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A. Bloom Filters > Use Cases
Use Cases
This section lists a number of common use cases for Bloom filters. In any application that can benefit from a Boolean test prior to some sort of expensive operation, a Bloom filter can most likely be utilized to reduce a large number of unneeded operations.
One of the most basic uses of a Bloom filter is to represent very large data sets in applications. A data set with millions of elements can take up gigabytes of memory, as well as the expensive I/O required simply to pull the data set off disk. A Bloom filter can drastically reduce the number of bytes required to represent this data set, allowing it to fit in memory and decrease the amount of time required to read. The obvious downside to representing a large data set with a Bloom filter is the false positives. Whether or not this is a big deal varies from one use case to another, but there are ways to get a 100% validation of each test. A post-process join operation on the actual ....
