
Or maybe it's just that this form of computation is too foreign. By the time you need it, the project is so large that it's too late to redesign it to use it. I'm also unclear on how it would handle changing requirements and recomputing new aggregations over old data. Better docs with more convincing examples would be helpful here. The GitHub page showing counting isn't very compelling.
[1] https://github.com/ballista-compute/ballista
- ballista-compute/ballista proof-of-concept distributed compute platform primarily implemented in Rust, using Apache Arrow as the memory model.
What are some alternatives?
julia - The Julia Programming Language
spark-rapids - Spark RAPIDS plugin - accelerate Apache Spark with GPUs
Makie.jl - Interactive data visualizations and plotting in Julia
differential-dataflow - An implementation of differential dataflow using timely dataflow on Rust.
HTTP.jl - HTTP for Julia
delta-rs - A native Rust library for Delta Lake, with bindings into Python
FromFile.jl - Julia enhancement proposal (Julep) for implicit per file module in Julia
dagster - An orchestration platform for the development, production, and observation of data assets.
julia-numpy-fortran-test - Comparing Julia vs Numpy vs Fortran for performance and code simplicity
Prefect - The easiest way to build, run, and monitor data pipelines at scale.
DataFramesMeta.jl - Metaprogramming tools for DataFrames
roapi - Create full-fledged APIs for slowly moving datasets without writing a single line of code.