bloom-filter

I am working with a large set (5-20 million) of String keys (average length 10 chars) which I need to store in an in memory data structure that supports the following operation in constant time or near constant time: // Returns true if the input is present in the container, false otherwise public boolean contains(String input) Java's Hashmap is proving to be more than satisfactory as far as throughput is concerned but is taking up a lot of memory. I am looking for a solution that is memory efficient and still supports a throughput that is decent (comparable with or nearly as good as hashing).

I'm trying to optimize a piece of software which is basically running millions of tests. These tests are generated in such a way that there can be some repetitions. Of course, I don't want to spend time running tests which I already ran if I can avoid it efficiently. So, I'm thinking about using a Bloom filter to store the tests which have been already ran. However, the Bloom filter errs on the unsafe side for me. It gives false positives. That is, it may report that I've ran a test which I haven't. Although this could be acceptable in the scenario I'm working on, I was wondering if there's an

I've got the following question about choosing hash functions for Bloom filters: Which functions to use? In nearly every document/paper you can read that the hash functions used in a Bloom filter should be independent and uniformly distributed. I know what is meant by this (independent and uniformly distributed), but I'm having trouble to find a argumentation or a discussion, which hash functions fulfill those requirements and are therefore suitable. In a lot of posts I've read about suggestions for the usage of the FNV or Murmur hash function , but not why (or at least without a proof) they

Wikipedia says: An empty Bloom filter is a bit array of m bits, all set to 0. There must also be k different hash functions defined, each of which maps or hashes some set element to one of the m array positions with a uniform random distribution. I read the article, but what I don't understand is how k is determined. Is it a function of the table size? Also, in hash tables I've written I used a simple but effective algorithm for automatically growing the hash's size. Basically, if ever more than 50% of the buckets in the table were filled, I would double the size of the table. I suspect you

I am reading up on bloom filters and they just seem silly. Anything you can accomplish with a bloom filter, you could accomplish in less space, more efficiently, using a single hash function rather than multiple, or that's what it seems. Why would you use a bloom filter and how is it useful? From Wikipedia : Bloom filters have a strong space advantage over other data structures for representing sets, such as self-balancing binary search trees, tries, hash tables, or simple arrays or linked lists of the entries. Most of these require storing at least the data items themselves, which can require