A Rust-implementation of a stable Bloom filter for filtering duplicates out of data streams, port of BoomFilters
This is an implementation of Stable Bloom Filters as described by Deng and Rafiei in Approximately Detecting Duplicates for Streaming Data using Stable Bloom Filters.
A Stable Bloom Filter (SBF) continuously evicts stale information so that it has room for more recent elements. Like traditional Bloom filters, an SBF has a non-zero probability of false positives, which is controlled by several parameters. Unlike the classic Bloom filter, an SBF has a tight upper bound on the rate of false positives while introducing a non-zero rate of false negatives. The false-positive rate of a classic Bloom filter eventually reaches 1, after which all queries result in a false positive. The stable-point property of an SBF means the false-positive rate asymptotically approaches a configurable fixed constant. A classic Bloom filter is actually a special case of SBF where the eviction rate is zero and the cell size is one, so this provides support for them as well (in addition to bitset-based Bloom filters).
Stable Bloom Filters are useful for cases where the size of the data set isn't known a priori and memory is bounded. For example, an SBF can be used to deduplicate events from an unbounded event stream with a specified upper bound on false positives and minimal false negatives.
toml
stable-bloom-filter = "0.3"
```rust use stable-bloom-filter::StableBloomFilter;
let mut f = StableBloomFilter::newdefault(10000, 0.01); assert!(!f.test(b"a"));
f.add(b"a"); assert!(f.test(b"a"));
assert!(f.testandadd(b"a"));
assert!(!f.testandadd(b"b")); assert!(f.test(b"a"));
assert!(f.test(b"b"));
assert!(!f.test(b"c")); ```