From a stream of relational queries to distributed stream processing

From a Stream of Relational Queries to Distributed Stream Processing

Applications from several domains are now being written to process live data originating from hardware and softwarebased streaming sources. Many of these applications have been written relying solely on database and data warehouse technologies, despite their lack of need for transactional support and ACID properties. In several extreme high-load cases, this approach does not scale to the proces...

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...

Survey of Distributed Stream Processing

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 ...