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Eventual Consistency
Delves into eventual consistency, a relaxed consistency model widely adopted in large-scale distributed systems, discussing its benefits for availability and partition tolerance, alongside its challenges.
Lecture guide · Written by AI from the lecture. For the lecture’s own words, open the Transcript tab.
Explores eventual consistency as an alternative to linearizability in distributed systems, examining its use in applications like calendar sync. It defines eventual consistency alongside strong eventual consistency and evaluates how these models trade availability and performance against coordination costs. The session concludes by comparing consistency models and protocols based on the strength of their system model assumptions.
This lecture assumes
- Replicationtaught in Introduction & MapReduce at 36:12
- Linearizabilitytaught in Linearizability at 0:45
- Availabilitytaught in Introduction & MapReduce at 31:45
Key Takeaways
- Linearizability requires quorum communication and partial synchrony, which limits availability and introduces latency bottlenecks.
- Eventual consistency allows replicas to process reads and writes locally without waiting for quorum communication.
- Strong eventual consistency guarantees that updates will be delivered and that replicas processing the same set of updates will converge to identical states.
- Concurrent edits in eventually consistent systems require conflict resolution strategies, such as last-writer-wins or sophisticated merge algorithms.
Concepts Explained3
Eventual consistency—A consistency model where replicas process read and write requests locally, and if updates stop, all replicas will eventually reach the same state.
CAP theorem—A theorem stating that a distributed system in the presence of a network partition can provide either strong consistency or availability, but not both.
Strong eventual consistency—A strengthening of eventual consistency requiring both eventual delivery of updates and convergence of replicas that have processed the same set of updates regardless of order.
Examples, Proofs & Derivations