Rename "score" to "_distance"

python/python/tests/test_lance.py

@@ -94,10 +94,10 @@ def test_nearest(tmp_path):
     top10 = dataset.to_table(
         nearest={"column": "emb", "q": arr[0].values, "k": 10, "nprobes": 10}
     )
-    scores = l2sq(arr[0].values, npvals.reshape((100, 32)))
-    indices = np.argsort(scores)
+    distances = l2sq(arr[0].values, npvals.reshape((100, 32)))
+    indices = np.argsort(distances)
     assert tbl.take(indices[:10]).to_pandas().equals(top10.to_pandas()[["emb"]])
-    assert np.allclose(scores[indices[:10]], top10.to_pandas().score.values)
+    assert np.allclose(distances[indices[:10]], top10.to_pandas()["_distance"].values)
 
 
 def l2sq(vec, mat):
@@ -114,8 +114,10 @@ def test_nearest_cosine(tmp_path):
         nearest={"column": "vector", "q": q, "k": 10, "metric": "cosine"}
     ).to_pandas()
     for i in range(len(rs)):
-        assert rs.score[i] == pytest.approx(cosine_distance(rs.vector[i], q), abs=1e-6)
-        assert 0 <= rs.score[i] <= 1
+        assert rs["_distance"][i] == pytest.approx(
+            cosine_distance(rs.vector[i], q), abs=1e-6
+        )
+        assert 0 <= rs["_distance"][i] <= 1
 
 
 def cosine_distance(vec1, vec2):

python/python/tests/test_vector_index.py

@@ -70,7 +70,7 @@ def run(ds, q=None, assert_func=None):
             expected_columns.extend(ds.schema.names)
         else:
             expected_columns.extend(columns)
-        for c in ["vector", "score"]:
+        for c in ["vector", "_distance"]:
             if c not in expected_columns:
                 expected_columns.append(c)
 
@@ -96,8 +96,8 @@ def run(ds, q=None, assert_func=None):
                     assert len(inmem.to_table(filter=filter_)) == len(rs)
                 else:
                     assert len(rs) == 15
-                    scores = rs["score"].to_numpy()
-                    assert (scores.max() - scores.min()) > 1e-6
+                    distances = rs["_distance"].to_numpy()
+                    assert (distances.max() - distances.min()) > 1e-6
                     if assert_func is not None:
                         assert_func(rs)
     return times

rust/src/dataset/scanner.rs

@@ -316,7 +316,7 @@ impl Scanner {
         self
     }
 
-    /// The Arrow schema of the output, including projections and vector / score
+    /// The Arrow schema of the output, including projections and vector / _distance
     pub fn schema(&self) -> Result<SchemaRef> {
         let schema = self
             .output_schema()
@@ -336,7 +336,7 @@ impl Scanner {
                 message: format!("Failed to convert vector field: {}", e),
             })?;
             extra_columns.push(vector_field);
-            extra_columns.push(ArrowField::new("score", DataType::Float32, false));
+            extra_columns.push(ArrowField::new("_distance", DataType::Float32, false));
         };
         if self.with_row_id {
             extra_columns.push(ArrowField::new(ROW_ID, DataType::UInt64, false));
@@ -488,14 +488,14 @@ impl Scanner {
                 }
             }
 
-            let knn_node = self.ann(q, index)?; // score, _rowid
+            let knn_node = self.ann(q, index)?; // _distance, _rowid
             let with_vector = self.dataset.schema().project(&[&q.column])?;
             let knn_node_with_vector = self.take(knn_node, &with_vector)?;
             let mut knn_node = if q.refine_factor.is_some() {
                 self.flat_knn(knn_node_with_vector, q)?
             } else {
                 knn_node_with_vector
-            }; // vector, score, _rowid
+            }; // vector, _distance, _rowid
 
             knn_node = self.knn_combined(&q, index, knn_node).await?;
 
@@ -545,8 +545,8 @@ impl Scanner {
                 let topk_appended = self.flat_knn(scan_node, q)?;
 
