29-bloom-filters

README

Bloom Filters

Bloom filter is a probabilistic data structure that's used to determine if an element is in a set.

Implement Bloom Filters and measure

1. False Positive Rate vs Size of the filter
2. False Positive Rate vs Number of Hash Function

False positive Rate vs Size of Bloom Filter - (Single Hash Function)

Here's the data formatted as a table:

False Positive Rate	Bloom Filter Size (Single Hash)
1.000000	16
1.000000	32
1.000000	64
1.000000	128
1.000000	256
0.983000	512
0.857000	1024
0.629000	2048
0.403000	4096
0.234000	8192
0.131000	16384
0.071000	32768
0.032000	65536
0.015000	131072
0.006000	262144

False positive Rate vs Size of Bloom Filter - (10 Hash Function)

False Positive Rate	Bloom Filter Size (10 hash)
1	16
1	32
1	64
1	128
1	256
1	512
1	1024
1	2048
0.929	4096
0.418	8192
0.033	16384
0	32768
0	65536
0	131072
0	262144

False Positive Rate vs Number of Hash Function

The sudden increase in the false positive rate after adding more hash functions is due to overhashing and the saturation of bits in the Bloom filter.

Conclustion

• With 10 hash functions the number of FPR reaches a low value much quick.

• But With too many hash functions FPR increases again 'cause oversaturation of the Bloom filter