Fast
Arrow enables execution engines to take advantage of the latest SIMD (Single input multiple data) operations included in modern processors, for native vectorized optimization of analytical data processing. Columnar layout of data also allows for a better use of CPU caches by placing all data relevant to a column operation in as compact of a format as possible.
Flexible
Arrow acts as a new high-performance interface between various systems. It is also focused on supporting a wide variety of industry-standard programming languages. Java, C, C++, Python are underway and more languages are expected soon.
Standard
Apache Arrow is backed by key developers of 13 major open source projects, including Calcite, Cassandra, Drill, Hadoop, HBase, Ibis, Impala, Kudu, Pandas, Parquet, Phoenix, Spark, and Storm making it the de-facto standard for columnar in-memory analytics.
Developer Mailing List
Performance Advantage of Columnar In-Memory
Advantages of a Common Data Layer
- Each system has its own internal memory format
- 70-80% CPU wasted on serialization and deserialization
- Similar functionality implemented in multiple projects
- All systems utilize the same memory format
- No overhead for cross-system communication
- Projects can share functionality (eg, Parquet-to-Arrow reader)
Committers
| Name | Alias (email is <alias>@apache.org) |
|---|---|
| Jacques Nadeau | jacques |
| Todd Lipcon | todd |
| Ted Dunning | tdunning |
| Michael Stack | stack |
| P. Taylor Goetz | ptgoetz |
| Julian Hyde | jhyde |
| Reynold Xin | rxin |
| James Taylor | jamestaylor |
| Julien Le Dem | julien |
| Jake Luciani | jake |
| Jason Altekruse | json |
| Alex Levenson | alexlevenson |
| Parth Chandra | parthc |
| Marcel Kornacker | marcel |
| Steven Phillips | smp |
| Hanifi Gunes | hg |
| Abdelhakim Deneche | adeneche |
| Wes McKinney | wesm |