@inproceedings{KlettkeStoerlShenavaietal., author = {Klettke, Meike and St{\"o}rl, Uta and Shenavai, Manuel and Scherzinger, Stefanie and Storl, Uta}, title = {NoSQL schema evolution and big data migration at scale}, series = {2016 IEEE International Conference on Big Data (Big Data), 5-8 Dec. 2016, Washington, DC}, booktitle = {2016 IEEE International Conference on Big Data (Big Data), 5-8 Dec. 2016, Washington, DC}, publisher = {IEEE}, doi = {10.1109/BigData.2016.7840924}, pages = {2764 -- 2774}, abstract = {This paper explores scalable implementation strategies for carrying out lazy schema evolution in NoSQL data stores. For decades, schema evolution has been an evergreen in database research. Yet new challenges arise in the context of cloud-hosted data backends: With all database reads and writes charged by the provider, migrating the entire data instance eagerly into a new schema can be prohibitively expensive. Thus, lazy migration may be more cost-efficient, as legacy entities are only migrated in case they are actually accessed by the application. Related work has shown that the overhead of migrating data lazily is affordable when a single evolutionary change is carried out, such as adding a new property. In this paper, we focus on long-term schema evolution, where chains of pending schema evolution operations may have to be applied. Chains occur when legacy entities written several application releases back are finally accessed by the application. We discuss strategies for dealing with chains of evolution operations, in particular, the composition into a single, equivalent composite migration that performs the required version jump. Our experiments with MongoDB focus on scalable implementation strategies. Our lineup further compares the number of write operations, and thus, the operational costs of different data migration strategies.}, language = {en} } @inproceedings{HillenbrandLevchenkoStoerletal., author = {Hillenbrand, Andrea and Levchenko, Maksym and St{\"o}rl, Uta and Scherzinger, Stefanie and Klettke, Meike}, title = {MigCast : Putting a Price Tag on Data Model Evolution in NoSQL Data Stores}, series = {Proceedings of the 2019 International Conference on Management of Data (SIGMOD/PODS '19) June 2019, Amsterdam, Netherlands}, booktitle = {Proceedings of the 2019 International Conference on Management of Data (SIGMOD/PODS '19) June 2019, Amsterdam, Netherlands}, editor = {Boncz, Peter and Manegold, Stefan and Ailamaki, Anastasia and Deshpande, Amol and Kraska, Tim}, publisher = {ACM}, address = {New York, NY, USA}, isbn = {9781450356435}, doi = {10.1145/3299869.3320223}, pages = {1925 -- 1928}, abstract = {We demonstrate MigCast, a tool-based advisor for exploring data migration strategies in the context of developing NoSQL-backed applications. Users of MigCast can consider their options for evolving their data model along with legacy data already persisted in the cloud-hosted production data-base. They can explore alternative actions as the financial costs are predicted respective to the cloud provider chosen. Thereby they are better equipped to assess potential consequences of imminent data migration decisions. To this end, MigCast maintains an internal cost model, taking into account characteristics of the data instance, expected work-load, data model changes, and cloud provider pricing models. Hence, MigCast enables software project stakeholders to remain in control of the operative costs and to make informed decisions evolving their applications.}, language = {en} }