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Storage-compute disaggregation has recently emerged as a novel architecture in modern data centers, particularly in the cloud. By decoupling compute from storage, this new architecture enables independent and elastic scaling of compute and storage resources, potentially increasing resource utilization and reducing overall costs. To best leverage the disaggregated architecture, a new breed of database systems termed storage-disaggregated databases has recently been developed, such as Amazon Aurora, Microsoft Socrates, Google AlloyDB, Alibaba PolarDB, and Huawei Taurus. However, little is known about the effectiveness of the design principles in these databases since they are typically developed by industry giants, and only the overall performance results are presented without detailing the impact of individual design principles. As a result, many critical research questions remain unclear, such as the performance impact of storage-disaggregation, the log-as-the-database design, shared-storage, and various log-replay methods. In this paper, we investigate the performance implications of the design principles that are widely adopted in storage-disaggregated databases for the first time. As these databases were usually not open-sourced, we have made a significant effort to implement a storage-disaggregated database prototype based on PostgreSQL v13.0. By fully controlling and instrumenting the codebase, we are able to selectively enable and disable individual optimizations and techniques to evaluate their impact on performance in various scenarios. Furthermore, we open-source our storage-disaggregated database prototype for use by the broader database research community, fostering collaboration and innovation in this field. 

Cytonuclear disruption may accompany allopolyploid evolution as a consequence of the merger of different nuclear genomes in a cellular environment having only one set of progenitor organellar genomes. One path to reconcile potential cytonuclear mismatch is biased expression for maternal gene duplicates (homoeologs) encoding proteins that target to plastids and/or mitochondria. Assessment of this transcriptional form of cytonuclear coevolution at the level of individual cells or cell types remains unexplored. Using single-cell (sc-) and single-nucleus (sn-) RNAseq data from eight tissues in three allopolyploid species, we characterized cell type–specific variations of cytonuclear coevolutionary homoeologous expression and demonstrated the temporal dynamics of expression patterns across development stages during cotton fiber development. Our results provide unique insights into transcriptional cytonuclear coevolution in plant allopolyploids at the single-cell level.