With the development of semantic web technology, RDF data grow rapidly. The single node RDF query system cannot meet the practical needs. Building distributed RDF query system has become one of the hotspots in the academia and industry. The existing RDF query system is based on Hadoop and general distributed technology. The disk I/O of the former is too high and the latter is less scalable. Besides, the two systems perform poorly in the basic pattern matching mode. In order to solve these problems, we design a distributed system architecture based on Spark and Redis, and improve the query plan generation algorithm. We call the prototype system RDF-SR. This system reduces the disk I/O by Spark, improves the data mapping rate by Redis and reduces the data shuffling process with improved algorithms. Our evaluation shows that RDF-SR performs better in the basic pattern matching mode compared with other systems.