Query Rewriting for Continuously Evolving NoSQL Databases

  • Mark Lukas MöllerEmail author
  • Meike Klettke
  • Andrea Hillenbrand
  • Uta Störl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11788)


In agile software development settings, applications are typically backed by schema-flexible NoSQL databases. New application code frequently implies data model changes to the effect of multiple schema versions within the NoSQL database. Here, a query rewriting approach can handle the issue of how to access legacy data, otherwise datasets in previous schema versions would seem to disappear for the application. Our NoSQL query rewriting approach for multi-versioned databases takes evolution operations into account, their reverse operations as well as the heterogeneity of data. For that purpose we specify four NoSQL heterogeneity classes from relational up to completely unstructured NoSQL records. Furthermore, we propose a NoSQL query rewriting algorithm that generates subqueries compatible to all existing structural versions.


Multi-versioned NoSQL databases NoSQL query rewriting NoSQL Schema Evolution NoSQL data heterogeneity classes 



The article is published in the scope of the project “NoSQL Schema Evolution und Big Data Migration at Scale” which is funded by the Deutsche Forschungsgemeinschaft (DFG) under the number 385808805. A special thanks goes to Stefanie Scherzinger, Dennis Marten, Tanja Auge, and Hannes Grunert for their support, comments on this work, and several discussions.


  1. 1.
    Hamadou, H.B., Ghozzi, F., Péninou, A., et al.: Towards schema-independent querying on document data stores. In: Proceedings of EDBT/ICDT (2018)Google Scholar
  2. 2.
    Herrmann, K., Voigt, H., Rausch, J., et al.: Living in parallel realities: co-existing schema versions with a bidirectional database evolution language. In: Proceedings of SIGMOD 2017 (2017)Google Scholar
  3. 3.
    Klettke, M., Störl, U., Shenavai, M., et al.: NoSQL schema evolution and big data migration at scale. In: Proceedings of SCDM@Big Data 2016 (2016)Google Scholar
  4. 4.
    Moon, H.J., Curino, C.A., Zaniolo, C.: Scalable architecture and query optimization for transaction-time DBs with evolving schemas. In: Proceedings of SIGMOD 2010 (2010)Google Scholar
  5. 5.
    Möller, M.L., Klettke, M., Störl, U.: Formal semantics of NoSQL evolution operations under different heterogeneity levels. Technical report, Rostock University (2018)Google Scholar
  6. 6.
    Saur, K., Dumitras, T., Hicks, M.W.: Evolving NoSQL databases without downtime. In: Proceedings of ICSME 2016 (2016)Google Scholar
  7. 7.
    Scherzinger, S., Klettke, M., Störl, U.: Managing schema evolution in NoSQL data stores. In: Proceedings of DBPL@VLDB (2013)Google Scholar
  8. 8.
    Stenzel, J.: Query rewriting in NoSQL-Datenbanksystemen. Master’s thesis, University of Applied Sciences Darmstadt (2017)Google Scholar
  9. 9.
    Störl, U., et al.: Curating variational data in application development. In: Proceedings of ICDE 2018 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mark Lukas Möller
    • 1
    Email author
  • Meike Klettke
    • 1
  • Andrea Hillenbrand
    • 2
  • Uta Störl
    • 2
  1. 1.University of RostockRostockGermany
  2. 2.University of Applied Sciences DarmstadtDarmstadtGermany

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