Christian Tinnefeld
ABSTRACT
A shared-nothing architecture is state-of-the-art for deploying a distributed analytical in-memory database management system: it preserves the in-memory performance advantage by processing data locally on each node but is difficult to scale out. Modern switched fabric communication links such as InfiniBand narrow the performance gap between local and remote DRAM data access to a single order of magnitude. Based on these premises, we introduce a distributed in-memory database architecture that separates the query execution engine and data access: this enables a) the usage of a large-scale DRAM-based storage system such as Stanford's RAMCloud and b) the push-down of bandwidth-intensive database operators into the storage system. We address the resulting challenges such as finding the optimal operator execution strategy and partitioning scheme. We demonstrate that such an architecture delivers both: the elasticity of a shared-storage approach and the performance characteristics of operating on local DRAM.
- D. J. Abadi, D. S. Myers, D. J. DeWitt, and S. Madden. Materialization Strategies in a Column-Oriented DBMS. In Proceedings of the 23rd International Conference on Data Engineering, ICDE 2007, The Marmara Hotel, Istanbul, Turkey, April 15--20, 2007, pages 466--475. IEEE, 2007.Google Scholar
Cross Ref
- P. A. Boncz, M. L. Kersten, and S. Manegold. Breaking the memory wall in MonetDB. Commun. ACM, 51(12):77--85, 2008. Google ScholarDigital Library
- D. Borthakur. The Hadoop Distributed File System: Architecture and Design. The Apache Software Foundation, 2007.Google Scholar
- M. Brantner, D. Florescu, D. A. Graf, D. Kossmann, and T. Kraska. Building a database on S3. In Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2008, Vancouver, BC, Canada, June 10--12, 2008, pages 251--264. ACM, 2008. Google ScholarDigital Library
- C. Curino, E. P. Jones, S. Madden, and H. Balakrishnan. Workload-aware database monitoring and consolidation. In Proceedings of the 2011 ACM SIGMOD International Conference on Management of data, SIGMOD '11, pages 313--324, New York, NY, USA, 2011. ACM. Google ScholarDigital Library
- J. Dean and S. Ghemawat. Mapreduce: a flexible data processing tool. Commun. ACM, 53(1):72--77, Jan. 2010. Google ScholarDigital Library
- F. Färber, N. May, W. Lehner, P. Große, I. Müller, H. Rauhe, and J. Dees. The SAP HANA Database -- An Architecture Overview. IEEE Data Eng. Bull., 35(1):28--33, 2012.Google Scholar
- InfiniBand Trade Association. The InfiniBand Architecture.Google Scholar
- Intel Coporation. Intel Xeon Processor E5-4650 Specification.Google Scholar
- D. Kossmann. The state of the art in distributed query processing. ACM Comput. Surv., 32(4):422--469, Dec. 2000. Google ScholarDigital Library
- S. Melnik, A. Gubarev, J. J. Long, G. Romer, S. Shivakumar, M. Tolton, and T. Vassilakis. Dremel: interactive analysis of web-scale datasets. Proc. VLDB Endow., 3(1-2):330--339, Sept. 2010. Google ScholarDigital Library
- H. Montaner, F. Silla, H. Fröning, and J. Duato. Memscale: in-cluster-memory databases. In Proceedings of the 20th ACM international conference on Information and knowledge management, CIKM '11, pages 2569--2572, New York, NY, USA, 2011. ACM. Google ScholarDigital Library
- B. Nitzberg and V. Lo. Distributed shared memory: A survey of issues and algorithms. Computer, 24(8):52--60, Aug. 1991. Google ScholarDigital Library
- P. E. O'Neil, E. J. O'Neil, X. Chen, and S. Revilak. The star schema benchmark and augmented fact table indexing. In R. O. Nambiar and M. Poess, editors, TPCTC, volume 5895 of Lecture Notes in Computer Science, pages 237--252. Springer, 2009. Google ScholarDigital Library
- O'Neil, P. E. and O'Neil, E. J. and Chen, X. The Star Schema Benchmark (SSB).Google Scholar
