Merge pull request #2837 from quickwit-oss/congxie/replaceHll

Replace hyperloglogplus with Apache DataSketches HLL (lg_k=11)

Cargo.toml

@@ -65,7 +65,7 @@ tantivy-bitpacker = { version = "0.9", path = "./bitpacker" }
 common = { version = "0.10", path = "./common/", package = "tantivy-common" }
 tokenizer-api = { version = "0.6", path = "./tokenizer-api", package = "tantivy-tokenizer-api" }
 sketches-ddsketch = { version = "0.3.0", features = ["use_serde"] }
-hyperloglogplus = { version = "0.4.1", features = ["const-loop"] }
+datasketches = "0.2.0"
 futures-util = { version = "0.3.28", optional = true }
 futures-channel = { version = "0.3.28", optional = true }
 fnv = "1.0.7"

src/aggregation/metric/cardinality.rs

@@ -1,12 +1,11 @@
-use std::collections::hash_map::DefaultHasher;
-use std::hash::{BuildHasher, Hasher};
+use std::hash::Hash;
 
 use columnar::column_values::CompactSpaceU64Accessor;
 use columnar::{Column, ColumnType, Dictionary, StrColumn};
 use common::f64_to_u64;
-use hyperloglogplus::{HyperLogLog, HyperLogLogPlus};
+use datasketches::hll::{HllSketch, HllType, HllUnion};
 use rustc_hash::FxHashSet;
-use serde::{Deserialize, Serialize};
+use serde::{Deserialize, Deserializer, Serialize, Serializer};
 
 use crate::aggregation::agg_data::AggregationsSegmentCtx;
 use crate::aggregation::intermediate_agg_result::{
@@ -16,29 +15,17 @@ use crate::aggregation::segment_agg_result::SegmentAggregationCollector;
 use crate::aggregation::*;
 use crate::TantivyError;
 
-#[derive(Clone, Debug, Serialize, Deserialize)]
-struct BuildSaltedHasher {
-    salt: u8,
-}
-
-impl BuildHasher for BuildSaltedHasher {
-    type Hasher = DefaultHasher;
-
-    fn build_hasher(&self) -> Self::Hasher {
-        let mut hasher = DefaultHasher::new();
-        hasher.write_u8(self.salt);
-
-        hasher
-    }
-}
+/// Log2 of the number of registers for the HLL sketch.
+/// 2^11 = 2048 registers, giving ~2.3% relative error and ~1KB per sketch (Hll4).
+const LG_K: u8 = 11;
 
 /// # Cardinality
 ///
 /// The cardinality aggregation allows for computing an estimate
 /// of the number of different values in a data set based on the
-/// HyperLogLog++ algorithm. This is particularly useful for understanding the
-/// uniqueness of values in a large dataset where counting each unique value
-/// individually would be computationally expensive.
+/// Apache DataSketches HyperLogLog algorithm. This is particularly useful for
+/// understanding the uniqueness of values in a large dataset where counting
+/// each unique value individually would be computationally expensive.
 ///
 /// For example, you might use a cardinality aggregation to estimate the number
 /// of unique visitors to a website by aggregating on a field that contains
@@ -184,7 +171,7 @@ impl SegmentCardinalityCollectorBucket {
 
             term_ids.sort_unstable();
             dict.sorted_ords_to_term_cb(term_ids.iter().map(|term| *term as u64), |term| {
-                self.cardinality.sketch.insert_any(&term);
+                self.cardinality.insert(term);
                 Ok(())
             })?;
             if has_missing {
@@ -195,17 +182,17 @@ impl SegmentCardinalityCollectorBucket {
                     );
                 match missing_key {
                     Key::Str(missing) => {
-                        self.cardinality.sketch.insert_any(&missing);
+                        self.cardinality.insert(missing.as_str());
                     }
                     Key::F64(val) => {
                         let val = f64_to_u64(*val);
-                        self.cardinality.sketch.insert_any(&val);
+                        self.cardinality.insert(val);
                     }
                     Key::U64(val) => {
-                        self.cardinality.sketch.insert_any(&val);
+                        self.cardinality.insert(*val);
                     }
                     Key::I64(val) => {
-                        self.cardinality.sketch.insert_any(&val);
+                        self.cardinality.insert(*val);
                     }
                 }
             }
@@ -296,11 +283,11 @@ impl SegmentAggregationCollector for SegmentCardinalityCollector {
                 })?;
             for val in col_block_accessor.iter_vals() {
                 let val: u128 = compact_space_accessor.compact_to_u128(val as u32);
-                bucket.cardinality.sketch.insert_any(&val);
+                bucket.cardinality.insert(val);
             }
         } else {
             for val in col_block_accessor.iter_vals() {
-                bucket.cardinality.sketch.insert_any(&val);
+                bucket.cardinality.insert(val);
             }
         }
 
@@ -321,11 +308,18 @@ impl SegmentAggregationCollector for SegmentCardinalityCollector {
     }
 }
 
-#[derive(Clone, Debug, Serialize, Deserialize)]
-/// The percentiles collector used during segment collection and for merging results.
+#[derive(Clone, Debug)]
+/// The cardinality collector used during segment collection and for merging results.
+/// Uses Apache DataSketches HLL (lg_k=11, Hll4) for compact binary serialization
+/// and cross-language compatibility (e.g. Java `datasketches` library).
 pub struct CardinalityCollector {
-    sketch: HyperLogLogPlus<u64, BuildSaltedHasher>,
+    sketch: HllSketch,
+    /// Salt derived from `ColumnType`, used to differentiate values of different column types
+    /// that map to the same u64 (e.g. bool `false` = 0 vs i64 `0`).
+    /// Not serialized — only needed during insertion, not after sketch registers are populated.
+    salt: u8,
 }
+
 impl Default for CardinalityCollector {
     fn default() -> Self {
         Self::new(0)
@@ -338,25 +332,52 @@ impl PartialEq for CardinalityCollector {
     }
 }
 
