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As you have already seen in 4, good connection pooling is critical to the performance of your application in a highly concurrent environment. This is because database connections are expensive resources. The one big difference between a data reader and a DataSet is that a DataSet is database independent and is completely disconnected from the underlying data source. In other words, in order to retrieve data out of a DataSet, you do not need to connect with the underlying data source. While it may be true that to fill the DataSet originally, and to update its changes back into the underlying data source, you will still need to reconnect to the data source, the entire upshot of this disconnected nature of data access is that you are able to keep the connection closed while you are actually working with the data. But calling Close on the DbConnection object, such as SqlConnection or OracleConnection, doesn t necessarily mean that your physical network or database connection is really closed. In fact, while you weren t using it, ADO.NET took the same physical database connection and possibly served a bunch of other connection requests in the meantime.
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n this chapter you ll learn how use multiple gem versions in your code. We ll cover installing new gem versions, getting rid of old ones, and making sure your code uses the correct ones. Gem versioning is very useful particularly because of the ever-changing nature of the Ruby community. Many gems are changing very fast, and sometimes interfaces change as well. In some ways, this is good it means that software is improving. On the other hand, it breaks older software. If you have to rely on a certain version of a gem for example, because a later version disables or changes functionality you currently use then gem versioning can make your old code run with just a one- or two-line change.
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This is in contrast with the data reader where, even if your result set had two rows in it, while you were working on the first row and hadn t explicitly closed the data reader and the connection, your connection is still open and cannot be used by anyone else. Thus, in such a situation even if the data reader is faster in a single-user scenario, in a highly concurrent scenario it will actually be slower and, hence, not a good choice. You might raise an eyebrow here and bring up the question that a data adapter internally uses a data reader, too. But, again, even if the data adapter uses a data reader, it frees up the data reader as soon as it can. It fills up a DataSet or DataTable and returns the object. The time that it takes you to actually process upon the data is when a connection will stay closed and, hence, effectively pooled.
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DataSets out of the box suffer from one big problem: even though they try to mimic an in-memory relational data structure, they fall short on representing the actual data types contained within. Everything is an object. While that is the most portable mechanism, you would typically then need to implement checks and conditions at each point to ensure that the data contained within is not corrupted. The other way out is to create a strongly typed DataSet. Strongly typed DataSets are as the name suggests strongly typed, but they, too, suffer from their own set of disadvantages. The biggest problem with strongly typed DataSets is that their structure needs to be continually updated to reflect the underlying table structure. This involves code generation and recompilation. Also, as the data structure underneath changes, the table adapters associated with the strongly typed DataSet continually get out of date as well. This is probably not an issue in a small application, but maintaining and deploying such a library of strongly typed DataSets can turn into a major headache. Most of all, DataSets and strongly typed DataSets provide you with a ready-to-use, in-memory disconnected data cache, but at the end of the day, they contain no true knowledge of what that data truly is. It s just a dumb bucket of data. Strongly typed DataSets try to come close to the true representation of your data, but they fall short by far. It is in these instances where you must resort to the use of a custom business object.
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