Refactoring dyn Column

Author: fulmicoton

examples/custom_collector.rs

@@ -7,11 +7,12 @@
 // Of course, you can have a look at the tantivy's built-in collectors
 // such as the `CountCollector` for more examples.
 
+use std::sync::Arc;
+
 use fastfield_codecs::Column;
 // ---
 // Importing tantivy...
 use tantivy::collector::{Collector, SegmentCollector};
-use tantivy::fastfield::DynamicFastFieldReader;
 use tantivy::query::QueryParser;
 use tantivy::schema::{Field, Schema, FAST, INDEXED, TEXT};
 use tantivy::{doc, Index, Score, SegmentReader};
@@ -96,7 +97,7 @@ impl Collector for StatsCollector {
 }
 
 struct StatsSegmentCollector {
-    fast_field_reader: DynamicFastFieldReader<u64>,
+    fast_field_reader: Arc<dyn Column<u64>>,
     stats: Stats,
 }
 

examples/warmer.rs

@@ -2,7 +2,6 @@ use std::cmp::Reverse;
 use std::collections::{HashMap, HashSet};
 use std::sync::{Arc, RwLock, Weak};
 
-use fastfield_codecs::Column;
 use tantivy::collector::TopDocs;
 use tantivy::query::QueryParser;
 use tantivy::schema::{Field, Schema, FAST, TEXT};

fastfield_codecs/src/column.rs

@@ -1,3 +1,5 @@
+use std::marker::PhantomData;
+
 pub trait Column<T = u64> {
     /// Return the value associated to the given idx.
     ///
@@ -42,8 +44,125 @@ pub trait Column<T = u64> {
     fn max_value(&self) -> T;
 
     fn num_vals(&self) -> u64;
+
     /// Returns a iterator over the data
     fn iter<'a>(&'a self) -> Box<dyn Iterator<Item = T> + 'a> {
         Box::new((0..self.num_vals()).map(|idx| self.get_val(idx)))
     }
 }
+
+pub struct VecColumn<'a, T> {
+    values: &'a [T],
+    min_value: T,
+    max_value: T,
+}
+
+impl<'a, T: Copy + PartialOrd> Column<T> for VecColumn<'a, T> {
+    fn get_val(&self, position: u64) -> T {
+        self.values[position as usize]
+    }
+
+    fn iter<'b>(&'b self) -> Box<dyn Iterator<Item = T> + 'b> {
+        Box::new(self.values.iter().copied())
+    }
+
+    fn min_value(&self) -> T {
+        self.min_value
+    }
+
+    fn max_value(&self) -> T {
+        self.max_value
+    }
+
+    fn num_vals(&self) -> u64 {
+        self.values.len() as u64
+    }
+}
+
+impl<'a, T: Copy + Ord + Default> From<&'a [T]> for VecColumn<'a, T> {
+    fn from(values: &'a [T]) -> Self {
+        if values.is_empty() {
+            return VecColumn {
+                min_value: T::default(),
+                max_value: T::default(),
+                values: &[],
+            };
+        }
+        let (min_value, max_value) = values[1..]
+            .iter()
+            .copied()
+            .fold((values[0], values[0]), |(min, max), val| {
+                (min.min(val), max.max(val))
+            });
+        Self {
+            values,
+            min_value,
+            max_value,
+        }
+    }
+}
+
+struct MonotonicMappingColumn<C, T, Input> {
+    from_column: C,
+    monotonic_mapping: T,
+    _phantom: PhantomData<Input>,
+}
+
+/// Creates a view of a column transformed by a monotonic mapping.
+pub fn monotonic_map_column<C, T, Input, Output>(
+    from_column: C,
+    monotonic_mapping: T,
+) -> impl Column<Output>
+where
+    C: Column<Input>,
+    T: Fn(Input) -> Output,
+{
+    MonotonicMappingColumn {
+        from_column,
+        monotonic_mapping,
+        _phantom: PhantomData,
+    }
+}
+
+impl<C, T, Input, Output> Column<Output> for MonotonicMappingColumn<C, T, Input>
+where
+    C: Column<Input>,
+    T: Fn(Input) -> Output,
+{
+    fn get_val(&self, idx: u64) -> Output {
+        let from_val = self.from_column.get_val(idx);
+        (self.monotonic_mapping)(from_val)
+    }
+
+    fn min_value(&self) -> Output {
+        let from_min_value = self.from_column.min_value();
+        (self.monotonic_mapping)(from_min_value)
+    }
+
+    fn max_value(&self) -> Output {
+        let from_max_value = self.from_column.max_value();
+        (self.monotonic_mapping)(from_max_value)
+    }
+
+    fn num_vals(&self) -> u64 {
+        self.from_column.num_vals()
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_monotonic_mapping() {
+        let vals = &[1u64, 3u64][..];
+        let col = VecColumn::from(vals);
+        let mapped = monotonic_map_column(col, |el| el + 4);
+        assert_eq!(mapped.min_value(), 5u64);
+        assert_eq!(mapped.max_value(), 7u64);
+        assert_eq!(mapped.num_vals(), 2);
+        assert_eq!(mapped.num_vals(), 2);
+        assert_eq!(mapped.get_val(0), 5);
+        assert_eq!(mapped.get_val(0), 7);
+    }
+}

