Predictive topology refinements in distributed stream processing system

Distributed Reactive Stream Processing

Reactive programming paradigm successfully overcomes the limitations of observer pattern which has traditionally been used for developing event-driven distributed systems. Due to its declarative style, compositionality and automatic management of dependencies, reactive programming offers a promising new way for building complex distributed data-flow systems. This article outlines some open chal...

Scalable Distributed Stream Processing

Many stream-based applications are naturally distributed. Applications are often embedded in an environment with numerous connected computing devices with heterogeneous capabilities. As data travels from its point of origin (e.g., sensors) downstream to applications, it passes through many computing devices, each of which is a potential target of computation. Furthermore, to cope with time-vary...

Benchmarking Distributed Stream Processing Engines

Over the last years, stream data processing has been gaining atten— tion both in industry and in academia due to its wide range of appli— cations. To fulfill the need for scalable and efficient stream analyt— ics, numerous open source stream data processing systems (SDPSs) have been developed, with high throughput and low latency being their key performance targets. In this paper, we propose a ...

Survey of Distributed Stream Processing

Fault-tolerant stream processing using a distributed, replicated file system

We present SGuard, a new fault-tolerance technique for distributed stream processing engines (SPEs) running in clusters of commodity servers. SGuard is less disruptive to normal stream processing and leaves more resources available for normal stream processing than previous proposals. Like several previous schemes, SGuard is based on rollback recovery [18]: it checkpoints the state of stream pr...