http://eclipse.dzone.com/news/effective-eclipse-shortcut-key

That is, if we want to find any of a large number, say k, fixed length patterns in a text, we can create a simple variant of Rabin–Karp that uses a Bloom filter or a set data structure to check whether the hash of a given string belongs to a set of hash values of patterns we are looking for:

 function RabinKarpSet(string s[1..n], set of string subs, m) {
     set hsubs := emptySet
     for each sub in subs
         insert hash(sub[1..m]) into hsubs
     hs := hash(s[1..m])
     for i from 1 to n-m+1
         if hs ∈ hsubs
             if s[i..i+m-1] = a substring with hash hs
                 return i
         hs := hash(s[i+1..i+m])
     return not found
 }

Here we assume all the substrings have a fixed length m, but this assumption can be eliminated. We simply compare the current hash value against the hash values of all the substrings simultaneously using a quick lookup in our set data structure, and then verify any match we find against all substrings with that hash value.

Other algorithms can search for a single pattern in O(n) time, and hence they can be used to search for k patterns in O(n k) time. In contrast, the variant Rabin–Karp above can find all kpatterns in O(n+k) time in expectation, because a hash table checks whether a substring hash equals any of the pattern hashes in O(1) time.