SPEAr: Expediting Stream Processing with Accuracy Guarantees

Stream Processing Engines (SPEs) are used for realtime and continuous processing with stateful operations. This type of processing poses numerous challenges due to its associated complexity, unpredictable input, and need for timely results. As a result, users tend to overprovision resources, and online scaling is required in order to overcome overloaded situations. Current attempts for expediting stateful processing are impractical, due to their inability to uphold the quality of results, maintain performance, and reduce memory requirements.In this paper, we present the SPEAr system, which can expedite processing of stateful operations automatically by trading accuracy for performance. SPEAr detects when it can accelerate processing by employing online sampling and accuracy estimation at no additional cost. We built SPEAr on top of Storm and our experiments indicate that it can reduce processing times by more than an order of magnitude, use more than an order of magnitude less memory, and offer accuracy guarantees in real-world benchmarks.