Rusts Potential for Big Data | Generated by AI
Rust is for Big Data (#rust2018)
Author: Andy Grove
Published: January 28, 2018
This blog post is a forward-looking piece written as part of the “#rust2018” initiative, where the author shares his vision for Rust’s role in the coming year. Andy Grove, a developer who’s been building distributed data processing systems with Apache Spark, argues that Rust has untapped potential to revolutionize big data processing due to its core strengths in memory safety, performance, and predictability—without the pitfalls of garbage collection or runtime overheads common in languages like Java.
Key Arguments for Rust in Big Data
Grove starts by recounting his journey into Rust: introduced by a coworker a few years prior and hooked after attending RustConf in 2016. He praises Rust’s ability to eliminate common security vulnerabilities like buffer overflows while delivering C-like speed. For server-side work, he highlights crates like futures and tokio for building scalable async applications. But his real passion is big data, where Rust could address pain points in existing tools.
In his day job, Grove works with Apache Spark, which has become the go-to for distributed data processing but started as a simple academic project and scaled through heroic engineering fixes. Early Spark struggled with:
- Java serialization overhead: Data shuffling between nodes was slow and memory-intensive.
- Garbage collection (GC) pauses: These caused unpredictable performance, leading to “OutOfMemory” errors that required endless tuning.
Spark’s “Project Tungsten” (launched around 2014) mitigated this by:
- Storing data off-heap in binary formats (e.g., columnar like Parquet) to bypass GC.
- Using whole-stage code generation to optimize CPU execution via bytecode.
These changes shifted bottlenecks from JVM quirks to raw CPU limits, proving that performance gains come from low-level efficiency rather than higher-level abstractions.
Grove’s bold hypothesis: If Spark had been built in Rust from day one, even a basic implementation would have nailed performance and reliability out of the gate. Rust’s ownership model ensures memory safety without GC, avoiding serialization bloat and erratic pauses. No more tweaking JVM flags—just predictable, fast execution. He sees this as Rust’s “unique opportunity” to outdo incumbents like Spark, especially as data volumes explode.
The DataFusion Project
To put this vision into action, Grove launched DataFusion, an open-source query engine prototype in Rust. At the time of writing (early 2018), it’s in alpha but already demos:
- A DataFrame API for loading Parquet files and running operations like filters, joins, and aggregations (example: parquet_dataframe.rs).
- A SQL API for declarative queries on the same data (example: parquet_sql.rs).
He plans to hack on it in his spare time through 2018 to sharpen his Rust skills and build something useful. Inviting the community to contribute, he points to the repo: github.com/apache/arrow-datafusion.
Updates (as of May 2024)
The post includes a retrospective addendum highlighting DataFusion’s growth:
- Donated to Apache Arrow in February 2019.
- Ballista (distributed execution layer) announced in July 2019: Announcing Ballista.
- Self-published book How Query Engines Work (Feb 2020) demystifying DataFusion/Ballista internals: Leanpub link.
- Ballista donated to Apache Arrow in April 2021.
- Apple donated DataFusion Comet (March 2024) to supercharge Spark with Rust-based acceleration: Arrow blog and GitHub.
- Grove joined Apple in April 2024 to work on Comet.
- DataFusion graduated to top-level Apache project in May 2024: Arrow blog.
Grove wraps up by encouraging readers to dive into query engines via his book, positioning Rust as a game-changer for data-intensive apps.
Rust is for Big Data (#rust2018)