Related Concepts
- OmniData: pushes operators of the big data engine to storage nodes or offload nodes to implement near-data computing, which reduces network bandwidth consumption and improves the query performance of the query engine.
- OmniOperator: uses native code (C/C++) to implement big data SQL operators to improve query performance. It uses columnar storage and vectorized execution technologies as well as the Kunpeng acceleration library to improve operator execution efficiency and query performance of the query engine.
- Homogeneous acceleration framework (HAF): provides user-friendly programming methods and application programming interfaces (APIs) to quickly, effectively, and securely push specified acceleration segments of your service programs to offload nodes, optimizing the offload effect.
- OmniShuffle: runs in big data clusters of the customer's data center as a performance acceleration component of the big data engine Spark. It employs effective features such as unified addressing of the memory pool, data exchange in memory semantics, and converged shuffle to reduce the drive I/O overhead, quicken the data analysis process, and improve cluster resource utilization.
- OmniData combined with OmniOperator: OmniData optimizes the data loading process and OmniOperator improves the operator execution efficiency to improve the end-to-end engine query performance.
- OmniShuffle combined with OmniOperator: OmniOperator improves the operator execution efficiency and OmniShuffle optimizes the data interaction process to improve the end-to-end engine query performance.
Parent topic: Feature Description