TY - CHAP A1 - Klettke, Meike A1 - Störl, Uta A1 - Shenavai, Manuel A1 - Scherzinger, Stefanie A1 - Storl, Uta T1 - NoSQL schema evolution and big data migration at scale T2 - 2016 IEEE International Conference on Big Data (Big Data), 5-8 Dec. 2016, Washington, DC N2 - 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. KW - Big data KW - Context KW - Data Migration Strategies KW - Data models KW - Databases KW - Incremental Migration KW - Lazy Composite Migration KW - Lazy Migration KW - NoSQL databases KW - Predictive Migration KW - Production KW - Runtime KW - schema evolution KW - Software Y1 - 2016 U6 - https://doi.org/10.1109/BigData.2016.7840924 SP - 2764 EP - 2774 PB - IEEE ER - TY - CHAP A1 - Störl, Uta A1 - Tekleab, Alexander A1 - Klettke, Meike A1 - Scherzinger, Stefanie A1 - Storl, Uta T1 - In for a Surprise When Migrating NoSQL Data T2 - 2018 IEEE 34th International Conference on Data Engineering (ICDE), 16-19 April 2018, Paris, France N2 - Schema-flexible NoSQL data stores lend themselves nicely for storing versioned data, a product of schema evolution. In this lightning talk, we apply pending schema changes to records that have been persisted several schema versions back. We present first experiments with MongoDB and Cassandra, where we explore the trade-off between applying chains of pending changes stepwise (one after the other), and as composite operations. Contrary to intuition, composite migration is not necessarily faster. The culprit is the computational overhead for deriving the compositions. However, caching composition formulae achieves a speed up: For Cassandra, we can cut the runtime by nearly 80%. Surprisingly, the relative speedup seems to be system-dependent. Our take away message is that in applying pending schema changes in NoSQL data stores, we need to base our design decisions on experimental evidence rather than on intuition alone. KW - composite migration KW - Conferences KW - Data engineering KW - data migration KW - Indexes KW - Lightning KW - NoSQL databases KW - Runtime KW - schema evolution KW - Tools Y1 - 2018 U6 - https://doi.org/10.1109/ICDE.2018.00202 SP - 1662 PB - IEEE ER - TY - CHAP A1 - Störl, Uta A1 - Müller, Daniel A1 - Tekleab, Alexander A1 - Tolale, Stephane A1 - Stenzel, Julian A1 - Klettke, Meike A1 - Scherzinger, Stefanie A1 - Storl, Uta A1 - Muller, Daniel T1 - Curating Variational Data in Application Development T2 - 2018 IEEE 34th International Conference on Data Engineering, 16-19 April 2018, Paris, France N2 - Building applications for processing data lakes is a software engineering challenge. We present Darwin, a middleware for applications that operate on variational data. This concerns data with heterogeneous structure, usually stored within a schema-flexible NoSQL database. Darwin assists application developers in essential data and schema curation tasks: Upon request, Darwin extracts a schema description, discovers the history of schema versions, and proposes mappings between these versions. Users of Darwin may interactively choose which mappings are most realistic. Darwin is further capable of rewriting queries at runtime, to ensure that queries also comply with legacy data. Alternatively, Darwin can migrate legacy data to reduce the structural heterogeneity. Using Darwin, developers may thus evolve their data in sync with their code. In our hands-on demo, we curate synthetic as well as real-life datasets. KW - data migration KW - Data mining KW - Evolution (biology) KW - history KW - NoSQL databases KW - query rewriting KW - schema evolution KW - schema management KW - Software KW - Task analysis KW - variational data Y1 - 2018 U6 - https://doi.org/10.1109/ICDE.2018.00187 SP - 1605 EP - 1608 PB - IEEE ER - TY - CHAP A1 - Klettke, Meike A1 - Awolin, Hannes A1 - Störl, Uta A1 - Müller, Daniel A1 - Scherzinger, Stefanie A1 - Storl, Uta A1 - Muller, Daniel T1 - Uncovering the evolution history of data lakes T2 - 2017 IEEE International Conference on Big Data (Big Data),,11-14 Dec. 2017, Boston, MA, USA N2 - Data accumulating in data lakes can become inaccessible in the long run when its semantics are not available. The