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 (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.
- Apache Arrow Flight Protocol for efficient data transfer between processes.
- Google Protocol Buffers for serializing query plans.
- Docker for packaging up executors along with user-defined code.
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.
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.
Ballista was donated to the Apache Arrow project in April 2021 and should be considered experimental.
The Ballista Developer Documentation and the DataFusion User Guide are currently the best sources of information for getting started with Ballista.