2016 IEEE 32nd International Conference on Data Engineering (ICDE) (2016)
May 16, 2016 to May 20, 2016
Fernando Chirigati , New York University, United States
Jerome Simeon , IBM Watson Research, United States
Martin Hirzel , IBM Watson Research, United States
Juliana Freire , New York University, United States
Increasingly, applications that deal with big data need to run analytics concurrently with updates. But bridging the gap between big and fast data is challenging: most of these applications require analytics' results that are fresh and consistent, but without impacting system latency and throughput. We propose virtual lightweight snapshots (VLS), a mechanism that enables consistent analytics without blocking incoming updates in NoSQL stores. VLS requires neither native support for database versioning nor a transaction manager. Besides, it is storage-efficient, keeping additional versions of records only when needed to guarantee consistency, and sharing versions across multiple concurrent snapshots. We describe an implementation of VLS in MongoDB and present a detailed experimental evaluation which shows that it supports consistency for analytics with small impact on query evaluation time, update throughput, and latency.
Databases, Throughput, Middleware, Context, Big data, Airports, Stability analysis
F. Chirigati, J. Simeon, M. Hirzel and J. Freire, "Virtual lightweight snapshots for consistent analytics in NoSQL stores," 2016 IEEE 32nd International Conference on Data Engineering (ICDE), Helsinki, Finland, 2016, pp. 1310-1321.