Spatial data analytics systems are widely studied in both the academia and industry. However, existing systems are limited when handling a large number of moving objects and real time spatial queries. In this work, we architect a scalable and efficient system CheetahGIS to process streaming spatial queries over massive moving objects. In particular, CheetahGIS is built upon Apache Flink Stateful Functions (StateFun), an API for building distributed streaming applications with an actor-like model. CheetahGIS enjoys excellent scalability due to its modular architecture, which clearly decomposes different components and allows scaling individual components. To improve the efficiency and scalability of CheetahGIS, we devise a suite of optimizations, e.g., lightweight global grid-based index, metadata synchroniza tion strategies, and load balance mechanisms. We also formulate a generic paradigm for spatial query processing in CheetahGIS, and verify its generality by processing three representative streaming queries (i.e., object query, range count query, and k nearest neighbor query). We conduct extensive experiments on both real and synthetic datasets to evaluate CheetahGIS.