- D. Ongaro, S. M. Rumble, R. Stutsman, J. K. Ousterhout, and M. Rosenblum. Fast crash recovery in RAMCloud. In Proceedings of the 23rd ACM Symposium on Operating Systems Principles 2011, SOSP 2011, Cascais, Portugal, October 23--26, 2011, pages 29--41. ACM, 2011. Google ScholarDigital Library
- J. K. Ousterhout, P. Agrawal, D. Erickson, C. Kozyrakis, J. Leverich, D. Mazières, S. Mitra, A. Narayanan, D. Ongaro, G. M. Parulkar, M. Rosenblum, S. M. Rumble, E. Stratmann, and R. Stutsman. The case for RAMCloud. Commun. ACM, 54(7):121--130, 2011. Google ScholarDigital Library
- M. T. Ozsu. Principles of Distributed Database Systems. Prentice Hall Press, Upper Saddle River, NJ, USA, 3rd edition, 2007. Google ScholarDigital Library
- A. Raghuveer, S. W. Schlosser, and S. Iren. Enabling database-aware storage with osd. In MSST, pages 129--142. IEEE Computer Society, 2007. Google ScholarDigital Library
- E. Rahm. Parallel query processing in shared disk database systems. SIGMOD Rec., 22(4):32--37, Dec. 1993. Google ScholarDigital Library
- S. M. Rumble, D. Ongaro, R. Stutsman, M. Rosenblum, and J. K. Ousterhout. It's time for low latency. In Proceedings of the 13th USENIX conference on Hot topics in operating systems, HotOS'13, pages 11--11, Berkeley, CA, USA, 2011. USENIX Association. Google ScholarDigital Library
- M. Sivathanu, L. N. Bairavasundaram, A. C. Arpaci-dusseau, and R. H. Arpaci-dusseau. Database-aware semantically-smart storage. In In Proceedings of the 4th USENIX Conference on File and Storage Technologies. USENIX Association, pages 239--252, 2005. Google ScholarDigital Library
- M. Stonebraker. The case for shared nothing. IEEE Database Eng. Bull., 9(1):4--9, 1986.Google Scholar
- M. Stonebraker, D. Abadi, D. J. DeWitt, S. Madden, E. Paulson, A. Pavlo, and A. Rasin. Mapreduce and parallel dbmss: friends or foes? Commun. ACM, 53(1):64--71, Jan. 2010. Google ScholarDigital Library
- M. Stonebraker, C. Bear, U. Çetintemel, M. Cherniack, T. Ge, N. Hachem, S. Harizopoulos, J. Lifter, J. Rogers, and S. B. Zdonik. One Size Fits All? Part 2: Benchmarking Studies. In CIDR 2007, Third Biennial Conference on Innovative Data Systems Research, Asilomar, CA, USA, January 7--10, 2007, Online Proceedings, pages 173--184. www.cidrdb.org, 2007.Google Scholar
- Texas Memory Systems. TMS RamSan-440 Details.Google Scholar
- C. Tinnefeld, A. Zeier, and H. Plattner. Cache-conscious data placement in an in-memory key-value store. In 15th International Database Engineering and Applications Symposium (IDEAS 2011), September 21--27, 2011, Lisbon, Portugal, pages 134--142. ACM, 2011. Google ScholarDigital Library
- E. Wong and R. H. Katz. Distributing a database for parallelism. SIGMOD Rec., 13(4):23--29, May 1983. Google ScholarDigital Library
Index Terms
- Elastic online analytical processing on RAMCloud
Recommendations
The RAMCloud Storage System
RAMCloud is a storage system that provides low-latency access to large-scale datasets. To achieve low latency, RAMCloud stores all data in DRAM at all times. To support large capacities (1PB or more), it aggregates the memories of thousands of servers ...
Fast crash recovery in RAMCloud
SOSP '11: Proceedings of the Twenty-Third ACM Symposium on Operating Systems PrinciplesRAMCloud is a DRAM-based storage system that provides inexpensive durability and availability by recovering quickly after crashes, rather than storing replicas in DRAM. RAMCloud scatters backup data across hundreds or thousands of disks, and it ...
A RAMCloud Storage System based on HDFS: Architecture, implementation and evaluation
Few cloud storage systems can handle random read accesses efficiently. In this paper, we present a RAMCloud Storage System, RCSS, to enable efficient random read accesses in cloud environments. Based on the Hadoop Distributed File System (HDFS), RCSS ...
Comments