Hardware Utilisation Techniques for Data Stream Processing

Abstract: Recent years have seen an increase in use of the stream processing architecture to compose continuous analytics applications. This thesis presents the design of a Rust-based stream processor that adopts two separate techniques to tackle existing weaknesses in modern production-grade stream processors. The first technique employs a data analytics language on top of the streaming runtime, in order to provide both dataflow and low-level compiler optimisations. This technique is motivated by an analysis of the impact that the lack of compiler integration may have on the end-to-end performance of streaming pipelines in Apache Flink. In the second technique streaming operators are scheduled using a task-parallel approach to boost performance for skewed data distributions. The experimental results for data-parallel streaming pipelines in this thesis demonstrate, that the scheduling model of the prototype achieves performance improvements in skewed scenarios without exhibiting any significant losses in performance during uniform distributions.