float_to_timestamp

Author: coderfender

native/spark-expr/Cargo.toml

@@ -103,3 +103,7 @@ path = "tests/spark_expr_reg.rs"
 [[bench]]
 name = "cast_from_boolean"
 harness = false
+
+[[bench]]
+name = "cast_non_int_numeric_timestamp"
+harness = false

native/spark-expr/benches/cast_non_int_numeric_timestamp.rs

@@ -0,0 +1,136 @@
+// 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::{BooleanBuilder, Decimal128Builder, Float32Builder, Float64Builder};
+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) {
+    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_non_int_numeric_to_timestamp");
+
+    // Float32 -> Timestamp
+    let batch_f32 = create_float32_batch();
+    let expr_f32 = Arc::new(Column::new("a", 0));
+    let cast_f32_to_ts = Cast::new(expr_f32, timestamp_type.clone(), spark_cast_options.clone());
+    group.bench_function("cast_f32_to_timestamp", |b| {
+        b.iter(|| cast_f32_to_ts.evaluate(&batch_f32).unwrap());
+    });
+
+    // Float64 -> Timestamp
+    let batch_f64 = create_float64_batch();
+    let expr_f64 = Arc::new(Column::new("a", 0));
+    let cast_f64_to_ts = Cast::new(expr_f64, timestamp_type.clone(), spark_cast_options.clone());
+    group.bench_function("cast_f64_to_timestamp", |b| {
+        b.iter(|| cast_f64_to_ts.evaluate(&batch_f64).unwrap());
+    });
+
+    // Boolean -> Timestamp
+    let batch_bool = create_boolean_batch();
+    let expr_bool = Arc::new(Column::new("a", 0));
+    let cast_bool_to_ts = Cast::new(expr_bool, timestamp_type.clone(), spark_cast_options.clone());
+    group.bench_function("cast_bool_to_timestamp", |b| {
+        b.iter(|| cast_bool_to_ts.evaluate(&batch_bool).unwrap());
+    });
+
+    // Decimal128 -> Timestamp
+    let batch_decimal = create_decimal128_batch();
+    let expr_decimal = Arc::new(Column::new("a", 0));
+    let cast_decimal_to_ts =
+        Cast::new(expr_decimal, timestamp_type.clone(), spark_cast_options.clone());
+    group.bench_function("cast_decimal_to_timestamp", |b| {
+        b.iter(|| cast_decimal_to_ts.evaluate(&batch_decimal).unwrap());
+    });
+
+    group.finish();
+}
+
+fn create_float32_batch() -> RecordBatch {
+    let schema = Arc::new(Schema::new(vec![Field::new("a", DataType::Float32, true)]));
+    let mut b = Float32Builder::with_capacity(BATCH_SIZE);
+    for i in 0..BATCH_SIZE {
+        if i % 10 == 0 {
+            b.append_null();
+        } else {
+            b.append_value(rand::random::<f32>());
+        }
+    }
+    RecordBatch::try_new(schema, vec![Arc::new(b.finish())]).unwrap()
+}
+
+fn create_float64_batch() -> RecordBatch {
+    let schema = Arc::new(Schema::new(vec![Field::new("a", DataType::Float64, true)]));
+    let mut b = Float64Builder::with_capacity(BATCH_SIZE);
+    for i in 0..BATCH_SIZE {
+        if i % 10 == 0 {
+            b.append_null();
+        } else {
+            b.append_value(rand::random::<f64>());
+        }
+    }
+    RecordBatch::try_new(schema, vec![Arc::new(b.finish())]).unwrap()
+}
+
+fn create_boolean_batch() -> RecordBatch {
+    let schema = Arc::new(Schema::new(vec![Field::new("a", DataType::Boolean, true)]));
+    let mut b = BooleanBuilder::with_capacity(BATCH_SIZE);
+    for i in 0..BATCH_SIZE {
+        if i % 10 == 0 {
+            b.append_null();
+        } else {
+            b.append_value(rand::random::<bool>());
+        }
+    }
+    RecordBatch::try_new(schema, vec![Arc::new(b.finish())]).unwrap()
+}
+
+fn create_decimal128_batch() -> RecordBatch {
+    let schema = Arc::new(Schema::new(vec![Field::new(
+        "a",
+        DataType::Decimal128(18, 6),
+        true,
+    )]));
+    let mut b = Decimal128Builder::with_capacity(BATCH_SIZE);
+    for i in 0..BATCH_SIZE {
+        if i % 10 == 0 {
+            b.append_null();
+        } else {
+            b.append_value(rand::random::<i64>() as i128);
+        }
+    }
+    let array = b.finish().with_precision_and_scale(18, 6).unwrap();
+    RecordBatch::try_new(schema, vec![Arc::new(array)]).unwrap()
+}
+
+fn config() -> Criterion {
+    Criterion::default()
+}
+
+criterion_group! {
+    name = benches;
+    config = config();
+    targets = criterion_benchmark
+}
+criterion_main!(benches);