Feature Description

The big data features of OmniRuntime are presented in the form of plugins to improve the performance of data loading, computing, and exchange from end to end.

Data volumes generated from Internet services have been growing much faster than CPUs' computing power. The open-source big data ecosystem is also developing on a fast track. However, diversified computing engines and open source components make it difficult to improve data processing performance throughout the lifecycle. Different big data engines use their own unique tuning policies and technologies to improve performance and efficiency. Some tuning items may be applied across multiple engines, which may cause resource contention and conflicts, reducing overall computing performance.

OmniRuntime consists of a series of features provided by Kunpeng BoostKit for Big Data in terms of application acceleration. It aims to improve the performance of end-to-end data loading, computing, and exchange through plugins, thereby improving the performance of big data analytics.

OmniShuffle is a subfeature of OmniRuntime. As a performance acceleration component of the big data engine Spark, OmniShuffle runs in big data clusters of the customer's data center. 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. 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 open source Shuffle and Broadcast of Spark in a non-intrusive manner.

It has been adapted to the following open source components and versions:

• Spark 3.1.1
• Spark 3.3.1
• Hive 3.1.0