notfilippo commented on code in PR #53: URL: https://github.com/apache/datasketches-rust/pull/53#discussion_r2655447014
########## datasketches/src/bloom/sketch.rs: ########## @@ -0,0 +1,732 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +use std::hash::Hash; +use std::hash::Hasher; + +use crate::codec::SketchBytes; +use crate::codec::SketchSlice; +use crate::error::Error; +use crate::hash::XxHash64; + +// Serialization constants +const PREAMBLE_LONGS_EMPTY: u8 = 3; +const PREAMBLE_LONGS_STANDARD: u8 = 4; +const FAMILY_ID: u8 = 21; // Bloom filter family ID +const SERIAL_VERSION: u8 = 1; +const EMPTY_FLAG_MASK: u8 = 1 << 2; + +/// A Bloom filter for probabilistic set membership testing. +/// +/// Provides fast membership queries with: +/// - No false negatives (inserted items always return `true`) +/// - Tunable false positive rate +/// - Constant space usage +/// +/// Use [`super::BloomFilterBuilder`] to construct instances. +#[derive(Debug, Clone, PartialEq)] +pub struct BloomFilter { + /// Hash seed for all hash functions + pub(super) seed: u64, + /// Number of hash functions to use (k) + pub(super) num_hashes: u16, + /// Total number of bits in the filter (m) + pub(super) capacity_bits: u64, + /// Count of bits set to 1 (for statistics) + pub(super) num_bits_set: u64, + /// Bit array packed into u64 words + /// Length = ceil(capacity_bits / 64) + pub(super) bit_array: Vec<u64>, +} + +impl BloomFilter { + /// Tests whether an item is possibly in the set. + /// + /// Returns: + /// - `true`: Item was **possibly** inserted (or false positive) + /// - `false`: Item was **definitely not** inserted + /// + /// # Examples + /// + /// ``` + /// # use datasketches::bloom::BloomFilterBuilder; + /// let mut filter = BloomFilterBuilder::with_accuracy(100, 0.01).build(); + /// filter.insert("apple"); + /// + /// assert!(filter.contains(&"apple")); // true - was inserted + /// assert!(!filter.contains(&"grape")); // false - never inserted (probably) + /// ``` + pub fn contains<T: Hash>(&self, item: &T) -> bool { + if self.is_empty() { + return false; + } + + let (h0, h1) = self.compute_hash(item); + self.check_bits(h0, h1) + } + + /// Tests and inserts an item in a single operation. + /// + /// Returns whether the item was possibly already in the set before insertion. + /// This is more efficient than calling `contains()` then `insert()` separately. + /// + /// # Examples + /// + /// ``` + /// # use datasketches::bloom::BloomFilterBuilder; + /// let mut filter = BloomFilterBuilder::with_accuracy(100, 0.01).build(); + /// + /// let was_present = filter.contains_and_insert(&"apple"); + /// assert!(!was_present); // First insertion + /// + /// let was_present = filter.contains_and_insert(&"apple"); + /// assert!(was_present); // Now it's in the set + /// ``` + pub fn contains_and_insert<T: Hash>(&mut self, item: &T) -> bool { + let (h0, h1) = self.compute_hash(item); + let was_present = self.check_bits(h0, h1); + self.set_bits(h0, h1); + was_present + } + + /// Inserts an item into the filter. + /// + /// After insertion, `contains(item)` will always return `true`. + /// + /// # Examples + /// + /// ``` + /// # use datasketches::bloom::BloomFilterBuilder; + /// let mut filter = BloomFilterBuilder::with_accuracy(100, 0.01).build(); + /// + /// filter.insert("apple"); + /// filter.insert(42_u64); + /// filter.insert(&[1, 2, 3]); + /// + /// assert!(filter.contains(&"apple")); + /// ``` + pub fn insert<T: Hash>(&mut self, item: T) { + let (h0, h1) = self.compute_hash(&item); + self.set_bits(h0, h1); + } + + /// Resets the filter to its initial empty state. + /// + /// Clears all bits while preserving capacity and configuration. + /// + /// # Examples + /// + /// ``` + /// # use datasketches::bloom::BloomFilterBuilder; + /// let mut filter = BloomFilterBuilder::with_accuracy(100, 0.01).build(); + /// filter.insert("apple"); + /// assert!(!filter.is_empty()); + /// + /// filter.reset(); + /// assert!(filter.is_empty()); + /// assert!(!filter.contains(&"apple")); + /// ``` + pub fn reset(&mut self) { + for word in &mut self.bit_array { + *word = 0; + } Review Comment: Always appreciate learning about new rust API. 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