Ballista is a distributed SQL query engine primarily implemented in Rust, and powered by Apache Arrow and DataFusion. It is built on an architecture that allows other programming languages (such as Python, C++, and Java) to be supported as first-class citizens without paying a penalty for serialization costs.

The foundational technologies in Ballista are:

• Apache Arrow memory model and compute kernels for efficient processing of data.
• DataFusion Query Engine for query execution
• Apache Arrow Flight Protocol for efficient data transfer between processes.
• Google Protocol Buffers for serializing query plans, with plans to eventually use substrait.io here.

Ballista implements a similar design to Apache Spark (particularly Spark SQL), but there are some key differences:

• The choice of Rust as the main execution language 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.

Ballista can be deployed as a standalone cluster and also supports Kubernetes. In either case, the scheduler can be configured to use etcd as a backing store to (eventually) provide redundancy in the case of a scheduler failing.

Ballista is currently a proof-of-concept and provides batch execution of SQL queries. Although it is already capable of executing complex queries, it is not yet scalable or robust.

There is an excellent discussion in apache#30 about the future of the project and we encourage you to participate and add your feedback there if you are interested in using or contributing to Ballista.

The current initiatives being considered are:

• Continue to improve the current batch-based execution
• Add support for low-latency query execution based on a streaming model
• Adopt substrait.io to allow other query engines to be integrated

The easiest way to get started is to run one of the standalone or distributed examples. After that, refer to the Getting Started Guide.

• Refer to the developer documentation for the Architecture Overview
• Watch the Ballista: Distributed Compute with Rust and Apache Arrow talk from the New York Open Statistical Programming Meetup (Feb 2021)

Please see Contribution Guide for information about contributing to DataFusion.