int_timestamp_add_benchmarks

Author: coderfender

native/spark-expr/Cargo.toml

@@ -92,6 +92,10 @@ harness = false
 name = "to_csv"
 harness = false
 
+[[bench]]
+name = "cast_int_to_timestamp"
+harness = false
+
 [[test]]
 name = "test_udf_registration"
 path = "tests/spark_expr_reg.rs"

native/spark-expr/benches/cast_int_to_timestamp.rs

@@ -0,0 +1,131 @@
+// 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.
+
+use arrow::array::builder::{Int16Builder, Int32Builder, Int64Builder, Int8Builder};
+use arrow::array::RecordBatch;
+use arrow::datatypes::{DataType, Field, Schema, TimeUnit};
+use criterion::{criterion_group, criterion_main, Criterion};
+use datafusion::physical_expr::{expressions::Column, PhysicalExpr};
+use datafusion_comet_spark_expr::{Cast, EvalMode, SparkCastOptions};
+use std::sync::Arc;
+
+const BATCH_SIZE: usize = 8192;
+
+fn criterion_benchmark(c: &mut Criterion) {
+    // Test with UTC timezone
+    let spark_cast_options = SparkCastOptions::new(EvalMode::Legacy, "UTC", false);
+    let timestamp_type = DataType::Timestamp(TimeUnit::Microsecond, Some("UTC".into()));
+
+    let mut group = c.benchmark_group("cast_int_to_timestamp");
+
+    // Int8 -> Timestamp
+    let batch_i8 = create_int8_batch();
+    let expr_i8 = Arc::new(Column::new("a", 0));
+    let cast_i8_to_ts = Cast::new(expr_i8, timestamp_type.clone(), spark_cast_options.clone());
+    group.bench_function("cast_i8_to_timestamp", |b| {
+        b.iter(|| cast_i8_to_ts.evaluate(&batch_i8).unwrap());
+    });
+
+    // Int16 -> Timestamp
+    let batch_i16 = create_int16_batch();
+    let expr_i16 = Arc::new(Column::new("a", 0));
+    let cast_i16_to_ts = Cast::new(expr_i16, timestamp_type.clone(), spark_cast_options.clone());
+    group.bench_function("cast_i16_to_timestamp", |b| {
+        b.iter(|| cast_i16_to_ts.evaluate(&batch_i16).unwrap());
+    });
+
+    // Int32 -> Timestamp
+    let batch_i32 = create_int32_batch();
+    let expr_i32 = Arc::new(Column::new("a", 0));
+    let cast_i32_to_ts = Cast::new(expr_i32, timestamp_type.clone(), spark_cast_options.clone());
+    group.bench_function("cast_i32_to_timestamp", |b| {
+        b.iter(|| cast_i32_to_ts.evaluate(&batch_i32).unwrap());
+    });
+
+    // Int64 -> Timestamp
+    let batch_i64 = create_int64_batch();
+    let expr_i64 = Arc::new(Column::new("a", 0));
+    let cast_i64_to_ts = Cast::new(expr_i64, timestamp_type.clone(), spark_cast_options.clone());
+    group.bench_function("cast_i64_to_timestamp", |b| {
+        b.iter(|| cast_i64_to_ts.evaluate(&batch_i64).unwrap());
+    });
+
+    group.finish();
+}
+
+fn create_int8_batch() -> RecordBatch {
+    let schema = Arc::new(Schema::new(vec![Field::new("a", DataType::Int8, true)]));
+    let mut b = Int8Builder::with_capacity(BATCH_SIZE);
+    for i in 0..BATCH_SIZE {
+        if i % 10 == 0 {
+            b.append_null();
+        } else {
+            b.append_value(rand::random::<i8>());
+        }
+    }
+    RecordBatch::try_new(schema, vec![Arc::new(b.finish())]).unwrap()
+}
+
+fn create_int16_batch() -> RecordBatch {
+    let schema = Arc::new(Schema::new(vec![Field::new("a", DataType::Int16, true)]));
+    let mut b = Int16Builder::with_capacity(BATCH_SIZE);
+    for i in 0..BATCH_SIZE {
+        if i % 10 == 0 {
+            b.append_null();
+        } else {
+            b.append_value(rand::random::<i16>());
+        }
+    }
+    RecordBatch::try_new(schema, vec![Arc::new(b.finish())]).unwrap()
+}
+
+fn create_int32_batch() -> RecordBatch {
+    let schema = Arc::new(Schema::new(vec![Field::new("a", DataType::Int32, true)]));
+    let mut b = Int32Builder::with_capacity(BATCH_SIZE);
+    for i in 0..BATCH_SIZE {
+        if i % 10 == 0 {
+            b.append_null();
+        } else {
+            b.append_value(rand::random::<i32>());
+        }
+    }
+    RecordBatch::try_new(schema, vec![Arc::new(b.finish())]).unwrap()
+}
+
+fn create_int64_batch() -> RecordBatch {
+    let schema = Arc::new(Schema::new(vec![Field::new("a", DataType::Int64, true)]));
+    let mut b = Int64Builder::with_capacity(BATCH_SIZE);
+    for i in 0..BATCH_SIZE {
+        if i % 10 == 0 {
+            b.append_null();
+        } else {
+            b.append_value(rand::random::<i64>());
+        }
+    }
+    RecordBatch::try_new(schema, vec![Arc::new(b.finish())]).unwrap()
+}
+
+fn config() -> Criterion {
+    Criterion::default()
+}
+
+criterion_group! {
+    name = benches;
+    config = config();
+    targets = criterion_benchmark
+}
+criterion_main!(benches);

