If you are a fan of set theory, you might agree there are two sets of people who write computer programs: those who know what a Bloom filter is and those who don’t. How could you efficiently test to see if someone is one set or another? Well, you could use a Bloom filter. [SamWho] takes us through the whole thing in general terms that you could apply in any situation.
The Bloom filter does perform a trade-off for its speed. It is subject to false positives but not false negatives. That is, if a Bloom filter algorithm tells you that X is not part of a set, it is correct. But if it tells you it is, you may have to investigate more to see if that’s true.
If it can’t tell you that something is definitely in a set, why bother? Usually, when you use a Bloom filter, you want to reduce searching through a huge amount of data. The example in the post talks about having a 20-megabyte database of “bad” URLs. You want to warn users if they enter one, but downloading that database is prohibitive. But a Bloom filter could be as small as 1.8 megabytes. However, there would be a 1 in 1000 chance of a false positive.
Increase the database size to 3.59 megabytes, and you can reduce false positives to one in a million. Presumably, if you got a positive, you could accept the risk it is false, or you could do more work to search further.
Imagine, for example, a web cache device or program. Many web pages are loaded one time and ne ..
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