perf: improve performance of levenshtein by reusing cache buffer

datafusion/common/src/utils/mod.rs

@@ -693,10 +693,14 @@ pub mod datafusion_strsim {
     }
 
     /// Calculates the minimum number of insertions, deletions, and substitutions
-    /// required to change one sequence into the other.
-    fn generic_levenshtein<'a, 'b, Iter1, Iter2, Elem1, Elem2>(
+    /// required to change one sequence into the other, using a reusable cache buffer.
+    ///
+    /// This is the generic implementation that works with any iterator types.
+    /// The `cache` buffer will be resized as needed and reused across calls.
+    fn generic_levenshtein_with_buffer<'a, 'b, Iter1, Iter2, Elem1, Elem2>(
         a: &'a Iter1,
         b: &'b Iter2,
+        cache: &mut Vec<usize>,
     ) -> usize
     where
         &'a Iter1: IntoIterator<Item = Elem1>,
@@ -709,7 +713,9 @@ pub mod datafusion_strsim {
             return b_len;
         }
 
-        let mut cache: Vec<usize> = (1..b_len + 1).collect();
+        // Resize cache to fit b_len elements
+        cache.clear();
+        cache.extend(1..=b_len);
 
         let mut result = 0;
 
@@ -729,6 +735,21 @@ pub mod datafusion_strsim {
         result
     }
 
+    /// Calculates the minimum number of insertions, deletions, and substitutions
+    /// required to change one sequence into the other.
+    fn generic_levenshtein<'a, 'b, Iter1, Iter2, Elem1, Elem2>(
+        a: &'a Iter1,
+        b: &'b Iter2,
+    ) -> usize
+    where
+        &'a Iter1: IntoIterator<Item = Elem1>,
+        &'b Iter2: IntoIterator<Item = Elem2>,
+        Elem1: PartialEq<Elem2>,
+    {
+        let mut cache = Vec::new();
+        generic_levenshtein_with_buffer(a, b, &mut cache)
+    }
+
     /// Calculates the minimum number of insertions, deletions, and substitutions
     /// required to change one string into the other.
     ///
@@ -741,6 +762,15 @@ pub mod datafusion_strsim {
         generic_levenshtein(&StringWrapper(a), &StringWrapper(b))
     }
 
+    /// Calculates the Levenshtein distance using a reusable cache buffer.
+    /// This avoids allocating a new Vec for each call, improving performance
+    /// when computing many distances.
+    ///
+    /// The `cache` buffer will be resized as needed and reused across calls.
+    pub fn levenshtein_with_buffer(a: &str, b: &str, cache: &mut Vec<usize>) -> usize {
+        generic_levenshtein_with_buffer(&StringWrapper(a), &StringWrapper(b), cache)
+    }
+
     /// Calculates the normalized Levenshtein distance between two strings.
     /// The normalized distance is a value between 0.0 and 1.0, where 1.0 indicates
     /// that the strings are identical and 0.0 indicates no similarity.

datafusion/functions/Cargo.toml

@@ -264,3 +264,8 @@ required-features = ["string_expressions"]
 harness = false
 name = "ends_with"
 required-features = ["string_expressions"]
+
+[[bench]]
+harness = false
+name = "levenshtein"
+required-features = ["unicode_expressions"]

