Performance Characterization of Modern Databases on Out-of-Order

Abstract—Big data revolution has created an unprecedented demand for intelligent data management solutions on a large scale. While data management has traditionally been used as a synonym for relational data processing, in recent years a new group popularly known as NoSQL databases have emerged as a competitive alternative. There is a pressing need to gain greater understanding of the characteristics of modern databases to architect targeted computers. In this paper, we investigate four popular NoSQL/SQL-style databases and evaluate their hardware performance on modern computer systems. Based on data collected from real hardware, we evaluate how efficiently modern databases utilize the underlying systems and make several recommendations to improve their performance efficiency. We observe that performance of modern databases is severely limited by poor cache/memory performance. Nonetheless, we demonstrate that dynamic execution techniques are still effective in hiding a significant fraction of the stalls, thereby improving performance. We further show that NoSQL databases suffer from greater performance inefficiencies than their SQL counterparts. SQL databases outperform NoSQL databases for most operations and are beaten by NoSQL databases only in a few cases. NoSQL databases provide a promising competitive alternative to SQL-style databases, however, they are yet to be optimized to fully reach the performance of contemporary SQL systems. We also show that significant diversity exists among different database implementations and big-data benchmark designers can leverage our analysis to incorporate representative workloads to encapsulate the full spectrum of data-serving applications. In this paper, we also compare data-serving applications with other popular benchmarks such as SPEC CPU2006 and SPECjbb2005. I