Change Description
The OmniRuntime OmniOperator feature of Kunpeng BoostKit for Big Data uses a unified infrastructure 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