@inproceedings{StoerlTekleabKlettkeetal., author = {St{\"o}rl, Uta and Tekleab, Alexander and Klettke, Meike and Scherzinger, Stefanie and Storl, Uta}, title = {In for a Surprise When Migrating NoSQL Data}, series = {2018 IEEE 34th International Conference on Data Engineering (ICDE), 16-19 April 2018, Paris, France}, booktitle = {2018 IEEE 34th International Conference on Data Engineering (ICDE), 16-19 April 2018, Paris, France}, publisher = {IEEE}, doi = {10.1109/ICDE.2018.00202}, pages = {1662}, abstract = {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.}, language = {en} } @inproceedings{MauererScherzinger, author = {Mauerer, Wolfgang and Scherzinger, Stefanie}, title = {Nullius in Verba: Reproducibility for Database Systems Research, Revisited}, series = {2021 IEEE 37th International Conference on Data Engineering (ICDE 2021): 19-22 April 2021, Chania, Greece}, booktitle = {2021 IEEE 37th International Conference on Data Engineering (ICDE 2021): 19-22 April 2021, Chania, Greece}, editor = {Ailamaki, Anastasia}, publisher = {IEEE}, address = {Piscataway, NJ}, isbn = {978-1-7281-9184-3}, doi = {10.1109/ICDE51399.2021.00270}, pages = {2377 -- 2380}, abstract = {Over the last decade, reproducibility of experimental results has been a prime focus in database systems research, and many high-profile conferences award results that can be independently verified. Since database systems research involves complex software stacks that non-trivially interact with hardware, sharing experimental setups is anything but trivial: Building a working reproduction package goes far beyond providing a DOI to some repository hosting data, code, and setup instructions.This tutorial revisits reproducible engineering in the face of state-of-the-art technology, and best practices gained in other computer science research communities. In particular, in the hands-on part, we demonstrate how to package entire system software stacks for dissemination. To ascertain long-term reproducibility over decades (or ideally, forever), we discuss why relying on open source technologies massively employed in industry has essential advantages over approaches crafted specifically for research. Supplementary material shows how version control systems that allow for non-linearly rewriting recorded history can document the structured genesis behind experimental setups in a way that is substantially easier to understand, without involvement of the original authors, compared to detour-ridden, strictly historic evolution.}, language = {en} }