Cited by 29Advanced Database Systems and QueriesHistory of Computing TechnologiesData Management and Algorithms

The DOI matches its Crossref record.

What Goes Around Comes Around... And Around...

Open access
Stonebraker, Michael, Pavlo, Andrew·2024·ACM SIGMOD Record

Abstract

Two decades ago, one of us co-authored a paper commenting on the previous 40 years of data modelling research and development [188]. That paper demonstrated that the relational model (RM) and SQL are the prevailing choice for database management systems (DBMSs), despite efforts to replace either them. Instead, SQL absorbed the best ideas from these alternative approaches. We revisit this issue and argue that this same evolution has continued since 2005. Once again there have been repeated efforts to replace either SQL or the RM. But the RM continues to be the dominant data model and SQL has been extended to capture the good ideas from others. As such, we expect more of the same in the future, namely the continued evolution of SQL and relational DBMSs (RDBMSs). We also discuss DBMS implementations and argue that the major advancements have been in the RM systems, primarily driven by changing hardware characteristics.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1145/3685980.3685984

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 170

  1. TeraData Forums. https://downloads.teradata.com/forum/ database/what-is-the-difference-between-a-ynet-andbynet, September 2011.
  2. Netezza TwinFin Architecture. https://www.iexpertify.com/ learn/netezza-twinfin-architecture/#.YYq5_S1h17Y, April 2020.
  3. Georaster in oracle database. https://www.oracle.com/a/ tech/docs/georaster-2021.pdf, mar 2021.
  4. Apache Hudi. https://hudi.apache.org/, 2023.
  5. Apache Iceberg. https://iceberg.apache.org/, 2023.
  6. Oracle introduces integrated vector database to augment generative ai and dramatically increase developer productivity. https://www.oracle.com/news/announcement/ocwintegrated- vector-database-augments-generative-ai- 2023-09--19/, sep 2023.
  7. Introducing vector search on rockset. https://rockset.com/ blog/introducing-vector-search-on-rockset/, apr 2023.
  8. Apache AGE. https://age.apache.org, 2024.
  9. Apache Arrow. https://arrow.apache.org, 2024.
  10. BigchainDB. https://www.bigchaindb.com/, 2024.
  11. Brytlyt. https://brytlyt.io/, 2024.
  12. Apache Cassandra. https://cassandra.apache.org, 2024.
  13. Clustrix. https://clustrix.com, 2024.
  14. Apache Arrow DataFusion. https://arrow.apache.org/ datafusion/, 2024.
  15. Dremio. https://dremio.com/, 2024.
  16. Apache drill. https://drill.apache.org, 2024.
  17. Elasticsearch. https://www.elastic.co, 2024.
  18. Fluree. https://flur.ee/, 2024.
  19. Apache Giraph. https://giraph.apache.org, 2024.
  20. Apache Hbase. https://hbase.apache.org, 2024.
  21. The hdf5 library & file format. https://www.hdfgroup.org/ solutions/hdf5, 2024.
  22. Apache Hive. https://hive.apache.org, 2024.
  23. Janusgraph. https://janusgraph.org/, 2024.
  24. Apache Kafka. https://kafka.apache.org/, 2024.
  25. kdb+. https://kx.com/, 2024.
  26. Kinetica. https://www.kinetica.com/, 2024.
  27. LangChain. https://langchain.com, 2024.
  28. Apache Lucene. https://lucene.apache.org, 2024.
  29. Milvus. https://milvus.io/, 2024.
  30. MongoDB. https://mongodb.com, 2024.
