[GitHub] [arrow] jorgecarleitao commented on a change in pull request #8116: ARROW-9919: [Rust][DataFusion] Speedup math operations by 15%+

[GitBox <gi...@apache.org>]

jorgecarleitao commented on a change in pull request #8116:
URL: https://github.com/apache/arrow/pull/8116#discussion_r483971257



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File path: rust/datafusion/benches/math_query_sql.rs
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@@ -0,0 +1,100 @@
+// 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.
+
+#[macro_use]
+extern crate criterion;
+use criterion::Criterion;
+
+use std::sync::Arc;
+
+extern crate arrow;
+extern crate datafusion;
+
+use arrow::{
+    array::{Float32Array, Float64Array},
+    datatypes::{DataType, Field, Schema},
+    record_batch::RecordBatch,
+};
+use datafusion::error::Result;
+
+use datafusion::datasource::MemTable;
+use datafusion::execution::context::ExecutionContext;
+
+fn query(ctx: &mut ExecutionContext, sql: &str) {
+    // execute the query
+    let df = ctx.sql(&sql).unwrap();
+    let results = df.collect().unwrap();
+
+    // display the relation
+    for _batch in results {}
+}
+
+fn create_context(array_len: usize, batch_size: usize) -> Result<ExecutionContext> {
+    // define a schema.
+    let schema = Arc::new(Schema::new(vec![
+        Field::new("f32", DataType::Float32, false),
+        Field::new("f64", DataType::Float64, false),
+    ]));
+
+    // define data.
+    let batches = (0..array_len / batch_size)
+        .map(|i| {
+            RecordBatch::try_new(
+                schema.clone(),
+                vec![
+                    Arc::new(Float32Array::from(vec![i as f32; batch_size])),
+                    Arc::new(Float64Array::from(vec![i as f64; batch_size])),
+                ],
+            )
+            .unwrap()
+        })
+        .collect::<Vec<_>>();
+
+    let mut ctx = ExecutionContext::new();
+
+    // declare a table in memory. In spark API, this corresponds to createDataFrame(...).
+    let provider = MemTable::new(schema, vec![batches])?;
+    ctx.register_table("t", Box::new(provider));
+
+    Ok(ctx)
+}
+
+fn criterion_benchmark(c: &mut Criterion) {
+    c.bench_function("sqrt_20_12", |b| {

Review comment:
       I agree. I will reduce samples. I needed them for the perform the scaling, which often only shows up in larger due to fixed costs.




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