Auron Joins Apache Incubator: High‑Performance Vectorized Engine Accelerates Big Data Workloads

1. Introduction to Auron

Auron is a native execution engine built with vectorization technology, implemented in Rust and leveraging SIMD instructions to reduce resource overhead and accelerate execution.

2. Core Capabilities

Native Execution : Implemented in Rust, eliminating JVM overhead for better performance.

Vectorized Computation : Built on Apache Arrow columnar format and optimized batch processing with SIMD instructions.

Pluggable Architecture : Seamlessly integrates with Apache Spark and is designed for future extensions to other compute engines.

Production‑grade Optimizations : Multi‑level memory management, optimized shuffle format, and adaptive execution strategies applied at large scale.

3. Performance Benchmark

On TPC‑DS workloads, Auron achieves more than 2× speedup compared with Spark.

4. History and Community

The project began in January 2022 as the Blaze engine within Kuaishou's big‑data Spark team, embracing open‑source from the start. By September 2023 it demonstrated significant performance gains on TPC‑H/TPC‑DS benchmarks and was deployed at massive scale, processing petabytes of data daily and saving millions of dollars in server costs.

Since its open‑source release, more than ten versions have been published, garnering over 1.5K GitHub stars and contributions from 30+ developers worldwide. Companies such as Didi, Ctrip, Autohome, 58.com, and OPPO have adopted the engine and reported positive feedback.

5. Apache Incubation

In August 2025 the project entered the Apache Software Foundation incubator and was renamed Auron (pronounced [ˈɔːrɑːn]), inspired by “Aura”. The name reflects the powerful performance the engine brings to big‑data workloads. Future plans include extensions for Flink, data‑lake systems, and integration with GPU/DPU hardware.

The community commits to Apache’s governance model, ensuring transparent code, documentation, and decision‑making to attract more contributors and complement other Apache big‑data projects such as Spark, Flink, and Celeborn.

6. Acknowledgements

Thanks to all Auron community contributors, upstream project developers, and users who have trusted and promoted the project. Special thanks to champion Calvin Kirs and mentors Xuanwo, Becket Qin, and Nicholas Jiang for their support during the incubation process.

Join the community via GitHub ( https://github.com/apache/auron/ ), the official website ( https://auron.apache.org/ ), or the mailing list ([email protected]).