What is a Bloom filter?
Q: What is a Bloom filter?
A: A Bloom filter is a data structure that allows computers to see if a given element occurs in a set. It uses hash functions to do this by calculating the hash value of each element added and comparing it to the other elements in the set.
Q: What type of data structure is a Bloom filter?
A: A Bloom filter is a probabilistic data structure, meaning there is a possibility of getting false positives but not false negatives.
Q: Who proposed the Bloom filter?
A: Edward Bloom proposed the Bloom filter in 1970.
Q: What was Edward's example for using his technique?
A: Edward's example was hyphenating about 500,000 words; he found that using his technique, he could eliminate most lookups and reduce disk accesses by 15%.
Q: How many bits per element are required for 1% false positive probability?
A: Fewer than 10 bits per element are required for 1% false positive probability, independent of the size or number of elements in the set.
Q: Is it possible to remove elements from a bloom filter once they have been added?
A: No, elements can only be added to the set but not removed.
Q: Does adding more elements increase or decrease the probability of getting a false positive result?
A: Adding more elements increases the probability of getting a false positive result.
