Fork-join Consistency

Notes Causal consistency and logical timestamps Logical Timestamps(Lamport timestamps) donโ€™t imply causal consistency 13) Causal Consistency, COPS#Causal

Causal Consistency

Notes Requirements for consistencies Linearizability Global ordering Completion to Invocation globally Sequential Global ordering Completion to Invocation per client Causal Completion to invocation per client Write and then later read dependency Transitivity for 1) #Causal and 2) #Causal Fork-join Causal Eventual

Eventual Consistency

Notes Dynamo is similar to a Distributed Hash Table meaning it uses consistent hashing. Dynamo is called a zero-hop DHT because each node has enough information about the whole consistent hashing ring that it can directly transfer any client to the server responsible for serving that key. Causes for the system not being linearizable Initializing replication factor but not having the same number of writes/reads Membership changes Discovery of membership changes is at different times, due to gossip protocol, causing writes to happen at different servers than intended....


Notes Consistency is a spectrum with weak to strong levels. GFS/Eventual consistency is an example of weak consistency. Linearizability is an example of strong consistency. Consistency can be defined as the following properties: Completion to Invocation(C2I) Globally Locally Sequential ordering If all of these properties are satisfied then the system is linearizable. If only local C2I and sequential ordering is guaranteed then the system is sequential consistency(second strongest consistency model). Sequential consistency is the one being used in x86 CPUsโ€™ memory model....