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Week 8, Friday (March 7, 2014)—Two eventually consistent algorithms
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Some basic issues From Distributed Databases

Eventually consistent: Vague commitment that system will eventually converge to a consensus, if you wait long enough after the "last update".

  • No Guarantee what value system will converge to
  • Simply means "not strongly consistent"
  • Different degrees and kinds

Handling conflicting writes

Conflict detection: Detect that a conflict has occurred and either

  • Pick a tie-breaker by algorithm
  • Return both values to the application to resolve (Used in Riak)

Conflict prevention: Don't allow conflicting writes

  • Slows system down due to communication costs
  • Necessarily intolerant of network partitions

Anti-entropy via gossip

Algorithm that is:

  • Scalable
  • Partition-tolerante
  • Weakly consistent
    • Not even guaranteed to converge to latest value

Read Quorum/ Write Quorum

Given N replications

  • Write W versions synchronously
  • Read R replicats

If R + W > N

  • Ensures read-after-write consistency for a given client
  • But different readers can get different values (during update)
  • If W > N/2, can detect conflicts
  • Not tolerant of network partitions
    • Can be modified to achieve that

Guide to reading for next class

Read {{site.data.bibliography.katsov2012.title}} section "Sharding and Replication in Dynamic Environments". Stop just before "Multi-Attribute Sharding".

In our discussion of availability/partition-tolerance tradeoffs so far (and in the Tea Emporium assignment as well), we have assumed a fixed number of replications of our data. But what do you do if you need to increase the number of replications to meet higher demand or reduce replications when demand drops? This section considers the challenges that arise when you change your number of replications as your application runs. Nearly all real applications do dynamic replica management, so these techniques are necessary to building real apps.