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Relying on Linearizability

In what circumstances is linearizability useful? Viewing the final score of a sporting match is perhaps a frivolous example: a result that is outdated by a few seconds is unlikely to cause any real harm in this situation. However, there a few areas in which linearizability is an important requirement for making a system work correctly.

Locking and leader election

A system that uses single-leader replication needs to ensure that there is indeed only one leader, not several (split brain). One way of electing a leader is to use a lock: every node that starts up tries to acquire the lock, and the one that succeeds becomes the leader [14]. No matter how this lock is implemented, it must be linearizable: all nodes must agree which node owns the lock; otherwise it is useless.

Coordination services like Apache ZooKeeper [15] and etcd [16] are often used to implement distributed locks and leader election. They use consensus algorithms to implement linearizable operations in a fault-tolerant way (we discuss such algorithms later in this chapter, in “Fault-Tolerant Consensus” on page 364).iU There are still many subtle details to implementing locks and leader election correctly (see for example the fencing issue in “The leader and the lock” on page 301), and libraries like Apache Curator [17] help by providing higher-level recipes on top of ZooKeeper. However, a linearizable storage service is the basic foundation for these coordination tasks.

Distributed locking is also used at a much more granular level in some distributed databases, such as Oracle Real Application Clusters (RAC) [18]. RAC uses a lock per disk page, with multiple nodes sharing access to the same disk storage system. Since these linearizable locks are on the critical path of transaction execution, RAC deployments usually have a dedicated cluster interconnect network for communication between database nodes.

 
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