With the development of location-aware mobile devices, communication technologies and GPS systems, location based queries have become an important research issue in the area of database. This paper studies the problem of snapshot based spatial range query which searches for the moving objects within a specific query range in a specific time interval. Range query is the building block of other types of spatial queries, such as k nearest neighbor query and reverse k nearest neighbor query. A series of algorithms have been proposed to process range queries of moving objects. However, these algorithms are either designed for fast response time or high update performance. They are not purposely designed for the situation of big data where throughput is more important as both queries and updates arrive at a very high rate. For the query stream and object update stream, a high throughput main memory algorithm—Dual Stream Join algorithm is proposed for moving object range query. DSJ uses a snapshot approach. In each snapshot, DSJ builds a new index structure based on the update of the moving objects, which avoids maintaining a sophisticate structures and gives full play to the performance of the hardware. DSJ executes a batch queries at each run, which increases the data locality and improves the efficiency of the algorithm. DSJ also employs the SIMD technology to accelerate the query processing and makes sure that the system has high throughput. A comprehensive performance evaluation of the proposed techniques is conducted using the German network generated data. The results show that DSJ is highly efficient.