apache/auron
Apache Auron is an accelerator for big data engines, leveraging native vectorized execution to accelerate query processing.
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
No releases in the last 90 days; the most recent was 103 days ago, with 65 contributors on GitHub.
Gained 152% more stars in the last 90 days than in the 90 days before.
Use Cases & Benefits
- Accelerates big data query processing by leveraging native vectorized execution and integrating with distributed computing frameworks.
- Eliminates JVM overhead using Rust and Apache Arrow's SIMD-optimized columnar format for predictable, high-performance query execution.
- Use for boosting Apache Spark query speeds in large-scale data analytics environments requiring efficient resource utilization.
- Use for extending Apache DataFusion capabilities with custom operators, functions, or file format support in Rust-based pipelines.
- Use for integrating native execution acceleration into big data engines like Flink to improve batch processing throughput.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
Apache 2.0
Permissive: commercial use, modification and redistribution are allowed with attribution; includes an explicit patent grant from contributors.
Community files
- README: present
- Contributing guide: present
- Code of conduct: present
Watch-outs
No watch-outs from GStars' checks.
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From the README
The Auron accelerator for big data engine (e.g., Spark, Flink) leverages native vectorized execution to accelerate query processing. It combines the power of the Apache DataFusion library and the scale of the distributed computing framework.
Auron takes a fully optimized physical plan from distributed computing framework, mapping it into DataFusion's execution plan, and performs native plan computation.
Based on the inherent well-defined extensibility of DataFusion, Auron can be easily extended to support:
We encourage you to extend DataFusion capability directly and add the supports in Auron with simple modifications in plan-serde and extension translation.
To build Auron from source, follow the steps below:
Auron's native execution lib is written in Rust. You need to install Rust (nightly) before compiling.
We recommend using rustup for installation.
Auron has been well tested with JDK 8, 11, and 17.
Make sure
JAVA_HOMEis properly set and points to your desired version.You can build Auron either locally or inside Docker with CentOS7 using a unified script:
auron-build.sh.Run
./auron-build.sh --helpto see all available options.After the build completes, a fat JAR with all dependencies will be generated in either the
target/directory (for local builds) ortarget-docker/directory (for Docker builds), depending on the selected build mode.This section describes how to submit and configure a Spark Job with Auron support.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
Forks
247+16
Open issues
154+14
Compared with peers in Backend
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| anthropics/skills | 187.8K | +2.9K | 15 | — |
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| NationalSecurityAgency/ghidra | 98.2K | +2.3K | 64 | 14 days ago |
| microsoft/VibeVoice | 56.0K | +1.1K | 29 | — |
| apache/auron | 1.8K | — | 59 | 103 days ago |
| Category median | 56.0K | +2.3K | 29 | — |
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