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Hybrid Transactional and Analytical Processing

Encyclopedia of Big Data Technologies

Synonyms

Hybrid transactional and analytical processing; HTAP; Operational analytics; Transactional analytics

Definitions

Hybrid transactional and analytical processing (HTAP) refers to system architectures and techniques that enable modern database management systems (DBMSs) to perform real-time analytics on data that is ingested and modified in the transactional database engine. It is a term that was originally coined by Gartner where Pezzini et al. (2014) highlight the need of enterprises to close the gap between analytics and action for better business agility and trend awareness.

Overview

The goal of running transactions and analytics on the same data has been around for decades but has not fully been realized due to technology limitations. Today, businesses can no longer afford to miss the real-time insights from data that is in their transactional system as they may lose competitive edge unless business decisions are made on latest data(i.e., allowing the query to run on any...

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References

  • Ailamaki A, DeWitt DJ, Hill MD, Skounakis M (2001) Weaving relations for cache performance. In: VLDB, pp 169–180

    Google Scholar 

  • Alagiannis I, Idreos S, Ailamaki A (2014) H2O: a hands-free adaptive store. In: SIGMOD, pp 1103–1114

    Google Scholar 

  • Appuswamy R, Karpathiotakis M, Porobic D, Ailamaki A (2017) The Case For Heterogeneous HTAP. In: CIDR

    Google Scholar 

  • Arulraj J, Pavlo A, Menon P (2016) Bridging the archipelago between row-stores and column-stores for hybrid workloads. In: SIGMOD, pp 583–598

    Google Scholar 

  • Athanassoulis M, Bøgh KS, Idreos S (2019) Optimal column layout for hybrid workloads. Proc VLDB Endow 12(13):2393–2407. http://www.vldb.org/pvldb/vol12/p2393-athanassoulis.pdf

    Article  Google Scholar 

  • Barber R, Huras M, Lohman G, Mohan C, Mueller R, Özcan F, Pirahesh H, Raman V, Sidle R, Sidorkin O, Storm A, Tian Y, Tözun P (2016) Wildfire: concurrent blazing data ingest and analytics. In: SIGMOD ’16, pp 2077–2080

    Google Scholar 

  • Barber R, Garcia-Arellano C, Grosman R, Müller R, Raman V, Sidle R, Spilchen M, Storm AJ, Tian Y, Tözün P, Zilio DC, Huras M, Lohman GM, Mohan C, Özcan F, Pirahesh H (2017) Evolving databases for new-gen big data applications. In: Online Proceedings of CIDR

    Google Scholar 

  • Dittrich J, Jindal A (2011) Towards a one size fits all database architecture. In: CIDR

    Google Scholar 

  • Duggan J, Elmore AJ, Stonebraker M, Balazinska M, Howe B, Kepner J, Madden S, Maier D, Mattson T, Zdonik S (2015) The BigDAWG polystore system. SIGMOD Rec 44(2):11–16

    Article  Google Scholar 

  • Dziedzic A, Wang J, Das S, Ding B, Narasayya VR, Syamala M (2018) Columnstore and B+ tree – are hybrid physical designs important? In: Das G, Jermaine CM, Bernstein PA (eds) Proceedings of the 2018 International Conference on Management of Data, SIGMOD Conference 2018, Houston, TX, USA, 10–15 June 2018. ACM, pp 177–190. URL https://doi.org/10.1145/3183713.3190660

  • Goel AK, Pound J, Auch N, Bumbulis P, MacLean S, Färber F, Gropengiesser F, Mathis C, Bodner T, Lehner W (2015) Towards scalable real-time analytics: an architecture for scale-out of olxp workloads. PVLDB 8(12):1716–1727

    Google Scholar 

  • Grund M, Krüger J, Plattner H, Zeier A, Cudré-Mauroux P, Madden S (2010) HYRISE – A main memory hybrid storage engine. PVLDB pp 105–116

    Google Scholar 

  • Gupta S, Sadoghi M (2018) EasyCommit: A non-blocking two-phase commit protocol. In: Proceedings of the 21th International Conference on Extending Database Technology, EDBT 2018, Vienna, Austria, 26–29 Mar 2018, pp 157–168. https://doi.org/10.5441/002/edbt.2018.15

