A Bloom filter is a space-efficient probabilistic data structure
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| Name | Summary |
|---|---|
| Bloom filter is based on multiple hashing, instead of one | |
| Bloom filters are usually periodically rebuilt | |
| By default, you can’t remove element from bloom filter | |
| Reference | YouTube: Bloom Filters |
Q: What are the typical use cases of bloom filter?
- Cache filtering: Google Bigtable, Apache HBase and Apache Cassandra and PostgreSQL[11] use Bloom filters to reduce the disk lookups for non-existent rows or columns.
- Search engine: Microsoft Bing (search engine) uses multi-level hierarchical Bloom filters for its search index.
- Web browser: The Google Chrome web browser used to use a Bloom filter to identify malicious URLs.
- Data synchronization: Bloom filters can be used for approximate data synchronization.
- Bitcoin uses Bloom filters to speed up wallet synchronization
Q: How we know bloom filter needs to be rebuilt?
A: TODO
Q: How to resize bloom filter, if false positive ratio is too high?
A: TODO
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