cjrh-scalable-cuckoo-filter 0.3.1

Scalable Cuckoo Filter implemented in Rust with Python bindings

scalable-cuckoo-filter-py

Python wrapper for sile/scalable_cuckoo_filter

See the crate that this wraps: https://docs.rs/scalable_cuckoo_filter/

This can be used as a probabilistic data structure for set membership tests. A cuckoo filter is similar to a Bloom filter, but it supports deletions.

This implementation is scalable, meaning that it can grow and shrink dynamically while maintinaing a low false positive rate.

Usage

Create the filter and add a few strings:

from scf import PyScalableCuckooFilter


scf = PyScalableCuckooFilter(1000, 0.001)
scf.insert("hello")
scf.insert("world")

Now we can do membership tests:

assert scf.might_contain("hello")
assert scf.might_contain("world")
assert not scf.might_contain("foo")

We can remove entries:

scf.remove("hello")
assert not scf.might_contain("hello")

We can save the filter to a file:

scf.write_to_file("filter.bin")

And load it back:

scf = PyScalableCuckooFilter.read_from_file("filter.bin")
assert scf.might_contain("world")

Types

These are the the types that can be used with the filter:

• str
• bytes
• bytearray
• int
• float
• bool
• Iterables of the above types, with nesting.

For example, you can insert a list of integers:

scf = PyScalableCuckooFilter(1000, 0.001)
scf.insert([1, 2, 3])  # list
scf.insert([1, (2, [3])])  # list(tuple(list)))

However, note that all iterables will hash the same as each other. For example, ["foo", "bar"] and ("foo", "bar") will be regarded as the same key. In fact, any iterable with the same elements will hash the same.

scf = PyScalableCuckooFilter(1000, 0.001)
scf.insert(range(3))  # iterable
assert scf.might_contain((0, 1, 2))  # tuple