fastfield_codecs/src/lib.rs

@@ -14,7 +14,7 @@ pub mod linear;
 
 mod column;
 
-pub use self::column::Column;
+pub use self::column::{monotonic_map_column, Column, VecColumn};
 
 #[derive(PartialEq, Eq, PartialOrd, Ord, Debug, Clone, Copy)]
 #[repr(u8)]
@@ -56,12 +56,12 @@ impl FastFieldCodecType {
 
 /// The FastFieldSerializerEstimate trait is required on all variants
 /// of fast field compressions, to decide which one to choose.
-pub trait FastFieldCodec {
+pub trait FastFieldCodec: 'static {
     /// A codex needs to provide a unique name and id, which is
     /// used for debugging and de/serialization.
     const CODEC_TYPE: FastFieldCodecType;
 
-    type Reader: Column<u64>;
+    type Reader: Column<u64> + 'static;
 
     /// Reads the metadata and returns the CodecReader
     fn open_from_bytes(bytes: OwnedBytes) -> io::Result<Self::Reader>;
@@ -90,35 +90,6 @@ pub struct FastFieldStats {
     pub num_vals: u64,
 }
 
-struct VecColum<'a>(&'a [u64]);
-impl<'a> Column for VecColum<'a> {
-    fn get_val(&self, position: u64) -> u64 {
-        self.0[position as usize]
-    }
-
-    fn iter<'b>(&'b self) -> Box<dyn Iterator<Item = u64> + 'b> {
-        Box::new(self.0.iter().cloned())
-    }
-
-    fn min_value(&self) -> u64 {
-        self.0.iter().min().cloned().unwrap_or(0)
-    }
-
-    fn max_value(&self) -> u64 {
-        self.0.iter().max().cloned().unwrap_or(0)
-    }
-
-    fn num_vals(&self) -> u64 {
-        self.0.len() as u64
-    }
-}
-
-impl<'a> From<&'a [u64]> for VecColum<'a> {
-    fn from(data: &'a [u64]) -> Self {
-        Self(data)
-    }
-}
-
 #[cfg(test)]
 mod tests {
     use proptest::prelude::*;
@@ -133,10 +104,10 @@ mod tests {
         data: &[u64],
         name: &str,
     ) -> Option<(f32, f32)> {
-        let estimation = Codec::estimate(&VecColum::from(data))?;
+        let estimation = Codec::estimate(&VecColumn::from(data))?;
 
         let mut out: Vec<u8> = Vec::new();
-        Codec::serialize(&mut out, &VecColum::from(data)).unwrap();
+        Codec::serialize(&mut out, &VecColumn::from(data)).unwrap();
 
         let actual_compression = out.len() as f32 / (data.len() as f32 * 8.0);
 
