Rice Univesrity Logo
    • FAQ
    • Deposit your work
    • Login
    View Item 
    •   Rice Scholarship Home
    • Faculty & Staff Research
    • Faculty Publications
    • View Item
    •   Rice Scholarship Home
    • Faculty & Staff Research
    • Faculty Publications
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Persisting in-memory databases using SCM

    Thumbnail
    Name:
    BigData16.pdf
    Size:
    912.4Kb
    Format:
    PDF
    View/Open
    Author
    Giles, Ellis
    Doshi, Kshitij
    Varman, Peter
    Date
    2016
    Citation
    Giles, Ellis, Doshi, Kshitij and Varman, Peter. "Persisting in-memory databases using SCM." 2016 IEEE International Conference on Big Data (Big Data), (2016) 2981-2990. https://doi.org/10.1109/BigData.2016.7840950.
    Published Version
    https://doi.org/10.1109/BigData.2016.7840950
    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.
    Type
    Journal article
    Citable link to this page
    http://hdl.handle.net/1911/96579
    Metadata
    Show full item record
    Collections
    • Computer Science Publications [84]
    • ECE Publications [1278]
    • Faculty Publications [3507]

    Home | FAQ | Contact Us
    Managed by the Digital Scholarship Services at Fondren Library, Rice University
    Physical Address: 6100 Main Street, Houston, Texas 77005
    Mailing Address: MS-44, P.O.BOX 1892, Houston, Texas 77251-1892
     

     

    Searching scope

    Browse

    Entire ArchiveCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsTypeThis CollectionBy Issue DateAuthorsTitlesSubjectsType

    My Account

    Login

    Statistics

    View Usage Statistics

    Home | FAQ | Contact Us
    Managed by the Digital Scholarship Services at Fondren Library, Rice University
    Physical Address: 6100 Main Street, Houston, Texas 77005
    Mailing Address: MS-44, P.O.BOX 1892, Houston, Texas 77251-1892