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Designing Data-Intensive Applications

Chapter 3 References

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  14. Michael McCandless: “Visualizing Lucene's Segment Merges,” blog.mikemccandless.com, February 11, 2011.

  15. Burton H. Bloom: “Space/Time Trade-offs in Hash Coding with Allowable Errors,” Communications of the ACM, volume 13, number 7, pages 422–426, July 1970. doi:10.1145/362686.362692

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  22. Bradley C. Kuszmaul: “A Comparison of Fractal Trees to Log-Structured Merge (LSM) Trees,” tokutek.com, April 22, 2014.

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  24. Peter Zaitsev: “Innodb Double Write,” percona.com, August 4, 2006.

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  26. Mark Callaghan: “The Advantages of an LSM vs a B-Tree,” smalldatum.blogspot.co.uk, January 19, 2016.

  27. Mark Callaghan: “Choosing Between Efficiency and Performance with RocksDB,” at Code Mesh, November 4, 2016.

  28. Michi Mutsuzaki: “MySQL vs. LevelDB” [URL inactive], August 2011.

  29. Benjamin Coverston, Jonathan Ellis, et al.: “CASSANDRA-1608: Redesigned Compaction, issues.apache.org, July 2011.

  30. Igor Canadi, Siying Dong, and Mark Callaghan: “RocksDB Tuning Guide,” github.com, 2016.

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  33. Joe Webb: “Using Covering Indexes to Improve Query Performance,” simple-talk.com, 29 September 2008.

  34. Frank Ramsak, Volker Markl, Robert Fenk, et al.: “Integrating the UB-Tree into a Database System Kernel,” at 26th International Conference on Very Large Data Bases (VLDB), September 2000.

  35. The PostGIS Development Group: “PostGIS 2.1.2dev Manual,” postgis.net, 2014.

  36. Robert Escriva, Bernard Wong, and Emin Gün Sirer: “HyperDex: A Distributed, Searchable Key-Value Store,” at ACM SIGCOMM Conference, August 2012. doi:10.1145/2377677.2377681

  37. Michael McCandless: “Lucene's FuzzyQuery Is 100 Times Faster in 4.0,” blog.mikemccandless.com, March 24, 2011.

  38. Steffen Heinz, Justin Zobel, and Hugh E. Williams: “Burst Tries: A Fast, Efficient Data Structure for String Keys,” ACM Transactions on Information Systems, volume 20, number 2, pages 192–223, April 2002. doi:10.1145/506309.506312

  39. Klaus U. Schulz and Stoyan Mihov: “Fast String Correction with Levenshtein Automata,” International Journal on Document Analysis and Recognition, volume 5, number 1, pages 67–85, November 2002. doi:10.1007/s10032-002-0082-8

  40. Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze: Introduction to Information Retrieval. Cambridge University Press, 2008. ISBN: 978-0-521-86571-5, available online at nlp.stanford.edu/IR-book

  41. Michael Stonebraker, Samuel Madden, Daniel J. Abadi, et al.: “The End of an Architectural Era (It’s Time for a Complete Rewrite),” at 33rd International Conference on Very Large Data Bases (VLDB), September 2007.

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  43. Stephen M. Rumble, Ankita Kejriwal, and John K. Ousterhout: “Log-Structured Memory for DRAM-Based Storage,” at 12th USENIX Conference on File and Storage Technologies (FAST), February 2014.

  44. Stavros Harizopoulos, Daniel J. Abadi, Samuel Madden, and Michael Stonebraker: “OLTP Through the Looking Glass, and What We Found There,” at ACM International Conference on Management of Data (SIGMOD), June 2008. doi:10.1145/1376616.1376713

  45. Justin DeBrabant, Andrew Pavlo, Stephen Tu, et al.: “Anti-Caching: A New Approach to Database Management System Architecture,” Proceedings of the VLDB Endowment, volume 6, number 14, pages 1942–1953, September 2013.

  46. Joy Arulraj, Andrew Pavlo, and Subramanya R. Dulloor: “Let's Talk About Storage & Recovery Methods for Non-Volatile Memory Database Systems,” at ACM International Conference on Management of Data (SIGMOD), June 2015. doi:10.1145/2723372.2749441

  47. Edgar F. Codd, S. B. Codd, and C. T. Salley: “Providing OLAP to User-Analysts: An IT Mandate,” E. F. Codd Associates, 1993.

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  49. Per-Åke Larson, Cipri Clinciu, Campbell Fraser, et al.: “Enhancements to SQL Server Column Stores,” at ACM International Conference on Management of Data (SIGMOD), June 2013.

  50. Franz Färber, Norman May, Wolfgang Lehner, et al.: “The SAP HANA Database – An Architecture Overview,” IEEE Data Engineering Bulletin, volume 35, number 1, pages 28–33, March 2012.

  51. Michael Stonebraker: “The Traditional RDBMS Wisdom Is (Almost Certainly) All Wrong,” presentation at EPFL, May 2013.

  52. Daniel J. Abadi: “Classifying the SQL-on-Hadoop Solutions,” hadapt.com, October 2, 2013.

  53. Marcel Kornacker, Alexander Behm, Victor Bittorf, et al.: “Impala: A Modern, Open-Source SQL Engine for Hadoop,” at 7th Biennial Conference on Innovative Data Systems Research (CIDR), January 2015.

  54. Sergey Melnik, Andrey Gubarev, Jing Jing Long, et al.: “Dremel: Interactive Analysis of Web-Scale Datasets,” at 36th International Conference on Very Large Data Bases (VLDB), pages 330–339, September 2010.

  55. Ralph Kimball and Margy Ross: The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd edition. John Wiley & Sons, July 2013. ISBN: 978-1-118-53080-1

  56. Derrick Harris: “Why Apple, eBay, and Walmart Have Some of the Biggest Data Warehouses You’ve Ever Seen,” gigaom.com, March 27, 2013.

  57. Julien Le Dem: “Dremel Made Simple with Parquet,” blog.twitter.com, September 11, 2013.

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  59. Peter Boncz, Marcin Zukowski, and Niels Nes: “MonetDB/X100: Hyper-Pipelining Query Execution,” at 2nd Biennial Conference on Innovative Data Systems Research (CIDR), January 2005.

  60. Jingren Zhou and Kenneth A. Ross: “Implementing Database Operations Using SIMD Instructions,” at ACM International Conference on Management of Data (SIGMOD), pages 145–156, June 2002. doi:10.1145/564691.564709

  61. Michael Stonebraker, Daniel J. Abadi, Adam Batkin, et al.: “C-Store: A Column-oriented DBMS,” at 31st International Conference on Very Large Data Bases (VLDB), pages 553–564, September 2005.

  62. Andrew Lamb, Matt Fuller, Ramakrishna Varadarajan, et al.: “The Vertica Analytic Database: C-Store 7 Years Later,” Proceedings of the VLDB Endowment, volume 5, number 12, pages 1790–1801, August 2012.

  63. Julien Le Dem and Nong Li: “Efficient Data Storage for Analytics with Apache Parquet 2.0,” at Hadoop Summit, San Jose, June 2014.

  64. Jim Gray, Surajit Chaudhuri, Adam Bosworth, et al.: “Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals,” Data Mining and Knowledge Discovery, volume 1, number 1, pages 29–53, March 2007. doi:10.1023/A:1009726021843