In addition to selecting an appropriate concurrency strategy, developers should also be aware of how a storage platform isolates changes, particularly changes to the same object across transactions.
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Azure Storage was designed to embrace a strong consistency model that guarantees that after the service performs an insert or update operation, subsequent read operations return the latest update. It can also be useful where short-lived data streams are being processed.Īzure Storage supports all three strategies, although it is distinctive in its ability to provide full support for optimistic and pessimistic concurrency. This approach is typically used when data is partitioned in such a way that multiple users will not access the same data at the same time. Last writer wins: An approach that allows update operations to proceed without first determining whether another application has updated the data since it was read. For example, in a primary/secondary data replication scenario in which only the primary performs updates, the primary typically holds an exclusive lock on the data for an extended period of time to ensure no one else can update it. Pessimistic concurrency: An application looking to perform an update will take a lock on an object preventing other users from updating the data until the lock is released. This strategy is most often used in web applications.
It must also ensure that both users understand whether their update was successful. For example, if two users viewing a wiki page make an update to that page, then the wiki platform must ensure that the second update does not overwrite the first update. Optimistic concurrency: An application performing an update will, as part of its update, determine whether the data has changed since the application last read that data.
There are three main data concurrency strategies that developers typically consider:
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Application developers need to think carefully about how to provide a predictable experience to their end users, particularly for scenarios where multiple users can update the same data. Modern applications often have multiple users viewing and updating data simultaneously.