  31. Neo4j. https://neo4j.com/, 2024.
  32. Amazon Neptune. https://aws.amazon.com/neptune/, 2024.
  33. Nuodb. https://nuodb.com, 2024.
  34. Heavydb. https://www.heavy.ai, 2024.
  35. openCypher. https://opencypher.org, 2024.
  36. Oracle graph database. https://www.oracle.com/database/ graph/, 2024.
  37. PGQL -- Property Graph Query Language. https://pgqllang. org/, 2024.
  38. Oracle Text. https://www.oracle.com/database/ technologies/datawarehouse-bigdata/text.html, 2024.
  39. Apache ORC. https://orc.apache.org/, 2024.
  40. Paradigm4 platform overview. https://www.paradigm4.com/ technology/scidb-platform-overview/, 2024.
  41. Apache Parquet. https://parquet.apache.org/, 2024.
  42. Partiql -- sql-compatible access to relational, semi-structured, and nested data. https://partiql.org/, 2024.
  43. Apache Phoenix. https://phoenix.apache.org, 2024.
  44. Pinecone. https://www.pinecone.io/, 2024.
  45. Apache Pinot. https://pinot.apache.org/, 2024.
  46. PlanetScale. https://planetscale.com/, 2024.
  47. Polars. https://www.pola.rs, 2024.
  48. PrestoDB. https://prestodb.io/, 2024.
  49. PRQL -- A Proposal for a Better SQL. https://prqllang. org/, 2024.
  50. Amazon Quantum Ledger Database (QLDB). https:// aws.amazon.com/qldb/, 2024.
  51. The rasdaman raster array database. http:// www.rasdaman.org, 2024.
  52. Redis. https://redis.io/, 2024.
  53. RocksDB. https://rocksdb.org, 2024.
  54. Singestore. https://www.singlestore.com/, 2024.
  55. Apache Solr. https://solr.apache.org/, 2024.
  56. SQLite. https://www.sqlite.org, 2024.
  57. Sql++ -- the next-generation query language for managing json data. https://www.couchbase.com/sqlplusplus, 2024.
  58. Tigergraph. https://www.tigergraph.com/, 2024.
  59. Tigergraph -- gsql. https://www.tigergraph.com/gsql/, 2024.
  60. Tiledb. https://tiledb.com, 2024.
  61. Trino. https://trino.io/, 2024.
  62. Turi. http://turi.com/, 2024.
  63. Vespa. https://vespa.ai/, 2024.
  64. Vitess. https://vitess.io, 2024.
  65. Vitesse Deepgreen DB. https://www.vitessedata.com/ products/deepgreen-db/, 2024.
  66. Project Voldemort. https://www.project-voldemort.com, 2024.
  67. Voltdb. https://www.voltactivedata.com/, 2024.
  68. Weaviate. https://weaviate.io, 2024.
  69. YugabyteDB. https://www.yugabyte.com/, 2024.
  70. 10.1007/s00778-019- · DOI
  71. A. Ailamaki, D. J. DeWitt, M. D. Hill, and M. Skounakis. Weaving relations for cache performance. In VLDB, volume 1, pages 169--180, 2001.
  72. 10.14778/3415478.3415560 · DOI
  73. M. Armbrust, A. Ghodsi, R. Xin, and M. Zaharia. Lakehouse: a new generation of open platforms that unify data warehousing and advanced analytics. In Proceedings of CIDR, page 8, 2021.
  74. 10.1145/3514221.3526045 · DOI
  75. M. Aslett. How will the database incumbents respond to NoSQL and NewSQL? The 451 Group, April 2011.
  76. M. Aslett. Ten years of NewSQL: Back to the future of distributed relational databases. The 451 Group, June 2021.
  77. S. Babu and P. Bizarro. Adaptive query processing in the looking glass. In CIDR, pages 238--249, January 2005.
  78. 10.1145/3035918.3056103 · DOI
  79. J. Baker, C. Bond, J. C. Corbett, J. Furman, A. Khorlin, J. Larson, J.-M. Leon, Y. Li, A. Lloyd, and V. Yushprakh. Megastore: Providing scalable, highly available storage for interactive services. In Proceedings of the Conference on Innovative Data system Research (CIDR), pages 223--234, 2011.
