Spinstreams: A static optimization tool for data stream processing applications

The ubiquity of data streams in different fields of computing has led to the emergence of Stream Processing Systems (SPSs) used to program applications that extract insights from unbounded sequences of data items. Streaming applications demand various kinds of optimizations. Most of them are aimed at increasing throughput and reducing processing latency, and need cost models used to analyze the steady-state performance by capturing complex aspects like backpressure and bottleneck detection. In those systems, the tendency is to support dynamic optimizations of running applications which, although with a substantial run-time overhead, are unavoidable in case of unpredictable workloads. As an orthogonal direction, this paper proposes SpinStreams, a static optimization tool able to leverage cost models that programmers can use to detect and understand the inefficiencies of an initial application design. SpinStreams suggests optimizations for restructuring applications by generating code to be run on the SPS. We present the theory behind our optimizations, which cover more general classes of application structures than the ones studied in the literature so far. Then, we assess the accuracy of our models in Akka, an actor-based streaming framework providing a Java and Scala API.