• DocumentCode
    249492
  • Title

    Inherent Replica Inconsistency in Cassandra

  • Author

    Xiangdong Huang ; Jianmin Wang ; Jian Bai ; Guiguang Ding ; Mingsheng Long

  • Author_Institution
    Sch. of Software, Tsinghua Univ., Beijing, China
  • fYear
    2014
  • fDate
    June 27 2014-July 2 2014
  • Firstpage
    740
  • Lastpage
    747
  • Abstract
    Inherent replica inconsistency refers to the difference among the replicas of the same logical data item in the write propagation process of a normally running distributed storage system. In this paper, we formalize the write propagation process model of Cassandra, a widely used NoSQL storage system. In the write propagation process we explore two queueing systems, sending task queues and mutation queues, which locate at each replica node and are determinants of the replica inconsistency. The departure time difference from the mutation queue is used as the measure of inconsistency between two replicas. Furthermore, Request Per Second (RPS) and Mutation Threads Number (MTN), which affect the inherent inconsistency, are discussed and the MTN adaptation algorithm is proposed. Finally, A Cassandra inconsistency measurement framework is implemented using the source instrumentation approach. The empirical results conform well with our proposed inconsistency measurement model.
  • Keywords
    queueing theory; replicated databases; Cassandra inconsistency measurement framework; NoSQL storage system; inherent replica inconsistency; mutation queues; mutation threads number; normally running distributed storage system; queueing system; request per second; task queues; write propagation process; Computational modeling; Data models; Distributed databases; Message systems; Queueing analysis; Synchronization; Writing; Cassandra; queuing theory; replica consistency; write process;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Big Data (BigData Congress), 2014 IEEE International Congress on
  • Conference_Location
    Anchorage, AK
  • Print_ISBN
    978-1-4799-5056-0
  • Type

    conf

  • DOI
    10.1109/BigData.Congress.2014.109
  • Filename
    6906852