Abstract
CHive is a new streaming analytics platform to run distributed SQL-style queries on edge clouds. However, CHive is currently tightly coupled to a specific stream processing system (SPS), Apache Storm. In this paper we address the decoupling of the CHive query planner and optimizer from the runtime environment, and also extend the latter to support pluggable runtimes through a common API. As runtimes, we currently support Apache Spark and Flink streaming. The fundamental contribution of this paper is to assess the cost of employing interstream parallelism in SPS. Experimental evaluation indicates that we can enable popular SPS to be distributed on edge clouds with stable overhead in terms of throughput
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