datafusion/functions/benches/levenshtein.rs

@@ -0,0 +1,87 @@
+// 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.
+
+extern crate criterion;
+
+use arrow::array::OffsetSizeTrait;
+use arrow::datatypes::{DataType, Field};
+use arrow::util::bench_util::create_string_array_with_len;
+use criterion::{Criterion, SamplingMode, criterion_group, criterion_main};
+use datafusion_common::DataFusionError;
+use datafusion_common::config::ConfigOptions;
+use datafusion_expr::{ColumnarValue, ScalarFunctionArgs};
+use datafusion_functions::string;
+use std::hint::black_box;
+use std::sync::Arc;
+use std::time::Duration;
+
+fn create_args<O: OffsetSizeTrait>(size: usize, str_len: usize) -> Vec<ColumnarValue> {
+    let string1_array = Arc::new(create_string_array_with_len::<O>(size, 0.1, str_len));
+    let string2_array = Arc::new(create_string_array_with_len::<O>(size, 0.1, str_len));
+
+    vec![
+        ColumnarValue::Array(string1_array),
+        ColumnarValue::Array(string2_array),
+    ]
+}
+
+fn invoke_levenshtein_with_args(
+    args: Vec<ColumnarValue>,
+    number_rows: usize,
+) -> Result<ColumnarValue, DataFusionError> {
+    let arg_fields = args
+        .iter()
+        .enumerate()
+        .map(|(idx, arg)| Field::new(format!("arg_{idx}"), arg.data_type(), true).into())
+        .collect::<Vec<_>>();
+    let config_options = Arc::new(ConfigOptions::default());
+
+    string::levenshtein().invoke_with_args(ScalarFunctionArgs {
+        args,
+        arg_fields,
+        number_rows,
+        return_field: Field::new("f", DataType::Int32, true).into(),
+        config_options: Arc::clone(&config_options),
+    })
+}
+
+fn criterion_benchmark(c: &mut Criterion) {
+    for size in [1024, 4096] {
+        let mut group = c.benchmark_group(format!("levenshtein size={size}"));
+        group.sampling_mode(SamplingMode::Flat);
+        group.sample_size(10);
+        group.measurement_time(Duration::from_secs(10));
+
+        for str_len in [8, 32] {
+            let args = create_args::<i32>(size, str_len);
+            group.bench_function(
+                format!("levenshtein_string [size={size}, str_len={str_len}]"),
+                |b| {
+                    b.iter(|| {
+                        let args_cloned = args.clone();
+                        black_box(invoke_levenshtein_with_args(args_cloned, size))
+                    })
+                },
+            );
+        }
+
+        group.finish();
+    }
+}
+
+criterion_group!(benches, criterion_benchmark);
+criterion_main!(benches);

datafusion/functions/src/string/levenshtein.rs

@@ -151,12 +151,18 @@ fn levenshtein<T: OffsetSizeTrait>(args: &[ArrayRef]) -> Result<ArrayRef> {
             DataType::Utf8View => {
                 let str1_array = as_string_view_array(&str1)?;
                 let str2_array = as_string_view_array(&str2)?;
+
+                // Reusable buffer to avoid allocating for each row
+                let mut cache = Vec::new();
+
                 let result = str1_array
                     .iter()
                     .zip(str2_array.iter())
                     .map(|(string1, string2)| match (string1, string2) {
                         (Some(string1), Some(string2)) => {
-                            Some(datafusion_strsim::levenshtein(string1, string2) as i32)
+                            Some(datafusion_strsim::levenshtein_with_buffer(
+                                string1, string2, &mut cache,
+                            ) as i32)
                         }
                         _ => None,
                     })
@@ -166,12 +172,18 @@ fn levenshtein<T: OffsetSizeTrait>(args: &[ArrayRef]) -> Result<ArrayRef> {
             DataType::Utf8 => {
                 let str1_array = as_generic_string_array::<T>(&str1)?;
                 let str2_array = as_generic_string_array::<T>(&str2)?;
+
+                // Reusable buffer to avoid allocating for each row
+                let mut cache = Vec::new();
+
                 let result = str1_array
                     .iter()
                     .zip(str2_array.iter())
                     .map(|(string1, string2)| match (string1, string2) {
                         (Some(string1), Some(string2)) => {
-                            Some(datafusion_strsim::levenshtein(string1, string2) as i32)
+                            Some(datafusion_strsim::levenshtein_with_buffer(
+                                string1, string2, &mut cache,
+                            ) as i32)
                         }
                         _ => None,
                     })
@@ -181,12 +193,18 @@ fn levenshtein<T: OffsetSizeTrait>(args: &[ArrayRef]) -> Result<ArrayRef> {
             DataType::LargeUtf8 => {
                 let str1_array = as_generic_string_array::<T>(&str1)?;
                 let str2_array = as_generic_string_array::<T>(&str2)?;
+
+                // Reusable buffer to avoid allocating for each row
+                let mut cache = Vec::new();
+
                 let result = str1_array
                     .iter()
                     .zip(str2_array.iter())
                     .map(|(string1, string2)| match (string1, string2) {
                         (Some(string1), Some(string2)) => {
-                            Some(datafusion_strsim::levenshtein(string1, string2) as i64)
+                            Some(datafusion_strsim::levenshtein_with_buffer(
+                                string1, string2, &mut cache,
+                            ) as i64)
                         }
                         _ => None,
                     })