# apache/auron

The Auron accelerator for distributed computing framework (e.g., Spark) leverages native vectorized execution to accelerate query processing

Repository: https://github.com/apache/auron
Canonical: https://ross.abutalabs.com/products/auron
Homepage: https://auron.apache.org/
Language: Rust
License: Apache-2.0
License Family: permissive
Topics: big-data, spark, datafusion, rust-lang, arrow
Last push: 2026-08-26T01:36:40+00:00

## Health v2 (maintenance only)
Score: 89/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 69, longevity 100
- inputs: {"age_days": 1892, "days_push": 8, "days_rel": 46, "gap_med": 137, "n_releases_24m": 6}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1795, forks 241 (observed 2026-08-28T04:05:37.431613+00:00)

## What it is
Apache Auron (Incubating) is a native vectorized query accelerator for distributed big data engines like Apache Spark, built on Apache DataFusion and Apache Arrow. It translates Spark's optimized physical plans into Rust-based native execution plans, eliminating JVM overhead and delivering roughly 2x faster TPC-DS performance with lower resource usage.

## Use cases
- speed up Apache Spark SQL queries with native vectorized execution
- reduce cluster resource costs for large-scale Spark workloads
- run TPC-DS style analytical queries faster on Spark
- avoid JVM overhead in distributed SQL execution
- integrate DataFusion-based native operators into a Spark pipeline
- support data lake formats like Hudi and Paimon with accelerated queries

## When to choose
- you run large-scale Spark SQL workloads and need better performance or lower resource usage
- you want production-hardened native execution with fine-grained failback to Spark
- you use HDFS, S3, Hudi, Paimon, or Celeborn in your Spark stack
- you need predictable performance without rewriting existing Spark jobs

## When to avoid
- your workloads are small or not SQL/Spark-centric
- you cannot add a custom shuffle manager and Spark extensions to your cluster
- you need a fully graduated (non-incubating) Apache project with long-term guarantees
- you rely on operators or UDFs not yet supported by Auron's native engine

## Facets
- artifact type: library
- maturity: active
- function: plugin-system
- domain: big-data, analytics, microservices, performance
- platform: jvm, rust, cloud
- tags: apache-spark, datafusion, apache-arrow, native-execution, simd, tpc-ds, query-optimizer, shuffle, sql, columnar, query-execution, vectorized-execution, query-acceleration, sql-engine, data-engineering, linux, docker

## Member repositories
- apache/auron (main) score 89

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:37.431613+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:22:50.847313+00:00, confidence not recorded.
  - readme: https://github.com/apache/auron (fetched 2026-08-28T04:05:37.431613+00:00, sha 31ac0bd9205a)
  - homepage: https://auron.apache.org/ (fetched 2026-08-29T11:01:36.660678+00:00, sha cf43b4796367)
  - site_page: https://auron.apache.org/documents/getting-started.html (fetched 2026-08-29T11:01:36.664564+00:00, sha e837dfa9f358)
  - site_page: https://auron.apache.org/documents/getting-started (fetched 2026-08-29T11:01:36.668972+00:00, sha e837dfa9f358)
  - site_page: https://auron.apache.org/archives/all-releases.html (fetched 2026-08-29T11:01:36.667172+00:00, sha 58def31fc813)
- Data as of 2026-08-30T08:39:29.467469+00:00.