Skip to Main content Skip to Navigation
Conference papers

Invisible Glue: Scalable Self-Tuning Multi-Stores

Francesca Bugiotti 1, 2, 3 Damian Bursztyn 2, 3, 1 Alin Deutsch 4, 1 Ioana Ileana 5, 1 Ioana Manolescu 2, 3, 1
2 OAK - Database optimizations and architectures for complex large data
Inria Saclay - Ile de France, LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : Next-generation data centric applications often involve di-verse datasets, some very large while others may be of mod-erate size, some highly structured (e.g., relations) while others may have more complex structure (e.g., graphs) or little structure (e.g., text or log data). Facing them is a variety of storage systems, each of which can host some of the datasets (possibly after some data migration), but none of which is likely to be best for all, at all times. Deploying and efficiently running data-centric applications in such a complex setting is very challenging. We propose Estocada, an architecture for efficiently han-dling highly heterogeneous datasets based on a dynamic set of potentially very different data stores. Estocada pro-vides to the application/programming layer access to each data set in its native format, while hosting them internally in a set of potentially overlapping fragments, possibly dis-tributing (fragments of) each dataset across heterogeneous stores. Given workload information, Estocada self-tunes for performance, i.e., it automatically choses the fragments of each data set to be deployed in each store so as to op-timize performance. At the core of Estocada lie powerful view-based rewriting and view selection algorithms, required in order to correctly handle the features (nesting, keys, con-straints etc.) of the diverse data models involved, and thus to marry correctness with high performance.
Document type :
Conference papers
Complete list of metadata

Cited literature [20 references]  Display  Hide  Download
Contributor : Francesca Bugiotti Connect in order to contact the contributor
Submitted on : Wednesday, November 26, 2014 - 2:10:43 PM
Last modification on : Thursday, July 8, 2021 - 3:49:16 AM
Long-term archiving on: : Friday, April 14, 2017 - 7:46:48 PM


Files produced by the author(s)


  • HAL Id : hal-01087624, version 1


Francesca Bugiotti, Damian Bursztyn, Alin Deutsch, Ioana Ileana, Ioana Manolescu. Invisible Glue: Scalable Self-Tuning Multi-Stores. Conference on Innovative Data Systems Research (CIDR), Jan 2015, Asilomar, United States. ⟨hal-01087624⟩



Les métriques sont temporairement indisponibles