Comet Overview¶
Comet runs Spark SQL queries using the native Apache DataFusion runtime, which is typically faster and more resource efficient than JVM based runtimes.
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 illustrates the architecture of Comet:
Current Status¶
Comet currently supports the following versions of Apache Spark:
3.3.x
3.4.x
Experimental support is provided for the following versions of Apache Spark and is intended for development/testing use only and should not be used in production yet.
3.5.x
4.0.0-preview1
Note that Comet may not fully work with proprietary forks of Apache Spark such as the Spark versions offered by Cloud Service Providers.
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.