                 // To do a union, we need to make the schemas match. Right now
-                // knn_node: score, _rowid, vector
-                // topk_appended: vector, _rowid, score
+                // knn_node: _distance, _rowid, vector
+                // topk_appended: vector, _rowid, _distance
                 let new_schema = Schema::try_from(&topk_appended.schema().project(&[2, 1, 0])?)?;
                 let topk_appended = ProjectionExec::try_new(topk_appended, Arc::new(new_schema))?;
                 assert_eq!(topk_appended.schema(), knn_node.schema());
@@ -948,7 +948,7 @@ mod test {
                         ),
                         true,
                     ),
-                    ArrowField::new("score", DataType::Float32, false),
+                    ArrowField::new("_distance", DataType::Float32, false),
                 ])
             );
 
@@ -1107,7 +1107,7 @@ mod test {
                     ),
                     true,
                 ),
-                ArrowField::new("score", DataType::Float32, false),
+                ArrowField::new("_distance", DataType::Float32, false),
             ])
         );
 
@@ -1157,7 +1157,7 @@ mod test {
                     ),
                     true,
                 ),
-                ArrowField::new("score", DataType::Float32, false),
+                ArrowField::new("_distance", DataType::Float32, false),
             ])
         );
 
@@ -1376,7 +1376,7 @@ mod test {
     /// Query: nearest(vec, [...], 10) + filter(i > 10 and i < 20)
     ///
     /// Expected plan:
-    ///  KNNIndex(vec) -> Take(i) -> filter(i) -> take(s, vec) -> projection(s, vec, score)
+    ///  KNNIndex(vec) -> Take(i) -> filter(i) -> take(s, vec) -> projection(s, vec, _distance)
     #[tokio::test]
     async fn test_ann_with_index() {
         let test_dir = tempdir().unwrap();
@@ -1398,14 +1398,14 @@ mod test {
                 .iter()
                 .map(|f| f.name())
                 .collect::<Vec<_>>(),
-            vec!["s", "vec", "score"]
+            vec!["s", "vec", "_distance"]
         );
 
         let take = &plan.children()[0];
         let take = take.as_any().downcast_ref::<TakeExec>().unwrap();
         assert_eq!(
             take.schema().field_names(),
-            ["score", "_rowid", "vec", "i", "s"]
+            ["_distance", "_rowid", "vec", "i", "s"]
         );
         assert_eq!(
             take.extra_schema
@@ -1420,12 +1420,15 @@ mod test {
         assert!(filter.as_any().is::<FilterExec>());
         assert_eq!(
             filter.schema().field_names(),
-            ["score", "_rowid", "vec", "i"]
+            ["_distance", "_rowid", "vec", "i"]
         );
 
         let take = &filter.children()[0];
         let take = take.as_any().downcast_ref::<TakeExec>().unwrap();
-        assert_eq!(take.schema().field_names(), ["score", "_rowid", "vec", "i"]);
+        assert_eq!(
+            take.schema().field_names(),
+            ["_distance", "_rowid", "vec", "i"]
+        );
         assert_eq!(
             take.extra_schema
                 .fields
@@ -1438,7 +1441,7 @@ mod test {
         // TODO: Two continuous take execs, we can merge them into one.
         let take = &take.children()[0];
         let take = take.as_any().downcast_ref::<TakeExec>().unwrap();
-        assert_eq!(take.schema().field_names(), ["score", "_rowid", "vec"]);
+        assert_eq!(take.schema().field_names(), ["_distance", "_rowid", "vec"]);
         assert_eq!(
             take.extra_schema
                 .fields
@@ -1450,7 +1453,7 @@ mod test {
 
         let knn = &take.children()[0];
         assert!(knn.as_any().is::<KNNIndexExec>());
-        assert_eq!(knn.schema().field_names(), ["score", "_rowid"]);
+        assert_eq!(knn.schema().field_names(), ["_distance", "_rowid"]);
     }
 
