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If you have huge amount of money, you can just keep throwing hardware at scaling problems. For the rest of us, though, performance tuning is a must.

Note that you first checked to see if the table existed. If it did, you dropped it so you could re-create it.



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INFORMATION_SCHEMA view of the same name. It limits the tables you can see to the ones you can access. Microsoft recommends you use the new catalog views to get database metadata in SQL Server 2005, and SQL Server itself uses them internally. The catalog view for this query would be sys.tables and the column name would be name. We ve used the INFORMATION SCHEMA view here because you may still see it often.





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Under each key, you store a list of people. In this case, the lists contain only me. Now, if you want a list of all the people registered who have the middle name Lie, you could do the following: >>> storage['middle']['Lie'] ['Magnus Lie Hetland'] As you can see, adding people to this structure is a bit tedious, especially when you get more people with the same first, middle, or last names, because then you have to extend the list that is already stored under that name. Let s add my sister, for example, and let s assume you don t know what is already stored in the database: >>> my_sister = 'Anne Lie Hetland' >>> storage['first'].setdefault('Anne', []).append(my_sister) >>> storage['middle'].setdefault('Lie', []).append(my_sister) >>> storage['last'].setdefault('Hetland', []).append(my_sister) >>> storage['first']['Anne'] ['Anne Lie Hetland'] >>> storage['middle']['Lie'] ['Magnus Lie Hetland', 'Anne Lie Hetland'] Imagine writing a large program filled with updates like this it would quickly become quite unwieldy. The point of abstraction is to hide all the gory details of the updates, and you can do that with functions. Let s first make a function to initialize a data structure: def init(data): data['first'] = {} data['middle'] = {} data['last'] = {} In the preceding code, I ve simply moved the initialization statements inside a function. You can use it like this:

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Instead of the BinaryReader you used for images, GetTextFile used a StreamReader (derived from System.IO.TextReader) to read the contents of the file into a string:

Unfortunately, performance tuning is much more of an art than a science, and it is even more difficult to write about than scaling. If you re serious about deploying a large-scale Django application, you should spend a great deal of time learning how to tune each piece of your stack. The following sections, though, present a few Django-specific tuning tips we ve discovered over the years.

Function GetTextFile(ByVal textFile As String) As String Console.WriteLine("Loading File: " + textFile) Dim fs As New FileStream(textFile, FileMode.Open, FileAccess.Read) Dim sr As StreamReader = New StreamReader(fs) Dim textBytes As String = sr.ReadToEnd() Console.WriteLine( _ "TextBytes has length {0} bytes.", _ textBytes.Length _ ) Return textBytes End Function

>>> storage = {} >>> init(storage) >>> storage {'middle': {}, 'last': {}, 'first': {}} As you can see, the function has taken care of the initialization, making the code much more readable.

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