While writing my post analyzing the new .NET 4 ConcurrentDictionary class (here), I fell into one of the classic blunders that I myself always love to warn about.  After analyzing the differences of time between a Dictionary with locking versus the new ConcurrentDictionary class, I noted that the ConcurrentDictionary was faster with read-heavy multi-threaded operations.  Then, I made the classic blunder of thinking that because the original Dictionary with locking was faster for those write-heavy uses, it was the best choice for those types of tasks.  In short, I fell into the premature-optimization anti-pattern.

Basically, the premature-optimization anti-pattern is when a developer is coding very early for a perceived (whether rightly-or-wrongly) performance gain and sacrificing good design and maintainability in the process.  At best, the performance gains are usually negligible and at worst, can either negatively impact performance, or can degrade maintainability so much that time to market suffers or the code becomes very fragile due to the complexity.

Keep in mind the distinction above.  I'm not talking about valid performance decisions.  There are decisions one should make when designing and writing an application that are valid performance decisions.  Examples of this are knowing the best data structures for a given situation (Dictionary versus List, for example) and choosing performance algorithms (linear search vs. binary search).  But these in my mind are macro optimizations.  The error is not in deciding to use a better data structure or algorithm, the anti-pattern as stated above is when you attempt to over-optimize early on in such a way that it sacrifices maintainability.

In my case, I was actually considering trading the safety and maintainability gains of the ConcurrentDictionary (no locking required) for a slight performance gain by using the Dictionary with locking.  This would have been a mistake as I would be trading maintainability (ConcurrentDictionary requires no locking which helps readability) and safety (ConcurrentDictionary is safe for iteration even while being modified and you don't risk the developer locking incorrectly) -- and I fell for it even when I knew to watch out for it. 

I think in my case, and it may be true for others as well, a large part of it was due to the time I was trained as a developer.  I began college in in the 90s when C and C++ was king and hardware speed and memory were still relatively priceless commodities and not to be squandered.  In those days, using a long instead of a short could waste precious resources, and as such, we were taught to try to minimize space and favor performance. 

This is why in many cases such early code-bases were very hard to maintain.  I don't know how many times I heard back then to avoid too many function calls because of the overhead -- and in fact just last year I heard a new hire in the company where I work declare that she didn't want to refactor a long method because of function call overhead. 

Now back then, that may have been a valid concern, but with today's modern hardware even if you're calling a trivial method in an extremely tight loop (which chances are the JIT compiler would optimize anyway) the results of removing method calls to speed up performance are negligible for the great majority of applications.  Now, obviously, there are those coding applications where speed is absolutely king (for example drivers, computer games, operating systems) where such sacrifices may be made.  But I would strongly advice against such optimization because of it's cost. 

Many folks that are performing an optimization think it's always a win-win.  That they're simply adding speed to the application, what could possibly be wrong with that?  What they don't realize is the cost of their choice.  For every piece of straight-forward code that you obfuscate with performance enhancements, you risk the introduction of bugs in the long term technical debt of the application.  It will become so fragile over time that maintenance will become a nightmare. 

I've seen such applications in places I have worked.  There are times I've seen applications where the designer was so obsessed with performance that they even designed their own memory management system for their application to try to squeeze out every ounce of performance.  Unfortunately, the application stability often suffers as a result and it is very difficult for anyone other than the original designer to maintain.

I've even seen this recently where I heard a C++ developer bemoaning that in VS2010 the iterators are about twice as slow as they used to be because Microsoft added range checking (probably as part of the 0x standard implementation).  To me this was almost a joke.  Twice as slow sounds bad, but it almost never as bad as you think -- especially if you're gaining safety.  The only time twice is really that much slower is when once was too slow to begin with.  Think about it.  2 minutes is slow as a response time because 1 minute is slow.  But if an iterator takes 1 microsecond to move one position and a new, safer iterator takes 2 microseconds, this is trivial!  The only way you'd ever really notice this would be in iterating a collection just for the sake of iterating (i.e. no other operations).  To my mind, the added safety makes the extra time worth it.

Always favor safety and maintainability when you can.  I know it can be a hard habit to break, especially if you started out your career early or in a language such as C where they are very performance conscious.  But in reality, these type of micro-optimizations only end up hurting you in the long run.

Remember the two laws of optimization.  I'm not sure where I first heard these, but they are so true:

1. For beginners: Do not optimize.
2. For experts: Do not optimize yet.

This is so true.  If you're a beginner, resist the urge to optimize at all costs.  And if you are an expert, delay that decision.  As long as you have chosen the right data structures and algorithms for your task, your performance will probably be more than sufficient.  Chances are it will be network, database, or disk hits that will be your slow-down, not your code.  As they say, 98% of your code's bottleneck is in 2% of your code so premature-optimization may add maintenance and safety debt that won't have any measurable impact.  Instead, code for maintainability and safety, and then, and only then, when you find a true bottleneck, then you should go back and optimize further.