docs/source/user-guide/distributed/introduction.md

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# Overview

Ballista is a distributed compute platform primarily implemented in Rust, and powered by Apache Arrow. It is
built on an architecture that allows other programming languages to be supported as first-class citizens without paying
a penalty for serialization costs.

The foundational technologies in Ballista are:

- [Apache Arrow](https://arrow.apache.org/) memory model and compute kernels for efficient processing of data.
- [Apache Arrow Flight Protocol](https://arrow.apache.org/blog/2019/10/13/introducing-arrow-flight/) for efficient data transfer between processes.
- [Google Protocol Buffers](https://developers.google.com/protocol-buffers) for serializing query plans.
- [DataFusion](https://github.com/apache/arrow-datafusion/) for query execution.

## How does this compare to Apache Spark?

Although Ballista is largely inspired by Apache Spark, there are some key differences.

- The choice of Rust as the main execution language means that memory usage is deterministic and avoids the overhead
  of GC pauses.
- Ballista is designed from the ground up to use columnar data, enabling a number of efficiencies such as vectorized
  processing (SIMD and GPU) and efficient compression. Although Spark does have some columnar support, it is still
  largely row-based today.
- The combination of Rust and Arrow provides excellent memory efficiency and memory usage can be 5x - 10x lower than
  Apache Spark in some cases, which means that more processing can fit on a single node, reducing the overhead of
  distributed compute.
- The use of Apache Arrow as the memory model and network protocol means that data can be exchanged between executors
  in any programming language with minimal serialization overhead.

## Status

Ballista is still in the early stages of development but is capable of executing complex analytical queries at scale.