We get these ~50GB data files consisting of 16 byte codes, and I want to find any code that occurs 1/2% of the time or more. Is there any way I can do that in a single pass over the data?

Edit: There are tons of codes - it's possible that every code is different.

EPILOGUE: I've selected Darius Bacon as best answer, because I think the best algorithm is a modification of the majority element he linked to. The majority algorithm should be modifiable to only use a tiny amount of memory - like 201 codes to get 1/2% I think. Basically you just walk the stream counting up to 201 distinct codes. As soon as you find 201 distinct codes, you drop one of each code (deduct 1 from the counters, forgetting anything that becomes 0). At the end, you have dropped at most N/201 times, so any code occurring more times than that must still be around.

But it's a two pass algorithm, not one. You need a second pass to tally the counts of the candidates. It's actually easy to see that any solution to this problem must use at least 2 passes (the first batch of elements you load could all be different and one of those codes could end up being exactly 1/2%)

Thanks for the help!