ERIS: A NUMA-Aware In-Memory Storage Engine for Analytical Workload

The ever-growing demand for more computing power forces hardware vendors to put an increasing number of multiprocessors into a single server system, which usually exhibits a non-uniform memory access (NUMA). In-memory database systems running on NUMA platforms face several issues such as the increased latency and the decreased bandwidth when accessing remote main memory. To cope with these NUMA-related issues, NUMA-awareness has to be considered as a major design principle for the fundamental architecture of a database system. In this paper we present ERIS, a NUMA-aware inmemory storage engine that is based on a data-oriented architecture. In contrast to existing approaches that focus on transactional workloads on a disk-based DBMS, ERIS aims at tera-scale analytical workloads that are executed entirely in main memory. ERIS uses an adaptive partitioning approach that exploits the topology of the underlying NUMA platform and significantly reduces NUMA-related issues. We evaluate ERIS on widespread standard server systems as well as on a system consisting of 64 multiprocessors and 512 cores. On these platforms, we achieve a more than linear speedup for index lookups and scalable parallel scan operations that are only limited by the available local bandwidth of the multiprocessor. Moreover, we measured a performance gain of up to 200% (index lookups) respectively 660% (column scans) in the memory-bound case compared to a NUMA-agnostic storage subsystem.