Key Features

OmniOperator

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 Kunpeng vectorization instructions to improve operator execution efficiency and query performance of the query engine.

OmniShuffle

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. OmniShuffle supports RSS and ESS modes. The two modes have little difference except in the deployment and configuration methods, and you can switch between the two modes when needed.

As a performance acceleration component of Spark, OmniShuffle uses the plugin mechanism provided by Spark to implement the Shuffle Manager and Broadcast Manager plugin interfaces and replace the native Shuffle and Broadcast of Spark in a non-intrusive manner.

OmniAdvisor

OmniAdvisor uses AI algorithms to train optimal parameters of the big data engines Spark and Hive, improving Spark and Hive running efficiencies.