     /// Test KNN index with refine factor
@@ -1459,7 +1462,7 @@ mod test {
     ///
     /// Expected plan:
     ///  KNNIndex(vec) -> Take(vec) -> KNNFlat(vec, 10) -> Take(i) -> Filter(i)
-    ///     -> take(s, vec) -> projection(s, vec, score)
+    ///     -> take(s, vec) -> projection(s, vec, _distance)
     #[tokio::test]
     async fn test_knn_with_refine() {
         let test_dir = tempdir().unwrap();
@@ -1482,14 +1485,14 @@ mod test {
                 .iter()
                 .map(|f| f.name())
                 .collect::<Vec<_>>(),
-            vec!["s", "vec", "score"]
+            vec!["s", "vec", "_distance"]
         );
 
         let take = &plan.children()[0];
         let take = take.as_any().downcast_ref::<TakeExec>().unwrap();
         assert_eq!(
             take.schema().field_names(),
-            ["score", "_rowid", "vec", "i", "s"]
+            ["_distance", "_rowid", "vec", "i", "s"]
         );
         assert_eq!(
             take.extra_schema
@@ -1504,12 +1507,15 @@ mod test {
         assert!(filter.as_any().is::<FilterExec>());
         assert_eq!(
             filter.schema().field_names(),
-            ["score", "_rowid", "vec", "i"]
+            ["_distance", "_rowid", "vec", "i"]
         );
 
         let take = &filter.children()[0];
         let take = take.as_any().downcast_ref::<TakeExec>().unwrap();
-        assert_eq!(take.schema().field_names(), ["score", "_rowid", "vec", "i"]);
+        assert_eq!(
+            take.schema().field_names(),
+            ["_distance", "_rowid", "vec", "i"]
+        );
         assert_eq!(
             take.extra_schema
                 .fields
@@ -1525,7 +1531,7 @@ mod test {
 
         let take = &flat.children()[0];
         let take = take.as_any().downcast_ref::<TakeExec>().unwrap();
-        assert_eq!(take.schema().field_names(), ["score", "_rowid", "vec"]);
+        assert_eq!(take.schema().field_names(), ["_distance", "_rowid", "vec"]);
         assert_eq!(
             take.extra_schema
                 .fields
@@ -1537,7 +1543,7 @@ mod test {
 
         let knn = &take.children()[0];
         assert!(knn.as_any().is::<KNNIndexExec>());
-        assert_eq!(knn.schema().field_names(), ["score", "_rowid"]);
+        assert_eq!(knn.schema().field_names(), ["_distance", "_rowid"]);
     }
 
     #[tokio::test]

rust/src/index/vector.rs

@@ -64,7 +64,7 @@ use crate::{
 };
 pub use traits::*;
 
-pub(crate) const SCORE_COL: &str = "score";
+pub(crate) const DIST_COL: &str = "_distance";
 const INDEX_FILE_NAME: &str = "index.idx";
 
 /// Query parameters for the vector indices

rust/src/index/vector/diskann/search.rs

@@ -31,7 +31,7 @@ use crate::{
     index::{
         vector::{
             graph::{GraphReadParams, PersistedGraph},
-            SCORE_COL,
+            DIST_COL,
         },
         Index,
     },
@@ -218,16 +218,16 @@ impl VectorIndex for DiskANNIndex {
         let state = greedy_search(&self.graph, 0, query.key.values(), query.k, query.k * 2).await?;
         let schema = Arc::new(Schema::new(vec![
             Field::new(ROW_ID, DataType::UInt64, false),
-            Field::new(SCORE_COL, DataType::Float32, false),
+            Field::new(DIST_COL, DataType::Float32, false),
         ]));
 
         let mut candidates = Vec::with_capacity(query.k);
-        for (score, row) in state.candidates {
+        for (distance, row) in state.candidates {
             if candidates.len() == query.k {
                 break;
             }
             if !self.deletion_cache.as_ref().is_deleted(row as u64).await? {
-                candidates.push((score, row));
+                candidates.push((distance, row));
             }
         }
 
@@ -236,11 +236,14 @@ impl VectorIndex for DiskANNIndex {
             .take(query.k)
             .map(|(_, id)| *id as u64)
             .collect();
-        let scores: Float32Array = candidates.iter().take(query.k).map(|(d, _)| **d).collect();
+        let distances: Float32Array = candidates.iter().take(query.k).map(|(d, _)| **d).collect();
 
         let batch = RecordBatch::try_new(
             schema,
-            vec![Arc::new(row_ids) as ArrayRef, Arc::new(scores) as ArrayRef],
+            vec![
+                Arc::new(row_ids) as ArrayRef,
+                Arc::new(distances) as ArrayRef,
+            ],
         )?;
         Ok(batch)
     }

rust/src/index/vector/flat.rs

@@ -24,7 +24,7 @@ use arrow_select::{concat::concat_batches, take::take};
 use futures::future;
 use futures::stream::{repeat_with, Stream, StreamExt, TryStreamExt};
 
