Improving index performance through prefetching

ABSTRACT

This paper proposes and evaluate Prefetching B⁺-Trees (pB⁺-Trees), which use prefetching to accelerate two important operations on B⁺-Tree indices: searches and range scans. To accelerate searches, pB⁺-Trees use prefetching to effectively create wider nodes than the natural data transfer size: e.g., eight vs. one cache lines or disk pages. These wider nodes reduce the height of the B⁺-Tree, thereby decreasing the number of expensive misses when going from parent to child without significantly increasing the cost of fetching a given node. Our results show that this technique speeds up search and update times by a factor of 1.21-1.5 for main-memory B⁺-Trees. In addition, it outperforms and is complementary to “Cache-Sensitive B⁺-Trees.” To accelerate range scans, pB⁺-Trees provide arrays of pointers to their leaf nodes. These allow the pB⁺-Tree to prefetch arbitrarily far ahead, even for nonclustered indices, thereby hiding the normally expensive cache misses associated with traversing the leaves within the range. Our results show that this technique yields over a sixfold speedup on range scans of 1000+ keys. Although our experimental evaluation focuses on main memory databases, the techniques that we propose are also applicable to hiding disk latency.

REFERENCES

• 1: Anastassia Ailamaki , David J. DeWitt , Mark D. Hill , David A. Wood, DBMSs on a Modern Processor: Where Does Time Go?, Proceedings of the 25th International Conference on Very Large Data Bases, p.266-277, September 07-10, 1999
• 2: Luiz André Barroso , Kourosh Gharachorloo , Edouard Bugnion, Memory system characterization of commercial workloads, Proceedings of the 25th annual international symposium on Computer architecture, p.3-14, June 27-July 02, 1998, Barcelona, Spain
• 3: L. A. Barroso, K. Gharachorloo, A.Nowatzyk, and B. Verghese. Impact of Chip-Level Integration on Performance of OLTP Workloads. In Proceedings of the 6th HPCA, pages 3-14, Jan. 2000.
• 4: Rudolf Bayer , Karl Unterauer, Prefix B-trees, ACM Transactions on Database Systems (TODS), v.2 n.1, p.11-26, March 1977 [doi>10.1145/320521.320530]
• 5: M. A. Bender , E. D. Demaine , M. Farach-Colton, Cache-oblivious B-trees, Proceedings of the 41st Annual Symposium on Foundations of Computer Science, p.399, November 12-14, 2000
• 6: Philip Bohannon , Peter Mcllroy , Rajeev Rastogi, Main-memory index structures with fixed-size partial keys, Proceedings of the 2001 ACM SIGMOD international conference on Management of data, p.163-174, May 21-24, 2001, Santa Barbara, California, United States
• 7: S. Chen, P. B. Gibbons, and T. C. Mowry. Improving Index Performance through Prefetching. Technical Report CMU-CS-00-177, School of Computer Science, Carnegie Mellon University, Dec. 2000.
• 8: Zarka Cvetanovic , R. E. Kessler, Performance analysis of the Alpha 21264-based Compaq ES40 system, Proceedings of the 27th annual international symposium on Computer architecture, p.192-202, June 2000, Vancouver, British Columbia, Canada
• 9: Jonathan Goldstein , Raghu Ramakrishnan , Uri Shaft, Compressing Relations and Indexes, Proceedings of the Fourteenth International Conference on Data Engineering, p.370-379, February 23-27, 1998
• 10: Kimberly Keeton , David A. Patterson , Yong Qiang He , Roger C. Raphael , Walter E. Baker, Performance characterization of a Quad Pentium Pro SMP using OLTP workloads, Proceedings of the 25th annual international symposium on Computer architecture, p.15-26, June 27-July 02, 1998, Barcelona, Spain
• 11: Tobin J. Lehman , Michael J. Carey, A Study of Index Structures for Main Memory Database Management Systems, Proceedings of the 12th International Conference on Very Large Data Bases, p.294-303, August 25-28, 1986
• 12: Tobin J. Lehman , Michael J. Carey, Query processing in main memory database management systems, Proceedings of the 1986 ACM SIGMOD international conference on Management of data, p.239-250, May 28-30, 1986, Washington, D.C., United States
• 13: Chi-Keung Luk , Todd C. Mowry, Compiler-based prefetching for recursive data structures, Proceedings of the seventh international conference on Architectural support for programming languages and operating systems, p.222-233, October 01-04, 1996, Cambridge, Massachusetts, United States
• 14: Chi-Keung Luk , Todd C. Mowry, Automatic Compiler-Inserted Prefetching for Pointer-Based Applications, IEEE Transactions on Computers, v.48 n.2, p.134-141, February 1999 [doi>10.1109/12.752654]
• 15: S. McFarling. Combining Branch Predictors. Technical Report WRL Technical Note TN-36, Digital Equipment Corporation, June 1993.
• 16: Todd C. Mowry , Monica S. Lam , Anoop Gupta, Design and evaluation of a compiler algorithm for prefetching, Proceedings of the fifth international conference on Architectural support for programming languages and operating systems, p.62-73, October 12-15, 1992, Boston, Massachusetts, United States
• 17: Chris Nyberg , Tom Barclay , Zarka Cvetanovic , Jim Gray , Dave Lomet, AlphaSort: a RISC machine sort, Proceedings of the 1994 ACM SIGMOD international conference on Management of data, p.233-242, May 24-27, 1994, Minneapolis, Minnesota, United States
• 18: Parthasarathy Ranganathan , Kourosh Gharachorloo , Sarita V. Adve , Luiz André Barroso, Performance of database workloads on shared-memory systems with out-of-order processors, Proceedings of the eighth international conference on Architectural support for programming languages and operating systems, p.307-318, October 02-07, 1998, San Jose, California, United States
• 19: Jun Rao , Kenneth A. Ross, Cache Conscious Indexing for Decision-Support in Main Memory, Proceedings of the 25th International Conference on Very Large Data Bases, p.78-89, September 07-10, 1999
• 20: Jun Rao , Kenneth A. Ross, Making B+- trees cache conscious in main memory, Proceedings of the 2000 ACM SIGMOD international conference on Management of data, p.475-486, May 15-18, 2000, Dallas, Texas, United States
• 21: Ambuj Shatdal , Chander Kant , Jeffrey F. Naughton, Cache Conscious Algorithms for Relational Query Processing, Proceedings of the 20th International Conference on Very Large Data Bases, p.510-521, September 12-15, 1994
• 22: Abraham Silberschatz , Henry F. Korth , S. Sudershan, Database System Concepts, McGraw-Hill, Inc., New York, NY, 1998
• 23: Kenneth C. Yeager, The MIPS R10000 Superscalar Microprocessor, IEEE Micro, v.16 n.2, p.28-40, April 1996 [doi>10.1109/40.491460]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Indexing methods

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Search process
      H.3.4 Systems and Software
          Subjects: Performance evaluation (efficiency and effectiveness)

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.8 Problem Solving, Control Methods, and Search
          Subjects: Graph and tree search strategies

General Terms:
Algorithms, Design, Experimentation, Management, Measurement, Performance, Theory