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I remember hearing about the following algorithm some years back, but can't find any reference to it online. It identifies the top k elements (or heavy hitters) in a data stream of n elements using only m counters. This is particularly useful for finding top search terms, network abusers, etc. while using minimal memory. The algorithm: for each element, If the element does not already have a counter and counters < m , create a counter for the element and initialize to 1. Else if the element does have a counter, increment it. Else if the element does not have a counter and counters > m , decrement an existing counter c . If c reaches 0, replace its corresponding element, with the current element. ( c is an index into the list of existing counters, where c increases in round robin fashion for each element that reaches this step.) I have found many other similar algorithms (many of which are listed, though not described, in this wikipedia article about streaming algorithms ), but not this one. I particularly like it because it is as simple to implement as it is to describe. But I'd like to learn more about its probabilistic characteristics- if I'm only interested in the top 100 items, what effect does using 1,000 counters instead of 100 counters have?

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