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  2. CAP theorem - Wikipedia

    en.wikipedia.org/wiki/CAP_theorem

    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).

  3. NoSQL - Wikipedia

    en.wikipedia.org/wiki/NoSQL

    The particular suitability of a given NoSQL database depends on the problem it must solve. Sometimes the data structures used by NoSQL databases are also viewed as "more flexible" than relational database tables. [10] Many NoSQL stores compromise consistency (in the sense of the CAP theorem) in

  4. Consistency (database systems) - Wikipedia

    en.wikipedia.org/wiki/Consistency_(database_systems)

    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.

  5. PACELC theorem - Wikipedia

    en.wikipedia.org/wiki/PACELC_theorem

    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 ...

  6. Column (data store) - Wikipedia

    en.wikipedia.org/wiki/Column_(data_store)

    A column is used as a store for the value and has a timestamp that is used to differentiate the valid content from stale ones. According to the CAP theorem, distributed data stores cannot guarantee consistency, as availability and partition tolerance are more important issues. Therefore, the data store or the application programmer will use the ...

  7. Aerospike (database) - Wikipedia

    en.wikipedia.org/wiki/Aerospike_(database)

    Since version 4.0 (2018), Aerospike Database can be configured both as Available and Partition-tolerant (AP) or Consistent and Partition-tolerant (CP) under the CAP theorem. [37] [38] The client cluster-aware layer is used to track the cluster configuration in the database, and manages client direct communications to all the nodes in the ...

  8. Voldemort (distributed data store) - Wikipedia

    en.wikipedia.org/wiki/Voldemort_(distributed...

    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 ...

  9. Replication (computing) - Wikipedia

    en.wikipedia.org/wiki/Replication_(computing)

    When data is replicated in a database, they will be constrained by CAP theorem or PACELC theorem. In the NoSQL movement, data consistency is usually sacrificed in exchange for other more desired properties, such as availability (A), partition tolerance (P), etc. Various data consistency models have also been developed to serve as Service Level ...