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

Linearizability

• 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