  80. 10.14778/2350229.2350242 · DOI
  81. 10.1007/3--540--48521-X_7 · DOI
  82. 10.1145/3183713.3190662 · DOI
  83. 10.1145/3514221.3526054 · DOI
  84. P. A. Boncz, M. Zukowski, and N. Nes. Monetdb/x100: Hyperpipelining query execution. In CIDR, pages 225--237, 2005.
  85. 10.1007/978--3--642--69419--6 · DOI
  86. T. Bray. AWS and Blockchain. https://www.tbray.org/ ongoing/When/202x/2022/11/19/AWS-Blockchain, nov 2019.
  87. P. Carbone, A. Katsifodimos, S. Ewen, V. Markl, S. Haridi, and K. Tzoumas. Apache flink: Stream and batch processing in a single engine. The Bulletin of the Technical Committee on Data Engineering, 38(4), 2015.
  88. 10.1145/1978915.1978919 · DOI
  89. F. Chang, J. Dean, S. Ghemawat, W. C. Hsieh, D. A. Wallach, M. Burrows, T. Chandra, A. Fikes, and R. E. Gruber. Bigtable: A distributed storage system for structured data. In Proceedings of the USENIX Symposium on Operating Systems Design and Implementation, OSDI '06, pages 205--218, 2006.
  90. S. Chaudhuri and G. Weikum. Rethinking database system architecture: Towards a self-tuning risc-style database system. In VLDB 2000, Proceedings of 26th International Conference on Very Large Data Bases, pages 1--10, 2000.
  91. 10.1109/TPAMI.1984.4767553 · DOI
  92. 10.1145/362384.362685 · DOI · Paper page
  93. E. F. Codd. Further normalization of the data base relational model. Research Report / RJ / IBM / San Jose, California, RJ909, 1971.
  94. J. C. Corbett, J. Dean, M. Epstein, A. Fikes, C. Frost, J. Furman, S. Ghemawat, A. Gubarev, C. Heiser, P. Hochschild, W. Hsieh, S. Kanthak, E. Kogan, H. Li, A. Lloyd, S. Melnik, D. Mwaura, D. Nagle, S. Quinlan, R. Rao, L. Rolig, M. S. Yasushi Saito, C. Taylor, R. Wang, and D. Woodford. Spanner: Google's Globally-Distributed Database. In OSDI, 2012.
  95. A. Crotty, V. Leis, and A. Pavlo. Are you sure you want to use MMAP in your database management system? In Conference on Innovative Data Systems Research. www.cidrdb.org, 2022.
  96. 10.1145/2882903.2903741 · DOI
  97. J. Dean and S. Ghemawat. MapReduce: Simplified data processing on large clusters. In 6th Symposium on Operating Systems Design & Implementation (OSDI 04). USENIX Association, Dec. 2004.
  98. 10.1145/1629175.1629198 · DOI
  99. 10.1145/1323293.1294281 · DOI
  100. 10.1145/3514221.3526057 · DOI
  101. 10.1145/129888.129894 · DOI
  102. C. Diaconu, C. Freedman, E. Ismert, P. Larson, P. Mittal, R. Stonecipher, N. Verma, and M. Zwilling. Hekaton: SQL server's memory-optimized OLTP engine. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 1243--1254, 2013. doi: 10.1145/ 2463676.2463710.
  103. M. Elhemali, N. Gallagher, N. Gordon, J. Idziorek, R. Krog, C. Lazier, E. Mo, A. Mritunjai, S. Perianayagam, T. Rath, S. Sivasubramanian, J. C. S. III, S. Sosothikul, D. Terry, and A. Vig. Amazon DynamoDB: A scalable, predictably performant, and fully managed NoSQL database service. In USENIX Annual Technical Conference, pages 1037--1048, July 2022.
  104. J. Fan, A. G. S. Raj, and J. M. Patel. The case against specialized graph analytics engines. In Seventh Biennial Conference on Innovative Data Systems Research, CIDR, 2015.
  105. B. Fitzpatrick. Distributed caching with memcached. Linux J., 2004(124):5, aug 2004. ISSN 1075--3583.
  106. 10.14778/3407790.3407797 · DOI
  107. 10.14778/3583140.3583165 · DOI
