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Stream processing in the last decade has seen broad adoption in both commercial and research settings. One key element for this success is the ability of modern stream processors to handle failures while ensuring exactly-once processing guarantees. At the moment of writing, virtually all stream processors that guarantee exactly-once processing implement a variant of Apache Flink's coordinated checkpoints - an extension of the original Chandy-Lamport checkpoints from 1985. However, the reasons behind this prevalence of the coordinated approach remain anecdotal, as reported by practitioners of the stream processing community. At the same time, common checkpointing approaches, such as the uncoordinated and the communication-induced ones, remain largely unexplored. This paper is the first to address this gap by i) shedding light on why practitioners have favored the coordinated approach and ii) by investigating whether there are viable alternatives. To this end, we implement three checkpointing approaches that we surveyed and adapted for the distinct needs of streaming dataflows. Our analysis shows that the coordinated approach outperforms the uncoordinated and communication-induced protocols under uniformly distributed workloads. To our surprise, however, the uncoordinated approach is not only competitive to the coordinated one in uniformly distributed workloads, but it also outperforms the coordinated approach in skewed workloads. We conclude that rather than blindly employing coordinated checkpointing, research should focus on optimizing the very promising uncoordinated approach, as it can address issues with skew and support prevalent cyclic queries. We believe that our findings can trigger further research into checkpointing mechanisms.

The sequential recommendation problem has attracted considerable research attention in the past few years, leading to the rise of numerous recommendation models. In this work, we explore how Large Language Models (LLMs), which are nowadays introducing disruptive effects in many AI-based applications, can be used to build or improve sequential recommendation approaches. Specifically, we design three orthogonal approaches and hybrids of those to leverage the power of LLMs in different ways. In addition, we investigate the potential of each approach by focusing on its comprising technical aspects and determining an array of alternative choices for each one. We conduct extensive experiments on three datasets and explore a large variety of configurations, including different language models and baseline recommendation models, to obtain a comprehensive picture of the performance of each approach. Among other observations, we highlight that initializing state-of-the-art sequential recommendation models such as BERT4Rec or SASRec with embeddings obtained from an LLM can lead to substantial performance gains in terms of accuracy. Furthermore, we find that fine-tuning an LLM for recommendation tasks enables it to learn not only the tasks, but also concepts of a domain to some extent. We also show that fine-tuning OpenAI GPT leads to considerably better performance than fine-tuning Google PaLM 2. Overall, our extensive experiments indicate a huge potential value of leveraging LLMs in future recommendation approaches. We publicly share the code and data of our experiments to ensure reproducibility., Comment: 33 pages, 12 figures, 7 tables