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In a distributed database system, data is often replicated to improve performance and availability. By storing copies of shared data on processors where it is frequently accessed, the need for expensive, remote read accesses is decreased. By storing copies of critical data on processors with independent failure modes, the probability that at least one copy of the data will be accessible increases. In theory, data replication makes it possible to provide arbitrarily high data availability.
In practice, realizing the benefits of data replication is difficult since the correctness of data must be maintained. One important aspect of correctness with replicated data is mutual consistency: all copies of the same logical data-item must agree on exactly one "current value" for the data-item. Furthermore, this value should "make sense" in terms of the transactions executed on copies of the data-item. When communication fails between sites containing copies of the same logical data-item, mutual consistency between copies becomes complicated to ensure. The most disruptive of these communication failures are partition failures, which fragment the network into isolated subnetworks called partitions. Unless partition failures are detected and recognized by all affected processors, independent and uncoordinated updates may be applied to different copies of the data, thereby compromising the correctness of data. Consider, for example, an Airline Reservation System implemented by a distributed database which splits into two partitions when the communication network fails. If, at the time of the failure, all the nodes have one seat remaining for PAN AM 537, reservations could be made in both partitions. This would violate correctness: who should get the last seat? There should not be more seats reserved for a flight than physically exist on the plane. (Some airlines do not implement this constraint and allow overbookings.)
The design of a replicated data management algorithm tolerating partition failures (or partition processing strategy) is a notoriously hard problem. Typically, the cause or extent of a partition failure cannot be discerned by the processors themselves. At best, a processor may be able to identify the other processors in its partition; but, for the processors outside of its partition, it will not be able to distinguish between the case where those processors are simply isolated from it and the case where those processors are down. In addition, slow responses can cause the network to appear partitioned even when it is not, further complicating the design of a fault-tolerant algorithm.
Susan B. Davidson, "Replicated Data and Partition Failures", . January 1989.
Date Posted: 20 November 2007