Simple Multiple BloomFilters data structure implementation on Java

As a follow up on my previous post about using Multiple Bloom Filters data structure to identify hot values, I decided to write a simple dumb implementation on Java. And open source it.

The project can be found on GitHub and is proudly named multi-bloom-filter.

It comes with basic little class called MultiBloomFilter which does most of the job. It accepts the number of enclosed bloom filters, capacity of each BF and duration before the the head BF will be reset. One can also specify what hash function is used and how many times hash function should be applied for each value.

Simple example:

This short example shows that MBF will reset only one of the internal BFs. Means, whenever reset happens, it will remove only part of the data, and whenever the hot key is added again, it would be identified as such.

Once again, MBF is a great solution if you need to find a set of hot values for some period of time. In particular, this helps to put only hot values into the cache. If we have many hosts that use a single distributed cache service, then using MBF might save from redundant traffic of putting cold data into the cache, where they would be evicted pretty fast. Also, as hot keys are in MBF, means there is a high chance they are in the distributed cache as well. Thus application has some kind of "bloom filter" to check what is the chance that value could be found in the cache for specified key.

There are much more use cases for the MBF data structure. Being able to work in concurrent scalable environment is another "feature" that I love about BloomFilters and MultiBloomFilter in particular. For me, good implementation of BloomFilter, that is able to grow and scale correctly, has different mechanism to evict data and fight the false positives, sounds as a very useful service.