  108. 10.1145/1165389.945450 · DOI
  109. 10.1109/ICDE.1996.492099 · DOI
  110. 10.14778/3380750.3380757 · DOI
  111. E. Hanson and A. Comet. Why Your Vector Database Should Not be a Vector Database. https://www.singlestore.com/ blog/why-your-vector-database-should-not-be-avector- database/, April 2023.
  112. G. Harrison. How WiredTiger Revolutionized MongoDB. https://www.dbta.com/Columns/MongoDB-Matters/How- WiredTiger-Revolutionized-MongoDB-145510.aspx, mar 2021.
  113. 10.1145/320251.320253 · DOI
  114. 10.1145/2588555.2595630 · DOI
  115. 10.14778/3415478.3415535 · DOI
  116. K. E. Iverson. A Programming Language. John Wiley & Sons Inc. 1962. ISBN 0471430145.
  117. A. Jindal, S. Madden, M. Castellanos, and M. Hsu. Graph analytics using vertica relational database. In 2015 IEEE International Conference on Big Data, pages 1191--1200, 2015. · DOI
  118. 10.14778/1454159.1454211 · DOI
  119. 10.1109/ICDE.2011.5767867 · DOI
  120. T. Kersten, V. Leis, A. Kemper, T. Neumann, A. Pavlo, and P. Boncz. Everything you always wanted to know about compiled and vectorized queries but were afraid to ask. Proc. VLDB Endow., 11(13):2209--2222, jan 2019. doi: 10.14778/ 3275366.3284966. · DOI
  121. R. Kimball. The DataWarehouse Toolkit: Practical Techniques for Building Dimensional Data Warehouses. John Wiley, 1996.
  122. 10.1145/211990.212023 · DOI
  123. M. Kornacker, A. Behm, V. Bittorf, T. Bobrovytsky, C. Ching, A. Choi, J. Erickson, M. Grund, D. Hecht, M. Jacobs, I. Joshi, L. Kuff, D. Kumar, A. Leblang, N. Li, I. Pandis, H. Robinson, D. Rorke, S. Rus, J. Russell, D. Tsirogiannis, S. Wanderman- Milne, and M. Yoder. Impala: A modern, open-source sql engine for hadoop. In CIDR, 2015.
  124. 10.1145/3183713.3196909 · DOI · Paper page
  125. S. Krishnan, Z. Yang, K. Goldberg, J. Hellerstein, and I. Stoica. Learning to optimize join queries with deep reinforcement learning, 2018. URL https://arxiv.org/abs/1808.03196. · arXiv
  126. 10.14778/2850583.2850594 · DOI
  127. 10.1145/153850.153851 · DOI
  128. 10.1145/3448016.3452838 · DOI
  129. W. McKinney et al. Data structures for statistical computing in python. In Proceedings of the 9th Python in Science Conference, volume 445, pages 51--56, 2010.
  130. F. McSherry. Scalability! but at what cost? http: //www.frankmcsherry.org/graph/scalability/cost/2015/ 01/15/COST.html, January 2015.
  131. 10.14778/1920841.1920886 · DOI
  132. 10.14778/3415478.3415568 · DOI
  133. 10.14778/3425879.3425882 · DOI
  134. 10.1145/3299887.3299897 · DOI
  135. 10.14778/3352063.3352116 · DOI
  136. 10.1145/2590989.2590991 · DOI
  137. M. A. Olson, K. Bostic, and M. I. Seltzer. Berkeley DB. In Proceedings of the FREENIX Track: 1999 USENIX Annual Technical Conference, pages 183--191, 1999.
  138. 10.1145/3003665.3003674 · DOI
  139. 10.1145/1559845.1559865 · DOI
  140. A. Pavlo, G. Angulo, J. Arulraj, H. Lin, J. Lin, L. Ma, P. Menon, T. Mowry, M. Perron, I. Quah, S. Santurkar, A. Tomasic, S. Toor, D. V. Aken, Z. Wang, Y. Wu, R. Xian, and T. Zhang. Self-driving database management systems. In CIDR 2017, Conference on Innovative Data Systems Research, 2017.
  141. A. Pavlo, M. Butrovich, A. Joshi, L. Ma, P. Menon, D. V. Aken, L. Lee, and R. Salakhutdinov. External vs. internal: An essay on machine learning agents for autonomous database management systems. IEEE Data Eng. Bull., 42(2):32--46, 2019.
