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myarbrough@oreilly.com 2014-11-26 06:12:39 -08:00
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@ -207,12 +207,13 @@ The bloom filter is initialized so that the array of bits is all zeros. To add a
<<bloom2>> is an example of adding a pattern "A" to the simple bloom filter shown in <<bloom1>>.
Adding a second pattern is as simple as repeating this process. The pattern is hashed by each hash function in turn and the result is recorded by setting the bits to +1+. Note that as a bloom filter is filled with more patterns, a hash function result might coincide with a bit that is already set to +1+, in which case the bit is not changed. In essence, as more patterns record on overlapping bits, the bloom filter starts to become saturated with more bits set to +1+ and the accuracy of the filter decreases. This is why the filter is a probabilistic data structure—it gets less accurate as more patterns are added. The accuracy depends on the number of patterns added versus the size of the bit array (N) and number of hash functions (M). A larger bit array and more hash functions can record more patterns with higher accuracy. A smaller bit array or fewer hash functions will record fewer patterns and produce less accuracy.
[[bloom2]]
.Adding a pattern "A" to our simple bloom filter
image::images/msbt_0609.png["Bloom2"]
Adding a second pattern is as simple as repeating this process. The pattern is hashed by each hash function in turn and the result is recorded by setting the bits to +1+. Note that as a bloom filter is filled with more patterns, a hash function result might coincide with a bit that is already set to +1+, in which case the bit is not changed. In essence, as more patterns record on overlapping bits, the bloom filter starts to become saturated with more bits set to +1+ and the accuracy of the filter decreases. This is why the filter is a probabilistic data structure—it gets less accurate as more patterns are added. The accuracy depends on the number of patterns added versus the size of the bit array (N) and number of hash functions (M). A larger bit array and more hash functions can record more patterns with higher accuracy. A smaller bit array or fewer hash functions will record fewer patterns and produce less accuracy.
<<bloom3>> is an example of adding a second pattern "B" to the simple bloom filter.
[[bloom3]]