Distributed Query Engine for Multiple-Query Optimization over Data Stream
Query processing over data stream has attracted much attention in real-time applications. While many efforts have been paid for query processing of data streams in distributed environment, no previous study focused on multiple-query optimization. To address this problem, we propose EsperDist, a distributed query engine for multiple-query optimization over data stream. EsperDist can significant reduce the overhead of network transmission and memory usage by reusing operators in the query plan. Moreover, EsperDist also makes best effort to minimize the query cost so as to avoid resource bottle neck in a single machine. In this demo, we will present the architecture and work-flow of EsperDist using datasets collected from real world applications. We also propose a user-friendly to monitor query results and interact with the system in real time.
This work was supported by NSFC (91646202), National Key R&D Program of China (SQ2018YFB140235), and the 1000-Talent program.
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