A Distributed Rule Engine for Streaming Big Data.

The rules engine has been widely used in industry and academia, because it can separate the rules from the execution logic and incorporate the features of expert knowledge. With the advent of big data era, the amount of data has grown at an unprecedented rate. However, traditional rule engines based on PCs or servers are hard to handle streaming big data owing to limitation of hardware performance. The structured streaming computing framework can provide new solutions for these challenges. In this paper, we design a distributed rule engine based on Kafka and Structured Streaming (KSSRE), and propose a rule-fact matching strategy using the Spark SQL engine to support a large number of event stream inferences. KSSRE uses DataFrame to store data and inherits the load balancing, scalability and fault-tolerance mechanisms of Spark2.x. In addition, in order to remove the possible repetitive rules and optimize the matching process, we use the ternary grid model [1] for representing rules and design a scheduling model to improve the memory sharing in the matching process. The evaluation shows that KSSRE has a better performance, scalability and fault tolerance based on DBLP data sets.