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Lecture

Eventual Consistency

In course

CS-322: Introduction to database systems

This course provides a deep understanding of the concepts behind data management systems. It covers fundamental data management topics such as system architecture, data models, query processing and optimization, database design, storage organization, and t ...

Description

This lecture covers the concept of eventual consistency in distributed systems, where data will eventually be consistent if no further updates are made, explaining the protocol used in Dynamo to achieve this. It also discusses the challenges of programming eventually consistent systems, the CAP Theorem, the tradeoff between consistency and latency, and the use of multiple-granularity locks. The instructor explains the Phantom Problem, deadlock prevention and detection, and the Two-Phase Locking protocol. The lecture concludes with a summary of different consistency models and their applications.

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Instructors (3)

Christoph Koch

Angelos Christos Anadiotis

Anastasia Ailamaki

Official source

Related concepts (32)

Lock (computer science)

In computer science, a lock or mutex (from mutual exclusion) is a synchronization primitive: a mechanism that enforces limits on access to a resource when there are many threads of execution. A lock is designed to enforce a mutual exclusion concurrency control policy, and with a variety of possible methods there exists multiple unique implementations for different applications. Generally, locks are advisory locks, where each thread cooperates by acquiring the lock before accessing the corresponding data.

CAP theorem

In theoretical computer science, the CAP theorem, also named Brewer's theorem after computer scientist Eric Brewer, states that any distributed data store can provide only two of the following three guarantees: Consistency Every read receives the most recent write or an error. Availability Every request receives a (non-error) response, without the guarantee that it contains the most recent write. Partition tolerance The system continues to operate despite an arbitrary number of messages being dropped (or delayed) by the network between nodes.

Two-phase locking

In databases and transaction processing, two-phase locking (2PL) is a concurrency control method that guarantees serializability. It is also the name of the resulting set of database transaction schedules (histories). The protocol uses locks, applied by a transaction to data, which may block (interpreted as signals to stop) other transactions from accessing the same data during the transaction's life. By the 2PL protocol, locks are applied and removed in two phases: Expanding phase: locks are acquired and no locks are released.

Deadlock

In concurrent computing, deadlock is any situation in which no member of some group of entities can proceed because each waits for another member, including itself, to take action, such as sending a message or, more commonly, releasing a lock. Deadlocks are a common problem in multiprocessing systems, parallel computing, and distributed systems, because in these contexts systems often use software or hardware locks to arbitrate shared resources and implement process synchronization.

Consistency model

In computer science, a consistency model specifies a contract between the programmer and a system, wherein the system guarantees that if the programmer follows the rules for operations on memory, memory will be consistent and the results of reading, writing, or updating memory will be predictable. Consistency models are used in distributed systems like distributed shared memory systems or distributed data stores (such as s, databases, optimistic replication systems or web caching).

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