  • Gupta S, Sadoghi M (2020) Efficient and non-blocking agreement protocols. Distrib Parallel Databases 38(2):287–333. https://doi.org/10.1007/s10619-019-07267-w

    Article  Google Scholar 

  • Hassan MS, Kuznetsova T, Jeong HC, Aref WG, Sadoghi M (2018a) Extending in-memory relational database engines with native graph support. In: Proceedings of the 21th International Conference on Extending Database Technology, EDBT 2018, Vienna, Austria, 26–29 Mar 2018, pp 25–36. https://doi.org/10.5441/002/edbt.2018.04

  • Hassan MS, Kuznetsova T, Jeong HC, Aref WG, Sadoghi M (2018b) GRFusion: Graphs as first-class citizens in main-memory relational database systems. In: Proceedings of the 2018 International Conference on Management of Data, SIGMOD Conference 2018, Houston, TX, USA, 10–15 June 2018, pp 1789–1792 https://doi.org/10.1145/3183713.3193541

  • Lahiri T, Chavan S, Colgan M, Das D, Ganesh A, Gleeson M, Hase S, Holloway A, Kamp J, Lee TH, Loaiza J, Macnaughton N, Marwah V, Mukherjee N, Mullick A, Muthulingam S, Raja V, Roth M, Soylemez E, Zait M (2015) Oracle database in-memory: a dual format in-memory database. In: ICDE, pp 1253–1258. https://doi.org/10.1109/ICDE.2015.7113373

    Google Scholar 

  • Lang H, Mühlbauer T, Funke F, Boncz PA, Neumann T, Kemper A (2016) Data blocks: Hybrid oltp and olap on compressed storage using both vectorization and compilation. In: Proceedings of the 2016 International Conference on Management of Data, Association for Computing Machinery, New York, NY, USA, SIGMOD ’16, pp 311–326. https://doi.org/10.1145/2882903.2882925

  • Larson PA, Birka A, Hanson EN, Huang W, Nowakiewicz M, Papadimos V (2015) Real-time analytical processing with SQL server. PVLDB 8(12):1740–1751

    Google Scholar 

  • Makreshanski D, Giceva J, Barthels C, Alonso G (2017) BatchDB: efficient isolated execution of hybrid OLTP+OLAP workloads for interactive applications. In: SIGMOD ’17, pp 37–50

    Google Scholar 

  • Najafi M, Sadoghi M, Jacobsen H (2015) The FQP vision: flexible query processing on a reconfigurable computing fabric. SIGMOD Record 44(2):5–10

    Article  Google Scholar 

  • Najafi M, Zhang K, Sadoghi M, Jacobsen H (2017) Hardware acceleration landscape for distributed real-time analytics: virtues and limitations. In: ICDCS, pp 1938–1948

    Google Scholar 

  • Najafi M, Sadoghi M, Jacobsen H (2018) A scalable circular pipeline design for multi-way stream joins in hardware. In: 34th IEEE International Conference on Data Engineering, ICDE 2018, Paris, France, 16–19 Apr 2018, pp 1280–1283. https://doi.org/10.1109/ICDE.2018.00130

  • Neumann T, Mühlbauer T, Kemper A (2015) Fast serializable multi-version concurrency control for main-memory database systems. In: SIGMOD, pp 677–689

    Google Scholar 

  • Pezzini M, Feinberg D, Rayner N, Edjali R (2014) Hybrid transaction/analytical porcessing will foster opportunities for dramatic business innovation. https://www.gartner.com/doc/2657815/hybrid-transacti onanalytical-processing-foster-opportunities

  • Pilman M, Bocksrocker K, Braun L, Marroquín R, Kossmann D (2017) Fast scans on key-value stores. PVLDB 10(11):1526–1537

    Google Scholar 

  • Pirk H, Funke F, Grund M, Neumann T, Leser U, Manegold S, Kemper A, Kersten ML (2013) CPU and cache efficient management of memory-resident databases. In: ICDE, pp 14–25