-use super::{Query, SCORE_COL};
+use super::{Query, DIST_COL};
 use crate::arrow::*;
 use crate::{Error, Result};
 
@@ -41,9 +41,9 @@ pub async fn flat_search(
         .map(|(batch, mt)| async move {
             let k = query.key.clone();
             let mut batch = batch?;
-            if batch.column_by_name(SCORE_COL).is_some() {
-                // Ignore the score calculated from inner vector index.
-                batch = batch.drop_column(SCORE_COL)?;
+            if batch.column_by_name(DIST_COL).is_some() {
+                // Ignore the distance calculated from inner vector index.
+                batch = batch.drop_column(DIST_COL)?;
             }
             let vectors = batch
                 .column_by_name(&query.column)
@@ -52,7 +52,7 @@ pub async fn flat_search(
                 })?
                 .clone();
             let flatten_vectors = as_fixed_size_list_array(vectors.as_ref()).values().clone();
-            let scores = tokio::task::spawn_blocking(move || {
+            let distances = tokio::task::spawn_blocking(move || {
                 mt.batch_func()(
                     k.values(),
                     as_primitive_array::<Float32Type>(flatten_vectors.as_ref()).values(),
@@ -62,19 +62,21 @@ pub async fn flat_search(
             .await? as ArrayRef;
 
             // TODO: use heap
-            let indices = sort_to_indices(&scores, None, Some(query.k))?;
-            let batch_with_score = batch
-                .try_with_column(ArrowField::new(SCORE_COL, DataType::Float32, false), scores)?;
-            let struct_arr = StructArray::from(batch_with_score);
+            let indices = sort_to_indices(&distances, None, Some(query.k))?;
+            let batch_with_distance = batch.try_with_column(
+                ArrowField::new(DIST_COL, DataType::Float32, false),
+                distances,
+            )?;
+            let struct_arr = StructArray::from(batch_with_distance);
             let selected_arr = take(&struct_arr, &indices, None)?;
             Ok::<RecordBatch, Error>(as_struct_array(&selected_arr).into())
         })
         .buffer_unordered(16)
         .try_collect::<Vec<_>>()
         .await?;
     let batch = concat_batches(&batches[0].schema(), &batches)?;
-    let scores = batch.column_by_name(SCORE_COL).unwrap();
-    let indices = sort_to_indices(scores, None, Some(query.k))?;
+    let distances = batch.column_by_name(DIST_COL).unwrap();
+    let indices = sort_to_indices(distances, None, Some(query.k))?;
 
     let struct_arr = StructArray::from(batch);
     let selected_arr = take(&struct_arr, &indices, None)?;

rust/src/index/vector/ivf.rs

@@ -149,16 +149,19 @@ impl VectorIndex for IVFIndex {
             .await?;
         let batch = concat_batches(&batches[0].schema(), &batches)?;
 
-        let score_col = batch.column_by_name("score").ok_or_else(|| Error::IO {
-            message: format!("score column does not exist in batch: {}", batch.schema()),
+        let dist_col = batch.column_by_name("_distance").ok_or_else(|| Error::IO {
+            message: format!(
+                "_distance column does not exist in batch: {}",
+                batch.schema()
+            ),
         })?;
 
         // TODO: Use a heap sort to get the top-k.
         let limit = query.k * query.refine_factor.unwrap_or(1) as usize;
-        let selection = sort_to_indices(score_col, None, Some(limit))?;
+        let selection = sort_to_indices(dist_col, None, Some(limit))?;
         let struct_arr = StructArray::from(batch);
-        let taken_scores = take(&struct_arr, &selection, None)?;
-        Ok(as_struct_array(&taken_scores).into())
+        let taken_distances = take(&struct_arr, &selection, None)?;
+        Ok(as_struct_array(&taken_distances).into())
     }
 
     fn is_loadable(&self) -> bool {