There’s a useful but non-obvious property of these filters.
If you have two Bloom filters which use same set of possible keys and same filter setup, you can compute intersection of their represented sets with a bitwise AND operation over the bitsets in these filters.
If you store bitsets aligned and padded by SIMD vector size (for example, an array of __m128i in C++ as opposed to the array of uint64 values in the golang example), computing such intersection is very efficient on modern computers.
I once used that technique to filter 3D objects from a large set using the intersection of 3 range queries, one per coordinate. Compute two Bloom filters filled with the objects returned by X and Y queries, intersect the filters, then iterate over the objects returned by the remaining Z query testing against the intersected Bloom filter. Due to probabilistic nature of the Bloom filter you still need to test for X and Y queries for the objects tested positive by the filter, however with proper setup the filter should reject vast majority of them.
If you have two Bloom filters which use same set of possible keys and same filter setup, you can compute intersection of their represented sets with a bitwise AND operation over the bitsets in these filters.
If you store bitsets aligned and padded by SIMD vector size (for example, an array of __m128i in C++ as opposed to the array of uint64 values in the golang example), computing such intersection is very efficient on modern computers.
I once used that technique to filter 3D objects from a large set using the intersection of 3 range queries, one per coordinate. Compute two Bloom filters filled with the objects returned by X and Y queries, intersect the filters, then iterate over the objects returned by the remaining Z query testing against the intersected Bloom filter. Due to probabilistic nature of the Bloom filter you still need to test for X and Y queries for the objects tested positive by the filter, however with proper setup the filter should reject vast majority of them.