lambdo VS ballista

It will return the sum of all values in column A. For large tables it will take some time to compute the result. Now assume we append a new record and want to get the new result. The traditional approach is execute this query again. A better approach is to process this new record only by adding its value in A to the result of the previous query. It is important in (stateful) stream processing.

Something similar is implemented in these libraries which however rely on a different data processing conception (alternative to map-reduce):

https://github.com/asavinov/prosto - Functions matter! No join-groupby, No map-reduce.

https://github.com/asavinov/lambdo - Feature engineering and machine learning: together at last!

I find this project quite interesting because sklearn has a good general design including data transformations and it does make sense to provide compatible functionality for Go.

Feature engineering in general is a hot topic and especially if features are not simple hard-coded transformations but rather can be learned from data. For example, I developed a toolkit intended for combining feature engineering and ML:

    https://github.com/asavinov/lambdo - Feature engineering and machine learning: together at last!

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.

https://github.com/ballista-compute/ballista/blob/main/rust/executor/src/flight_service.rs#L193-L228

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?

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.

https://github.com/ballista-compute/ballista is also a cool project worth checking out.

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

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