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| |
| # Comet Overview |
| |
| Apache DataFusion Comet is a high-performance accelerator for Apache Spark, built on top of the powerful |
| [Apache DataFusion] query engine. Comet is designed to significantly enhance the |
| performance of Apache Spark workloads while leveraging commodity hardware and seamlessly integrating with the |
| Spark ecosystem without requiring any code changes. |
| |
| [Apache DataFusion]: https://datafusion.apache.org |
| |
| The following diagram provides an overview of Comet's architecture. |
| |
|  |
| |
| Comet aims to support: |
| |
| - a native Parquet implementation, including both reader and writer |
| - full implementation of Spark operators, including |
| Filter/Project/Aggregation/Join/Exchange etc. |
| - full implementation of Spark built-in expressions. |
| - a UDF framework for users to migrate their existing UDF to native |
| |
| ## Architecture |
| |
| The following diagram shows how Comet integrates with Apache Spark. |
| |
|  |
| |
| ## Feature Parity with Apache Spark |
| |
| The project strives to keep feature parity with Apache Spark, that is, |
| users should expect the same behavior (w.r.t features, configurations, |
| query results, etc) with Comet turned on or turned off in their Spark |
| jobs. In addition, Comet extension should automatically detect unsupported |
| features and fallback to Spark engine. |
| |
| To achieve this, besides unit tests within Comet itself, we also re-use |
| Spark SQL tests and make sure they all pass with Comet extension |
| enabled. |
| |
| ## Getting Started |
| |
| Refer to the [Comet Installation Guide] to get started. |
| |
| [Comet Installation Guide]: installation.md |
