Research on Rule Matching Model Based on Spark

Abstract

Abstract Rule engines are widely used in engineering and academic fields because they can flexibly separate facts and rules from the rule matching process. Since the rule matching process is very time-consuming, and the traditional rule matching uses single-computer operation when many facts and rules exceed the computer’s memory and computational capacity limit, it will cause the application to crash and paralyze. To solve the above problems, this paper investigates the Spark framework and Rete algorithm to take advantage of Spark’s in-memory computation to alleviate the time-consuming problem of the traditional rule matching process. A high-performance distributed rule matching model is designed by combining the actual rule matching scenario and the development process. In addition, according to the form of rules and facts in the actual scenario, this paper effectively divides the matching process and improves the scalability of the rule matching model.