Lecture

Eventual Consistency

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

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.

Instructors (3)

Anastasia Ailamaki

Expertise Database management systems, scientific applications, computer architecture Teaching & PhD PhD Students Hamish Nicholson, Anna Herlihy, Yi Jiang, Eleni Zapridou, Georgiy Konstantinovich Lebedev, Ioanna Tsakalidou Past EPFL PhD Students Farhan Tauheed, Radu Ioan Stoica, Manos Athanassoulis, Pinar Tözün, Adrian Daniel Popescu, Ioannis Alagiannis, Renata Borovica-Gajic, Danica Porobic, Iraklis Psaroudakis, Manos Karpathiotakis, Matthaios Alexandros Olma, Mirjana Pavlovic, Eleni Tzirita Zacharatou,

Christoph Koch

Expertise knows everything ;-) Publications My publications in Google ScholarMy publications in DBLPMy papers on EPFL's publications server (InfoScience)Prospective PhD students and postdocs please read this before contacting me. Christoph Koch is a professor of Computer Science at EPFL, specializing in data management. Until 2010, he was an Associate Professor in the Department of Computer Science at Cornell University. Previously to this, from 2005 to 2007, he was an Associate Professor of Computer Scienc

Angelos Christos Anadiotis

Official source

Related concepts (32)

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.

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.

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.

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).

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.

Related lectures (53)

Transactions & Concurrency Control: Pessimistic CC

Explores transactions, concurrency control, deadlock handling, and challenges in dynamic databases.

Transaction Management & Concurrency Control

Explores transaction management, concurrency control, ACID properties, anomalies, and conflict serializability in database systems.

Transactional Memory: Two-phase Locking and Invisible Reads

Explores historical perspectives and mechanisms of transactional memory, emphasizing the importance and challenges of its implementation in modern computing systems.

Lecture 13 - Concurrency Control and Eventual Consistency & Concurrency II

Transactions and ACID: Overview