    Google Scholar 

  • Plattner H (2009) A common database approach for oltp and olap using an in-memory column database. In: SIGMOD, pp 1–2

    Google Scholar 

  • Psaroudakis I, Wolf F, May N, Neumann T, Böhm A, Ailamaki A, Sattler KU (2014) Scaling up mixed workloads: a battle of data freshness, flexibility, and scheduling. In: TPCTC 2014, pp 97–112

    Google Scholar 

  • Qadah T, Sadoghi M (2018) QueCC: A queue-oriented, control-free concurrency architecture. In: Proceedings of the 19th International Middleware Conference, Middleware 2018, Rennes, France, 10–14 Dec 2018, pp 13–25. https://doi.org/10.1145/3274808.3274810

  • Qadah T, Gupta S, Sadoghi M (2020) Q-Store: Distributed, multi-partition transactions via queue-oriented execution and communication. In: Proceedings of the 23nd International Conference on Extending Database Technology, EDBT 2020, Copenhagen, Denmark, 30 March–02 Apr 2020, pp 73–84. https://doi.org/10.5441/002/edbt.2020.08

  • Ramamurthy R, DeWitt DJ, Su Q (2002) A case for fractured mirrors. In: VLDB ’02, pp 430–441

    Google Scholar 

  • Sadoghi M, Blanas S (2019) Transaction Processing on Modern Hardware. Synthesis Lectures on Data Management, Morgan & Claypool Publishers. https://doi.org/10.2200/S00896ED1V01Y201901DTM058

    Book  Google Scholar 

  • Sadoghi M, Ross KA, Canim M, Bhattacharjee B (2013) Making updates disk-I/O friendly using SSDs. PVLDB 6(11):997–1008

    Google Scholar 

  • Sadoghi M, Canim M, Bhattacharjee B, Nagel F, Ross KA (2014) Reducing database locking contention through multi-version concurrency. PVLDB 7(13):1331–1342

    Google Scholar 

  • Sadoghi M, Bhattacherjee S, Bhattacharjee B, Canim M (2016a) L-store: a real-time OLTP and OLAP system. CoRR abs/1601.04084

    Google Scholar 

  • Sadoghi M, Ross KA, Canim M, Bhattacharjee B (2016b) Exploiting SSDs in operational multiversion databases. VLDB J 25(5):651–672

    Article  Google Scholar 

  • Sadoghi M, Bhattacherjee S, Bhattacharjee B, Canim M (2018) L-Store: A real-time OLTP and OLAP system. In: Proceedings of the 21th International Conference on Extending Database Technology, EDBT 2018, Vienna, Austria, 26–29 Mar 2018, pp 540–551, https://doi.org/10.5441/002/edbt.2018.65

  • Sikka V, Färber F, Lehner W, Cha SK, Peh T, Bornhövd C (2012) Efficient transaction processing in sap hana database: the end of a column store myth. In: SIGMOD ’12, pp 731–742

    Google Scholar 

  • Stonebraker M, Cetintemel U (2005) ”One Size Fits All”: An idea whose time has come and gone. In: ICDE, pp 2–11

    Google Scholar 

  • Teubner J, Woods L (2013) Data processing on FPGAs. Synthesis Lectures on Data Management. Morgan & Claypool Publishers, Switzerland. https://doi.org/10.1007/978-3-031-01849-7

    Book  Google Scholar 

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Correspondence to Jana Giceva .

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Giceva, J., Sadoghi, M. (2022). Hybrid Transactional and Analytical Processing. In: Zomaya, A., Taheri, J., Sakr, S. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-63962-8_179-2

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  • DOI: https://doi.org/10.1007/978-3-319-63962-8_179-2

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Chapter history

  1. Latest

    Hybrid Transactional and Analytical Processing
    Published:
    24 May 2022

    DOI: https://doi.org/10.1007/978-3-319-63962-8_179-2

  2. Original

    Hybrid OLTP and OLAP
    Published:
    19 February 2018

    DOI: https://doi.org/10.1007/978-3-319-63962-8_179-1