-impl CardinalityCollector {
-    /// Compute the final cardinality estimate.
-    pub fn finalize(self) -> Option<f64> {
-        Some(self.sketch.clone().count().trunc())
+impl Serialize for CardinalityCollector {
+    fn serialize<S: Serializer>(&self, serializer: S) -> Result<S::Ok, S::Error> {
+        let bytes = self.sketch.serialize();
+        serializer.serialize_bytes(&bytes)
     }
+}
 
+impl<'de> Deserialize<'de> for CardinalityCollector {
+    fn deserialize<D: Deserializer<'de>>(deserializer: D) -> Result<Self, D::Error> {
+        let bytes: Vec<u8> = Deserialize::deserialize(deserializer)?;
+        let sketch = HllSketch::deserialize(&bytes).map_err(serde::de::Error::custom)?;
+        Ok(Self { sketch, salt: 0 })
+    }
+}
+
+impl CardinalityCollector {
     fn new(salt: u8) -> Self {
         Self {
-            sketch: HyperLogLogPlus::new(16, BuildSaltedHasher { salt }).unwrap(),
+            sketch: HllSketch::new(LG_K, HllType::Hll4),
+            salt,
         }
     }
 
-    pub(crate) fn merge_fruits(&mut self, right: CardinalityCollector) -> crate::Result<()> {
-        self.sketch.merge(&right.sketch).map_err(|err| {
-            TantivyError::AggregationError(AggregationError::InternalError(format!(
-                "Error while merging cardinality {err:?}"
-            )))
-        })?;
+    /// Insert a value into the HLL sketch, salted by the column type.
+    /// The salt ensures that identical u64 values from different column types
+    /// (e.g. bool `false` vs i64 `0`) are counted as distinct.
+    pub(crate) fn insert<T: Hash>(&mut self, value: T) {
+        self.sketch.update((self.salt, value));
+    }
 
+    /// Compute the final cardinality estimate.
+    pub fn finalize(self) -> Option<f64> {
+        Some(self.sketch.estimate().trunc())
+    }
+
+    /// Serialize the HLL sketch to its compact binary representation.
+    /// The format is cross-language compatible with Apache DataSketches (Java, C++, Python).
+    pub fn to_sketch_bytes(&self) -> Vec<u8> {
+        self.sketch.serialize()
+    }
+
+    pub(crate) fn merge_fruits(&mut self, right: CardinalityCollector) -> crate::Result<()> {
+        let mut union = HllUnion::new(LG_K);
+        union.update(&self.sketch);
+        union.update(&right.sketch);
+        self.sketch = union.get_result(HllType::Hll4);
         Ok(())
     }
 }
@@ -518,4 +539,75 @@ mod tests {
 
         Ok(())
     }
+
+    #[test]
+    fn cardinality_collector_serde_roundtrip() {
+        use super::CardinalityCollector;
+
+        let mut collector = CardinalityCollector::default();
+        collector.insert("hello");
+        collector.insert("world");
+        collector.insert("hello"); // duplicate
+
+        let serialized = serde_json::to_vec(&collector).unwrap();
+        let deserialized: CardinalityCollector = serde_json::from_slice(&serialized).unwrap();
+
+        let original_estimate = collector.finalize().unwrap();
+        let roundtrip_estimate = deserialized.finalize().unwrap();
+        assert_eq!(original_estimate, roundtrip_estimate);
+        assert_eq!(original_estimate, 2.0);
+    }
+
+    #[test]
+    fn cardinality_collector_merge() {
+        use super::CardinalityCollector;
+
+        let mut left = CardinalityCollector::default();
+        left.insert("a");
+        left.insert("b");
+
+        let mut right = CardinalityCollector::default();
+        right.insert("b");
+        right.insert("c");
+
+        left.merge_fruits(right).unwrap();
+        let estimate = left.finalize().unwrap();
+        assert_eq!(estimate, 3.0);
+    }
+
+    #[test]
+    fn cardinality_collector_serialize_deserialize_binary() {
+        use datasketches::hll::HllSketch;
+
+        use super::CardinalityCollector;
+
+        let mut collector = CardinalityCollector::default();
+        collector.insert("apple");
+        collector.insert("banana");
+        collector.insert("cherry");
+
+        let bytes = collector.to_sketch_bytes();
+        let deserialized = HllSketch::deserialize(&bytes).unwrap();
+        assert!((deserialized.estimate() - 3.0).abs() < 0.01);
+    }
+
+    #[test]
+    fn cardinality_collector_salt_differentiates_types() {
+        use super::CardinalityCollector;
+
+        // Without salt, same u64 value from different column types would collide
+        let mut collector_bool = CardinalityCollector::new(5); // e.g. ColumnType::Bool
+        collector_bool.insert(0u64); // false
+        collector_bool.insert(1u64); // true
+
+        let mut collector_i64 = CardinalityCollector::new(2); // e.g. ColumnType::I64
+        collector_i64.insert(0u64);
+        collector_i64.insert(1u64);
+
+        // Merge them
+        collector_bool.merge_fruits(collector_i64).unwrap();
+        let estimate = collector_bool.finalize().unwrap();
+        // Should be 4 because salt makes (5, 0) != (2, 0) and (5, 1) != (2, 1)
+        assert_eq!(estimate, 4.0);
+    }
 }