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Stream processing applications extract value from raw data through DirectedAcyclic Graphs of data analysis tasks. Shared-nothing (SN) parallelism is thede-facto standard to scale stream processing applications. Given anapplication, SN parallelism instantiates several copies of each analysis task,making each instance responsible for a dedicated portion of the overallanalysis, and relies on dedicated queues to exchange data among connectedinstances. On the one hand, SN parallelism can scale the execution ofapplications both up and out since threads can run task instances within andacross processes/nodes. On the other hand, its lack of sharing can causeunnecessary overheads and hinder the scaling up when threads operate on datathat could be jointly accessed in shared memory. This trade-off motivated us instudying a way for stream processing applications to leverage shared memory andboost the scale up (before the scale out) while adhering to the widely-adoptedand SN-based APIs for stream processing applications.  We introduce STRETCH, a framework that maximizes the scale up and offersinstantaneous elastic reconfigurations (without state transfer) for streamprocessing applications. We propose the concept of Virtual Shared-Nothing (VSN)parallelism and elasticity and provide formal definitions and correctnessproofs for the semantics of the analysis tasks supported by STRETCH, showingthey extend the ones found in common Stream Processing Engines. We also providea fully implemented prototype and show that STRETCH's performance exceeds thatof state-of-the-art frameworks such as Apache Flink and offers, to the best ofour knowledge, unprecedented ultra-fast reconfigurations, taking less than 40ms even when provisioning tens of new task instances.

STRETCH: Virtual Shared-Nothing Parallelism for Scalable and Elastic Stream Processing