Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2016 | 2016 32ND IEEE INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE) | Data mining,Latency (engineering),Computer science,NoSQL,Throughput,Analytics,Snapshot (computer storage),Big data,Distributed transaction,Database,Software versioning |
DocType | ISSN | Citations |
Conference | 1084-4627 | 1 |
PageRank | References | Authors |
0.35 | 20 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fernando Seabra Chirigati | 1 | 205 | 16.38 |
| Jérôme Siméon | 2 | 1515 | 210.75 |
| Martin Hirzel | 3 | 704 | 47.87 |
| Juliana Freire | 4 | 3956 | 270.89 |