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POLoading multiple large ADO.NET DataTables/DataReaders - Performance improvements
 

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    <p>I need to load multiple sql statements from SQL Server into DataTables. Most of the statements return some 10.000 to 100.000 records and each take up to a few seconds to load. My guess is that this is simply due to the amount of data that needs to be shoved around. The statements themselves don't take much time to process.</p> <p>So I tried to use Parallel.For() to load the data in parallel, hoping that the overall processing time would decrease. I do get a 10% performance increase, but that is not enough. A reason might be that my machine is only a dual core, thus limiting the benefit here. The server on which the program will be deployed has 16 cores though.</p> <p>My question is, how I could improve the performance more? Would the use of Asynchronous Data Service Queries be a better solution (BeginExecute, etc.) than PLINQ? Or maybe some other approach?</p> <p>The SQl Server is running on the same machine. This is also the case on the deployment server.</p> <p>EDIT: I've run some tests with using a DataReader instead of a DataTable. This already decreased the load times by about 50%. Great! Still I am wondering whether parallel processing with BeginExecute would improve the overall load time if a multiprocessor machine is used. Does anybody have experience with this? Thanks for any help on this!</p> <p>UPDATE: I found that about half of the loading time was consumed by processing the sql statement. In SQL Server Management Studio the statements took only a fraction of the time, but somehow they take much longer through ADO.NET. So by using DataReaders instead of loading DataTables and adapting the sql statements I've come down to about 25% of the initial loading time. Loading the DataReaders in parallel threads with Parallel.For() does not make an improvement here. So for now I am happy with the result and leave it at that. Maybe when we update to .NET 4.5 I'll give the asnchronous DataReader loading a try.</p>
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    1. COplease show some source code...
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    2. COthere is always a good chance that loading 1000s of records is a sign of bad design ... (unless you do it for caching for example)
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    3. COAnd describe your use case... how timely does the data need to be? What caching options do you have? Can you restrict the elements you need to load, reducing network traffic? Can you optimize your server-side database to facilitate the query?
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