V1.4.0

Change Description

The OmniOperator operator acceleration 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.

Version 1.4.0 has the following changes:

• Optimized the execution plan and added three new rules: DeduplicateRightSideOfLeftSemiJoin, RewriteSelfJoinInInPredicate, and MergeSubqueryFilters.
• Added support for the NullType data type.
• Added the SubqueryBroadcastExec, CoalesceExecTransformer, and Limit Omni operators.
• Optimized operator functions: HashAggregator RollUp optimization, TableScan operator Parquet data read optimization, and Radix Sort for Sort operator.
• Optimized end expressions: adding the instr, startswith, and endswith functions, allowing conversion between the string type and int/long type, optimizing decimal data processing, and optimizing the expressions of the string type.
• Optimized functions in a Kerberos security cluster: operator acceleration in Spark local or Yarn mode, and ORC/Parquet data read in native mode.
• Added the spill function for Window and HashAggregator operators.
• Optimized NEON instructions, covering HashJoin, Sort, and Aggregator operators.

Resolved Issues

None

Known Issues

None