Argyros, T.

Boncz, P.A. (Peter)

Dietterich, D.

Harizopoulos, S.

Madden, S. (Samuel)

Waas, F.

English

Waas, F. (Florian)

CWI's Institutional Repository

Database Architecture (R)evolution:New Hardware vs. New SoftwareStavros Harizopoulos (Panel Moderator, HP Labs), Tassos Argyros (Aster Data), Peter A. Boncz (CWI),Dan Dietterich (Netezza), Samuel Madden (MIT), Florian M. Waas (Greenplum)I. PANEL OVERVIEWThe last few years have been exciting for data managementsystem designers. The explosion in user and enterprise datacoupled with the availability of newer, cheaper, and morecapable hardware have lead system designers and researchersto rethink and, in some cases, reinvent the traditional DBMSarchitecture. In the space of data warehousing and analyticsalone, more than a dozen new database product offerings haverecently appeared, and dozens of research system papers areroutinely published each year. Among these efforts, oneschool of thought promotes research on exploiting andanticipating new hardware (many-core CPUs [4, 7, 8], GPUs[3], FPGAs [5, 11], flash SSDs [6], other non-volatile storagetechnologies). Another school of thought focuses on softwareand algorithmic issues (column and hybrid stores [1, 10, 13],scale out architectures using commodity hardware [2, 9, 10,13], optimizations in network and OS software stack [9]). And,at the same time, there are approaches that combine hardware-specific optimizations with from-scratch database softwaredesign [12].In this panel, we will ask our panelists, a mix of industryand academic experts, which of those trends will have lastingeffects on database system design, and which directions holdthe biggest potential for future research. We are particularlyinterested in the differences in views and approaches betweenacademic and industrial research.Some of the questions that will be addressed during thepanel are the following: Is the use of non-conventional CPUs, from GPUs toFPGAs and to custom chips, a research exercise, or aglimpse of things to come? In a few years, a computing node will feature 10s to100s of cores, and data will fit in fast non-volatilestorage. Data volume and workloads will scale orders ofmagnitude. How well are existing systems prepared forthis? Scalability means different things to different people.What is it to you? Do academic researchers think “big”enough? Column-store start-ups are flourishing, yet “big”database vendors so far adhere to row-oriented dataprocessing. Why? Are flash SSDs simply faster disks, or a whole new andexciting research playground? Scale out (shared nothing) database architectures areconstantly gaining ground against scale up (sharedmemory/disk) ones. Will this trend hold or will historyrepeat itself?ACKNOWLEDGMENTWe would like to thank the ICDE 2010 General Chairs,Umeshwar Dayal and Vassilis Tsotras, and the ICDE 2010Panel Chairs, Anastassia Ailamaki and Carlo Zaniolo, forsuggesting the panel topic and assisting with the panel’sorganization.REFERENCES[1] D. J. Abadi, P. A. Boncz, and S. Harizopoulos, “Column-orientedDatabase Systems,” in Proc. PVLDB’09, 2009.[2] A. Abouzeid, K. Bajda-Pawlikowski, D. J. Abadi, A. Rasin, and A.Silberschatz, “HadoopDB: An Architectural Hybrid of MapReduce andDBMS Technologies for Analytical Workloads,” in Proc. PVLDB’09,2009.[3] N. K. Govindaraju, B. Lloyd, W. Wang, M. C. Lin, and D. Manocha,“Fast Computation of Database Operations using GraphicsProcessors,” in Proc. SIGMOD’04, 2004.[4] R. Lee, X. Ding, F. Chen, Q. Lu, and X. Zhang, “MCC-DB:Minimizing Cache Conflicts in Multi-core Processors for Databases,”in Proc. PVLDB’09, 2009.[5] R. Müller, J. Teubner, and G. Alonso, “Data Processing on FPGAs,” inProc. PVLDB’09, 2009.[6] D. Tsirogiannis, S. Harizopoulos, M. A. Shah, J. L. Wiener, and G.Graefe, “Query Processing Techniques for Solid State Drives,” in Proc.SIGMOD’09, 2009.[7] F. M. Waas and J. M. Hellerstein, “Parallelizing extensible queryoptimizers,” in Proc. SIGMOD’09, 2009.[8] T. Willhalm, N. Popovici, Y. Boshmaf, H. Plattner, A. Zeier, and J.Schaffner, “SIMD-Scan: Ultra Fast in-Memory Table Scan using on-Chip Vector Processing Units,” in Proc. PVLDB’09, 2009.[9] Aster Data, http://www.asterdata.com/[10] Greenplum, http://www.greenplum.com/[11] Netezza, http://www.netezza.com/[12] VectorWise, http://www.vectorwise.com/[13] Vertica, http://www.vertica.com/

Database Architecture (R)evolution:New Hardware vs. New Software

Database Architecture (R)evolution:New Hardware vs. New Software

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