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The PACELC theorem, introduced in 2010, [8] builds on CAP by stating that even in the absence of partitioning, there is another trade-off between latency and consistency. PACELC means, if partition (P) happens, the trade-off is between availability (A) and consistency (C); Else (E), the trade-off is between latency (L) and consistency (C).
In a distributed database system, a transaction could execute its operations at multiple sites. Since atomicity requires every distributed transaction to be atomic, the transaction must have the same fate (commit or abort) at every site.
The CAP theorem is based on three trade-offs, one of which is "atomic consistency" (shortened to "consistency" for the acronym), about which the authors note, "Discussing atomic consistency is somewhat different than talking about an ACID database, as database consistency refers to transactions, while atomic consistency refers only to a property of a single request/response operation sequence.
The PACELC theorem was first described by Daniel Abadi from Yale University in 2010 in a blog post, [2] which he later clarified in a paper in 2012. [3] The purpose of PACELC is to address his thesis that "Ignoring the consistency/latency trade-off of replicated systems is a major oversight [in CAP], as it is present at all times during system operation, whereas CAP is only relevant in the ...
The Algorand consensus protocol privileges consistency over availability (CAP theorem). [26] If the network is unable to reach consensus over the next step (or block), within a certain time, the protocol enters in a recovery mode, suspending the block production to prevent forks (contrary to what would happen in blockchains based on the ...
Eric Allen Brewer is professor emeritus of computer science at the University of California, Berkeley [1] and vice-president of infrastructure at Google. [2] His research interests include operating systems and distributed computing. He is known for formulating the CAP theorem about distributed network applications in the late 1990s. [3]
Voldemort does not try to satisfy arbitrary relations and the ACID properties, but rather is a big, distributed, persistent hash table. [2] A 2012 study comparing systems for storing application performance management data reported that Voldemort, Apache Cassandra, and HBase all offered linear scalability in most cases, with Voldemort having the lowest latency and Cassandra having the highest ...
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