Transactional stream processing engines (TSPEs) are central to modern stream applications handling shared mutable states. However, their full potential, particularly in adaptive scheduling, remains largely unexplored. We presentMorphStream, a TSPE designed to optimize parallelism and performance for transactional stream processing on multicores. Through a unique three-stage execution paradigm (i.e.,planning,scheduling, andexecution),MorphStreamenables adaptive scheduling under varying workload characteristics. Building on this foundation,MorphStreamis further enhanced with support for non-deterministic state access, employing a stateful task precedence graph to handle undefined read/write sets at runtime while guaranteeing transaction semantics. Additionally,MorphStreamincorporates a generalized framework for managing window-based operations, enabling efficient tracking and maintenance of overlapping windows using multi-versioned state management. These extensions enhance the system's ability to process dynamic and irregular workloads. Experimental results demonstrate up to 3.4 times higher throughput and 69.1% lower latency compared to state-of-the-art TSPEs, validating its scalability and adaptability in real-world streaming scenarios.

In the realm of transactional stream processing (TSP), the challenge lies in providing a unified execution model that seamlessly integrates transactional and stream-oriented capabilities. Existing TSP engines (TSPEs) largely employ non-adaptive scheduling techniques, leaving multicore parallelism underutilized due to intricate workload dependencies. We demonstrate MorphStream, a state-of-the-art TSPE built for unprecedented scalability on multicores. MorphStream distinguishes itself by employing an adaptive scheduling algorithm, explicitly designed to unlock the full potential of multicore architectures even under complex workload conditions. This enables MorphStream to make optimal trade-offs in performance metrics under varying workload characteristics. To enhance user engagement, the demonstration will showcase MorphStream's graphical user interface, specifically engineered to simplify the implementation and deployment of complex streaming applications while providing detailed and comprehensive performance monitoring and analytics for the job execution runtime.

Transactional stream processing engines (TSPEs) differ significantly in their designs, but all rely on non- adaptive scheduling strategies for processing concurrent state transactions. Subsequently, none exploit multicore parallelism to its full potential due to complex workload dependencies. This paper introduces MorphStream, which adopts a novel approach by decomposing scheduling strategies into three dimensions and then strives to make the right decision along each dimension, based on analyzing the decision trade-offs under varying workload characteristics. Compared to the state-of-the-art, MorphStream achieves up to 3.4 times higher throughput and 69.1% lower processing latency for handling real-world use cases with complex and dynamically changing workload dependencies.

Stream processing is widely used for real-time data processing and decision-making, leading to tens of thousands of streaming jobs deployed in ByteDance cloud. Since those streaming jobs usually run for several days or longer and the input workloads vary over time, they usually face diverse runtime issues such as processing lag and varying failures. This requires runtime management to resolve such runtime issues automatically. However, designing a runtime management service on the ByteDance scale is challenging. In particular, the service has to concurrently manage cluster-wide streaming jobs in a scalable and extensible manner. Furthermore, it should also be able to manage diverse streaming jobs effectively. To this end, we propose StreamOps to enable cloud-native runtime management for streaming jobs in ByteDance. StreamOps has three main designs to address the challenges. 1) To allow for scalability, StreamOps is running as a standalone lightweight control plane to manage cluster-wide streaming jobs. 2) To enable extensible runtime management, StreamOps abstracts control policies to identify and resolve runtime issues. New control policies can be implemented with a detect-diagnose-resolve programming paradigm. Each control policy is also configurable for different streaming jobs according to the performance requirements. 3) To mitigate processing lag and handling failures effectively, StreamOps features three control policies, i.e., auto-scaler, straggler detector, and job doctor, that are inspired by state-of-the-art research and production experiences at ByteDance. In this paper, we introduce the design decisions we made and the experiences we learned from building StreamOps. We evaluate StreamOps in our production environment, and the experiment results have further validated our system design.

The intra-window join (IaWJ), i.e., joining two input streams over a single window, is a core operation in modern stream processing applications. This paper presents the first comprehensive study on parallelizing the IaWJ on modern multicore architectures. In particular, we classify IaWJ algorithms into lazy and eager execution approaches. For each approach, there are further design aspects to consider, including different join methods and partitioning schemes, leading to a large design space. Our results show that none of the algorithms always performs the best, and the choice of the most performant algorithm depends on: (i) workload characteristics, (ii) application requirements, and (iii) hardware architectures. Based on the evaluation results, we propose a decision tree that can guide the selection of an appropriate algorithm.