October 2019
Intermediate to advanced
370 pages
10h 23m
English
Content preview from Database Internals
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Chapter 11. Replication and Consistency
Before we move on to discuss consensus and atomic commitment algorithms, let’s put together the last piece required for their in-depth understanding: consistency models. Consistency models are important, since they explain visibility semantics and behavior of the system in the presence of multiple copies of data.
Fault tolerance is a property of a system that can continue operating correctly in the presence of failures of its components. Making a system fault-tolerant is not an easy task, and it may be difficult to add fault tolerance to the existing system. The primary goal is to remove a single point of failure from the system and make sure that we have redundancy in mission-critical components. Usually, redundancy is entirely transparent for the user.
A system can continue operating correctly by storing multiple copies of data so that, when one of the machines fails, the other one can serve as a failover. In systems with a single source of truth (for example, primary/replica databases), failover can be done explicitly, by promoting a replica to become a new master. Other systems do not require explicit reconfiguration and ensure consistency by collecting responses from multiple participants during read and write queries.
Data replication is a way of introducing redundancy by maintaining multiple copies of data in the system. However, since updating multiple copies of data atomically is a problem equivalent to consensus [MILOSEVIC11]
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