MonetDB/X100: Hyper-Pipelining Query Execution Peter Boncz, Marcin Zukowski, Niels Nes CWI Kruislaan 413 Amsterdam, The Netherlands fP.Boncz,M.Zukowski,
[email protected] Abstract One would expect that query-intensive database workloads such as decision support, OLAP, data- Database systems tend to achieve only mining, but also multimedia retrieval, all of which re- low IPC (instructions-per-cycle) efficiency on quire many independent calculations, should provide modern CPUs in compute-intensive applica- modern CPUs the opportunity to get near optimal IPC tion areas like decision support, OLAP and (instructions-per-cycle) efficiencies. multimedia retrieval. This paper starts with However, research has shown that database systems an in-depth investigation to the reason why tend to achieve low IPC efficiency on modern CPUs in this happens, focusing on the TPC-H bench- these application areas [6, 3]. We question whether mark. Our analysis of various relational sys- it should really be that way. Going beyond the (im- tems and MonetDB leads us to a new set of portant) topic of cache-conscious query processing, we guidelines for designing a query processor. investigate in detail how relational database systems The second part of the paper describes the interact with modern super-scalar CPUs in query- architecture of our new X100 query engine intensive workloads, in particular the TPC-H decision for the MonetDB system that follows these support benchmark. guidelines. On the surface, it resembles a The main conclusion we draw from this investiga- classical Volcano-style engine, but the cru- tion is that the architecture employed by most DBMSs cial difference to base all execution on the inhibits compilers from using their most performance- concept of vector processing makes it highly critical optimization techniques, resulting in low CPU CPU efficient.