The system proposes a distributed k-NN search (DKNN) algorithm based on DSI. And also presents a new index structure called Dynamic Strip Index (DSI) . These solutions that can support scalable distributed processing of k-NN queries. Dynamic Strip Index (DSI), which can better adapt to different data distributions than exiting grid indexes. it can be naturally distributed across the cluster, therefore lending itself well to distributed processing. DKNN avoids having an uncertain number of potentially expensive iterations, and is thus more efficient and more predictable than existing approaches. DSI and DKNN are implemented on Apache S4, an open-source platform for distributed stream processing. We perform extensive experiments to study the characteristics of DSI and DKNN, and compare them with three baseline methods. Experimental results show that our proposal scales well and significantly outperforms the alternative methods. This model has been used in many well-known systems including recently proposed MapReduce and Google File System. This DKNN uses DSI for processing k-NN queries. The DSI structure and the DKNN algorithm strike a good balance between the cost of index maintenance and that of query processing. DKNN supports more efficient updates. compared with the grid-based approaches that require an uncertain number of iterations in query processing, the DKNN algorithm is able to identify the final search space for each query with only two iterations, leading to great savings in communication cost as well as more predictable performance.