Depends on what you are programming on. If its in a language far removed from the machine your mileage may vary. I have had the opportunity to try out Google's

Depends on what you are programming on. If its in a language far removed from the machine your mileage may vary. I have had the opportunity to try out Google's implementation of mapreduce implemented in C++ way back in time (6 years ago). These would run on fairly impoverished processors, essentially laptop grade. Have done stuff on Yahoo's Hadoop setup as well, these used high end multicore machines provisioned with oodles of RAM (I dont think I should share more than that). If I were to be generous, Hadoop ran 4 times slower as measured by wall clock times. Not only that, Hadoop required about 4 times more memory for similar sized jobs. So you ended up requiring more RAM, running for longer and potentially burning more electricity. This is by no means a benchmark or anything like that, just an anecdote. That Hadoop would require much more memory did not surprise me, that was expected. What was really surprising was that it was so much slower. JVM is one of the most well optimized virtual machines we have out there, but its view of the processor is very antiquated and it does not surface those hardware level advances to the programmer. You pay for a hot-rod machine but run it like an old faithful crown victoria. Four times might not seem like much, for one thing I am being generous, and it makes a big difference when you can make multiple run through the data in a single day and make changes to the code/model. Debugging and ironing out issues is a lot more efficient. I think Hadoop gave Google a significant competitive advantage over the rest, probably still does.