Ballista is a distributed compute platform primarily implemented in Rust, 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.
April 2021: Ballista has been donated to the Apache Arrow project!
This repository is now archived and development is now happening in the Apache Arrow repository.
We will now use the official ASF Slack for
informal discussions and coordination. This is a great place to meet other contributors and get guidance on where to
contribute. Join us in the #arrow-rust channel.
Also, see Arrow Rust Community for more information on how to interact with the project.
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.
The following diagram highlights some of the integrations that will be possible with this unique architecture. Note that not all components shown here are available yet.
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.
The following examples should help illustrate the current capabilities of Ballista:
To follow the progress of this project, please refer to the "This Week in Ballista" series of blog posts. Follow @BallistaCompute on Twitter to receive notifications when the blog is updated.
Ballista releases are now available on crates.io, Maven Central and Docker Hub. Please refer to the user guide for instructions on using a released version of Ballista.
The user guide is hosted at https://ballistacompute.org, along with the blog where news and release notes are posted.
Developer documentation can be found in the docs directory.
See CONTRIBUTING.md for information on contributing to this project.