Persisting in-memory databases using SCM
| dc.citation.firstpage | 2981 | |
| dc.citation.journalTitle | 2016 IEEE International Conference on Big Data (Big Data) | |
| dc.citation.lastpage | 2990 | |
| dc.contributor.author | Giles, Ellis | |
| dc.contributor.author | Doshi, Kshitij | |
| dc.contributor.author | Varman, Peter | |
| dc.date.accessioned | 2017-08-04T12:30:00Z | |
| dc.date.available | 2017-08-04T12:30:00Z | |
| dc.date.issued | 2016 | |
| dc.description.abstract | Big Data applications need to be able to access large amounts of variable data as fast as possible. Emerging Storage Class Memory (SCM) fit this need by making memory available in large capacity while making changes endure as a seamless continuation of load-store accesses through processor caches. However, when writing values into a persistent memory tier, programmers are faced with the dual problems of controlling untimely cache evictions that might commit changes prematurely, and of grouping changes and making them durable as a unit so that consistency can be guaranteed in the event of sudden failure. In this paper, we present various methods to achieve high-performance byte-addressable persistence for an in-memory data store. We chose Redis, a popular high-performance memory oriented key value database. We modified its source code to use SCM such that updates to data and structures are performed in a failure resilient manner. We evaluated the changes using both internal benchmarks and the Yahoo! Cloud Servicing Benchmark (YCSB). We found that even though Redis uses many SCM read operations, it can benefit from highly optimized persistent SCM write based approaches, especially when SCM write times are longer than DRAM write times. The paper presents an innovative Local Alias Table Batched (LATB) method, and shows that it outperforms the alternatives. | |
| dc.identifier.citation | Giles, Ellis, Doshi, Kshitij and Varman, Peter. "Persisting in-memory databases using SCM." <i>2016 IEEE International Conference on Big Data (Big Data),</i> (2016) IEEE: 2981-2990. https://doi.org/10.1109/BigData.2016.7840950. | |
| dc.identifier.digital | BigData16 | |
| dc.identifier.doi | https://doi.org/10.1109/BigData.2016.7840950 | |
| dc.identifier.uri | https://hdl.handle.net/1911/96579 | |
| dc.language.iso | eng | |
| dc.publisher | IEEE | |
| dc.rights | Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. | |
| dc.title | Persisting in-memory databases using SCM | |
| dc.type | Journal article | |
| dc.type.dcmi | Text | |
| dc.type.publication | publisher version |
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