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How do you handle data concurrency in .NET ?
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One of the key features of the ADO.NET DataSet is that it can be a self-contained and disconnected data store. It can contain the schema and data from several rowsets in DataTable objects as well as information about how to relate the DataTable objects-all in memory. The DataSet neither knows nor cares where the data came from, nor does it need a link to an underlying data source. Because it is data source agnostic you can pass the DataSet around networks or even serialize it to XML and pass it across the Internet without losing any of its features. However, in a disconnected model, concurrency obviously becomes a much bigger problem than it is in a connected model.
In this column, I'll explore how ADO.NET is equipped to detect and handle concurrency violations. I'll begin by discussing scenarios in which concurrency violations can occur using the ADO.NET disconnected model. Then I will walk through an ASP.NET application that handles concurrency violations by giving the user the choice to overwrite the changes or to refresh the out-of-sync data and begin editing again. Because part of managing an optimistic concurrency model can involve keeping a timestamp (rowversion) or another type of flag that indicates when a row was last updated, I will show how to implement this type of flag and how to maintain its value after each database update.
Is Your Glass Half Full
There are three common techniques for managing what happens when users try to modify the same data at the same time: pessimistic, optimistic, and last-in wins. They each handle concurrency issues differently.
The pessimistic approach says: "Nobody can cause a concurrency violation with my data if I do not let them get at the data while I have it." This tactic prevents concurrency in the first place but it limits scalability because it prevents all concurrent access. Pessimistic concurrency generally locks a row from the time it is retrieved until the time updates are flushed to the database. Since this requires a connection to remain open during the entire process, pessimistic concurrency cannot successfully be implemented in a disconnected model like the ADO.NET DataSet, which opens a connection only long enough to populate the DataSet then releases and closes, so a database lock cannot be held.
Another technique for dealing with concurrency is the last-in wins approach. This model is pretty straightforward and easy to implement-whatever data modification was made last is what gets written to the database. To implement this technique you only need to put the primary key fields of the row in the UPDATE statement's WHERE clause. No matter what is changed, the UPDATE statement will overwrite the changes with its own changes since all it is looking for is the row that matches the primary key values. Unlike the pessimistic model, the last-in wins approach allows users to read the data while it is being edited on screen. However, problems can occur when users try to modify the same data at the same time because users can overwrite each other's changes without being notified of the collision. The last-in wins approach does not detect or notify the user of violations because it does not care. However the optimistic technique does detect violations. Contd....
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