heterogeneity of data formats and the sheer volumes of data collections prohibit cleaning and unifying the data manually. Thus, tools for automated data lake analysis are of great interest. In this paper, we target the particular problem of reconstructing the schema evolution history from data lakes. Knowing how the data is structured, and how this structure has evolved over time, enables programmatic access to the lake. By deriving a sequence of schema versions, rather than a single schema, we take into account structural changes over time. Moreover, we address the challenge of detecting inclusion dependencies. This is a prerequisite for mapping between succeeding schema versions, and in particular, detecting nontrivial changes such as a property having been moved or copied. We evaluate our approach for detecting inclusion dependencies using the MovieLens dataset, as well an adaption of a dataset containing botanical descriptions, to cover specific edge cases. KW - Data mining KW - evolution operations KW - Grippers KW - history KW - inclusion dependencies KW - integrity constraints KW - Lakes KW - NoSQL databases KW - Protocols KW - schema version extraction Y1 - 2017 U6 - https://doi.org/10.1109/BigData.2017.8258204 SP - 2462 EP - 2471 PB - IEEE ER - TY - JOUR A1 - Störl, Uta A1 - Klettke, Meike A1 - Scherzinger, Stefanie T1 - Kurz erklärt: Objekt-NoSQL-Mapping JF - Datenbank-Spektrum Y1 - 2016 U6 - https://doi.org/10.1007/s13222-016-0212-y VL - 16 IS - 1 SP - 83 EP - 87 PB - Springer ER - TY - JOUR A1 - Klettke, Meike A1 - Scherzinger, Stefanie A1 - Störl, Uta T1 - Datenbanken ohne Schema? JF - Datenbank-Spektrum N2 - In der Entwicklung von interaktiven Web-Anwendungen sind NoSQL-Datenbanksysteme zunehmend beliebt, nicht zuletzt, weil sie flexible Datenmodelle erlauben. Das erleichtert insbesondere ein agiles Projektmanagement, das sich durch häufige Releases und entsprechend häufige Änderungen am Datenmodell auszeichnet. In diesem Artikel geben wir einen Überblick über die besonderen Herausforderungen der agilen Anwendungsentwicklung gegen schemalose NoSQL-Datenbanksysteme. Wir stellen Strategien für die Schema-Evolution aus der Praxis vor, und postulieren unsere Vision einer eigenen Schema-Management-Komponente für NoSQL-Datenbanksysteme, die für eine kontinuierliche und systematische Schema-Evolution ausgelegt ist. KW - NoSQL-Datenbanksysteme KW - Schema-Evolution KW - Schema-Informationen Y1 - 2014 U6 - https://doi.org/10.1007/s13222-014-0156-z VL - 14 IS - 2 SP - 119 EP - 129 PB - Springer ER - TY - CHAP A1 - Scherzinger, Stefanie A1 - Störl, Uta A1 - Klettke, Meike ED - Cheney, James ED - Neumann, Thomas T1 - A Datalog-based protocol for lazy data migration in agile NoSQL Application development T2 - Proceedings of the 15th Symposium on Database Programming Languages : SPLASH '15: Conference on Systems, Programming, Languages, and Applications: Software for Humanity, Pittsburgh PA USA, 27.10.2015 - 27.10.2015 N2 - We address a practical challenge in agile web development against NoSQL data stores: Upon a new release of the web application, entities already persisted in production no longer match the application code. Rather than migrating all legacy entities eagerly (prior to the release) and at the cost of application downtime, lazy data migration is a popular alternative: When a legacy entity is loaded by the application, all pending structural changes are applied. Yet correctly migrating legacy data from several releases back, involving more than one entity at-a-time, is not trivial. In this paper, we propose a holistic Datalog ¬non-rec model model for reading, writing, and migrating data. In implementing our model, we may blend established Datalog evaluation algorithms, such as an incremental evaluation with certain rules evaluated bottom-up, and certain rules evaluated top-down with sideways information passing. Our systematic approach guarantees that from the viewpoint of the application, it remains transparent whether data is migrated eagerly or lazily. Y1 - 2015 SN - 9781450339025 U6 - https://doi.org/10.1145/2815072.2815078 SP - 41 EP - 44 PB - ACM CY - New York, NY, USA ER -