ballista

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?

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

Apache Arrow is a specification for in-memory columnar data, IPC format + Flight protocol, with implementations in a number of languages. Some of the implementations contain code to perform computations on the in-memory data. Some of the implementations contain some form of query engine. All of these are single process / libraries, rather than distributed systems. Apache Spark is a distributed compute platform, which does have some support for Arrow for interop purposes. One of the things that led me to get involved in Arrow originally was to explore the idea of building something like Apache Spark based on Arrow (and Rust) and my latest prototype of that concept is in the Ballista project [1]. [1] https://github.com/ballista-compute/ballista

There is also https://github.com/ballista-compute/ballista (shameless plug for my own project)

Arrow is tightly integrated in Spark's Scala code: https://github.com/apache/spark/search?q=arrow&unscoped_q=ar... Andy Grove is building a "distributed compute platform implemented in Rust, using the Apache Arrow memory model": https://github.com/ballista-compute/ballista. Seems possible.