Taking Analytic Databases to the Bank

The explosion of big data has spotlighted the bottlenecks of data movement in traditional von-Neumann architectures. Data analytic applications, such as online analytic query processing (OLAP) databases, are especially burdened by these bottlenecks, given that latency is a key driver in these workloads. Thus, these applications turn to specialized hardware to overcome these otherwise insurmountable challenges. While there are many hardware options, processing in memory (PIM) techniques have gained relevance due to their recent availability as commodity DDR DRAM devices and their relatively cheap expected cost (in terms of power, area, and monetary considerations). However, even with such prevalence, existing research has yet to explore the impact of PIM on end-to-end OLAP workloads fully. In this work, we consider every aspect within a database system; the storage and memory layout, operator implementation, and the data sharing considerations. In particular, we find that ensuring data layout interoperability between query operators is an under-explored consideration that has a significant impact on performance. Using the Star Schema Benchmark, we show that for conservative PIM hardware, up to $17.1 \times$ query latency improvement can be achieved over a state-of-the-art, CPU-focused DBMS.