Distributed consistency

Distributed consistency

  • usually describes behaviors of multiple copies of data
    • example, replicated chunks/log entries (GFS, Raft), CPU caches
  • challenges
    • concurrency
    • failures
  • systems
    • abstract data types
    • key-value store (memory, registers)
  • not defined by states, but by operations, example
    initially x=0, y=0
    c1: put(x, 1) put(y, 1)
    c2: get(y)=? get(x)=?

Consistency as a “contract”

  • clients’ expectation
    • knowing the system’s behavior without examining the system design
  • what would a good contract?
    • stronger, hard to implement, easy to use
    • weaker, easy to implement, hard to use


  • definition
    • equivalent sequential order, one-copy data
    • the order matches the global completion-to-issuing (C2I) order
      • why not C2C or I2I?
  • linearizability is the strongest consistency in practice?
  • linearizability is a “local” property
    • putting different linearizable systems together? still linearizable
    • proof strategy: order and C2I

Sequential consistency

  • definition
    • order
    • per-client C2I
  • usage: closer to today’s multi-core memory system
  • example (consider the case with and without c3)
    x,y=0 initially
    c1: put(x)=1        get(y)=?
    c2:      put(y)=1
    c3:                         get(y)=1  get(x)=0