TY - GEN
T1 - Virtual lightweight snapshots for consistent analytics in NoSQL stores
AU - Chirigati, Fernando
AU - Simeon, Jerome
AU - Hirzel, Martin
AU - Freire, Juliana
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/6/22
Y1 - 2016/6/22
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84980392030&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84980392030&partnerID=8YFLogxK
U2 - 10.1109/ICDE.2016.7498334
DO - 10.1109/ICDE.2016.7498334
M3 - Conference contribution
AN - SCOPUS:84980392030
T3 - 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016
SP - 1310
EP - 1321
BT - 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 32nd IEEE International Conference on Data Engineering, ICDE 2016
Y2 - 16 May 2016 through 20 May 2016
ER -