Usage
I like to use this component for concatenation of SQL query strings, both in VB and in ASP scripts. SQL Server devours big batches of SQL much better than lots of little requests. Since Catter is so efficient, great gains can be had when the size of the strings and the number of concatenations grows. As an example, I use this component to manufacture SQL that marks all of a set of database-modelled files and folders and their children folders and files as public or private. If there are just a few files and folders, you can get away with VB string concatenation. Throw in a few thousand files and deeply nested folders, and you need the improved performance of Catter.

Performance
Catter outperforms standard VB string concatenation after several dozen concatenations. Here's The results can be seen below. If you're interested in the code tests I used, check out the VB test code used. Note that is employs the use of a free component, ASP Profiler. To run this code you will need to download and install the component.

Concatenations	Length of String	VBScript Concatenation	StrCat.Catter Concatenation
1	44	0.01	0.05
5	44	0.02	0.06
10	44	0.02	0.08
25	44	0.05	0.31
50	44	0.14	0.35
100	44	1.07	0.44
200	44	1.86	0.81
500	44	12.19	2.30
1000	44	140.20	5.51
2000	44	940.53	12.41
200	484	150.13	11.31
500	484	1,467.19	25.16
1000	484	6,414.08	58.50
2000	484	26,419.20	112.30

You can see that it takes about three times longer to get Catter up to speed for a single concatenation, and even after a few dozen Catter lags behind. But once VB starts to slow down, it does so disastrously - quadratically, in fact - and Catter keeps its cool, staying almost linear in terms of time versus concatenations. I'd hate to be the person waiting 25 seconds for VB to concatenate some strings!

Implementation
Catter is an STL vector of STL wstrings. Catter's implementation calls on the reserve methods of both the vector and wstring classes. The implementations of these classes are designed so that their execution time stays linear under these circumstances. They accomplish this by doubling their allocated memory when they need to grow, thereby avoiding making too many allocations and fragmenting the heap.