@@ -233,7 +204,7 @@ mod tests {
     #[test]
     fn estimation_good_interpolation_case() {
         let data = (10..=20000_u64).collect::<Vec<_>>();
-        let data: VecColum = data.as_slice().into();
+        let data: VecColumn = data.as_slice().into();
 
         let linear_interpol_estimation = LinearCodec::estimate(&data).unwrap();
         assert_le!(linear_interpol_estimation, 0.01);
@@ -249,7 +220,7 @@ mod tests {
     fn estimation_test_bad_interpolation_case() {
         let data: &[u64] = &[200, 10, 10, 10, 10, 1000, 20];
 
-        let data: VecColum = data.into();
+        let data: VecColumn = data.into();
         let linear_interpol_estimation = LinearCodec::estimate(&data).unwrap();
         assert_le!(linear_interpol_estimation, 0.32);
 
@@ -259,8 +230,8 @@ mod tests {
     #[test]
     fn estimation_test_bad_interpolation_case_monotonically_increasing() {
         let mut data: Vec<u64> = (200..=20000_u64).collect();
+        let data: VecColumn = data.as_slice().into();
         data.push(1_000_000);
-        let data: VecColum = data.as_slice().into();
 
         // in this case the linear interpolation can't in fact not be worse than bitpacking,
         // but the estimator adds some threshold, which leads to estimated worse behavior

src/aggregation/agg_req_with_accessor.rs

@@ -4,14 +4,14 @@ use std::rc::Rc;
 use std::sync::atomic::AtomicU32;
 use std::sync::Arc;
 
+use fastfield_codecs::Column;
+
 use super::agg_req::{Aggregation, Aggregations, BucketAggregationType, MetricAggregation};
 use super::bucket::{HistogramAggregation, RangeAggregation, TermsAggregation};
 use super::metric::{AverageAggregation, StatsAggregation};
 use super::segment_agg_result::BucketCount;
 use super::VecWithNames;
-use crate::fastfield::{
-    type_and_cardinality, DynamicFastFieldReader, FastType, MultiValuedFastFieldReader,
-};
+use crate::fastfield::{type_and_cardinality, FastType, MultiValuedFastFieldReader};
 use crate::schema::{Cardinality, Type};
 use crate::{InvertedIndexReader, SegmentReader, TantivyError};
 
@@ -37,10 +37,16 @@ impl AggregationsWithAccessor {
 #[derive(Clone)]
 pub(crate) enum FastFieldAccessor {
     Multi(MultiValuedFastFieldReader<u64>),
-    Single(DynamicFastFieldReader<u64>),
+    Single(Arc<dyn Column<u64>>),
 }
 impl FastFieldAccessor {
-    pub fn as_single(&self) -> Option<&DynamicFastFieldReader<u64>> {
+    pub fn as_single(&self) -> Option<&dyn Column<u64>> {
+        match self {
+            FastFieldAccessor::Multi(_) => None,
+            FastFieldAccessor::Single(reader) => Some(&**reader),
+        }
+    }
+    pub fn into_single(self) -> Option<Arc<dyn Column<u64>>> {
         match self {
             FastFieldAccessor::Multi(_) => None,
             FastFieldAccessor::Single(reader) => Some(reader),
@@ -118,7 +124,7 @@ impl BucketAggregationWithAccessor {
 pub struct MetricAggregationWithAccessor {
     pub metric: MetricAggregation,
     pub field_type: Type,
-    pub accessor: DynamicFastFieldReader<u64>,
+    pub accessor: Arc<dyn Column>,
 }
 
 impl MetricAggregationWithAccessor {
@@ -134,9 +140,8 @@ impl MetricAggregationWithAccessor {
 
