Thursday, April 24, 2014

Don't Settle for Eventual Consistency

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Geo-replicated storage provides copies of the same data at multiple, geographically distinct locations. Facebook, for example, geo-replicates its data (profiles, friends lists, likes, etc.) to data centers on the east and west coasts of the United States, and in Europe. In each data center, a tier of separate Web servers accepts browser requests and then handles those requests by reading and writing data from the storage system, as shown in figure 1.

Geo-replication brings two key benefits to Web services: fault tolerance and low latency. It provides fault tolerance through redundancy: if one data center fails, others can continue to provide the service. It provides low latency through proximity: clients can be directed to and served by a nearby data center to avoid speed-of-light delays associated with cross-country or round-the-globe communication.

Geo-replication brings its challenges, however. The famous CAP theorem, conjectured by Brewer1 and proved by Gilbert and Lynch,7 shows it is impossible to create a system that has strong consistency, is always available for reads and writes, and is able to continue operating during network partitions. Each of these properties is highly desirable. Strong consistency—more formally known as linearizability—makes programming easier. Availability ensures that front-end Web servers can always respond to client requests. Partition tolerance ensures that the system can continue operating even when data centers cannot communicate with one another. Faced with the choice of at most two of these properties, many systems5,8,16 have chosen to sacrifice strong consistency to ensure availability and partition tolerance. Other systems—for example, those that deal with money—sacrifice availability and/or partition tolerance to achieve the strong consistency that is necessary for the applications built on top of them.

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