The stream processing systems

whether centralized or distributed

have to overcome the single-node bottleneck.Moreover

their static configurations also make them either shortage or surplus of resources.To this end

this paper proposes a scalable parallel-distributed stream processing system named SPSPS.The system splits a query into parallel sub-queries according to stateful query operators to minimize the communication overhead in parallel processing

and achieves order-preserving tuple processing through the stateful operator's distributor and collector.Moreover

the scalability techniques with load balancing and reconfiguration support effective adjustment of resources depending on the incoming load.The experiments on a cluster with 60 nodes prove the scalability.