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Data synchronization is the process of establishing consistency between source and target data stores, and the continuous harmonization of the data over time. It is fundamental to a wide variety of applications, including file synchronization and mobile device synchronization.
Optimistic replication, also known as lazy replication, [1] [2] is a strategy for replication, in which replicas are allowed to diverge. [3]Traditional pessimistic replication systems try to guarantee from the beginning that all of the replicas are identical to each other, as if there was only a single copy of the data all along.
A version vector is a mechanism for tracking changes to data in a distributed system, where multiple agents might update the data at different times.The version vector allows the participants to determine if one update preceded another (happened-before), followed it, or if the two updates happened concurrently (and therefore might conflict with each other).
Weak ordering classifies memory operations into two categories: data operations and synchronization operations. To enforce program order, a programmer needs to find at least one synchronisation operation in a program. Synchronization operations signal the processor to make sure it has completed and seen all previous operations done by all ...
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).
Synchronization was originally a process-based concept whereby a lock could be obtained on an object. Its primary usage was in databases. There are two types of (file) lock; read-only and read–write. Read-only locks may be obtained by many processes or threads.
Data integration refers to the process of combining, sharing, or synchronizing data from multiple sources to provide users with a unified view. [1] There are a wide range of possible applications for data integration, from commercial (such as when a business merges multiple databases) to scientific (combining research data from different bioinformatics repositories).
In multithreaded computing, the ABA problem occurs during synchronization, when a location is read twice, has the same value for both reads, and the read value being the same twice is used to conclude that nothing has happened in the interim; however, another thread can execute between the two reads and change the value, do other work, then change the value back, thus fooling the first thread ...