III: Small: Increase the Throughput of Non-Relational Databases through Theoretical Modeling and Optimization

数据的爆炸性增长正在推动海量数据存储系统的快速演变。这些系统被广泛应用，不仅在大规模的互联网服务中，而且在天文、地理和遗传学等不同领域的科学项目中也得到了广泛应用。该项目将提高这些数据存储系统的效率，从而能够以更低的成本处理更多的数据。随着科学和工程研究变得更具成本效益，可能会产生巨大的社会影响。更具体地说，该项目将致力于使用日志结构合并树存储体系结构来改进非关系数据库。一个主要的重点将是改进这类系统的一个关键组成部分，即压缩政策。压缩策略尚未得到很好的理解，但对系统性能至关重要。到目前为止，压实政策一直是通过试验和错误设计的，主要是由经验指导的。该项目将开发紧凑的分析模型，通过经验测试验证和改进模型，根据模型设计最优的改进策略，并在现场系统中部署这些策略。此外，开发的理论模型将被用来优化非关系数据库系统，以处理快速到达和终止的大量动态连续查询。

The explosive growth of data is driving the rapid evolution of massive data-storage systems. These systems are widely used, not only in large-scale Internet services, but also in scientific projects in diverse areas such as astronomy, geography, and genetics. This project will increase the efficiency of these data-storage systems, which will allow processing more data at lower cost. There is the potential for a large societal impact as science and engineering research is made more cost-effective.More specifically, this project will work on improving non-relational databases with log-structured merge-tree storage architectures. One main focus will be on improving a key component of such systems, namely, compaction policies. Compaction policies are not yet well understood, but are crucial for system performance. To date, compaction policies have been designed by trial and error, guided mainly by empirical experience. The project will develop analytical models for compaction, validate and refine the models with empirical testing, design improved policies that are optimal according to the models, and deploy these policies in live systems. Further, the developed theoretical models will be leveraged to optimize non-relational database systems in handling high volumes of dynamic continuous queries, which arrive and expire rapidly.