Reservoir sampling requires you to maintain a reservoir of n items that will be your sample. This can be prohibitive if the sample size itself is huge. The algo

Reservoir sampling requires you to maintain a reservoir of n items that will be your sample. This can be prohibitive if the sample size itself is huge. The algorithm described here decides (commits) whether an item is to be sampled as it sees it (i.e., online), while consuming a constant amount of memory. It differs from the usual reservoir sampling approach in that it cannot go back and "unsample" an item it already selected (whereas the crux of a reservoir sampling approach is evicting current candidate items and replacing them with new items).