Change Description
The OmniRuntime OmniOperator feature of Kunpeng BoostKit for Big Data uses one base to support different engines (such as Spark), reducing repeated optimization work, fully exploring common and heterogeneous computing power, and promoting the Kunpeng ecosystem.
New Features
- Optimized the Partial Agg logic to improve the query efficiency.
- Optimized the vectorized instructions of the Sort, HashAgg, and HashJoin operators.
- Optimized the execution plan for Spark operation acceleration in the Agg+Sort+Limit scenario and the Scan execution plan to reduce the performance overhead and improve the query efficiency.
- Added the ColumnarDataWritingCommandExec operator for Spark operator acceleration.
- Added stage-level operator rollback for Spark operator acceleration. In some scenarios, the performance loss caused by row-column conversion can be reduced.
- Added the timestamp data type for Spark operator acceleration.
- Added the unix_timestamp and from_unixtime expressions for Spark operator acceleration.
- Added SIMPLE_EDGE shuffle support for Hive operator acceleration and added fusion of the Filter and Select operators.
- Added the POWER expression for Hive operator acceleration.
Modified Features
None
Removed Features
None
Parent topic: V1.7.0