native/spark-expr/src/conversion_funcs/cast.rs

@@ -25,7 +25,6 @@ use arrow::array::{
     PrimitiveBuilder, StringArray, StructArray, TimestampMicrosecondBuilder,
 };
 use arrow::compute::can_cast_types;
-use arrow::datatypes::DataType::Int64;
 use arrow::datatypes::{
     i256, ArrowDictionaryKeyType, ArrowNativeType, DataType, Decimal256Type, GenericBinaryType,
     Schema,
@@ -614,6 +613,20 @@ macro_rules! cast_decimal_to_int32_up {
     }};
 }
 
+macro_rules! cast_int_to_timestamp_impl {
+    ($array:expr, $builder:expr, $primitive_type:ty) => {{
+        let arr = $array.as_primitive::<$primitive_type>();
+        for i in 0..arr.len() {
+            if arr.is_null(i) {
+                $builder.append_null();
+            } else {
+                let micros = (arr.value(i) as i64).saturating_mul(MICROS_PER_SECOND);
+                $builder.append_value(micros);
+            }
+        }
+    }};
+}
+
 // copied from arrow::dataTypes::Decimal128Type since Decimal128Type::format_decimal can't be called directly
 fn format_decimal_str(value_str: &str, precision: usize, scale: i8) -> String {
     let (sign, rest) = match value_str.strip_prefix('-') {
@@ -942,16 +955,18 @@ fn cast_int_to_timestamp(
     cast_options: &SparkCastOptions,
 ) -> SparkResult<ArrayRef> {
     // Input is seconds since epoch, multiply by MICROS_PER_SECOND to get microseconds.
-    let int64_array = cast_with_options(&array_ref, &Int64, &CAST_OPTIONS)?;
-    let int64_arr = int64_array.as_primitive::<Int64Type>();
-
-    let mut builder = TimestampMicrosecondBuilder::with_capacity(int64_arr.len());
-    for i in 0..int64_arr.len() {
-        if int64_arr.is_null(i) {
-            builder.append_null();
-        } else {
-            let micros = int64_arr.value(i).saturating_mul(MICROS_PER_SECOND);
-            builder.append_value(micros);
+    let mut builder = TimestampMicrosecondBuilder::with_capacity(array_ref.len());
+
+    match array_ref.data_type() {
+        DataType::Int8 => cast_int_to_timestamp_impl!(array_ref, builder, Int8Type),
+        DataType::Int16 => cast_int_to_timestamp_impl!(array_ref, builder, Int16Type),
+        DataType::Int32 => cast_int_to_timestamp_impl!(array_ref, builder, Int32Type),
+        DataType::Int64 => cast_int_to_timestamp_impl!(array_ref, builder, Int64Type),
+        dt => {
+            return Err(SparkError::Internal(format!(
+                "Unsupported type for cast_int_to_timestamp: {:?}",
+                dt
+            )))
         }
     }