  142. 10.14778/3554821.3554829 · DOI
  143. 10.14778/3603581.3603604 · DOI
  144. 10.14778/3407790.3407807 · DOI
  145. D. Petkovic. SQL/JSON standard: Properties and deficiencies. Datenbank-Spektrum, 17(3):277--287, 2017. doi: 10.1007/ s13222-017-0267--4. · DOI
  146. 10.1145/1394127.1394128 · DOI
  147. 10.1145/3299869.3320212 · DOI
  148. 10.25080/Majora-7b98e3ed-013 · DOI
  149. F. Rusu. Multidimensional array data management. Found. Trends Databases, 12(2--3):69--220, 2023. doi: 10.1561/ 1900000069.
  150. 10.1145/3434642 · DOI
  151. 10.1145/364955.364990 · DOI
  152. 10.1109/ICDE.2019.00196 · DOI
  153. 10.1145/2588555.2595637 · DOI
  154. M. Stonebraker. The case for polystores. https:// wp.sigmod.org/?p=1629, 2015.
  155. M. Stonebraker and J. Hellerstein. Readings in Database Systems, chapter What Goes Around Comes Around, pages 2--41. 4th edition, 2005.
  156. M. Stonebraker, S. Madden, D. J. Abadi, S. Harizopoulos, N. Hachem, and P. Helland. The end of an architectural era: (it's time for a complete rewrite). In Proceedings of the 33rd International Conference on Very Large Data Bases, VLDB '07, pages 1150--1160. VLDB Endowment, 2007.
  157. 10.1145/1629175.1629197 · DOI
  158. 10.1007/978--3--642--22351--8 · DOI
  159. L. Sullivan. Performance issues in mid-sized relational database machines. Master's thesis, Rochester Institute of Technology, 1989.
  160. 10.14778/3594512.3594528 · DOI
  161. Y. Sverdlik. Google dumps mapreduce in favor of new hyper-scale analytics system. https: //www.datacenterknowledge.com/archives/2014/06/ 25/google-dumps-mapreduce-favor-new-hyper-scaleanalytics- system, June 2014.
  162. 10.1145/3318464.3386134 · DOI
  163. D. ten Wolde, T. Singh, G. Szarnyas, and P. Boncz. Duckpgq: Efficient property graph queries in an analytical rdbms. In CIDR, 2023. URL https://www.cidrdb.org/cidr2023/ papers/p66-wolde.pdf.
  164. 10.1109/ICDE.2010.5447738 · DOI
  165. 10.1145/3079079.3079099 · DOI
  166. 10.1145/3035918.3064029 · DOI
  167. 10.1145/2934664 · DOI · Paper page
  168. C. Zaniolo. The database language GEM. In SIGMOD, pages 207--218, 1983.
  169. 10.14778/3626292.3626298 · DOI
  170. 10.14778/3538598.3538604 · DOI

Cited by

See all 29

Metadata

Authors
Stonebraker, M., & Pavlo, A.
Affiliations
  • Massachusetts Institute of Technology (US)
  • Carnegie Mellon University (US)
Venue
ACM SIGMOD Record
Published
2024-07-30
Volume
53
Issue
2
Pages
21-37
ISSN
0163-5808 (ISSN-L)
OpenAlex
W4401183274
Cited by
29
References
0
Citations by year
2026: 15 · 2025: 10 · 2024: 4
FWCI
4.84
Type
journal article
Language
en
Open access
Locations
ACM SIGMOD Record (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Advanced Database Systems and Queries
  • Physical Sciences › Computer Science › Computer Science Applications › History of Computing Technologies
  • Physical Sciences › Computer Science › Information Systems › Data Management and Algorithms
Keywords
relational model, SQL, database management systems, data modeling

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: What Goes Around Comes Around... And Around... https://app.cochat.ai/papers/10.1145/3685980.3685984
The paper
Stonebraker, M., & Pavlo, A. (2024). What Goes Around Comes Around... And Around... ACM SIGMOD Record. https://doi.org/10.1145/3685980.3685984