                 Ok(MetricAggregationWithAccessor {
                     accessor: accessor
-                        .as_single()
-                        .expect("unexpected fast field cardinality")
-                        .clone(),
+                        .into_single()
+                        .expect("unexpected fast field cardinality"),
                     field_type,
                     metric: metric.clone(),
                 })

src/aggregation/bucket/histogram/histogram.rs

@@ -15,7 +15,6 @@ use crate::aggregation::intermediate_agg_result::{
     IntermediateAggregationResults, IntermediateBucketResult, IntermediateHistogramBucketEntry,
 };
 use crate::aggregation::segment_agg_result::SegmentAggregationResultsCollector;
-use crate::fastfield::DynamicFastFieldReader;
 use crate::schema::Type;
 use crate::{DocId, TantivyError};
 
@@ -264,7 +263,7 @@ impl SegmentHistogramCollector {
         req: &HistogramAggregation,
         sub_aggregation: &AggregationsWithAccessor,
         field_type: Type,
-        accessor: &DynamicFastFieldReader<u64>,
+        accessor: &dyn Column<u64>,
     ) -> crate::Result<Self> {
         req.validate()?;
         let min = f64_from_fastfield_u64(accessor.min_value(), &field_type);

src/aggregation/bucket/range.rs

@@ -1,7 +1,6 @@
 use std::fmt::Debug;
 use std::ops::Range;
 
-use fastfield_codecs::Column;
 use fnv::FnvHashMap;
 use serde::{Deserialize, Serialize};
 

src/aggregation/metric/average.rs

@@ -4,7 +4,6 @@ use fastfield_codecs::Column;
 use serde::{Deserialize, Serialize};
 
 use crate::aggregation::f64_from_fastfield_u64;
-use crate::fastfield::DynamicFastFieldReader;
 use crate::schema::Type;
 use crate::DocId;
 
@@ -58,7 +57,7 @@ impl SegmentAverageCollector {
             data: Default::default(),
         }
     }
-    pub(crate) fn collect_block(&mut self, doc: &[DocId], field: &DynamicFastFieldReader<u64>) {
+    pub(crate) fn collect_block(&mut self, doc: &[DocId], field: &dyn Column<u64>) {
         let mut iter = doc.chunks_exact(4);
         for docs in iter.by_ref() {
             let val1 = field.get_val(docs[0] as u64);

src/aggregation/metric/stats.rs

@@ -2,7 +2,6 @@ use fastfield_codecs::Column;
 use serde::{Deserialize, Serialize};
 
 use crate::aggregation::f64_from_fastfield_u64;
-use crate::fastfield::DynamicFastFieldReader;
 use crate::schema::Type;
 use crate::{DocId, TantivyError};
 
@@ -164,7 +163,7 @@ impl SegmentStatsCollector {
             stats: IntermediateStats::default(),
         }
     }
-    pub(crate) fn collect_block(&mut self, doc: &[DocId], field: &DynamicFastFieldReader<u64>) {
+    pub(crate) fn collect_block(&mut self, doc: &[DocId], field: &dyn Column<u64>) {
         let mut iter = doc.chunks_exact(4);
         for docs in iter.by_ref() {
             let val1 = field.get_val(docs[0] as u64);

src/aggregation/segment_agg_result.rs

@@ -185,10 +185,10 @@ impl SegmentMetricResultCollector {
     pub(crate) fn collect_block(&mut self, doc: &[DocId], metric: &MetricAggregationWithAccessor) {
         match self {
             SegmentMetricResultCollector::Average(avg_collector) => {
-                avg_collector.collect_block(doc, &metric.accessor);
+                avg_collector.collect_block(doc, &*metric.accessor);
             }
             SegmentMetricResultCollector::Stats(stats_collector) => {
-                stats_collector.collect_block(doc, &metric.accessor);
+                stats_collector.collect_block(doc, &*metric.accessor);
             }
         }
     }