Add left function benchmark (#19600)

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- Closes #.

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Author: viirya

datafusion/functions/Cargo.toml

@@ -294,3 +294,8 @@ required-features = ["unicode_expressions"]
 harness = false
 name = "levenshtein"
 required-features = ["unicode_expressions"]
+
+[[bench]]
+harness = false
+name = "left"
+required-features = ["unicode_expressions"]

datafusion/functions/benches/left.rs

@@ -0,0 +1,111 @@
+// 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 std::hint::black_box;
+use std::sync::Arc;
+
+use arrow::array::{ArrayRef, Int64Array};
+use arrow::datatypes::{DataType, Field};
+use arrow::util::bench_util::create_string_array_with_len;
+use criterion::{BenchmarkId, Criterion, criterion_group, criterion_main};
+use datafusion_common::config::ConfigOptions;
+use datafusion_expr::{ColumnarValue, ScalarFunctionArgs};
+use datafusion_functions::unicode::left;
+
+fn create_args(size: usize, str_len: usize, use_negative: bool) -> Vec<ColumnarValue> {
+    let string_array = Arc::new(create_string_array_with_len::<i32>(size, 0.1, str_len));
+
+    // For negative n, we want to trigger the double-iteration code path
+    let n_values: Vec<i64> = if use_negative {
+        (0..size).map(|i| -((i % 10 + 1) as i64)).collect()
+    } else {
+        (0..size).map(|i| (i % 10 + 1) as i64).collect()
+    };
+    let n_array = Arc::new(Int64Array::from(n_values));
+
+    vec![
+        ColumnarValue::Array(string_array),
+        ColumnarValue::Array(Arc::clone(&n_array) as ArrayRef),
+    ]
+}
+
+fn criterion_benchmark(c: &mut Criterion) {
+    for size in [1024, 4096] {
+        let mut group = c.benchmark_group(format!("left size={size}"));
+
+        // Benchmark with positive n (no optimization needed)
+        let args = create_args(size, 32, false);
+        group.bench_function(BenchmarkId::new("positive n", size), |b| {
+            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());
+
+            b.iter(|| {
+                black_box(
+                    left()
+                        .invoke_with_args(ScalarFunctionArgs {
+                            args: args.clone(),
+                            arg_fields: arg_fields.clone(),
+                            number_rows: size,
+                            return_field: Field::new("f", DataType::Utf8, true).into(),
+                            config_options: Arc::clone(&config_options),
+                        })
+                        .expect("left should work"),
+                )
+            })
+        });
+
+        // Benchmark with negative n (triggers optimization)
+        let args = create_args(size, 32, true);
+        group.bench_function(BenchmarkId::new("negative n", size), |b| {
+            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());
+
+            b.iter(|| {
+                black_box(
+                    left()
+                        .invoke_with_args(ScalarFunctionArgs {
+                            args: args.clone(),
+                            arg_fields: arg_fields.clone(),
+                            number_rows: size,
+                            return_field: Field::new("f", DataType::Utf8, true).into(),
+                            config_options: Arc::clone(&config_options),
+                        })
+                        .expect("left should work"),
+                )
+            })
+        });
+
+        group.finish();
+    }
+}
+
+criterion_group!(benches, criterion_benchmark);
+criterion_main!(benches);